Economic Impact Assessment of using Congestion Management ...955077/FULLTEXT01.pdf · PowerExchange...

INOM EXAMENSARBETE ELEKTROTEKNIK,AVANCERAD NIVÅ, 30 HP

, STOCKHOLM SVERIGE 2016

Economic Impact Assessment of using Congestion Management Methods to enable increased Wind Power Integration on Gotland

Performed in collaboration with Vattenfall R&D

VINCENT GLINIEWICZ

KTHSKOLAN FÖR ELEKTRO- OCH SYSTEMTEKNIK

TRITA TRITA-EE 2016:121

www.kth.se

AbstractCongestion management methods are useful regulatory mechanisms toprevent transmission capacity problems. This thesis intends to assesswhether congestion management can be cost efficiently used to postponeor even avoid network capacity reinforcements while increasing the host-ing capacity of wind power on Gotland. Two methods, re-dispatch andmarket splitting, are studied in details and applied to the Gotland case.A simplified electricity market model using historical data from Gotlandwas designed to perform the simulations. While these methods pass thecost of lack of transmission capacity on different actors in the electricitymarket (mainly grid owner for re-dispatch and consumers and producersfor market splitting), simulations indicate that both method could beemployed to raise the installed production capacity of wind power onGotland by at least 25MW above the stated limit of 195MW withoutnegatively impacting the income of any actors. Moreover, market split-ting efficiently reflects the transmission problems on the energy price ofGotland, thus giving economic incentive for flexible power consumptionto alleviate the problems.

Referat

Metoder för flaskhalshantering är användbara verktyg för att förebyg-ga problem anknutna till begränsningar i överföring i elnätet. Dennauppsats syftar till att fastställa huruvida dessa metoder på ett kost-nadseffektivt sätt kan senarelägga eller till och med undvika utbyggnadav elnätet samtidigt som den installerade vindkraftskapaciteten på Got-land ökas. Två metoder för flaskhalshantering, Re-Dispatch och MarketSplitting, studeras i detalj och appliceras på fallet Gotland. En förenkladenergimarknadsmodell, som tillämpar historisk data från Gotland, hardesignats för simulering. Trots att dessa två metoder fördelar kostnadenrelaterad till brist på överföringskapacitet till olika marknadsaktörer(huvudsakligen nätägaren vid Re-Dispatch och konsumenter samt pro-ducenter vid Market Splitting), indikerar simuleringar att båda meto-derna kan appliceras för att öka den installerade vindkraftskapacitetenpå Gotland med minst 25MW över den nuvarande begränsningen på195MW utan att inkomsten för någon av aktörerna påverkas negativt.Dessutom, Market Splitting på ett effektivt sätt fångar överföringspro-blem i priset på Gotland, vilket ger ekonomiska incentiv för flexiblalaster att anpassa sig för att undvika flaskhalsar i systemet.

Acknowledgement

I would like to thank my supervisors at Vattenfall, Gaëlle Ryckebusch and DavidErol as well as my supervisor at KTH, Daniel Brodén. Thank you for taking thetime to read my reports and try to understand every little detail, and thank youfor being so engaged during our meetings. Your comments and feedback have beenextremely valuable!

I would also like to thank Richard Scharff from Vattenfall for take the time tolisten to all my questions about small details of the Nordic Electricity Market. Iwould like to show my appreciation for Lars Joelsson from Vattenfall for taking thetime to listen and comment on the results i have obtained. Your comments havehelped me see my thesis from a broader point of view. I would like to express mygratitude to all my colleagues at Vattenfall R&D for their warm welcome and foralways being open for discussion about their own projects or mine.

Finally, i would like to thank my room mate and fellow student Jakob Sahlin forall the discussions we had together. The time we spent speaking about my thesistogether help me clarify so many things, both in my head and on paper!

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose of the Master Thesis . . . . . . . . . . . . . . . . . . . . . . 21.3 Thesis Goals and Objective . . . . . . . . . . . . . . . . . . . . . . . 21.4 Delimitation of the Study . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theoretical Study 52.1 The Nordic Electricity Market . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.2 Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Electricity Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2.1 Future Market . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2.2 Day Ahead Market . . . . . . . . . . . . . . . . . . . . . . . . 92.2.3 Intraday Market . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.4 Real Time Market . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 Congestion Management . . . . . . . . . . . . . . . . . . . . . . . . . 142.3.1 Value of transmission capacity . . . . . . . . . . . . . . . . . 152.3.2 Locational Marginal Pricing . . . . . . . . . . . . . . . . . . . 152.3.3 Market Splitting . . . . . . . . . . . . . . . . . . . . . . . . . 172.3.4 Re-dispatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.5 Market Re-dispatch . . . . . . . . . . . . . . . . . . . . . . . 222.3.6 Explicit Auction . . . . . . . . . . . . . . . . . . . . . . . . . 232.3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.4 Demand Response (DR) . . . . . . . . . . . . . . . . . . . . . . . . . 242.4.1 End-User Flexibility . . . . . . . . . . . . . . . . . . . . . . . 252.4.2 Demand Response Programs . . . . . . . . . . . . . . . . . . 26

3 The Gotland Case 273.1 Electric Power System . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1.1 Interconnection with Mainland . . . . . . . . . . . . . . . . . 273.1.2 Gotland’s Power Production . . . . . . . . . . . . . . . . . . . 273.1.3 Consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1.4 Smart Grid Gotland . . . . . . . . . . . . . . . . . . . . . . . 28

3.2 Earlier Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2.1 Study 1: Analysis of Demand Response Solutions for Conges-

tion Management in Distribution Networks . . . . . . . . . . 293.2.2 Study 2: Analysis of Demand Response Participation Strate-

gies for Congestion Management in an Island DistributionNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4 Method 314.1 Spot Price Computation . . . . . . . . . . . . . . . . . . . . . . . . . 314.2 Trading Wind Power . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2.1 Profit for the wind power producers with the Two-Price System 324.2.2 Trading with Unknown Balance Prices . . . . . . . . . . . . . 33

5 Case Study 355.1 General Data and Assumptions . . . . . . . . . . . . . . . . . . . . . 35

5.1.1 Wind Power Producers . . . . . . . . . . . . . . . . . . . . . 355.1.2 Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.1.3 Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.1.4 Grid Owner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.1.5 Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5.2 Case 1: Using Re-dispatch to Manage Congestion between Gotlandand Mainland Sweden . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.1 Producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.2 Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.3 Grid Owner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.3 Case 2A: Using Market Splitting to Manage Congestion between Got-land and Mainland Sweden - Current Transmission Capacity . . . . 405.3.1 Transmission Capacity . . . . . . . . . . . . . . . . . . . . . . 405.3.2 Grid Owner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3.3 Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3.4 Producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.4 Case 2B: Using Market Splitting to Manage Congestion between Got-land and Mainland Sweden - Equal Transmission Capacity in BothDirection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.4.1 Transmission capacity . . . . . . . . . . . . . . . . . . . . . . 435.4.2 Market Assumptions . . . . . . . . . . . . . . . . . . . . . . . 435.4.3 Grid Owner . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.4.4 Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.4.5 Producers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.5 Market Splitting with Flexible Demand . . . . . . . . . . . . . . . . 445.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.5.2 Impact of Flexible Demand on Wind Power Dispatch . . . . . 475.5.3 Economic Factors . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.6 The Possible Effect of Optimal Wind Power Bidding on CongestionManagement Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 495.6.1 Data and Assumptions . . . . . . . . . . . . . . . . . . . . . . 505.6.2 Variation in Bidding quantities . . . . . . . . . . . . . . . . . 515.6.3 Detecting Congestion . . . . . . . . . . . . . . . . . . . . . . 52

6 Discussion 536.1 Accuracy of the Results . . . . . . . . . . . . . . . . . . . . . . . . . 536.2 Discussion about the Use Case Results . . . . . . . . . . . . . . . . . 53

6.2.1 Case 1: Using Re-dispatch . . . . . . . . . . . . . . . . . . . . 536.2.2 Case 2A and 2B: Using Market Splitting . . . . . . . . . . . . 546.2.3 Flexible Demand . . . . . . . . . . . . . . . . . . . . . . . . . 556.2.4 Re-Dispatch or Market Splitting . . . . . . . . . . . . . . . . 556.2.5 Congestion rate . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.3 Thesis Benefits for the Smart Grid Gotland Project . . . . . . . . . . 56

7 Conclusion 577.1 Conclusive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 577.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Bibliography 59

Chapter 1

Introduction

1.1 Background

Sweden has set an ambitious target for its share of renewable energy by 2020: 49%of its total energy production. This was however met as early as in 2013 (52.1%)and the Swedish government decided in 2015 to promote an increase of 5TWh ofelectricity produced from renewables by 2020 using the electricity certificate system.[1]. Most of this renewable power production will come from wind power which oftenis connected to the distribution grid. This new source of power is however muchmore intermittent than other conventional energy sources and therefore exposes thegrid to stresses and challenges that were not taken into account when the systemwas designed.

A modernization of the system has been undertaken (going from "simple" gridto smart grid), where the demand, previously considered inelastic, could take amore active part in maintaining a good power quality in the system: DemandResponse requires efficient information and communication technologies as well asmarket and/or regulatory mechanisms to incentivize and encourage demand sideparticipation.

The project Smart Grid Gotland has been initiated to study the potential ofcombining communication technologies with the actual distribution grid to facilitatewind power integration and improve electricity quality [2].

In situations of locally high wind power production coupled with low consump-tion on Gotland, the transmission capacity of the High Voltage Direct Current(HVDC) cable connecting Gotland to the rest of Sweden may be insufficient (itis then congested), and unless the extra production can be absorbed by the localdemand, wind power production must be curtailed for stability reasons. Althoughthe risk of congestion is presently infinitesimal, adding wind power capacity on theisland will increase the probability that it occurs. A congested network leads toa decrease in total social welfare since the resources are not used as efficiently asthey would be with sufficient transmission capacity. In theory however, DemandResponse could help mitigate and in some cases avoid congestion [3] by shifting the

1

CHAPTER 1. INTRODUCTION

demand from peak-load hours to peak-production hours.A previous study conducted in 2013 shows that it is technically feasible to bal-

ance 5MW beyond the capacity of the HVDC link by using the flexibility (longterm and short term demand response) of 1900 households as well as a batteryenergy storage system [4]. Following, [5] proposed and analysed two market-basedstrategies applied to detached houses for day-ahead congestion management in or-der to take into account economic considerations as well. Dynamic network tariffis used by the Distribution System Operator to reimburse the demand responseparticipants for higher electricity costs, and Spot price optimization tries to avoidcongestion by reallocating the flexible demand to hours with congestion problems(thereby absorbing the excess of production). Although the results were positive,increasing the capacity of the HVDC link appears to be much more economical.

The main problem with these strategies seems to be that, given the currentconditions, there is no correlation between Gotland’s energy export problems andthe electricity spot price. Indeed, Gotland is a small island part of a much biggerprice area and the availability (or unavailability) of cheap energy sources does notaffect much (or at all) the price of the area.

Hence, the suggestion of studying the consequences of turning Gotland into itsown price area on the actors located on Gotland.

1.2 Purpose of the Master ThesisThe purpose of this Master Thesis is to assess whether congestion managementmethods could be used to favor an increase in wind power integration on Gotlandby looking at how they would economically impact different actors of the electricitymarket, namely the grid owner, the wind power producers and finally the end-users.

1.3 Thesis Goals and ObjectiveThe following Master Thesis goals and objectives have been formulated:

• Performing a theoretical economic assessment of different conges-tion management methodsThe operating of the Nordic Electricity Market, the actors involved as well asthe price settlement mechanisms will be thoroughly reviewed and presented.Following, basic concepts of microeconomics will be used to understand andillustrate how different congestion management methods can deal with lack oftransmission capacity and the impacts they have on different actors.

• Modeling the electricity market of GotlandThe Gotland electricity market is modeled using MATLAB. The model shouldbe able to compute the spot price on Gotland as well as the generators dis-patched (and the energy produced) each hour and implement the congestionmanagement methods.

2

1.4. DELIMITATION OF THE STUDY

• Performing SimulationsSimulations will be performed in MATLAB for a set of relevant scenarios.Scenarios will be chosen so not only results for Gotland but also general con-clusions can be drawn. The simulation results will serve as reference materialfor the economic impact assessment, discussion and general conclusions.

1.4 Delimitation of the StudyThe study has been limited in certain aspects to ensure reasonable and qualitativedeliverables within the time frame of this thesis.

