Lecture #10 Operating power systems at very high VRE shares

Lecture #10Operating power systems at

very high VRE shares

PHYS-E0483_#3 Juha Kiviluoma & Peter Lund 2020

Contents Lecture # 10

PHYS_E0483_#10 JuhaKiviluoma & Peter Lund 2020

• Unit commitment and economic dispatch (balancing andreserves)

• Maintaining frequency• Maintaining voltage• Other stability issues• Effects on power market

• Exercise: Duration curve• Reading (same as in next lecture): Helistö et al.

“Backbone—An Adaptable Energy Systems ModellingFramework”, Energies 2019, 12(17), 3388.https://www.mdpi.com/1996-1073/12/17/3388

Voltage


Reactive power:Airplane Analogy

Slide from Bri-Mathias Hodge (University of Colorado, Boulder)

Frequency

7

Years, months: Forwards and futures, capacity adequacy (enough MW)

12-36 hours: Day-ahead market to schedule units (especially slow to start units)

1-24 hours: Intra-day market (new forecasts arrive and schedules can be updated)

Minutes:

Seconds, milli-seconds:

Normal operation: balancing market(manual correction of errors during operation)

Disturbances: fast reserves (to releave thereserves below)

Normal operation (49.9-50.1 Hz):Frequency controlled operating reserves(automatic correction of errors in realtime)

Disturbances (49.5-49.9 Hz):Frequency controlled disturbancereserve

Balancing the power system (maintainingfrequency)

Frequency ContainmentReserve - Normal

Frequency ContainmentReserve - Disturbance

automatic FrequencyRestoration Reserve

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Reserve activation time scales

50 Hz Time

Time0HoursSeconds

Pow

er

Minutes

Frequency

Freq

uenc

y

Load

Primary reserve

Secondaryreserve

Long term reserve

Kinetic energy Frequency dependentload decrease

Frequency deviation is large when a large power plant trips

/ balancing market

How much balancing reserve should youcarry? Allocating short term reserves.

• To cover for variability and uncertainty of loads (+wind/solar) andsudden failures of large thermal power plants

• High reliability, as a black out has very costly consequences• But, not too much costs§Often rules of thumb based on previous experience§ Primary regulation rule 5% of load, and on top of that 3% of load as secondary,

used in India§ N-1 reserve to cover for largest single loss of generation, and on top of that some

% of load

Slide from Hannele Holttinen (Recognis Consulting Oy)

Operational practices matter: more balancing forsmaller area and slower operation time scales

Milligan, Kirby, King, Beuning (2011), The Impact of Alternative Dispatch Intervals on OperatingReserve Requirements for Variable Generation. Presented at 10th International Workshop on Large-Scale Integration of Wind (and Solar) Power into Power Systems, Aarhus, Denmark. October

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Footprint Regional BAU

Aver

age T

otal

Regu

latio

n (M

W)

Average Total Regulation for 6 Dispatch/LeadSchedules by Aggegation (Dispatch interval -

Forecast lead time)

10-1030-1030-3060-1030-4060-40

ß Faster ß Faster ß Faster

Large Medium Small

Inertia

Inertia and synchronousgeneration

• Generators store energy in their rotation• Synchronous generators will decelerate if frequency goes

down – this transfers power from the rotation to electricity• Inverter connected generation does not do this• …but there are software based solutions to try to ‘emulate’

inertia or to turn them into ‘grid forming’ invertes

Thought opener

• I am a Balancing Authority (responsible forensuring that there is sufficient generation tomeet the electricity demand): How do I decidewhich units to use to serve my load?

• You have 100+ thermal power plants, VREgeneration in several locations, flexibledemand, hydro power with reservoirs andtransmission line bottlenecks.

Slide modified from Bri-Mathias Hodge (University of Colorado, Boulder)

Power System Timescales

Syst

emLo

ad (M

W)

Time of Day (hr)0 2412 16 204 8

seconds tominutes

Regulation

dayScheduling

minutes tohours

LoadFollowing

DaysUnit Commitment


Economic Dispatch• Closer to the operating hour perform a “true-

up” with better forecasts• Can change output levels of units that are on

(within ramping constraints) but only start upand shut down really fast units

• Also an optimization problem• LP if no start up shut down• MILP with fast start units


Dispatch Stacks

Source: PJM


Unit Commitment• Some decisions need to be made early (hours/days ahead)

• Some thermal units take a long time to start up, so need to tell them aday (or more) in advance if they will be on

• Optimal use of storages depends on how one expects to use them in thefuture

• Use forecasted load to schedule the generation mix• Optimization problem, minimize cost of serving expected load,

usually as a mixed-integer linear problem (MILP)• The optimization tells what units to have online on the next day

as well as an expectation at what levels they generate• Constraints to ensure a feasible solution: generator minimum

up/down times, ramping limits, minimum generation levels,transmission constraints, etc.

