Optimization in Smart Grids: Challenges and opportunities

Optimization in Smart Grids: Challenges and opportunities

Prof. El-Ghazali TALBI

Polytech’Lille - University Lille & INRIA

[email protected]

2© El-Ghazali TALBI, INRIA Lille – Nord Europe 2

Mastertitelformat bearbeiten

Power Grids

Traditional grids

Smart grids

Microgrids

Optimization principles

Generation optimization

Design & Placement

Unit commitment

Transmission optimization

Network design

Energy market

Pricing

Distribution optimization

Power flow

Consumer optimization

Demand Management

Outline



Designed 130 years ago

First coal plant: Thomas Edison,1882

Generation

Electrical power is centrally generated

at large power plants

Transmission

Grid: Large transmission network

Distribution

Consumption is distributed over a large

geographical area

Energy demand will triple by 2050

Deregulation/liberalization market

Power loss : 6% in US, worse for

other countries

USA: estimated $25 billion per year

Environment impact

Greenhouse Gas (GHG) emissions:

contribution of 34%

Traditional grid



Electricity difficult to store

Advances in battery design

Electrical vehicles (EVs)

Flexibility: Energy production is

very hard to change quickly

Most of the flexibility is provided by

fossil fuel power stations

Energy demand fluctuates widely

during the day/seasons/weather

Electricity generation must match

consumer demand every minute

(power flow equations)

Peak load versus off-peak load

Low utilization of the grid during

off-peak times

Volatility in prices

Challenges and constraints

gasification

fired


Mastertitelformat bearbeitenPower demand over a day


Mastertitelformat bearbeitenFrom Traditional Grid to Smart grids

Distributed heterogenous

efficient reliable generation

Plants are distributed almost the

same way the consumers are

Minimal transmission of power to

distant consumers

Two-way information flow

Real-time demand, …

Two-way power flow

Smart meters: usage data

Flexible controllable load &

generation

Renewable energy

Energy storage

Plug in electric vehicles

Smart appliances

Customers can respond to price

signals sent from the utility



“A Microgrid is a group of interconnected loads and distributed energy

resources within clearly defined electrical boundaries that acts as a single

controllable entity with respect to the grid. A microgrid can connect and

disconnect from the grid to enable it to operate in both grid-connected or island

mode.” Microgrid Exchange Group, October 2010

Microgrids are low voltage intelligent distribution networks comprising various

distributed generators, storage devices and controllable loads which can be

operated as interconnected or as islanded system

Microgrid


Mastertitelformat bearbeitenOptimization issues



Exact versus Approximation Algo.

Mathematical programming:

Linear programming,

Mixed integer programming

Relaxation (Lagrangian, SDP,…)

Dynamic programming, ADP, Monte

Carlo search

Artificial intelligence:

Constraint programming

Metaheuristics

Single-solution based algorithms:

local search, tabu search, …

Population based algorithms:

evolutionary algorithms, particle

swarm, …

Optimization methods



Multi-period optimization

Multiple time periods (time-

horizon)

Uncertainty management

Robust optimization, stochastic

optimization

Ex: wind and solar production

(weather), demand, prices, …

Optimization + Simulation

Meta-modeling, Surrogates

High performance computing

Optimization + Machine learning

Forecasting (demand, renewable

generation)

Neural networks, deep learning,

ensemble methods

Optimization principles



Economics

Keeping downward prices on electricity prices,

reducing the amount paid by consumers

Efficiency Reducing the cost to produce, deliver, and

consume electricity

Reliability

Reducing the cost of interruptions and power

quality disturbances

Reducing the probability and consequences of

widespread blackouts

Security

Reducing dependence on imported energy as

well as the probability and consequences of

manmade attacks and natural disasters.

Environmental friendliness

Reducing emissions by enabling a larger

penetration of renewables and improving

efficiency of generation, delivery,

consumption.