• Geographical DelimitationThe study is limited to the island of Gotland in Sweden. However, the modeldesigned during this thesis should be general enough to be adapted to anyother region working with the optimal dispatch mechanism (price cross).

• Simplified MarketOnly the day-ahead market is modelled (in a simplified version). The balancemarket, is not modeled due to lack of time and available data. However, itremains in the background so that conclusions are drawn with the mechanismsof this market in mind as well.

• Flexible ConsumptionThe flexible demand model is rudimentary. The associated results are there-fore more illustrative than they are specific.

• Grid CapacityThe installed wind power capacity on Gotland is increased during the simu-lations. It is assumed that the power grid of Gotland as well as the real timebalance mechanisms are able to cope with real time imbalances in the system.

3

Chapter 2

Theoretical Study

This chapter intends to present the architecture of the Nordic electricity market,the roles and responsibilities in such a deregulated market. The second part willanalyze several congestion management methods as well as their implications forthe different actors engaged in the electricity market.

2.1 The Nordic Electricity Market

2.1.1 Overview

Electricity is by its nature a special commodity: besides being regarded as an ab-solute necessity in modern society, electricity is hardly storable and is consumed atthe very instant it is generated. Demand and supply vary moreover continuouslyand it is impossible to trace back any consumed kWh to the producer that actuallygenerated it, thus putting special requirements on the billing system. Furthermore,power system infrastructures are extremely costly, making the transmission anddistribution grids natural monopolies. For these reasons, a specific market with aparticular set of actors and rules have to be designed for a physically and economi-cally efficient power supply.

Nord Pool, the power market of the Nordic and Baltic regions, was establishedin 1993 in Norway and extended to Sweden in 1996, becoming the first internationalmarket for electricity exchange. Nowadays, it encompasses 20 countries with 380companies trading on the market [6].

2.1.2 Actors

Regulator

The regulator represents the political authorities responsible for making sure thatlaws and regulation are followed and that public interests are taken into account.They have the responsibility for ensuring market efficiency and supervising themonopolistic entities. The introduction of electricity certificates to boost renewables

5

CHAPTER 2. THEORETICAL STUDY

investments is a good example of how the regulator can impact the market. Eachcountry has its own regulator meaning that parts of the market at different locationscan follow different rules (e.g. Sweden uses Green certificates while Denmark usesfeed-in tariff for wind power [7]).

Producers

The producers are companies generating active power and selling it on the market.For an efficient market, it is important that no company has a too big market share.The regulator, in this case the competition authority, can interfere in mergers if itbelieves the deal harmful for competition.

Grid owners

The grid companies own the physical infrastructure of the electricity network. Theyare responsible for investments to connect new customers and producers to thegrid, maintain and sometimes extend the network capacity. As they are naturalmonopolies, the regulator makes sure they operate and maintain the grid properlywith a set of requirements (e.g. System Average Interruption Duration Index). Inthe Nordic electricity market, the grid owners must pay for the power losses in theirnetwork and therefore transfer the costs to their customers through the grid tariff.

Customers or End-Users

The customers or end users are the power consuming part of the system. Theyare affected by tariffs and pricing but usually are not seen as directly controllable,meaning that the supply in general has to make up for their unpredictable vari-ations. However, modern information and communication technologies allow foran improved possibility of controlling end user consumption (to some extents) andthereby help improve the system efficiency. Unless they are major power consumers,end users are not directly in contact with the power market, but go through a re-tailer, which they are free to choose.

Power Exchange

The power exchange (sometimes called power pool) is responsible for matchingbids purchases and sales of electricity. It matches the bids using a social welfareoptimizing algorithm (today called EUPHEMIA [8]) so that the prices and thetrades quantities are known to all. In the Nordic electricity market, the powerexchange entity is Nord Pool. Its basic activity is the operation of the short termphysical electricity market (or spot market), where it computes the market clearingprice (or spot price) for each hour of the coming day. The power exchange in theNordic electricity market is named Nord Pool and is supervising energy trades forthe day-ahead and intra-day market (c.f. figure 2.1). These markets are thoroughlydescribed in their dedicated sections later in this chapter.

6

2.2. ELECTRICITY TRADING

System Operator

The system operator assures security of supply in the system. It is responsible forthe physical balance of the system, and for keeping sufficient capacity reserves inthe generating system and the grid to maintain stability. It is also responsible forkeeping the frequency stable in the system and for controlling the voltage. Thesystem operator can either be an independent entity (ISO), which is common in theUSA or be even responsible for the transmission grid (TSO). In the Nordic area,there are four TSOs which own, maintain and operate the main grid in addition tobeing system operators.

Congestion management is a task that is shared by both the system operatorsand the power exchange. The TSOs provide the power exchange with the trans-mission capacity limits of each regions of the network. The power exchange thencompares the volumes of traded energy with these limits to determine whether thereis a risk of congestion in some parts of the system. Nord Pool plays a major roleof relieving congestion between price areas whereas the TSOs are responsible forcongestion within the prices areas (more on that in section 2.3)

Balance Responsible Player (BRP)

The balance responsible player is financially liable for imbalances between its sub-mitted hour by hour schedules and the actual consumption/production for the cor-responding hours. A BRP can be balance responsible for production, consumptionand/or trade. Deviations, or imbalances are subject to penalties that are defined bythe state of the system (up- or down-regulation) and the costs of balancing reserves(more on that in section 2.2.4). In the Nordic electricity market, all generatorsand consumers have to be tied to a balance responsible entity. Large enough powerconsumers and producers such as big industries are usually balance responsiblethemselves. Smaller customers and generators however often turn to other entitiesthat are responsible for their balance (e.g. retailers). Between the day-ahead gateclosure and one hour before the delivery hour, BRPs may trade on the Intradaymarket to improve their balance (more on that in section 2.2.3).

Retailers

Retailers are market participants that sell electricity to end users. The majority ofthe retailers are producers or grid owners. From a demand response perspective,a retailer could also play the role of an aggregator (it does not necessarily have tobe the retailer), allowing the end users to take a more active part in mitigatingimbalances.

2.2 Electricity TradingThe electricity that a end-user is receiving has been traded several times over longperiods of times before being delivered. Figure 2.2 shows different important dead-

7


Figure 2.1: The three main markets of the Nordic electricity market [6]. The Spot marketor day-ahead market is a pool based market where the market clearing price for each hourof the coming day is decided. The Intraday market Elbas is a pay as bid market thatallows BRPs to improve their balance. The Real time market is used by the TSOs to buyregulating power to maintain physical balance in the system.

delivery hour

operation plan to TSO

intraday gate closure

intraday opens

h-45’h-1h

d-1,14:00

day ahead auction results

d-1,12:42

day ahead gate closure

d-1,12:00 h

future market(hedging)

day ahead market(ELSPOT)

intraday market(ELBAS)

real timemarket

post market(settlements)

Figure 2.2: The timeline of electricity trading in the Nordic electricity market.

lines for some participants trading this commodity.

2.2.1 Future Market

The future market consists in financial contracts with a time horizon up to severalyears. The contracts are mostly used for price hedging and risk management (this isbeyond the scope of this thesis but is nonetheless important, especially for investors).

8


2.2.2 Day Ahead Market

The day-ahead market is the main activity1 of the power exchange (Nord Pool).At this stage, generations bids and demand offers are matched through a poolingauction in order to obtain a uniform price for the whole region: the market clearingprice. The TSOs communicate the transmission capacity of the network, and if thelimits are exceeded during the price clearing process, the system is split into severalprice areas (cf. section 2.3)

Pooling

The system price is determined through pooling auctions. All generators, retailersand consumers place bids anonymously to the power exchange. A simple sell bidspecifies the lowest price for which a player is willing to sell, the quantity beingsold, the hour of delivery as well as the geographic location of the bid (known asbidding area). A simple purchase bid specifies the highest price willing to be paidfor a specific quantity, the hour of consumption as well as the geographic location.Block bids (bids running for several hours) are also possible. A sell block bid is e.g.fully accepted only if the price of the block is lower than the average area price ofthe specific block period [9].

Pricing

There are different procedures for price settlement in a power exchange. The twomain clearing processes are the periodic clearing and the continuous auction. NordPool uses the periodic clearing price settlement for the market clearing price (day-ahead market) but uses the continuous auction settlement for its Intraday market.The main difference between the two is that the periodic clearing price leads toa single price for all participants while in the continuous auction, participants arepaid (or pay) what they bid. Both settlement method have their own advantagesand disadvantages. The biggest advantage of the periodic clearing price settlementis that it provides high liquidity as all participants trade at the same time and place.The advantage of continuous market is that the participants have the flexibility tomake their trades whenever they want.

The spot price is determined using the price cross method2. Consumption offersare ranked in decreasing price order while supply offers are ranked in increasing priceorder (this is often called the merit order). The point where the sell and purchasebids meet defines the market clearing price and the traded quantity. All sell bids

1In 2013, 349TWh were traded on the day-ahead market and 4.2TWh were traded on theintraday market [6].

2The spot price calculation is actually slightly more complex. Euphemia, the algorithm used byNord Pool [8] takes into consideration, among other parameters, all the possible bid types and theirrules, the line capacity limits provided by the TSOs and solves an optimization problem with thegoal of optimizing the total social welfare. For simple systems and relatively simple bids however,the price cross is valid.

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quantity [MWh]

market clearing price

pric

e [€

/MW

h]

sell bids

purchase bids

Figure 2.3: Market clearing process of the spot price. Sell bids under the market clearingprice (MCP) and purchase bids over the MCP are accepted. The light grey part representsthe producers surplus and the dark grey part the consumer surplus. Their sum is the totalsurplus, or social welfare.

below and all the purchase bids above the market clearing price are accepted (cf.figure 2.3).

In practice, the bids for the next day have to be submitted before 12:00 CET,time at which the gate for day-ahead trade closes. The computed prices for eachhour are made public around 12.42 [6]. If no congestion has occurred between thebidding areas, a unique system price is announced, otherwise different area pricesmight be needed (more on that in section 2.3).

Bilateral Trading

It is also possible for actors to deal directly with each other without passing throughthe power exchange. This bilateral trading does not affect the spot price. Thesetrades must however be reported to the system operators as they are taken intoaccount for balance responsibility.

2.2.3 Intraday MarketThe Intraday market consists of a continuous trading platform between the day-ahead and the real time market. It allows BRPs to adjust their balance giventhat they have acquired better consumption/production forecasts or if changes in

10


schedule (e.g. outages) have occurred. The intraday market is based on bilateralcontracts3 with a pay-as-bid mechanism. Since this market is used for correctiveactions, the liquidity is usually low and highly variable. The intraday market is openfrom 14:00 CET the day before delivery up to 1 hour before the hour of operation.

2.2.4 Real Time MarketThe real time market, sometimes called balancing market, is used by the systemoperators to keep the system in physical balance. For this, the TSO uses regulatingpower that is bought in the real time market. At the end of the hour of delivery, thecosts of regulation are passed on to the BRPs. The costs vary for each participant,depending on the system imbalance as a whole as well as the size and direction ofthe player’s imbalance.

System Imbalance

There are three possible situations for the system as a whole:

• Positive imbalance: The total supply in the system is greater than the totaldemand. There is a need for downward regulation

• Negative imbalance: The total supply in the system is lower than the totaldemand. There is a need for upward regulation

• No imbalance: Supply and demand are about equal. No need for regulation.

Participants Imbalance

Similarly, participants may also have an imbalance, e.g. a producer might have (thecontrary applies for a consumer):

• Excess: The actual generation during the hour is greater than the scheduledgeneration.

• Deficit: The actual generation during the hour is lower than the scheduledgeneration.

• No imbalance: The actual generation and scheduled generation are equiva-lent.

Price of Balancing Power

The price of balancing power is determined by the area price as well as the amount ofregulation power needed. Up-regulation orders (increase of production or decrease of

3The intraday market Elbas is however centrally organised. The bids are all placed on theElbas platform and are visible to all players. However, in case of congestion, a player will only seethe bids that are physically possible to be met.

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quantity [MWh]

amount of up regulating power needed

up-regulation price

day ahead price

down regulation orders up regulation orders

pric

e [€

/MW

h]

Figure 2.4: Price setting in the real time market. Offers of varying price and volume areavailable for the TSO to use for regulating the physical balance of the system. The resultingprice is a single price used for settlement. Both producers and consumers are welcome toplace bids on the balance market.

consumption) must always have a price higher than the area price. Similarly, down-regulation orders must have a price lower than the area price. The TSO determinesthe amount of regulating power (as well as its direction) using the production andconsumption plans provided by the BRPs 45 minutes before the hour of delivery.The price of balancing power is a single price, meaning that all actors will be paid(or pay) the same price (similar to the market clearing price). Figure 2.4 illustratesthe price setting in the real time market with a system need for up-regulation.