• Security constrained unit commitment ensures feasible powerflows during contingencies (n-1: the largest asset in the systemtrips off and the system has to be stay stable)

• Usually co-optimize energy and ancillary services (reserves)Slide modified from Bri-Mathias Hodge (University of Colorado, Boulder)

Use of unit commitment models

• Actual operational decision making– In power pools: unit commitment model with unit data decides

actual system operation– In power markets: a similar model resolves bids made into the

market. System operator can still use UC to ensure sufficientresources.

• In planning studies– What kind of systems would work and how much they would

cost. Could be made after a planning model.– Evaluating costs and benefits of new technologies / resources

• Academic topic of commercial interest– Difficult problem (hundreds of units, complex networks)– Hundreds of articles on different formulations

PHYS_E0483_#10 Juha Kiviluoma& Peter Lund 2020

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Bri-Mathias Hodge, CarloBrancucci Martinez-Anido, QinWang, Erol Chartan, AnthonyFlorita, Juha Kiviluoma. Thecombined value of wind and solarpower forecasting improvementsand electricity storage. AppliedEnergy, 2018.

Value of better forecastsShort term view important:savings from thermal unit operation

Unit commitment problemformulations

Applies to Methods FlexTool Backbone

Commitmentdecisions

Mainly thermalpower plants

Realistic: Integer.Can be simplifiedas linear decisions.

Linear Integer or linear(and can be mixedup)

Dispatchdecisions

Generation,consumption andstorages

Nodal balance(generation,consumption,transfers, storages)

Nodal balance Nodal balance

Uncertainty(forecasts)

VRE, loads, plantfailures

Perfect foresight,single forecast,stochasticforecasts, robustoptimization

Perfect foresightonly

All

Power gridconstraints

Transmission lines Copper plate, nettransfer capacity(NTC), DCapproximation, fullAC load flow

NTC NTC or DC loadflow

Frequencyreservepresentation

Grids and nodes Variousrepresentations

Single upwardreserve

User selectablereserves.Reserves can alsobe released.

PHYS_E0483_#10 Juha Kiviluoma &Peter Lund 2020

Markets• Day-ahead

• Usually held at5:00 am – noon onDay 1 for electricitybeing produced forall of Day 2

• Utilize loadforecasts

• Wind and solar canparticipate in someareas usingforecasts

• Spot• Actual load and

variable generationthat “show up”

Source: Synapse Energy Economics


Marginal Cost Generators• Marginal cost of most thermal generators depends on

the heat rate• Lower heat rate equals less fuel equals lower cost

Source: NREL


Locational Marginal Prices (LMPs)

Source: Synapse Energy Economics


LMP Example


Rolling time window

• Unit commitment model performs multiple solves in order tocover a longer time period used for the analysis (typically ayear)

• This is rolling planning, where forecasts get updated anddecisions are made

• Backbone has the capability to aggregate later time steps


Stochastic UC

PHYS_E0483_#10Juha Kiviluoma &Peter Lund 2020

Robust UC

• Ensuring that the commitment decision is sufficient to coveralso the worst case


Source: Pinsen P, Madsen H, Nielsen HA, Papaefthymiou G, Klöckl B. From Probabilistic Forecasts toStatistical Scenarios of Short-term Wind Power Production. Wind Energy 2009; 12:51–62

44 hour wind power forecast with uncertainty:

How UC with a single forecasthandle the worst case?