Multi-Objective optimization




Unit commitment

Economic dispatch


Distribution optimization

Pricing and markets

Dynamic pricing

Customer management

Demand response

management

Optimization challenges in Smart Grids

NIST Model


Mastertitelformat bearbeitenGeneration optimization



Nuclear

Shut down only for refueling

Ex: Scheduling maintenance of nuclear pants

«Zero» cost renewable sources

Hydroelectric

Solar (photovoltaic, combustion)

Wind

Biomass

Geothermal

Gas < 200 Mw, some hours (8-24) to start

Coal Long start time (days), +250 MW

Generation sources



Lifecycle greenhouse gas emissions by electricity source

Technology Description50th percentile

(g CO2/kWhe)

Hydroelectric reservoir 4

Wind onshore 12

Nuclearvarious generation II

reactor types16

Biomass various 18

Solar thermal parabolic trough 22

Geothermal hot dry rock 45

Solar PV Polycrystaline silicon 46

Natural gasvarious combined cycle

turbines without scrubbing469

Coalvarious generator types

without scrubbing1001

Grenhouse gas emissions / sources

p.s: sometimes eletrical vehicles are not environment friendly (ex. Singapour & Tesla)

http://en.wikipedia.org/wiki/Hydroelectricity

http://en.wikipedia.org/wiki/Wind

http://en.wikipedia.org/wiki/List_of_onshore_wind_farms

http://en.wikipedia.org/wiki/Nuclear_power

http://en.wikipedia.org/wiki/Generation_II_reactor

http://en.wikipedia.org/wiki/Biomass

http://en.wikipedia.org/wiki/Concentrating_solar_power

http://en.wikipedia.org/wiki/Parabolic_trough

http://en.wikipedia.org/wiki/Geothermal

http://en.wikipedia.org/wiki/Hot_dry_rock

http://en.wikipedia.org/wiki/Solar_PV

http://en.wikipedia.org/wiki/Polycrystalline_silicon_photovoltaics

http://en.wikipedia.org/wiki/Natural_gas

http://en.wikipedia.org/wiki/Coal


Mastertitelformat bearbeitenCapital versus operating cost – Ramping (flexibility)Technology Capital Cost ($/kW) Operating Cost ($/kWh)

Coal-fired combustion turbine $500 — $1,000 0.20 — 0.04

Natural gas combustion turbine $400 — $800 0.04 — 0.10

Coal gasification combined-cycle

(IGCC)

$1,000 — $1,500 0.04 — 0.08

Natural gas combined-cycle $600 — $1,200 0.04 — 0.10

Wnd turbine (includes offshore

wind)

$1,200 — $5,000 Less than 0.01

Nuclear $1,200 — $5,000 0.02 — 0.05

Photovoltaic Solar $4,500 and up Less than 0.01

Hydroelectric $1,200 — $5,000 Less than 0.01

Technology Ramping Time Min. Run Time

Simple-cycle cumbustion turbine minutes to hours minutes

Combined-cycle cumbustion turbine hours hours to days

Nuclear days weeks to months

Wind Turbine (includes offshore

wind)

minutes none

Hydroeletric (includes pumped

storage)

minutes none


Mastertitelformat bearbeitenCentralized / distributed generation

Distributed generation

More independance

More flexibility on the source

Less electricity loss

Local generation

More suitable local sources: wind, solar, biomass, geothermal

More reliability

All your eggs aren’t in one basket

Brings a lot of problems (balancing, intermittent, uncertainty) !!



Engineering design

problems

Wind turbines

Solar systems, …

Placement problems

Placement of generators

Placement of storing

devices ...