Settlement in the Real Time Market

There are two different price systems for balance settlements 4 in the real timemarket: the one-price and two-price system.

For the one-price system, if the deviation from one player participates in puttingthe system off balance, it leads to a financial loss for the BRP. However, if thedeviation from one player helps the system go back to balance, it leads to a profit.

For the two-price system however, no deviation can lead to a profit. Thoseputting the system off balance will be financially penalized while the ones supporting

4The settlement methods and time resolution vary widely between European countries [10].The ones presented here are valid for the Nordic electricity market.

12


Table 2.1: Imbalance settlements for a BRP given the system state. λ represents themarket clearing price (day-ahead) while λ↑ represents the price for balancing power for up-regulation and λ↓ the price for balancing power for down-regulation. An italic font meansthat the player is making a profit compared to a trade of equivalent volume in the day-aheadmarket while a bold font means a loss.

One-price systemXXXXXXXXXXXBRP

System upward regulation no regulation downward regulation

Deficit buy deficit at λ↑ buy deficit at λ buy deficit at λ↓Excess sell excess at λ↑ sell excess at λ sell excess at λ↓

Two-price systemXXXXXXXXXXXBRP

System upward regulation no regulation downward regulation

Deficit buy deficit at λ↑ buy deficit at λ buy deficit at λExcess sell excess at λ sell excess at λ sell excess at λ↓

the system will not get rewarded (since it is not intentional).

Imbalance and binding plan

Imbalance settlements are based on the generation (or consumption) plan providedby the BRP to the TSO. In the Nordic electricity market, the BRP disposes of 15minutes after the gate closure of the intraday market to propose an operation plan.This plan is binding, meaning that the BRP is not allowed to reschedule generationor consumption in its own portfolio or do bilateral trades past this point. This planserves as the main component for financial imbalance settlement. In the Nordicelectricity market, BRPs are exposed to two different kind of imbalances5, eachsettled using its own price system. They are described as such in [11]:

Production imbalance = observed production - production plan (2.1)

Consumption imbalance = prod. plan - sales + purchases - observed cons. (2.2)

The production plan is plan submitted to the TSO, the observed production is whatthe TSO has metered at the end of the delivery hour. Sales and purchases representthe quantity of energy traded in the day-ahead market, the intraday market as wellas bilateral trades.

The production imbalance is subject to the two price system while the consump-tion imbalance is subject to the one price system.

Let’s take the example of a wind power producer (which is balance responsible)

5Production imbalance can be seen as a real time/planning imbalance while consumption im-balance can be seen as a trade/planning imbalance.

13


trading on the power exchange (the notation and price systems shown in table 2.1are used). The following events take place:

1. A first production forecast (50MWh) is made for day-ahead bidding for thecorresponding hour of delivery: 50MWh is sold at a the market clearing priceλ = 30 e/MWh.

2. An updated forecast (60MWh) comes 2 hours before the delivery hour.

3. The producer wants to sell the extra 10MWh but no satisfactory offer is foundon intraday.

4. The producer submits a production plan of 60MWh to the TSO.

5. The TSO measures 55MWh of production at the end of the delivery hour.

6. The state of the system for the delivery hour was down-regulation. The pricefor down-regulating power λ↓ was 20 e/MWh.

Using points 1 to 6, and equations 2.1 and 2.2, we see that the wind power producerhas a consumption imbalance of 60−50 = 10MWh (excess). However, the producerhas a production imbalance of 55− 60 = −5MWh (deficit).

50MWh were initially sold at λ on the day-ahead market, an excess of 10MWhsold at λ↓ (one price system) and a deficit of 5 MWh was bought at λ (two-pricesystem) on the real time market. The total profit of the wind power producerequals: 50 · 30 + 10 · 20− 5 · 30 = 1550e. If the wind power producer had had accessto a better forecast and sold 55MWh on the day-ahead market with no imbalanceinstead, they would have earned 55 · 30 = 1650e.

Balance Costs Minimization

Due to the difficulty to obtain accurate forecasts (especially for day-ahead bidding),wind power producers are exposed to high imbalance costs. They might thereforewant to minimize those by optimizing their bids on the day-ahead market (moreon that in section 4.2). In a market dominated by wind power, this optimizationprocess might have an impact on the market clearing price.

2.3 Congestion ManagementCongestion management by means of the electricity market was first discussed inacademic literature in 1992 [12]. It is now accepted that the electricity marketshould take into account not only the cost of producing power but also transportingit. The market should therefore even reflect the transmission limitations in orderto give the appropriate price signal to the market parties. In theory, each nodein the network (a node can be seen as a point connecting power generation orconsumption, e.g. a substation) should be treated separately in order to best reflect

14

2.3. CONGESTION MANAGEMENT

the network and its constraints. On the other hand, while this gives the rightprice incentive to the market participants, access to electricity is seen as a basicright in modern societies and one might argue that a different electricity price forend users is a form of social injustice: prices should be uniform within a countryor geographical zone. Moreover each congestion management method affects themarket actors differently (who should bare the responsibility for the scarcity oftransmission capacity?), making it difficult to find consensus over which methodshould prevail.

2.3.1 Value of transmission capacity

It should however be noted that having transmission capacity between two areasincreases the total social welfare of both areas.

To illustrate this, let’s take the example of two areas, A and B, with supply anddemand curves as shown in figure 2.5a. The price difference between areas indicatesthat some participants might benefit from being able to do transactions with theother area. For example, suppliers in A want to sell energy in B since the price ismuch higher. Similarly, consumers in B would want to buy electricity at a muchlower price.

Introduction of transfer capacity can be seen as a shift of the demand curves(consumers are able to trade with producers from another region). If we comparefigure 2.5b and 2.5a, we see that 50MW of transfer capacity is equivalent to shifting50MW from the demand curve in B to A (the demand is decreased in B and increasedin A). New supply and demand curves in both areas mean that prices will bechanged. Indeed, area A sees a price increase while it decreases in B.

On a social welfare perspective, both areas benefit transmission capacity. Whilethe welfare suplus of the demand in A is decreased (the area in dark grey diminishes),the gain in social welfare made by the producers is greater (light grey area increases).The total benefit for area A of a 50MW transmission capacity is represented as greentriangles in figure 2.5b. Similarly, the welfare gain made by the consumers in areaB is greater than what the suppliers loose.

Finally, when transmission capacity is sufficient (in this example 100MW), theprices in both areas is the same (cf. figure 2.5c). The total welfare is then maximum(the green triangles are the largest).

Although transmission capacity induces a net social welfare gain, congestionmanagement is nevertheless a necessity since it would be extremely costly to buildand maintain perfect6 transmission capacity everywhere in the system. Some meth-ods are presented in the sections below.

6perfect transmission capacity means that there is enough capacity to cover for the biggestpeaks which occur sometimes only a few times a year.

15


pA [€/MWh]

PA [MWh/h]

pB [€/MWh]

PB [MWh/h]

Area A Area B

pA0

Supply SASupply SB

Demand DADemand DB

pB0

100 200 100 200

Figure 2.5a: No transmission capacity between areas

pA [€/MWh]

PA [MWh/h]

pB [€/MWh]

PB [MWh/h]

Area A Area B

50MW trans. capacity 50MWtrans. capacity

pA0

Supply SASupply SB

Demand DADemand DB

pB0

pA50

pB50

100 200 100 200

Figure 2.5b: 50MW transmission capacity between areas

pA [€/MWh]

PA [MWh/h]

pB [€/MWh]

PB [MWh/h]

Area A Area B

100MWtrans. capacity

100MWtrans. capacity

pA0

Supply SASupply SB

Demand DADemand DB

pB0

pA50

pB50pAB pAB

100 200 100 200

Figure 2.5c: Full transmission capacity between areas

16


2.3.2 Locational Marginal PricingThe concept of locational marginal pricing (or sometimes nodal pricing) refers totreating each node in the transmission network separately for market clearing. Thiswas first discussed in [12] and was first implemented in New Zealand in 1997, andin some parts of the USA7 in 1998 [13].

Locational differences are due to congestion and losses in the system. Indeed,due to the physical characteristics of the transmission system, energy is lost as it istransmitted from generators to loads, and additional generation must be dispatchedto cover for the loss. Transmission congestion on the other hand prevents generationwith a lower marginal price to meet the consumption at another point in the system:more expensive generation must be dispatched instead, resulting in a higher nodalprice.

In order to calculate the market clearing price at each node, the following pro-cedure is used:

• Determine the least cost dispatch to serve load (social welfare optimization,as described in section 2.2.2) for entire region.

• Use dispatched units to determine power flow.If transmission capacity is exceeded, determine least cost generation dis-

patch for each uncongested region (including maximum transferred energythrough transmission link).

• Calculate prices by calculating the dispatch for one additional MW at eachnode.

Figure 2.6 shows an example of nodal price calculation between two nodes underdifferent conditions: sufficient transmission, congestion and transmission losses8

accounted for in the price. Demand is there assumed to be inelastic, meaningconsumers will accept any price.

2.3.3 Market SplittingMarket splitting is essentially a simplified form of locational marginal pricing. In-stead of treating each node of the power system separately, they are aggregatedinto zones. We speak then of zonal pricing and the zones are called bidding areas orprice areas. The author of [15] discusses however the difficulty (or impossibility) todefine zones that are truly efficient for congestion management. Efficient zones (i.e.congestion occurring almost always between zones and not within) often induce sideeffects such as market inefficiencies and arbitrage possibilities (e.g. too few actors

7Nodal pricing is now the main congestion management method in the entire USA, althoughthe implementations differ slightly between regions [13].

8The inclusion of transmission losses in the price is not uniform. In the USA for example,some TSO use nodal prices including losses and others use transmission tariff [14]. In the Nordicelectricity market, all the TSOs use transmission tariff.

17


node1

capacity 600MW

actual flow: 501MW

CASE 1: TRANSMISSION CAPACITY IS ENOUGH

CASE 2: TRANSMISSION CAPACITY IS NOT ENOUGHprice node 1&2: 40€/MWh

price node 1: 35€/MWh price node 2: 45€/MWh

node2

load299MW

load890MW

400MW@

30€/MWh

400MW@

35€/MWh

400MW@

40€/MWh

400MW@

45€/MWh

generators at node 1 generators at node 2

node1

capacity 300MW

actual flow: 300MWnode

2load

299MWload

890MW

400MW@

30€/MWh

400MW@

35€/MWh

400MW@

40€/MWh

400MW@

45€/MWh

generators at node 1 generators at node 2

CASE 3: LOSSES TAKEN INTO ACCOUNT

price node 3: 30€/MWh price node 4: 31.03€/MWh

price = = 31.03

node3

capacity 600MW

actual flow: 300MWline losses: 10MW

node4

load290MW

400MW@

30€/MWh

generator30 x 300

290

Figure 2.6: Illustration of nodal pricing under different conditions. Case 1: the threecheapest generating units are dispatched and the transmission link capacity is sufficient.Nodes 1 and 2 are treated as one and the price is equal to the one of the most expensiveunit. Case 2: The transmission capacity is not sufficient and a more expensive unit has tobe dispatched at node 2. Prices are different between nodes, each price corresponding tothe most expensive dispatched unit at each node. Case 3: Losses are taken into account.Although the load at node 4 is only 290MW, it pays for the entire amount of energydispatched, 300MW. This means that the cost for 290MW at node 4 must be equal to theprice for 300MW at node 3. A dispatched generator does not necessarily produce at fullcapacity, only the required quantity.

18


Figure 2.7: The different bidding areas in the Nording electricity market [6]. The UKbelongs to another market called N2EX which is doing exchanges with the Nordic electricitymarket through the Nord Pool power exchange.

if zone is too small). This is for example the reason why Finland still remains oneunique price area.

The Nordic electricity market is implementing the market splitting congestionmanagement method and is nowadays split into several price areas: five in Norway,four in Sweden, two in Denmark and one for each other country. The areas are shownin figure 2.7. To compute the price between bidding areas, the same principle asfor nodal pricing is followed, with the difference that all nodes encompassed by azone are treated as one for price calculation (i.e. congestion between nodes (if any)is disregarded and all generation and consumption within each zone is aggregated).