• Can you figure out?• The answer is reserves

PHYS_E0483_#10Juha Kiviluoma &Peter Lund 2020

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§ The core model offers energy conversions andenergy transfers that are applicable to anyconceivable energy transformation§ Minimize equations to keep the code tractable

§ Input data drives what forms of energy are actuallymodelled and how conversions and transfers are represented§ Allows stochastics for short-term forecasts and for long-term

statistics (e.g. reservoir hydro power)§ New models are defined through model definition files: allows to

build new implementations on top of the core engine as needed§ Different models can directly re-use each others results

(e.g. investments and operations)

Juha Kiviluoma, VTT / UCD

Backbone: Adaptable model forenergy systems and energy resources

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An example of a model that can do unitcommitment§ To introduce how these kinds of models are structured§ No need to understand all equations§ These kinds of models underlay most analysis that is used for

decision making in policy and in energy businesses

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• Nodes (n)• Grids (g)• Units (u)

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Some features

§MIP or LP§ The temporal structure of the model can be re-defined§ Backbone is really multiple models

§ Investments (using e.g. selected time periods)§ Storage value (to be passed to the scheduling model)§ Scheduling (stochastic UCED with DC OPF)§ Dispatch§ …

§GAMS does the solving (no de-composition within Backbonecurrently)§ Speed: depends on the problem and on the solver

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Stochastics

§ Backbone can take into account both short-term weatherforecasts and long-term (climatological / cost) uncertainty.§ Short-term uncertainty (e.g. day-ahead wind forecasts)

§ Wind data converted to wind power capacity factor time series.§ Backbone can use stochastic forecasts in the operational

optimization.§ Long-term uncertainty (e.g. different hydrological years)

§ In investment optimization, it is possible to consider the differentpossible costs simultaneously in a stochastic model run.

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Backbone inputs

§ Nodes in Grids (power, heat, gas,…)§ Units in Nodes§ Fuel prices for units using fuels§ Time series for units dependent on fluctuations (wind, PV, hydro,

air-source heat pump,…)§ Time series for energy demand (or influx)§ Constraints for different unit types (start-ups, ramps, reserve

provision, multiple outputs, conversion units…)§ Transfer limits between nodes§ Emission costs, taxes, etc.§ Investment costs§ Reserve demand

The devil is in the details.

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Outputs

§ For each unit, node, grid or the whole system:§ Generation and controllable consumption time series (including

conversions, which both consumes and generates)§ Start-ups, shut-downs§ Storage behaviour§ Curtailments and spills§ Energy transfers§ Emissions§ Costs (split into fuel, O&M, start-up, and emission costs)§ Prices§ Investments§ Violations of the energy balance or reserve requirements

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The objective function -A sum of all costs is minimized

= ,

,, + , + , + , + , +

+ _ + _

, = , , × + , , ×

p = parameterv = variablef = forecastt = time periodn = node

Objective function:minimize costs

+ fixed operation and maintenance costs [capacity×unittype: fixed_cost]+ variable operation and maintenance costs [v_gen | v_charge | v_convert×unittype: O&M_cost]+ fuel costs of units [v_fuelUse×fuel: fuel_price]+ CO2 emission costs [v_fuelUse×fuel: CO2_content×master: CO2_cost]+ start-up costs [v_startup×unittype: startup_cost]+ penalty cost for loss of load [v_slack×master: loss_of_load_penalty]+ penalty cost for insufficient upward reserves [v_reserveSlack×master: loss_of_reserves_penalty]+ penalty cost for insufficient capacity margin [v_capacitySlack×master: lack_of_capacity_penalty]+ penalty cost for curtailment of VRE [v_curtail×master: curtailment_penalty]+ penalty cost for insufficient inertia [v_inertiaSlack×master: lack_of_inertia_penalty]+ unit investment costs [v_invest×unit_type: inv.cost_kW×annuity]+ storage investment costs [v_investStorage×unit_type:inv.cost_kWh×annuity+ transmission line investment costs [v_investTransfer×nodeNode: inv.cost_kW×annuity]

Operation

Penalties

Investment

Capacity=+ pre-existing capacity [units: capacity]+ forced new capacity [units: invested_capacity]+ invested new capacity [v_invest | v_investTransfer]

FlexTool objective function is very similar (but easier to read slide)

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Rest of the equations

§ Constraint the solution space§Otherwise the model would minimize the objective function to

negative infinity§Otherwise the model would operate units at any output level§Otherwise the model would transfer any amount of power over a

transmission line§ Etc.