Maintenance

Predictive maintenance

scheduling

Nuclear sites [Dupin,

Talbi 2016]




Given

A set of geographical areas

A set of units

Find

A set of assignment of units to geographical areas

Objective

Cost

Power loss

Capacity (production)

Constraints

Unit specific

Examples

Placement of wind turbines

Optimal placement of units



Given

Land topology and leases

Wind resource variation

Operating and maintenance costs

Road and cabling costs

Find


Objective

Max global revenue, Max production

Constraints

Noise and environmental factors

Ensuring safety area (size turbines blades)

Inter-turbine interferences

Literature solutions: Metaheuristics CMA-ES [Wagner 2011]

Evolutionary algorithms [Xu 2011]

Particle Swarm Optimization [Chowdhury2012]




Given

Time horizon: short, term, long term

Generation sources

Demand prediction

Determine

For each t : power to produce

From which source

Objective

Min Cost, Min Risk, Max Sustainability, …

Constraints

Demand: Meet the demand

Ramp constraints (no fast change)

Power limits

Min Up/Down time constraints

Reserve, …

Literature solutions

Lagrangian relaxation, Mixed Integer integer

programming, Stochastic and robust opt.

Dynamic programming, metaheuristics, Contraint

programming, matheuristics [Talbi et al. 2015]

Unit Committment Problem (UCP) / Economic dispatch


Mastertitelformat bearbeitenTransmission Optimization



Transmission and distribution losses

Losses in transmission between sources of supply and points of

distribution and in the distribution to consumers

6% in USA, 6% in France, 17% in Brazil, 22% in India, http://data.worldbank.org/indicator/EG.ELC.LOSS.ZS


http://data.worldbank.org/indicator/EG.ELC.LOSS.ZS



Network design problems

Connection of new end users (factory, …)

Interconnection with other countries

Planning network evolution

Transmission and communication networks expansion

planning

Lines, substations, …

Presence of uncertainty

Load, …

Monte-Carlo simulations, …

Objectives: investment cost, reliability, power losses

Design problems


Mastertitelformat bearbeitenEnergy markets

Creates new markets attracting consumers and innovations



Traditional retail view

Customer choice and control

Customer decide when and how

much to consume

Fully hedged service

Flat, uniform price

Tiered (inclining or declining rates)

Time of use (TOU) rates

(peak load pricing)

Pricing : Competitive markets

Wholesale view (load as resource)

Customer bid to supply

Day ahead market

4 hour ahead

Real-time market (5mn ahead)

Reliability products

Capacity

Emergency

Ancillary services



Flat pricing

Fixed price for all periods

Time-of-use (TOU) rates - Peak-load pricing

The intended cycle is divided into several periods

Distinct price value for each period is announced at the beginning of the

operation

Offer very low rates to customers who can shift high-demand operations

away from the times of day when the utility receives its peak demand for

energy.

The utility benefits from a more consistent daily load pattern, the customer

pays less

Pricing tiers (i.e. off-peak, peak) are established to correspond to specific

time intervals. Utilities can publish these rates to consumers to provide

financial incentives to shift demand to off-peak hours and reduce overall

consumption.

Time-shifting consumption - Real-time pricing (RTP)

Some utilities now offer their major customers real-time pricing

The exact price value for each period is calculated in real-time and is

announced only at the beginning of each operation period

Pricing strategies in whole sale market



Interaction between smart meters and

energy providers

Given

Subscribers preferences (utility functions)

Energy consumption patterns

Energy provider(s) single, multiple

Find

Real-time prices, day-ahead prices, …

Objectives

Min imposed energy cost,

Max the agregate utility to all subscribers,…

Constraints

Power generation capacity


Bi-level optimization/Game theory [Sezin

2013]

Interior point methods [Samadi 2012]

Dynamic programming [Joe-Wang 2012]

Greedy heuristic algorithm [Wang 2012]

Multi-agent systems [Ramachandran 2010]

Optimal real-time pricing for revenue management


Mastertitelformat bearbeitenOptimal real-time pricing for revenue management

Bi-level model [Brotcorne 2017]

Decision process involving two

decision-makers with a

hierarchical structure

Two decision levels : a leader

and a follower, controlling their

decision variables, seeking to

optimize their objective function.

The leader sets his decision

variables first. Then the follower

reacts based on the choices of

the leader.

Bilevel programming is strongly

NP-hard.