Congestion Rent

When congestion between zones occurs, the spot price in the area with an energydeficit will be higher than in the area with an energy surplus, and power will flowfrom the surplus area to the deficit area9 (c.f. figure 2.6, case 2). The power flowingfrom one area to the other is bought at a lower price than it is sold. This tradingsurplus (also called congestion rent) can go to the system operator, the powerexchange or the grid company10. This rent equals the price difference between areastimes the transferred energy. Since the owner of the transmission link is making aprofit from congestion, there is a need for regulation. In Denmark and Norway, the

9This is slightly more complex in case more than two zones are concerned, and it is not trivialto find the location of the market split that maximizes social welfare. A thorough algorithm ispresented in [16]. We will however focus in this thesis on congestion management between twozones.

10In the Nordic electricity market, the congestion rent goes to the TSO. If the rent is betweencountries (and therefore TSOs), the rent is split between them.

19


pA [€/MWh]

PA [MWh/h]

pB [€/MWh]

PB [MWh/h]

Area A Area B

50MW trans. capacity 50MWtrans. capacity

pA0

Supply SA welfare gain for SAdue to 50MW transmission capacity

congestion rent

welfare gain for DBdue to 50MW transmission capacity

Supply SB

Demand DADemand DB

pB0

pA50

pB50pB50pAB pAB

100 200 100 200

Figure 2.8: Areas are split into several markets in case of congestion. The price in thearea with a surplus of energy (A) is lower than the system price (PAB) while the price inthe deficit area (B) is higher. Increased transmission leads to a smaller price difference.The grid owners makes a profit from the price difference (dark grey rectangle).

rent is deduced from the transmission tariff while in Sweden and Finland, it is usedto finance the building of new lines [17].

Economic Consequences

Figure 2.8 illustrates the market splitting mechanism between the two areas shownin the example of figure 2.5. With 100MW of transmission capacity between them,there would be no congestion and every participant would see the price pAB. How-ever, a reduced capacity (50MW) leads to a price difference (pA50 and pB50 respec-tively). The transmission capacity does not benefit all actors: in area A, producersreceive a higher price (pA50 instead of pA0) and sell more power than in case ofno transmission between areas. Consumers in A on the other hand consume lessat a higher price. The increased surplus for the producers however is greater thanthe loss for consumers, meaning that as a whole, actors in area A benefit from thetransmission capacity. Similarly, in area B, consumers will be able to purchase morepower at a lower price and get an increased surplus greater than the deficit of theproducers. Lastly, the grid owner will receive a congestion rent from transmittingpower from the low price area to the high price area.

2.3.4 Re-dispatch

The re-dispatch congestion management method is used in the Nordic electricitymarket within price areas (meaning that all participants see the same price). Incase of congestion, the TSO engages market transactions in order to avoid thetransmission capacity to be exceeded.

20


pA [€/MWh]

PA [MWh/h]

pB [€/MWh]

PB [MWh/h]

Area A Area B

50MW trans. capacitypA0

Supply SASupply SB

Demand DADemand DB

pB0

pA50

pB50pB50pAB pAB

100 200 100 200

cost for the TSO to increaseconsumption in A

This triangle is the profit made bygenerators in B to increase production

This rectangle represents thecost for the TSO to decrease

production in A

profit of consumers in Bdealing with TSO

50MWtrans. capacity

Figure 2.9: With Re-dispatch, the TSO must decrease production (or increase consump-tion) in area A and increase production (or decrease consumption) in area B. This has acost that is represented by the brown rectangles.

Counter trading

In order to determine which actor gets to trade with the TSO, the counter trad-ing mechanism is used: in the constrained-off area (where there is an excess ofenergy), producers offer to buy back their production at a price lower than themarket price (consumers offer to increase their consumption at a discount price).In the constrained on area (with a deficit of energy), producers ask for a financialcompensation (their marginal cost minus the market price) to sell energy at themarket price and consumers offer to decrease their consumption against compen-sation. The TSO chooses the least expensive bids from both sides for re-dispatchpurposes11.


Taking the same example as before, the redispatch mechanism is illustrated in figure2.9. Due to transmission limitations, the TSO must buy in B (i.e. pay generatorsto increase their production or consumers to decrease their consumption) and sellin A (i.e. pay generators to decrease their production or consumers to increase theirconsumption). These transactions cause the TSO to suffer a loss (represented byall the brown rectangles).

The participants involved in the re-dispatch process will make a profit (half ofeach rectangle respectively) from the different between their marginal price and theprice paid by the TSO (pA50 in A and pB50 in B)12.

11In the Nordic electricity market, a single price is used for transaction, meaning that everyparticipant will pay or get paid the same (c.f. 2.9). Another variant would be to treat each bidindividually and proceed using the pay-as-bid method

12In reality re-dispatch is handled in real time, meaning that the TSO will be exposed to the

21


pA [€/MWh]

PA [MWh/h]100 200

pB [€/MWh]

PB [MWh/h]

Area A Area B

50MW trans. capacitypA0

Supply SA Supply SB

Demand DADemand DB

pB0

pA50

pB50pB50pAB pAB

100 200

Profit for the acceptedgenerators in A

Cost for TSO to increaseproduction in B

Cost for TSO to decreaseconsumption in B

50MWtrans. capacity

Figure 2.10: With market redispatch, only the accepted generators in area A sell powerat the system price (pAB). The TSO must purchase more expensive generation (or decreaseconsumption) in area B and passes the cost (brown triangles) onto the generators in A.

2.3.5 Market Re-dispatch

The market re-dispatch method is presented in [18]. With this method, the TSOdetermines the volume of energy that needs to be re-dispatched. It looks at the bidsfrom each area and rejects the most expensive bids from the area with an excess ofenergy and accepts more bids (they are more expensive that the system price) fromthe deficit area until the congestion problem is solved. Constrained-off actors (i.e.who lost a bid) cannot sell power and are not compensated while constrained-onactors (i.e. who got their bids granted by the TSO) are paid based on the pay-as-bidprocedure. Finally, the cost of constrained-on power is passed onto the acceptedgenerators in the area with an energy excess (meaning they will receive less thanthe system price), with the constraint that they receive at least as much as the priceof the first constrained off generator (if not, the difference is covered by the TSO).Finally, the end users in each area pay the system price.


Figure 2.10 illustrates the implication of market re-dispatch for the market par-ticipants using the same example as in the previous sections. Consumers in eacharea see the price pAB, which benefits the consumers in B. Producers in A produce50MW that they sell at price pAB as well, making a profit. The TSO buys extrageneration (and decrease consumption)13 in B at the marginal price of the actors

price of the real time market instead of the spot price. However this does not change the totalsocial welfare loss, only the participants engaged with the TSO will make a greater profit while theTSO will suffer an even greater loss.

13[18] only uses generation, but for the sake of comparison between congestion managementmethods, we assume that consumers can be involved in each method.

22


(they do not make a profit) and passes the cost (the two brown triangles) onto theproducers in A.

It should also be noted that if the cost of dispatching more expensive units is onlypassed onto producers in area A, the total social welfare for both region is lowerthan for the other two methods. [18] assumes that the consumption is inelastic,which is not the case in our example. In case of elastic demand, both producersand consumers in area A should see a lower price.

2.3.6 Explicit Auction

The three previous congestion management methods are based on implicit auction,meaning that the auctioning of trading of transmission capacity is included im-plicitly in the auction of electrical energy in the market. Another possibility is toauction energy and transmission capacity separately and independently from eachother. This is called explicit auctioning. According to [19], this method is mainlyused for handling the capacity of international interconnections in Europe, and isnot used within the Nordic electricity market.

For explicit auction, the two areas are treated separately (no transmission ca-pacity) and the price difference between them is called the economic rent (pB0−pA0in our example). All producers in the energy surplus area A will be keen to bidto have access to B’s market since the price there is much higher (pB0). They willtherefore send a bid to the grid owner to acquire the right to transfer power to B.The auction takes place on a yearly, monthly and daily basis meaning that it ispossible to acquire rights for different time spans.

The auction does not however take place in conjunction with the electrical en-ergy market. This means that the economic rent is not known by the biddingparticipants. This lack of information can therefore result in an inefficient utiliza-tion of the transmission link, decreased social welfare and more frequent adverseflows (power flowing in the opposite direction of what was decided). [20] pointsout among other problems that explicit auctioning can be prone to gaming: somegenerators (with low marginal cost) may not require transmission capacity but stillbid in order to raise the price and damage the profit of their competitors.

These limitations make explicit auction a viable alternative for congestion man-agement only when no integrated market is available (i.e. usually between markets).

2.3.7 Discussion

Nodal vs Zonal Congestion Management

There are actually many arguments for implementing nodal pricing over zonal pric-ing:

1. Nodal pricing reflects in the most accurate way the network constraints andgives an appropriate price incentive to all participants.

23


2. Nodal and zonal pricing are essentially working in the same way. The onlydifference is that for zonal pricing nodes are aggregated in zones, and as [15]concludes, it is almost impossible to define an efficient zone without causingsome kind of market inefficiency (which must be corrected), and therefore theargument of keeping it simple and using nodal pricing.

3. Some state that aggregation can lead to more competition and therefore mit-igate market power. [21] argues however that only real elimination of thephysical constraints can effectively reduce market power.

On the other hand, zones favour electricity price equity (at least within them),which is probably the main argument for electricity markets in Europe to supportzonal pricing over nodal pricing.

Market Splitting, Re-dispatch and Market Re-dispatch

These three congestion management methods lead to the same social welfare. Alack of transmission capacity induce of welfare loss and these market mechanismsonly give different financial incentives to different actors:

1. Market splitting leads to a net income for the grid owner (TSO), re-dispatchleads to a net cost and market re-dispatch to low or no cost at all. Re-dispatchgives a strong financial incentive to reinforce transmission capacity14.

2. In market splitting, the market participants see a different price in each areawhile they see a unique price (the system price) with re-dispatch (except theones directly transacting with the TSO) and market re-dispatch.

There are both advantages and disadvantages with each solution. On the one hand,giving the grid owner an economic incentive to avoid congestion can be seen as themost efficient way to get sufficient capacity (since the grid owner is the most suitableactor to upgrade the grid). On the other hand, exposing all market participantsto the same price might not give the appropriate signal meaning that they mightnot invest in the optimal geographical location. Finally, market re-dispatch givesa strong incentive for producers not to invest in the area with an excess of energybut participates in giving the same electrical energy price (equal access) to all end-users. However, the method as presented in [18] should be used only when demandis inelastic, or should be adapted for elastic demand.

2.4 Demand Response (DR)Demand Response can be seen as an intentional modification of the end-users ownelectrical consumption pattern in response to a signal (usually a price signal). Fur-

14In market splitting, the (unregulated) grid owner would want to optimize its revenue andwould therefore have an incentive to build 50% transmission capacity (this is when the congestionrent is maximum). With re-dispatch however, more capacity means less cost.

24

2.4. DEMAND RESPONSE (DR)

Consumption[MWh/h]

Price offered [€/MWh]

a) b) c)

pAB

Figure 2.11: 3 examples of demand bids. a) inelastic demand b) linear decrease c) stepwisedecrease for an increased price. For prices higher or lower than pAB , the amount of energyconsumed will vary for each case.

ther, DR can be defined as "the incentive payments designed to induce lower elec-tricity use at times of high wholesale market15 prices or when system reliability isjeopardized" [22]. In case of congestion, DR can e.g. be used by the system operatorto increase or decrease consumption (see 2.3.4) to maintain system stability.

2.4.1 End-User Flexibility

Demand side participation is usually associated with the concept of loss of comfort(or provision of flexibility). Lighting for example is not seen as a good DR resource,as it is not likely that anybody will accept to remain in the dark for the sake ofsystem stability. Some appliances on the other hand are much more suited for DR.[23] claims that space heating, air conditioning and electrical water boiler shouldbe used in priority for DR programs, especially for residential end-users. Theseappliances amount for a substantial part of the residential power consumption andcan be altered with a minimal loss of comfort (e.g switching space heating off for amoment can be unnoticed thanks to the thermal inertia of the building).

Industrial consumption is very process dependant and the flexibility varies fromcompany to company.

Monetizing this loss of comfort (or flexibility) means that power demand is tosome extent price sensitive. This can be included in the bids made by consumers tothe electricity markets. Figure 2.11 shows three possible ways to represent consumerprice sensitivity. Consumption can be inelastic (case a) or vary linearly with theprice (b) or vary stepwise with the price (c)16.

15The contrary is not excluded as well: increased consumption when price is low. This isespecially good when there is congestion. The area with an excess of energy will have a lower price,and increasing consumption might participate in increasing social welfare. (c.f. e.g. 2.3.3)

16This is especially valid for industries. A process can be cost effective only from a givenelectricity price for example.

