Need for integrated simulations: Integration of electricity supply and demand

1. Need for integrated simulations: Integration of electricity supply and demand Kenneth Bruninx with Erik Delarue and William D’haeseleer

2. Basic principles of electricity generation Electric power • Travels at speed of light • Is difficult to store → Supply must meet demand instantaneously • Network required for transport • High voltage – Transmission • Low voltage – Distribution Demand [MW] Time [h]

3. Basic principles of electricity generation Different technologies used and scheduled to meet demand

4. Technologies Electricity generation system is mix of • Dispatchable units • Non-dispatchable units • Often intermittent • Storage

5. Technologies Dispatchable units: output can be activelycontrolled • Nuclear, coal, lignite • Rankine steam cycle • Large units ~1000 MW • Continuous flat operation (?) • Gas Combined Cycle • Braytongas cycle + Rankine steam cycle • η ≈ 50%-55% • Typical size: ~400 MW • Flexible operation • Gas turbine • Peak units, very flexible • Renewable Energy Sources (RES) • Biomass, hydro (basin)

6. Technologies Non-dispatchable units: output cannot (or only limitedly) be controlled • Intermittent units • Variableoutput • Output is predictableonly to limited extent • Wind and solar photovoltaics (PV) • Zero marginal cost

7. Technologies Variable output & limited predictability

8. Technologies • Storage • Pumped hydro storage • Compressed Air Energy Storage (CAES) • Flywheels • Batteries • H2, CH4 • …

9. Power plant scheduling Given portfolio of power plants, how to meet certain electricity demand? • At lowest variable cost • Significant amount can be non-dispatchable generation • Taking into account technical constraints of power plants • Taking into account safety margins • Dealing with uncertainties • Network restrictions, import/export This optimization problem is known as the unit commitment problem • Difficult to solve because of on/off nature of decision variables • Required to represent start-up costs and start-up behavior, minimum operating point, minimum up & down times

10. Power plant scheduling Example of simplified unit commitment • Given set of power plants • Capacity, minimum operating point, efficiency, fuel price, start-up cost, minimum up & down time • Meet certain demand profile • Minimize fuel and start-up costs • Fuel cost dependent on load level • Technical constraints • Respect minimum operating point if on • Respect minimum up & down time Optimized with Mixed Integer Linear Programming (MILP) (Operations Research technique)

11. Power plant scheduling Example of simplified unit commitment • Given set of power plants • Capacity, minimum operating point, efficiency, fuel price, start-up cost, minimum up & down time • Meet certain demand profile • Minimize fuel and start-up costs • Fuel cost dependent on load level • Technical constraints • Respect minimum operating point if on • Respect minimum up & down time Optimized with Mixed Integer Linear Programming (MILP) (Operations Research technique)

12. Power plant scheduling • Consider power plants i • Consider time periods j • E.g., one week, hourly time steps: j = 1 … 168

13. Power plant scheduling • How to meet demand at lowest cost? • Minimize • Supply = Demand With gi,jelectricity generation of plant i in period j [MW] and djelectricity demand in period j[MW]

14. Power plant scheduling

15. Power plant scheduling Fuel costs • Assume linear cost behavior between Pmin and Pmax • Other function possible, e.g., quadratic Linear : Fuel cost [€/h] c b ~ η δ Output, g [MW] Pmin Pmax

16. Power plant scheduling • Deriving c and b from data previous table yields

17. Power plant scheduling Startup costs • Bringing power online (from zero to 1) incurs a cost • E.g., amount of heat required to bring steam to appropriate temperature and pressure

18. Power plant scheduling: an example Total installed capacity: 6520 MW One week period (168 h) • Peak demand = 5244 MW (95% of dispatchable capacity) • Given certain intermittent profile

19. Power plant scheduling

20. Power plant scheduling Results

21. Challenges and issues:

22. Challenges and issues: a paradigm shift Residual load in Germany in 2050

23. Challenges and issues: a paradigm shift Yearly load duration curves of supluses due to fluctuating electricity supply

24. Challenges and issues Flexibility will be required • Generation side • Storage • Interconnections • Curtailment • Demand side activation • Through smart grids • Demand side response

25. Challenges and issues Activation of demand side • Modeling of demand response

26. Challenges and issues Modeling of demand response • Cost based models • Centrally planned • Incentive payment to consumers (function of amount shifted) • Explicit modeling of flexibility • E.g., heating/cooling systems, transport (including storage) • Price based models • Time-of-use pricing (e.g., two tariff system), critical peak pricing, real time pricing • Demand elasticities • Own and cross price elasticities • Maximizing overall social welfare • Quadratically constrained programming or iterative piecewise linear optimization

27. Challenges and issues Fixed demand -0.2 own-price elasticity Source: De Jonghe, Delarue, D’haeseleer, Belmans, 2011, PSCC

28. Challenges and issues Electricity price -0.2 own-price elasticity Source: De Jonghe, Delarue, D’haeseleer, Belmans, 2011, PSCC

29. Challenges and issues Source: EEX Spot prices

30. Challenges and issues • Old situation: • Load drives generation • New paradigm: • Generation drives load • Consume when & where there is electricity generation

31. Integrated modelling: a case study ‘Impact of intelligent thermal systems on electricity generation – a systems approach’ • Research questions: • Effect of DS flexibility on electricity generation • Cost • Carbon intensity • RES utilization • Quantify the usefulness of DS flexibility • Motivation: in literature • OR focus on DS: technology-based analysis • OR focus on SS: economics-based analysis (see example above) Here: integrated approach, where the (economic) flexibility stems from a technology-based model. • Ongoing research

32. Integrated modelling: a case study Modelling approach: cost-based • Minimize • Supply = Demand With gi,jelectricity generation of plant i in period j [MW] and djelectricity demand in period j[MW] Optimization variable

33. Integrated modelling: a case study Modelling approach: cost-based • Variable demand is sum of demand of all flexible devicesof all consumer groups • Constraints • Technology: peak power, stored heat • Comfort level

34. Integrated modelling: a case study No flexibility 20% flexible demand

35. Integrated modelling: a case study Challenges in modelling • Consumer behaviour • Attendance pattern determines optimal solution • Limited number of consumer groups currently considered (25) • Correct representation of the building stock and its energy storage potential • Problem size • computational effort rises as more flexible device

36. Conclusions • Forms of RES are non-dispatchable • Intermittent: variability & limited predictability • Impact on dispatchable generation system • Increases flexibility requirements • Can be absorbed by flexible generation if limited • At higher penetration additional flexibility required • Can lead to negative instantaneous residual demand • Storage, interconnections, curtailment, flexible demand • New paradigm: generation drives load • Also other technical, economic and regulatory challenges