Originated from Stackelberg

games, related to principal-agent

problem, mathematical programs

with equilibrium constraints.

prices reaction


Mastertitelformat bearbeitenDistribution optimization


Mastertitelformat bearbeiten Grid connected micro-grids: High variability & uncertainty, distributed

generation, storage, pricing

Given

A distribution network, storage, prices

Find

Power flow, voltage, storage (multi-periodic)

Objective

Min Energy Losses, Imported energy, Carbon emissions, Max profit

Constraints

Bounds: powers, pressure, voltage, frequency, generation, load

Power flow equations, generation/load balance, storing capacities/losses


Traditional gradient based solvers (such as Newton-Raphson), are inadequate in time

domain & cannot be applied to the combined network-storage problem (non-convex)

Semi-definite programming (SDP) relaxation [Dall’Anese 2014]

Dynamic programming [Levron 2017]

Particle swarm optim. [Srtomme 2009]

Optimal distributed power flow problem


Mastertitelformat bearbeitenConsumer: Demand management optimization



Reduce electricity consumption in

homes, offices, and factories by

continually monitoring electricity

consumption and actively managing

how appliances consume energy.

It consists of demand-response

programs, smart meters and

variable electricity pricing, smart

buildings with smart appliances,

and energy dashboards.

Combined, these innovations allow

utility companies and consumers to

manage and respond to the

variances in electricity demand

more effectively

Demand management



Currently Flat price for electricity regardless of

the time of day or actual demand.

Smart meters give customers the ability to

choose variable-rate pricing based on the time of

day.

Consumers can respond accordingly by shifting

their energy consumption from high-price to low-

price periods.

Joint benefit of reducing costs for typical

consumers while lowering demand peaks for

utility companies.

Shifting

Re-scheduling operations so that some activities

take place during off-peak times of the day

Shedding

Shutting off non-essential equipment during the

peak period

Load shifting (shedding) with smart meters



Given

Local production (wind, solar, …)

Local load

Energy tariff

Find

Home devices scheduling

Energy plan

Buy/sold/store

Objectives

Min Bill, Max Profit

Min Peak demand, Max Confort, …

Constraints

Production

Batterie constraints

Home devices scheduling

Time windows


ILP (Integer Linear Programming) [A. Barbato 2012]

Mixed Linear Integer Programming [D. Zhang 2011]

Multi-objective evolutionary algorithms [Z. Garroussi,

E-G. Talbi & R. Ellaia, 2016]

House energy demand response



Large scale problems

Huge number of generators, clients, big data

USA: 12M distributed generators, 3M miles lines, …

Multi-objective problems

reliability, availability, efficiency, sustainability, cost.

Dynamic optimization

Sensing and real-time measurements

Multi-periodic planning and optimization

Distribution networks evolution (different scenarios)

Optimization under uncertainty

Stochastic data

Optimization Challenges & Perspectives



Bi-level optimization

Dynamic pricing

Optimization & Simulation

Meta-modeling, Surrogates

High performance computing,

Parallel algorithms

GPU, Multi-cores, Cluster,

Heterogeneous computing

Optimization & Machine learning

Forecasting (demand, renewable

generation)

Short-term, medium-term, long-

term

Neural networks, deep learning,

…

Optimization Challenges & Perspectives

GPU

Upper-level / Leader

Lower-level / Follower

GPU



Smart grids and Logistic/Transportation systems

Electrical vehicles (charging systems)

Electric vehicle routing problem

[J. Serrar, E-G. Talbi, R. Ellaia, 2017]

Electrial buses

Smart grids and Cloud computing systems

Green data centers

Energy-aware Job Scheduling

Cooling

Smart grids and Smart city

Urban configuration

Smart green building

Electrical vehicles as storing devices

More flexibility

Smart Grid Application perspectives



https://bioma2018.sciencesconf.org

https://meta2018.sciencesconf.org

https://bioma2018.sciencesconf.org/

https://meta2018.sciencesconf.org/

Optimization in Smart Grids: Challenges and opportunities

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