25


Incentive Based ProgramsClassical

Direct ControlInterruptible / Curtailable Program

Market BasedDemand BiddingEmergency DRCapacity MarketAncillary Services Market

Price Based ProgramsTime of UseCritical Peak PricingExtreme Day PricingReal Time Pricing

Figure 2.12: List of DR programs as seen in [24].

2.4.2 Demand Response ProgramsThere are many different programs intended to incentivize demand side participa-tion. They can be classified into two categories: Incentive-Based Programs andPrice-Based Programs [24, 22]. An exhaustive list as presented in [24] is shown infigure 2.12.

Demand bidding (which can be illustrated by figure 2.11) takes advantage ofthe price sensitivity of consumption at a spot market level (day-ahead). It assumesthat consumers are likely to reduce their consumption if the spot price is high andincrease it when it is low. Ancillary services17 market is used closer to or duringthe delivery hour. DR is there used as a balancing resource and can be used by theTSO for balance or re-dispatch purposes. All programs presented in [24] intend onusing demand flexibility as an extra tool for maintaining system stability and arethoroughly described in that report. We will only use demand bidding and ancillaryservices market as this report focuses on the use of electricity markets for congestionmanagement.

17An ancillary service is a service used to support the transmission of electric power.

26

Chapter 3

The Gotland Case

3.1 Electric Power System

Gotland, located in the Baltic sea, is Sweden’s largest island. The island is one ofthe most windy regions in Sweden and is therefore a favored location for installingwind power. Gotland Energi AB, which is owned partly by Vattenfall AB and partlyby the region of Gotland, owns and maintains the power grid on Gotland [2].

3.1.1 Interconnection with Mainland

A HVDC link connects Gotland to Sweden’s mainland. It is composed of two cables,each with a capacity of 130MW. Power is transmitted from the mainland duringpeak load hours on the island. In case of great wind power production, one of thecables can be used to send power to the mainland instead. Since the HVDC linkis used to maintain the frequency of the island, one of the cable must always bedirected from the mainland to Gotland, meaning that only one cable can be usedto send the possible excess of wind power production from Gotland [25].

3.1.2 Gotland’s Power Production

In addition to the link, Gotland possesses power reserves in form of gas turbineswith a capacity of 160MW [26]. These are present in case of extreme peaks ofconsumption or if Gotland becomes isolated from the mainland due to some faultor undergoing maintenance on the HVDC link.

The main power production on Gotland consists however of wind power. In2012, the total wind power installed capacity was 170MW [26]. This number isincreasing year by year, but GEAB has set a limit of 195MW installed wind powercapacity due to the limited transmission capacity of the HVDC link1.

1The Swedish authorities have decided to build two HVAC cables with 500MW of transmissioncapacity each. This in order to cover for the planned installed wind power capacity by 2025:1000MW [2]

27

CHAPTER 3. THE GOTLAND CASE

Private Housing 210 GWh

25%Industries 290GWh

35%

Others 350GWh

40%

Figure 3.1: Electricity consumption per category in Gotland in 2011 [27]. Others includeoffices, agriculture, public sector...

3.1.3 ConsumptionDespite the fact that the island is not an industrial region, industry remains thebiggest consumer of electricity, representing about 35% of the yearly consumption.Housing (private houses and apartment building) amounts for 25%. The share ofenergy consumption by sector on Gotland is represented in figure 3.1. The averageelectricity consumption on Gotland is around 860GWh [27] per year (less than 1%of Sweden’s consumption), or about 100MWh/h in average but with peaks up to195MWh/h during the winter.

3.1.4 Smart Grid GotlandGotland has been home of several studies to analyze the potential of smart gridtechnologies on the distribution grid. The project Smart Grid Gotland was initiatedin 2012 with three overall objectives[2]:

1. Cost efficiently increase the hosting capacity for wind power in an existingdistribution system.

2. Show that novel technology can improve the power quality in a rural grid withlarge quantities of installed wind power.

3. Create possibilities for demand side participation in the electricity market, inorder to shift load from peak load hours to peak production hours.

3.2 Earlier WorksSeveral studies have been conducted as part of the Smart Grid Gotland project.Two of them are preceding the current thesis and are presented shortly below.

28

3.2. EARLIER WORKS

3.2.1 Study 1: Analysis of Demand Response Solutions for CongestionManagement in Distribution Networks

This work demonstrates that a minimum of 5 MW beyond the capacity of theHVDC link can be balanced during worst case scenarios using a set of ancillaryservices, mainly the demand flexibility of households on Gotland (less than 10%of all detached houses) as well a battery energy storage system [4]. The consumerflexibility is decomposed into two categories: long term and short term DemandResponse. The first one can be seen as the consumption that will be affected byinformation from the preceding day, while he second one is more connected to thereal time market, where short notice is expected.

3.2.2 Study 2: Analysis of Demand Response Participation Strategiesfor Congestion Management in an Island Distribution Network

A following study [5] intended to take into account the economical aspect of demandside management as well. Indeed, the first study only shows that it is technicallyfeasible to maintain system stability using the ancillary services. Two strategies arepresented: Dynamic network tariff and spot price optimization.

Dynamic network Tariff

As Gotland is part of SE03, the cost of congestion is passed onto the grid owner (seeredispatch in section 2.3), which is called GEAB on Gotland. Instead of curtailingwind in excess, the grid owner can ask the consumers to increase the demand toabsorb the excess of production, thus avoiding congestion. The consumers arein return reimbursed by the grid owner through the network tariff (in form of adiscount) for the extra cost induced by the shifting the consumption.

Spot Price Optimization

The spot price optimization strategy aims at optimally shifting load throughout theday in order to both solve transmission capacity problems with the constraint thatthe daily electricity cost for the consumer participating in the program is equal orlower than otherwise.

The main limitation with these two strategies is that, given the current conditions,there is no correlation between Gotland’s energy transmission problems and theelectricity spot price on Gotland. Indeed, it is a small island part of a much biggerprice area and the availability (or unavailability) of cheap energy sources does notaffect much (or at all) the price of the area.

29

Chapter 4

Method

4.1 Spot Price ComputationAs described in section 2.2.2, spot price calculation is done using the price crossmethod, which is the result of a social optimization welfare.

Given a set of consumers {Ci, i = 1, ..., NC} and a set of producers {Pj , j =1, ..., NP } participating in the bidding process, each with a bidding price (λCi andλPj respectively) and a maximum quantity per offer1 (Y C

i and Y Pj respectively), the

goal of the social welfare optimization is to determine the consumption or productionof each participant (yCi and yPj respectively) yielding the maximum social welfarepossible. This can be written as:

max∀yC ,yP

∑i

λCi yCi −

∑j

λPj yPj

subject to∑i

yCi −∑j

yPj = 0

with 0 ≤ yCi ≤ Y Ci ,∀i, and 0 ≤ yPj ≤ Y P

j ,∀j

(4.1)

Equation 4.1 represents what is called a linear program which can easily be solvedusing top-of-the-shelf software, such as in MATLAB. This linear program howeverwill only yield the dispatch for each actor. To get the system price, it is necessaryto retrieve the dual2 of the linear program (primal) presented in equation 4.1.

maxλS ,∀νC ,νP

−∑i

νCi YCi −

∑j

νPj YPj

subject to − λS − νCi ≤ −λCi , ∀i, and λS − νPj ≤ λPj ,∀jwith νCi ≥ 0, ∀i, and νPj ≥ 0,∀j

(4.2)

1In reality, participants are allowed to submit supply (or demand) curves, which are not con-stant for a given capacity but linearly increasing (or decreasing, c.f e.g. figure 2.11). These curvesin our case will be decomposed into stairs in order to be able to solve the linear program.

2[28] proposes a step by step method for obtaining the dual of a linear program.

31

CHAPTER 4. METHOD

where λS , νCi and νPj are the Lagrange multipliers associated to all equality andinequality constraints (line 2 and 3 in eq. 4.1) of the primal linear program.

λS represents the system price, i.e. the spot price, νCi and νPj represent theunitary benefit (e/MWh) for consumer i and producer j respectively.

Equation 4.2 is also a linear program and can be solved the same way at theprimal linear program. Solving both will yield the system price, the unitary benefitand the dispatch of each participant.

4.2 Trading Wind Power

Wind generation is stochastic, meaning that balance responsible wind power pro-ducers are much more exposed to balance costs than a more conventional powerproducer (hydro-, nuclear- and gas power has a controlled output). This particu-larity makes trading wind power a subject of its own, where balance costs ought tobe comprehensively considered before placing a bid on the spot market.

4.2.1 Profit for the wind power producers with the Two-Price System

Remembering balancing costs presented in 2.2.4, the loss of a wind power producerdepends on the system deviation as well as the producer’s own deviation during thedelivery hour. Following, the producer’s profit can be defined as:

ρ = λED + λUPEUP + λDWEDW (4.3)

where:

• λ and ED are the day-ahead price and the quantity of energy sold at day-aheadrespectively,

• λUP and λDW are the price for buying up or down regulating power respec-tively,

• EUP and EDW are the deficit and excess of energy between the productionplan3 and metered production,

• λUP can either be equal to λ or λ↑ depending on the system regulation state(c.f. table 2.1) and λDW can either be equal to λ or λ↓ (λ↑ and λ↓ are theprices for up- and down-regulation in the balancing market),

• EUP and EDW are defined as E − ED where E is the amount of energymetered by the TSO. Finally, EUP equals 0 if E − ED > 0 and EDW equals0 if E − ED ≤ 0.

3We assume here that ED is also the amount of energy proposed in the production plan.

32

4.2. TRADING WIND POWER

The profit can be rewritten as:

ρ = λE − [−ψUPEUP + ψDWEDW ]︸︷︷︸opportunity loss

(4.4)

with ψUP = λUP − λ and ψDW = λ− λDW , the unitary loss (positive) for tradingenergy at the balancing market.

4.2.2 Trading with Unknown Balance PricesIn reality, prices and imbalances are unknown for a wind power producer whenbidding. The producer tries therefore to optimize its expected profit (i.e. tries tominimize its expected opportunity loss) by making use of the fact that the windpower production estimate for every hour of delivery is given as a probabilisticforecast (which contains a lot more information than a single value (the estimateitself)). In chapter 7 of [29], it is shown that the optimal bidding quantity4 withstochastic prices under the assumption that the wind power producer is price taker(i.e. does not have market power) is defined as:

ED∗ = F−1E

[ψ̂DW

ψ̂DW + ψ̂UP

](4.5)

with F−1E being the inverse function of the cumulative distribution function of E

and ψ̂DW and ψ̂UP the expected unitary losses for trading down-regulated and up-regulated power respectively. The optimal bidding strategy will be illustrated insection 5.6. Historical market data can be used to estimate the expected unitarylosses.

4This quantity is obtained by choosing the critical fractile (see e.g. news-vendor model).

33

Chapter 5

Case Study

This chapter presents numerical results from simulations using the island of Got-land as a use case. In this study, the installed wind power capacity on Gotland isincreased stepwise and the economic impacts of the two main congestion manage-ment methods (namely Re-dispatch (Case 1) and Market Splitting (Case 2A and2B) on different actors are presented. Following, results from a simulation casecombining elastic demand and market splitting are presented. Lastly, the possi-ble consequences of optimal bidding strategies for wind power producers on thecongestion management mechanisms are illustrated using real world data.

5.1 General Data and Assumptions

5.1.1 Wind Power Producers• Historical wind power production data from Gotland for the year 2012 are

used. The data is provided with an hourly granularity.

• For each hour, the total wind power production is decomposed into 26 datapoints, one for every wind power producer1 on Gotland. Each data point iscomputed by multiplying the total hourly production by the production shareof each producer. For a wind power producer i:

productioni(hour) = totalproduction(hour) · sharei [MWh]

The production share is simply the yearly production of each actor dividedby the overall yearly production2.

• Wind power producers sell electricity certificate for each MWh delivered intothe grid. The price of the certificate (SEK/MWh) is determined through a

1There are actually 55 actors in the data. The smallest actors have however been aggregatedso that no actor has an installed capacity lower than 1MW.

2This means that the production of every wind power producer is entirely correlated for eachhour of the year. This is not the case in reality, even though this assumption is fairly reasonableconsidering the small size of Gotland.

35

CHAPTER 5. CASE STUDY

separate auction. Historical prices (one data point a week) from 2012 are used(data available at [30]).