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Energy balance

, , − , , ×

= , , + , , + , , − , , ×

+ , , − , , + , , − , , × ℎ

, ,

= , , × , − , , × ,

∈

∈

h = duration of the time stepht = duration to the previous time step

, , = , , , × , − , , ,

∈

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Reserve requirement

, , , + , , , , × ,

∈

, , , }∈ ,

= , , , + , , , , × ,

∈

r = reserve typeu = unit

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Unit operation

, , + , , , × − , , ,

∈≥ , , × , ,

, , + , , , × + , , ,

∈≤ , , × , ,

, , = , , + , , ,

∈− , ,

, , ≤ + , , − , ,

, , ≥ , , ,

∈

Output_1

Output_2

Fixed output ratio+ output_2 [v_gen(g,node2,u,t)]=+ output_1 [v_gen(g,node1,u,t)]

×eq_co-efficient [units: output2_eq_coeff]+ eq_constant [units: output2_eq_constant]

[Capacity]

eq constant1

eq coefficient

Output_2 cannot provide reserve(use output_1 for electricity)FUEL

OUTPUT_1

OUTPUT_2

Units with two outputs

Output_1

Output_2

Less than output ratio+ output to node 2 [v_gen(g,node2,u,t)]<=+ output to node 1 [v_gen(g,node1,u,t)]

×co-efficient [units: output2_lt_coeff+ constant [units: output2_lt_constant]

Upper limit for 2nd output:+ output to node 2 [v_gen(g,node2,u,t)]<=+ ratio between outputs [units: output2_max_capacity]

×1st output online [v_online(g,node1,u,t)]OR 1st output capacity [see orange box]

Greater than output ratio+ output to node 2 [v_gen(g,node2,u,t)]>=+ output to node 1 [v_gen(g,node1,u,t)]

×co-efficient [units: output2_gt_coeff]+ constant [units: output2_gt_constant]

‘lt’ constant

‘gt’ constant[Capacity]

FUEL

OUTPUT_1

OUTPUT_2

Units with two outputs

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Base nodeA heat_pumpnodeA Loss of loadnodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.

• Just one coal unit – easy to unit commitand dispatch without a model

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Peaker nodeA heat_pumpnodeA Loss of loadnodeA Engine_gasnodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.

• Adding a gas engine as a peaker• Can be done manually, but the engine is

also running all the time to provide reservethat the coal unit cannot do alone


• Adding a condensing biomass power plant,which has operational costs between thecoal unit and the gas unit

• Can be done with a spreadsheet

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Biomass nodeA heat_pumpnodeA Loss of loadnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.


• Adding wind power, which replaces themost expensive generation first

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Wind nodeA heat_pumpnodeA Loss of loadnodeA windnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.


• Adding also solar power, which incombination with wind power displacesquite a bit of base load generation as well

• Still possible to do in a spreadsheet

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PV nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.


• Startup costs mean that it’s better to keepthe thermal power plants running

• However, startup costs were inflated inorder to demonstrate the effect

• In reality, the impact is small, but it can stillbe lot of money in a large power system

• Kills the spreadsheet

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Startups nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.


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Hydro nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Hydro_RESnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.

• Adding reservoir hydro power andconsequently zero marginal cost electricityand flexibility (moving energy andreserves)

• Thermal units can be turned off again forlonger periods of time


• There is district heating system too – it hasbeen operating with gas boilers

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Peaker heatA Loss of loadheatA heat_pumpheatA gas_boilerheatA Excess loadAll Demand+exp.-imp.

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Hydro nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Hydro_RESnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.


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HeatPump nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Hydro_RESnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.

• Heat pumps connect district heating(below) to the power system

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HeatPump heatA Loss of loadheatA heat_pumpheatA gas_boilerheatA Excess loadAll Demand+exp.-imp.


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HeatStorage nodeA heat_pumpnodeA Loss of loadnodeA PVnodeA windnodeA Hydro_RESnodeA Engine_gasnodeA ST_bionodeA ST_coalnodeA Excess loadAll Demand+exp.-imp.

• Heat storages help to reduce fuel use in thedistrict heating (natural gas is expensive)

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HeatStorage heatA heat_storage_chargeheatA Loss of loadheatA heat_storage_dischargeheatA heat_pumpheatA gas_boilerheatA Excess loadAll Demand+exp.-imp.

Notes

• Changing parameter values would change the results– As I’ve been emphasizing: garbage in, garbage out– If the district heating had a cheaper fuel, the flexibility from heat

storages would impact the power system more• Also the order of the scenarios would change the story

– Wind power and PV in a power system with very little flexibility isnot necessarily the best idea, but it’s not realistic, sinceflexibility is possible and it becomes cost effective

• This is operational only – the power plant portfolios are notoptimized (next lecture)


Lecture #10 Operating power systems at very high VRE shares

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