• Wind power producers do not generally include their marginal production costwhen bidding at the market as they do not expect to be marginal producers3.However, with increased installed wind power capacity on Gotland in com-bination with the market splitting congestion management mechanism, windpower actors could become marginal producers (in that case it is importantto bid the actual marginal cost in order not to lose money). Two main costsrelated to production are identified:

1. marginal maintenance cost (the rotating parts of a wind turbine wearout): this cost is highly dependent of the age and brand of the wind pro-peller. We will assume a cost normally distributed (mean 30 SEK/MWh4

and standard deviation 5 SEK/MWh) for each wind power producer.

2. marginal grid feed-in tariff: the tariffing is different for each grid owner(and year), but usually includes a fee per MWh. In 2012, the grid feed-infee for a wind power producer on Gotland was 36 SEK/MWh [31].

The marginal production cost5 for a wind power producer i can finally beexpressed as:

costi(hour) = maintenancei + tariff − certificate(hour) [SEK/MWh]

5.1.2 Market

• Historical prices of Sweden’s bidding area SE03 are used for the simulations.Hourly data (available at [6]) for the year 2012 are used in priority, but datafrom 2013 to 2015 are used as well for comparison.

• Small bidding areas (such as Gotland with market splitting) are arguablymore prone to market gaming [15]. We will assume however that all actors onGotland are well behaved and regulated6.

3A marginal producer is a producers whose bidding price equals the spot price.4This was the cost estimated by experts from the wind division at Vattenfall.5It is important to observe that all wind power producer have a slightly different bidding price.

This means that in case of congestion, the most expensive power plants will not be dispatched (incase of market splitting).

6An obvious risk for Gotland is the fact that the market split mechanism can be harmful tothe profit of the wind power producers: indeed, when there is too much wind power productionon Gotland and the link capacity is exceeded, prices on Gotland become very low. Bidding lowerquantities (to avoid market splitting) could therefore be a strategy for wind power producers toincrease their profit. This is especially valid since all production on Gotland comes from windpower and that the wind does not vary much over the island, meaning that a wind power producercan guess fairly accurately what the other producers are able to produce.

36

5.1. GENERAL DATA AND ASSUMPTIONS

5.1.3 Consumers• Historical hourly consumption data on Gotland from 2012 are used for simu-

lations.

• In cases 1, 2A and 2B, the load is assumed to be inelastic. The assumptionsrelated to load elasticity will be introduced in section 5.5.

5.1.4 Grid Owner• The revenue of the grid owner associated with the wind power producers

is assumed to be proportional to the quantity of energy fed into the system.Some tariffs, including GEAB’s (c.f. [31]) also include a part related to power,but this is neglected for simplicity (we assume that GEAB does not have toreinforce its grid due to increased power capacity since congestion managementmethod are used.)

• For re-dispatch, the hourly income for the grid owner is defined as:

ρ(h) = tariff ·wprod(h)−loss·wprod(h)·spot(h)−wcurt(h)·(spot(h)−pcurt(h))

with:

– tariff [SEK/MWh], the feed in tariff charged by the grid owner to thewind power producers,

– wprod(h) [MWh], the quantity of wind energy fed into the grid at hour h,– loss [%], the percentage of energy lost in the grid. The grid owner buys

this lost energy at the electricity spot price,– spot(h) [SEK/MWh], the spot price at hour h,– wcurt(h) [MWh], the quantity of wind energy that must be curtailed at

hour h due to congestion,– pcurt(h) [SEK/MWh], the lowest bidding price among constrained-off

wind power producers at hour h.

5.1.5 Scenarios• In order to be able to obtain more general results, 100 scenarios7 are simulated

for a fixed installed wind power production capacity. Between each scenario,the wind power production is randomly rearranged (the production data foreach month are permuted, e.g. January become March, February becomesOctober,...). As a result, the difference between production and consumptionon Gotland varies with each scenario, meaning that cases of congestion willoccur at different time (and quantity) for each scenario.

7We assume that 100 scenarios are a sample big enough for the results to be general enough.Indeed, the variance was not reduced much when using 1000 scenarios, while the simulation speedwas greatly affected.

37


• Simulations are done for different installed wind power capacities. The ca-pacity is increased by 10% between each simulation, starting from 170MW(the installed capacity in 2012). Gotland’s grid owner (GEAB) considers that195MW to be the maximum allowable installed capacity without any risk ofcongestion on the transmission link.

• Increasing the capacity of energy production with a low marginal cost onGotland should in theory participate in lowering the average energy price ofSE03. However, we will neglect this variation as the volumes currently tradedon Gotland amount for only 1% of the current volumes traded in SE03 [6].This way, the prices of SE03 can be kept constant throughout the simulation.

• Since the transmission link capacity is unchanged, we assume that the networkis able to cope an with increased installed wind power production without gridreinforcements (over-production is curtailed, either by the grid owner or bythe market depending on the congestion management method).

5.2 Case 1: Using Re-dispatch to Manage Congestionbetween Gotland and Mainland Sweden

The re-dispatch congestion management method puts the responsibility of insuffi-cient transmission capacity solely on the grid owner. When congestion is expectedto occur, the grid owner must negotiate with consumers and producers on both sideof the congested transmission link to resolve the issue. Re-dispatch is describedthoroughly in section 2.3.4.

5.2.1 Producers

With re-dispatch, the spot price on both side of the congested link remains the same.Increasing the installed wind power production capacity on Gotland therefore simplymeans an increased volume of sold energy without affecting the marginal profit foreach MWh sold. Moreover, the producers counter trading with the grid owner mightslightly increase their profit (they buy back the energy they sold at a price lowerthan their marginal production cost.)

5.2.2 Consumers

Since the average energy price on Gotland is unchanged with re-dispatch, increas-ing the hosting capacity of wind power production on Gotland does not affect con-sumers. Only those directly involved in counter trading slightly profit from conges-tion.

38

5.2. CASE 1: USING RE-DISPATCH TO MANAGE CONGESTION BETWEENGOTLAND AND MAINLAND SWEDEN

-25

-20

-15

-10

-5

0

5

Installed wind power capacity [MW]

Yea

rly in

com

e fo

r Grid

ow

ner [

10M

SEK

]

91%75%50%25%9%

170 187 204 221 238 255 272 289

Figure 5.1: (Case 1) Income of the grid owner (as defined in the assumptions) for increasinginstalled wind power capacity. The grid losses here are set to 10% of the total energy fedinto the grid. The tariff is set as 36SEK/MWh

5.2.3 Grid Owner

It might seem that since the cost of insufficient transmission capacity entirely restswith the grid owner, increasing installed wind power capacity on Gotland to thepoint that congestion occurs is disadvantageous to them. However, the grid ownermight want to allow a little bit of congestion in return for higher feed-in income.Indeed, more wind power production connected to the grid means an increasedquantity of energy fed into the grid and therefore a higher income through gridfeed-in tariff for the grid owner. Figure 5.1 shows the yearly income of the gridowner on Gotland, GEAB, as a function of the total installed wind power capacityon Gotland. The income appears to slightly increase up to 221MW (i.e. 26MW overthe current limit) and starts to severely decrease with higher installed capacity. Upto 221MW, the increase in tariff income exceeds the total cost of congestion. Indeed,with 221MW installed wind power capacity, the HVDC link is only congested about0.2% of the time (15 hours a year on average). On the other hand, a link congesteda mere 0.78% of the time (c.f. table 5.1) is enough to impair the income of the gridowner.

39


Table 5.1: Congestion time and curtailed wind energy for increasing installed wind powercapacity (these values are valid for case 1 and 2A).

Installed Wind powercapacity [MW]

Time link is congested[%hour/year]

Curtailed wind energy[MWh]

170 0 0187 0 0204 0.01 6221 0.17 88238 0.78 585255 2.18 2352272 4.30 6163289 6.77 12576

5.3 Case 2A: Using Market Splitting to ManageCongestion between Gotland and Mainland Sweden -Current Transmission Capacity

The market splitting congestion management method uses the day-ahead market todeal with risks of congestion. If hourly energy exchanges between two areas exceedthe transmission capacity limit, the markets are split into two, each of them withits own spot price and optimal dispatch. Market splitting is thoroughly describedin section 2.3.3.

5.3.1 Transmission Capacity

By current transmission capacity, it is meant the transmission capacity of the HVDClink connecting Gotland to mainland Sweden. The link is made of two cables, eachwith a 130MW transmission capacity limit. However, the HVDC link plays a bigrole in maintaining the frequency of Gotland, and one cable must therefore alwaysbe energized and directed from mainland Sweden to Gotland. This means that evenwhen Gotland is a net producer (total wind production on the island exceeds totalconsumption for a specific hour), at least 15MW must be supplied to Gotland fromone cable. This effectively means that when the net production or consumption ofGotland exceeds 115MW and 260MW respectively, congestion is expected on theHVDC link and markets are split.

As it can be seen on figure 5.2 however, no market splitting is to be exceptedwith 170MW of installed wind power capacity. Indeed, the transmission capacityof the HVDC link clearly exceeds the current needs of Gotland.

40

5.3. CASE 2A: USING MARKET SPLITTING TO MANAGE CONGESTIONBETWEEN GOTLAND AND MAINLAND SWEDEN - CURRENT TRANSMISSIONCAPACITY

0 1000 2000 3000 4000 5000 6000 7000 8000 9000−300

−250

−200

−150

−100

−50

0

50

100

150production−consumptionexport limitimport limit

hours of the year

net p

rodu

ctio

n G

otla

nd [M

Wh/

h]

Figure 5.2: Hourly net production (total wind production minus total consumption) onGotland for 2012 (170MW of installed wind power capacity). A negative value meansGotland is a net consumer. Exceeding the limits would result in congestion.

5.3.2 Grid Owner

As described in section 2.3.3, the grid owner benefits from congestion with marketsplitting. With increased installed wind power capacity, congestion occurs morefrequently and the congestion rent for the grid owner increases.

Like in Case 1, the grid owner will also see its grid feed-in tariff income growwith increased installed wind power capacity as well.

5.3.3 Consumers

With the current transmission capacity of the HVDC link, increasing the installedwind power capacity on Gotland would only result in congestion only occurringwhen Gotland is a net producers8, meaning that market splitting will always resultin the electricity price on Gotland being lower than the SE03’s spot price (over-production). A lower spot price (c.f. figure 5.3) on average is simply better for theconsumers.

8c.f. figure 5.2 (Increasing wind production on Gotland would only shift the hourly net pro-duction curve upwards).

41


255

260

265

270

275

280


Aver

age

spot

pric

e on

Got

land

[SEK

/MW

h]

170 187 204 221 238 255 272 289

91%75%50%25%9%

Figure 5.3: (Case 2A) Box and whisker plot of the yearly average electricity price onGotland in 2012 (100 scenarios) with increasing installed wind power capacity.

5.3.4 Producers

As the yearly average price on Gotland decreases as the installed wind power capac-ity increases, the marginal profit per MWh for the wind power producers dwindles.This clearly sends a negative signal for increased installed wind power capacity in-vestments. However, Gotland is a very windy region, and one should not only lookat the marginal profit but also at their income. Indeed, a higher installed capacitymeans a higher production throughout the year (and therefore more energy sold).Figure 5.4 shows that the income of the wind power producers on Gotland increasesmore or less steadily up to 238MW of installed wind power capacity and reachesits maximum for 255MW and subsequently declines for higher installed wind powercapacity.

42

5.4. CASE 2B: USING MARKET SPLITTING TO MANAGE CONGESTIONBETWEEN GOTLAND AND MAINLAND SWEDEN - EQUAL TRANSMISSIONCAPACITY IN BOTH DIRECTION

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9


Year

ly in

com

e fo

r win

d pr

oduc

ers

[100

MSE

K]

170 187 204 221 238 255 272 289

91%75%50%25%9%

Figure 5.4: (case 2A) Box and whiskers plot of yearly average income for the wind powerproducers on Gotland from sold energy for increasing installed wind power capacity (100scenarios for each case). Expenses and electricity certificates are not taken into accounthere.

5.4 Case 2B: Using Market Splitting to ManageCongestion between Gotland and Mainland Sweden -Equal Transmission Capacity in Both Direction

5.4.1 Transmission capacity

In this case, the transmission capacity of the link is assumed to be equal in eachdirection. More precisely, 130MW can be transmitted from Gotland to mainlandSweden and vice-versa. This can be seen as a generalization of the Gotland case.

5.4.2 Market Assumptions

For this case, a few assumptions are necessary:

• Since the link capacity is not enough to cover for peaks of consumption onGotland, a reserve power plant (a gas power plant9) is introduced into the

9There are actually several gas power plant with a total aggregated capacity of 160MW (c.f.chapter 3) on Gotland, and they are currently used as power reserve should the HVDC link fail.

43


model. For simplicity, it bids at a constant price over the year, defined as aparameter: 700SEK/MWh (estimated by [32]).

• This gas power plant does not bid on the spot electricity market like the windpower plants. Indeed, with a capacity of 160MW, it can cause congestionby itself if it sells electricity to the mainland. Instead, when a market splitoccurs caused by excessive net power deficit on Gotland, the gas power plantis dispatched and the electricity spot price on Gotland is either the biddingprice of the gas power plant or the price of SE03 (if higher10)

5.4.3 Grid Owner

In this case, congestion can occur both when there is an over-production on Gotlandand when there is an over-consumption as well. More occurrences of congestionmeans that the yearly congestion rent for the grid owner will be greater than incase 2A for the same installed wind power capacity.

5.4.4 Consumers

With this transmission capacity and with 170MW installed wind power capacity,market split will often occur on Gotland due to a high net power deficit on theisland. This results in the yearly average spot price being much higher comparedwith mainland Sweden (circa 10SEK/MWh higher than in SE03), which is clearlya downturn for consumers on the island.

5.4.5 Producers

Since a high marginal profit is expected on Gotland, there is a clear incentive forinvestors to increase the installed wind power capacity on Gotland. With 130MWtransmission capacity in both direction, a substantial increase in installed windpower capacity is needed in order for the yearly average spot price on Gotland toequal SE03’s: at least 272MW (or +60% increase from 2012’s installed capacity, c.f.5.5).

5.5 Market Splitting with Flexible Demand

The price variations from case 2B can give a signal to consumers to shift the loadin order to save money (consuming more when the electricity price is low and lesswhen it is high). As it can be seen in figure 5.6, the price on Gotland can be bothconsiderably lower or higher than on SE03.

10When Gotland has a net power deficit, the price cannot be lower than the price of mainlandSweden.

44

5.5. MARKET SPLITTING WITH FLEXIBLE DEMAND

270

275

280

285

290

295


Aver

age

spot

pric

e on

Got

land

[SEK

/MW

h]

SE03

170 187 204 221 238 255 272 289

91%75%50%25%9%

Figure 5.5: (Case 2B) Box and whiskers plot (100 scenarios) of the yearly average spotelectricity price on Gotland for 2012 as a function of the installed wind power capacity. Themarginal cost of the gas turbine is set at 700SEK/MWh. The line marked SE03 representsthe yearly average spot price of mainland Sweden.

5.5.1 Assumptions

The flexible demand used for the simulation is very simplistic and is described asfollows:

• Each hour, the demand can increase or decrease up to a maximum limit: 2, 5 or10MWh/h11 (parameter). In reality, the load cannot be expected to have thesame flexibility for each hour of the day. Also, allocating a maximum flexibilityper day might be more adequate, but this was too complex to implement inthe time frame of this thesis.

• The load is price sensitive in the sense that it is a function of the spot electricityprice. It decreases as the electricity price increases and vice versa. A retailerwith price sensitive end-users will tend to adjust its bidding curve in order toavoid excessive balance costs. The resulting bidding curve is shown in figure5.7.

• A buffer (price region where demand is inelastic) is introduced so that the totalyearly consumption remains the same when the maximum demand flexibility

1110MWh/h represents about 10% of the average hourly consumption on Gotland.

45


0 1000 2000 3000 4000 5000 6000 7000 8000-200

0

200

400

600

800

1000

1200

1400

hours

spot

pric

e [S

EK/M

Wh]

Spot Price GotlandSpot Price SE03

Figure 5.6: (Case 2B) The spot electricity price on Gotland versus SE03 in 2012 from case2B with 272MW installed wind power capacity and 700SEK/MWh bidding price for thegas power plant. A lower price mean that there is an net hourly energy excess (MWh/h) onGotland over the transmission capacity limit (higher price indicates an excessive net deficit.

Consumption [MWh/h]

Price offered [SEK/MWh]

Meanprice

Inelasticdemand

Price region wheredemand isn’t flexible

Maximum demand flexibility

Maximum demand flexibility

Lowest windbidding price

(-250SEK/MWh)

Maximum priceacceptable

(5000SEK/MWh)

Figure 5.7: Bidding curve of a retailer (consumption bidding curve) with flexible demand.The region where demand is not affected by the price is used to keep the overall consumptionunchanged in each case. This region is decided upon using trial and error.

46

5.5. MARKET SPLITTING WITH FLEXIBLE DEMAND

parameter is changed. This is done to allow for comparison when varying theflexibility parameter.

5.5.2 Impact of Flexible Demand on Wind Power Dispatch

When there is an exceeding net power surplus on Gotland during a specific hour,markets are split and the electricity spot price on Gotland becomes drasticallylower than usual (c.f. figure 5.6). Wind power producers with the highest marginalproduction costs are curtailed by the market and can therefore not sell their expectedproduction. However, with a price sensitive load, consumption increases with lowerspot prices, meaning that more wind power can be dispatched, or in other words,less curtailment. We can see in figure 5.9 that with 10MWh/h of maximum flexibledemand (about 10% of the average hourly consumption), the annual curtailed windenergy on Gotland is reduced by over 50%. Put into perspective, this means thatnearly 0.75GWh (+0.1%, c.f. fig. 5.8) extra wind energy is being dispatched in thegrid of Gotland over the year.

5.5.3 Economic Factors

Spot price and Electricity Related Cost for Consumers

A price sensitive load has an effect on the electricity price of Gotland. Indeed, anincreased consumption at lower spot prices means that some more expensive windpower producers are being dispatched, thereby increasing12 the resulting spot price.While this is not a particularly sizable increase (+0.45%, or +1.17SEK/MWh with10MWh/h maximum flexible demand, c.f. figure 5.8), this can have a negative im-pact on the consumers’ economy. While consumers might be able to reduce theirentire spot price related cost by shifting consumption from high spot price hours tolow spot price hours, the increasing spot price affects the cost of the entire consump-tion, canceling out (and at some point exceeding) the possible savings. Simulationsshow that optimal maximum flexible demand from a consumer perspective is be-tween 2 and 5MWh/h, with savings up to 0.4MSEK yearly (c.f. see figure 5.10).

Income for Wind Producers

Wind producers not only are able to dispatch more energy throughout the year, butsince the yearly average spot price increases with demand flexibility, they are ableto do so at a higher average marginal profit. A price sensitive load is therefore verybeneficial for wind power producers as shown in figure 5.10: their income increases

12Observe that in case of excessing net power deficit, the spot price on Gotland is settled bythe gas power plant. A smaller load does not alter the spot price, only the quantity of energydispatched by the gas power plant (the gas power plant is still dispatched, hence the same price).In reality, if flexible demand participates in increasing the spot price during low price hours, itshould participate in decreasing the spot price during high price hours as well.

47


Overall dispatched wind energy Spot price0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Perc

enta

ge c

hang

e vs

No

flexi

ble

dem

and

2MW flex5MW flex10MW flex Flexible

Demand[MWh/h]

Overalldispatchedwind energy

[GWh]

Averageyearly spotprice [SEK]

0 690.90 276.722 691.03 276.905 691.32 277.2410 691.63 277.89

Figure 5.8: Relative and absolute variation of: 1. the total wind energy dispatched overthe year on Gotland, 2. the yearly average spot price on Gotland, as a function of themaximum flexible demand parameter. Installed wind power capacity : 272MW.

Congestion rent Total curtailed wind energy−60

−50

−40

−30

−20

−10

0

Perc

enta

ge c

hang

e vs

No

flexi

ble

dem

and

2MW flex5MW flex10MW flex

FlexibleDemand[MWh/h]

CongestionRent

[MSEK]

Totalcurtailed

wind energy[GWh]

0 5.1633 1.43182 4.9978 1.29835 4.6621 1.004710 4.0668 0.6986

Figure 5.9: Relative and absolute variation of: 1. the annual congestion rent earnedby the the grid owner on Gotland, 2. the overall quantity of wind energy that has beencurtailed by the market due to congestion over the year, as a function of the maximumflexible demand parameter. Installed wind power capacity : 272MW.

Income wind producers Consumer cost Total cost Gotland−2

−1.5

−1

−0.5

0

0.5

1

1.5

Perc

enta

ge c

hang

e vs

No

flexi

ble

dem

and

2MW flex5MW flex10MW flex Flexible

Demand[MWh/h]

IncomeWind

[MSEK]

ConsumerCost

[MSEK]

Total CostGotland[MSEK]

0 186.87 270.39 78.3612 187.20 269.89 77.6925 187.84 269.92 77.42110 189.05 270.04 76.925

Figure 5.10: Relative and absolute variation of: 1. the annual income of the wind powerproducers from sold energy, 2. the annual spot price related cost for consumers on Gotland,and 3. the annual spot price related cost for the society of Gotland (defined in eq. 5.1),as a function of the maximum flexible demand parameter. Installed wind power capacity :272MW.

48

5.6. THE POSSIBLE EFFECT OF OPTIMAL WIND POWER BIDDING ONCONGESTION MANAGEMENT METHODS

by up to 1% (over 2MSEK) yearly (excluding production costs and electricity cer-tificates).

Grid Owner

The grid owner sees its congestion rent decrease due to flexible demand. While aprice sensitive load does not affect the quantity of energy being exported to mainlandSweden, it affects the spot price on Gotland, and therefore the difference of pricesbetween Gotland and SE0313. With 10MWh/h of flexible demand, the annualcongestion rent is reduced by about 20% or over 1MSEK (c.f. figure 5.9).

Gotland as a Whole

Since the benefits of demand flexibility are spread out between different actors, itcan be interesting to look at Gotland as a whole. We define the annual electricityrelated cost for Gotland as:

costGotland = costconsumers − profitwindproducers − congestionrent [SEK] (5.1)

We can see in figure 5.10 that flexible demand profits Gotland as a whole. Indeed,the costs related to electricity for the society can be reduced by up to 1.5% (from78.3MSEK to 76.5MSEK) yearly. As presented in the previous sections, the actorsmost benefiting from price sensitive loads are the wind power producers. They couldin theory use a part of their increased profit to incentivize consumers to be evenmore flexible14.

5.6 The Possible Effect of Optimal Wind Power Biddingon Congestion Management Methods

The stochasticity of wind power production means that wind power producers do notknow for sure while bidding on the day-ahead market what their actual productionis going to be during the hour of delivery. Rather than bidding the quantity thatis the most probable, they can try to minimize their balance costs instead. This ispresented in more details in section 4.2.

Re-dispatch and especially Market Splitting rely on day-ahead bids as an inputto assess whether there is a risk for congestion.

If the bidding difference between most probable and most optimal productionquantity is too large however, congestion might not be detected (or detected erro-neously). Since the power production on Gotland is mainly issued from wind power

13The congestion rent is defined as the product between the quantity of energy transmittedbetween two areas and the difference of price between the two areas. It is only non zero duringmarket split hours. c.f section 2.3.3.

14It is quite often that suppliers also own some production units. For those, it could be veryeasy to introduce some kind of incentive mechanism for the consumer.

49


1 1.1 1.2 1.3 1.4 1.5 1.60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1P(

X<=

x)

Production [10MW]

Figure 5.11: Cumulative distribution function of the quantity of the expected wind energyproduced by Horns rev1 plant for Mars 2nd 2014 from 17:00 to 18:00.

producers, the purpose of this section is to illustrate how optimal bidding strategiesmay affect the day-ahead congestion management mechanism on Gotland.

5.6.1 Data and Assumptions

• No probabilistic forecast data were available from wind power producers fromGotland. Data from January to April 2014 from an offshore wind powerplant (Horns Rev1, located in DK1) were used. For each hour of planneddelivery, several forecasts, created by slightly varying the initial conditions,are available.

• We assume that the wind power producer obtains the cumulative distributionfunction (cdf) of the energy quantity produced each hour (FE in section 4.2)using the ecdf function available in the MATLAB statistical toolbox15.

• We assume that if a wind power producer does not try to minimize its bal-ance costs, the natural bidding quantity is 50% of the cumulative distributionfunction (the most probable production output).

15this function provides a quick way to create a cdf given a few observations. In reality, thewind power producer might try to refine the cdf using other tools as well.

50

5.6. THE POSSIBLE EFFECT OF OPTIMAL WIND POWER BIDDING ONCONGESTION MANAGEMENT METHODS

0 10 20 30 40 50 60 70 80 90 100-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

hours (from jan 1st 2014)

rela

tive

diff

eren

ce b

id q

uant

ity fr

om n

on o

ptim

ized

bid

[%]

0.40.450.550.6

Critical fractile

Figure 5.12: Variation (in %) in bidding quantity for a wind producer for a delivery hourfor various critical fractile. 0 represents non-optimized bidding.

• Since the producers of the Gotland bidding area essentially consist in windpower producers, the up and down regulation balance prices of DK1 (avail-able at [6]) are used16. We thereby assume here that the balancing prices ofGotland, in case the market split mechanism is used, would resemble DK1’s.

• The wind power producers use monthly expected unitary losses (ψ̂DW andψ̂UP in section 4.2).

5.6.2 Variation in Bidding quantities

The critical fractile (value used by the wind power producer for optimal bidding, c.f.equation 4.5) in DK1 varies from month to months but seems to stay between 0.4and 0.6. Using this range of variation, we can simulate the different outputs of thequantity that the wind producer from Horns rev1 would be bidding at the day-aheadmarket. Since the forecasts are not equally accurate from hour to hour (they dependon the weather forecasts), some hours contain an uncertainty in power productiongreater than others. This uncertainty coupled with balance cost optimization canresult in large deviation from the non-optimized bidding quantity. Indeed, as shown

16DK1 power production sources is somewhat similar to Gotland: many wind parks and a fewcondensed heat and power plants.

51


in figure 5.12, the optimal bidding quantity might be up to 30-40% lower or higherthat what would be obtained by simply choosing the most probable output.

5.6.3 Detecting CongestionWith 272MW of installed wind power capacity instead of 170MW, congestion oc-currences on the transmission link between Gotland and mainland Sweden are oftencaused by an excess (or deficit) of a few MW (up to 30MW for extreme cases). A30-40% deviation in bidding quantity from wind power producers could thereforeeasily create false positives (and negatives) congestion cases, thereby rendering suchcongestion management methods less efficient.

52

Chapter 6

Discussion

In this chapter, the results from the use case (and their validity) are discussed inorder to better understand how to interpret them.

6.1 Accuracy of the ResultsMany assumptions were used in order to simulate the use case: the electricitymarket modelled and used on Gotland is a simplified model of what Nordpoolis using. Thus, the case study does not provide unequivocal results about howmuch extra wind power capacity can be installed on Gotland but instead it ismeant to be a base for discussion about using regulatory alternatives instead of gridreinforcements to enable increased wind power integration. It seems from the usecase that both Re-Dispatch and Market Splitting are efficient (in the sense thatnone of the actors studied, i.e. the grid owner, the consumers and the producers,would be economically impaired by the introduction of such measures) to handlesporadic congestion problems on the transmission link.

6.2 Discussion about the Use Case Results

6.2.1 Case 1: Using Re-dispatchTwo main points should be discussed about the grid owner:

1. Today, the grid owner employs the re-dispatch mechanism after receiving theproduction plan (45 minutes before the hour of delivery) of each balance re-sponsible player: congestion management is dealt with in the real time market,where flexibility is limited (little time to react), participants are few (regulat-ing bids have a minimum size) and prices much more volatile than in the dayahead market. However, since Nordpool does not publish the energy flowswithin the bidding area (only between areas), the grid owner cannot knowin advance whether there is a risk of congestion for a given hour. It couldbe interesting for the grid owner to have access to energy flows earlier than

53

CHAPTER 6. DISCUSSION

from the production plans, allowing them to trade in the intra-day marketfor example. An idea is the use of sub bidding areas which could be a wayfor the grid owner to obtain day ahead information from the power exchange.The SE03 bidding area could be split into 2 sub areas, SE03-1 (SE03 withoutGotland) and SE03-2 (Gotland only). The market splitting mechanism wouldonly be used between main areas (i.e. SE02 and SE03) but not between subbidding areas (meaning the spot price would be the same in SE03-1 and SE03-2 even in case of congestion). The energy flow between sub bidding areas foreach hour would be accessible from Nordpool, allowing the grid owner to takeactions and prevent cases of congestion at an earlier stage.

2. It is not likely that the grid owner is able to optimize its tariff related income.Indeed, since grid owners are monopolies, the tariff they can charge is heavilyregulated. On the other hand, the fact that they do not seem to lose any moneywith up to 221MW of installed wind power capacity on Gotland indicates thatthe implementation of re-dispatch to enable a greater wind power integrationon Gotland could even be welcomed by the grid owner (the producers and theconsumers being rather neutral since the spot price would remain unchanged).

Finally, only the case with the HVDC transmission link was elected for simulations.Indeed, using the same transmission capacity as in case 2B is not economicallyrealistic for the grid owner. With 170MW of installed wind power capacity, theHVDC link is congested about 2.8% of the time, and as seen in the results fromcase 1, a link congested 0.8% of the time already leads to heavy congestion relatedlosses (c.f. figure 5.1).

6.2.2 Case 2A and 2B: Using Market Splitting

• Figure 5.4 only displays the possible yearly income from sold energy for thewind power producers when the market splitting congestion managementmethod is implemented. This increase in income (combined with the revenuefrom the electricity certificates) should be compared with the investment costand technical lifespan of new wind turbines to assess what is the limit to howmuch more wind power can be installed on Gotland at a profit. Looking atthe same figure, the slope of the income seems fairly linear for values be-tween 170MW and 221MW of installed wind power capacity (and decreasesafterwards). This indicates that any increase in capacity up to 221MW (from170MW) would have the same return on investment. In 2014, the installedcapacity on Gotland was increased to 185MW (the extra installed capacitycan therefore be assumed to be profitable). It is logical then to assume thatwithout the current physical constraint imposed by the grid owner (195MW),investors would be then willing to install up to 221MW.

• The power variation of 221MW installed wind power capacity on Gotlandmight be manageable at day-ahead level using the market split congestion

54

6.2. DISCUSSION ABOUT THE USE CASE RESULTS

management method, but this would require very efficient (and probablyquickly responsive) balancing power units and/or battery storage units tobe able to maintain the network stable within the hour of delivery (real-timebalance). On the other hand, the Swedish law imposes that the congestionrent should be re-invested in grid reinforcements, meaning that the transmis-sion link would probably be reinforced after some time (market splitting canbe seen as a buffer for the power grid to allow wind power to be installedbefore grid reinforcements are made, thus speeding up the process). Alterna-tively, the grid owner could also communicate a lower limit for transmissioncapacity in order to keep a certain buffer in the transmission link for balancingpurposes.

6.2.3 Flexible Demand

The flexible demand model used in the use case scenario is very rudimentary in thesense that it is just assumed to be somewhat proportional to the spot electricityprice. While this might be true to some extent, the consumption does not slavishlyfollow the spot electricity price but depends also on many other factors, most ofthem related to human behavior (time of the day, day of the week, ...). On theother hand, most smart home optimization tools make use of the spot price as aninput for consumption optimization, meaning that many devices possess the kindof price sensitivity proposed in the assumptions of the use case.

6.2.4 Re-Dispatch or Market Splitting

From the use case, there is no congestion management method that seems to beclearly better than the other. At least 221MW of wind power capacity could beinstalled on Gotland with both re-dispatch and market splitting while keeping theprofit of all actors unhurt. Market splitting seems however to be favoured if Gotlandwants to make use of the possible load flexibility as presented in the use case, but isprobably more sensitive to possible bidding strategies intending to minimize balancecosts. Implementing demand response programs with the re-dispatch congestionmanagement mechanism would require more advanced control schemes such as theones presented in e.g. [4]. Note that load price sensitivity does not exclude the useof these more advanced method.

6.2.5 Congestion rate

221MW of installed wind power capacity seems to be the limit at which both con-gestion management method show signs of ineffectiveness. 221MW of installed windpower capacity induces a congestion rate around 0.2%, while 238MW yields a 0.8%congestion rate. This indicates that we should only rely on these type of regulatorymechanism to handle congestion up to a given rate, i.e. only to deal with sporadiccongestion.

55

CHAPTER 6. DISCUSSION

6.3 Thesis Benefits for the Smart Grid Gotland ProjectThis thesis has led to a number discussion topics that are a great value for theSmart Grid Gotland project:

1. Congestion management methods, especially market splitting, can favour de-mand side participation. Price sensitive loads (probably simplest to imple-ment) can lead to reduced wind curtailment and increase welfare for the islandas a whole.

2. Congestion cases, especially if they are very infrequent, do not necessarily re-quire a technical solution. Regulatory mechanisms can, and without impairingthe economy of any actor of the electricity market, be advantageously used(or at least considered) to promote wind power integration.

3. When cases of congestion on the transmission link happen too often (morethan 0.3% of the time), technical solutions (such as grid reinforcements) shouldbe implemented in place of regulatory mechanisms (such as the congestionmanagement methods presented in this project).

56

Chapter 7

Conclusion

7.1 Conclusive SummaryIncreasing the installed wind power capacity on Gotland could lead to periods

where the transmission capacity from Gotland to mainland Sweden is insufficient.While it is theoretically feasible to balance 5MW beyond the capacity of the trans-mission link using ancillary services and demand response, grid reinforcement orregulatory mechanisms are required to further integrate wind power on Gotland.This thesis intends to assess whether congestion management methods can be costefficiently used to postpone or even avoid network capacity reinforcements whileincreasing the hosting capacity of wind power on Gotland.

A theoretical study was first conducted to analyse the operation mode and theactors (and their roles) of the Nordic electricity market. Furthermore, several con-gestion management methods were assessed using concepts of microeconomics tounderstand how each of them handle the lack of transmission capacity, and how theactors of the Nordic electricity market are affected from an economical perspective.Re-dispatch and market splitting proved to be the most appropriate methods forthe Gotland case. Re-dispatch passes the entirety of the cost of lack of transmissionon the grid owner and market splitting engages the producers and consumers bysetting different spot prices between bidding areas when congestion occurs.

An electricity market model capable of performing social welfare optimal dispatchwas designed in order to be able to illustrate the theoretical results with the Gotlanduse case.

Simulations implementing these two congestion management method show thatit is potentially possible to increase the installed wind power capacity on Gotland toat least 221MW (25MW over the current stated limit of 195MW) without impairingthe income of any actor. With re-dispatch, the increased in grid feed-in tariff incomefrom increased installed wind power capacity is more than enough to cover for the

57

CHAPTER 7. CONCLUSION

cost of congestion (up to 221MW) for the grid owner. With market splitting, theaverage marginal profit per MWh (i.e. the spot price on Gotland) for the windpower producers is negligibly affected with up to 221MW of installed wind powercapacity. Market splitting has the added advantage of reflecting the transmissionproblems on the energy spot price of Gotland, thus giving economic incentive forflexible power consumption to alleviate the problems.

The last part of the thesis shows that bidding strategies for wind power producerswith the goal of minimizing balance costs might jeopardize the day ahead conges-tion management process (i.e. especially market splitting) as bidding quantity andproduced quantity could greatly differ.

To conclude, both congestion management methods studied in this thesis (re-dispatch and market splitting) seem to be worthy add-ons to grid reinforcements inorder to enable increased wind power integration as these regulatory mechanismsoffer an extra degree of flexibility to the system to manage sporadic congestion casescaused by intermittent power production.

7.2 Future WorkA great focus was set on day ahead congestion in this thesis. One should not forgetthat the power system must be in balance at every instant. Some future workintending to treat real time congestion and the balancing market in general couldprove to be a valuable continuation of this work in order to confirm (or infirm) theresults presented in this work.

It would be interesting indeed to analyze the impact of these two congestionmanagement methods, coupled with 221MW of installed wind power capacity, on thebalancing market of Gotland. Wind power plant are rather easy to shut down whenneeded, meaning that down regulating power would probably be very affordable.On the other hand, it is not possible to for wind power plants to increase theirpower output, likely resulting in a great need (and expensive) up-regulating power.

Real time balance would also be put under greater pressure with 221MW ofinstalled wind power capacity. Wind power production varies within the hour,and therefore the question whether an extra 25MW can be balanced at real timeshould be answered. This is especially true since day ahead congestion managementmethod cannot treat real time congestion on the link, meaning that at times thelink is indeed congested, the island would have to be able to balance itself.

Finally, the concept of of the impact of bid optimization on day ahead priceshas been hardly touched upon in this thesis. Having a model implementing dayahead market with market splitting, balancing market and strategical bidding forbalance cost minimization could yield valuable results about market sensitivity tohigh wind power penetration.

58

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