Storage and d emand management in electricity distribution grids Andreas Schröder

1. Storage and demand management in electricity distribution gridsAndreas Schröder Presentation to the IAEE International Conference Stockholm 21 June 2011

2. Research question 2 options for generation cost reductions Demand Management Central Storage facilities + Grid Upgrade (vs. congestion) - DC Load flow analysis - Stochastic optimization reflects uncertainty in demand and generation Should a vertically integrated DSO and utility invest in storage and demand management facilities? Hardly beneficial Best option Not necessary

3. 1) Model

4. Extensive form Minimize expected generation cost + Investment cost s.t. Energy balance, storage power and capacity limit, DSM limits, DC load flow constraints

5. Two-stagestructure

6. Model algorithm Source: Gabriel, Eckhause, 2008

7. 2) Application #1

8. Grid Topology Stylized 5-node distribution grid configuration in series connection. Topology chosen so as to simulate a ‘worst-case’ grid (Source: own illustration).

9. Stochastic wind output • Inverse Weibull CDF • m shape parameter: 2 • k scale parameter: Time-varying • Average wind speed: 5,22 m/s • Cut-in: 2.6 m/s • Cut-out: 17 m/s • Rated: 8 m/s Frequency of wind speeds and sampled profiles. (Source: Own production based on Roy et al.. (2010))

10. Stochastic demand (Sources: own production based on BDEW (2010), Leitinger (2009), Grein et al. (2009), Stadler (2008))

11. DSM Limits Demand-management limits time-varying and asymmetric Differentiation by season and time of day Differentiation between household & commerce (Source: Based on Stadler (2008), Grein et al. (2009))

12. DSM investment cost Cost data for 2010 in EUR per annum for an AMM. [Sources: Based on EcoFys (2009)]

13. Storage cost data Storage investment cost data compiled from various sources. Mechanical bulk storage included for reference but not considered in our calculations. (Sources: Winter (2008), RWTH Aachen (2010), EcoFys (2009)) Schoenung (2008), and ESA (electricitystorage.org)

14. 3) Results

15. Model results Linear program executed in GAMS with CPLEX Solver. Deterministic model 3 sec, Stochastic model ca. 7-10 min. Ca. 6 MB work space allocated, 10 - 20 Iterations. Value of stochastic solution: 0.5 – 2 % of total cost

16. Model results Storage device beneficial up to 850 EUR/kWh DSM not beneficial beyond 200 EUR Increase in EV strengthens case for DSM and a bit for storage Stochastic model favours more investment (Source: Own production)

17. Model results Storage and DSM operation and line flows in one scenario. Summed over all nodes, there are 309 kWh storage (up right) and 1440 consumers have DSM appliances installed (up left). (Source: Own production)

18. 4) Conclusion

19. Conclusion Grid at 10 kV no shortage Smart meters and related appliances not beneficial at total beyond 200 EUR Storage facilities beneficial when cost is below 850 EUR/MWh capacity Uncoordinated EV charging strengthens the case for DSM and storage in most cases Deterministic model under-estimates value of DSM and storage

20. Application #2 (Source: Niederrheinwerke, 2011; NEW Netz, 2011)

21. Grid Supply Point Vorst 10,000 Grid Supply Point Süchteln 20,000 Skt Tönis 20,000 Viersen1 20,000 Grid Supply Point Viersen2 20,000 Dülken 20,000 Boisheim 2,000 Grid Supply Point Grid Supply Point

22. Application #2 (Source: NEW Netz, 2011)

23. End

24. Drawbacks No energy savings from DSM Storage cost determined by fixing cycles to 1 per day although cycles are endogeneous in the model Other business fields for storage (reserve market) Grid representation stylized

25. Model constraints lf line flow kW bnetwork susceptance matrix hweighted network matrix(hl,i = 1/xl lml,n) lmconnection matrix P Potential of node i(choose 1=0)

26. Generation cost data Generation capacities and marginal generation cost in EUR/kWh. (Sources: EWI, Prognos, GWS (2010); Forschungsstelle für Energiewirtschaft (2010))

27. demand With demand-side management technology time Low demand and prices at t = 0  use or store more energy (more load online) High demand and prices at t = 1  demand already satisfied (or use stored energy) price price Demand w/o demand-side mgmt. Demand with DSM supply Demand with DSM Demand w/o DSM supply DSM1 DSM0 - W1 W0 > W1 p0 +W0 p1* p1 p0* quantity quantity • If the demand side is provided with real-time prices, there’s an incentive for intertemporal load shifting - the extent to which this is possible depends on the installed technology (e.g. smart metering, storage capacity, etc.) • This can result in a better overall utilization of the more efficient generation technologies  welfare improvements (* = the rationale for storage devices is similar)

28. Welfare maximisation vs. Cost minimisation p0r2 p0r2 – m DSM2 pri(q) = p0ri + mq p2(q2) pr2(q2) pi(qi) = (p0ri - mDi) + mqi DSM2 p0r1 DSM1 pr1(q1) p1(q1) q, q1, q2 - DSM1 Example of an artificial overall welfare increase without cost decrease. (Source: Own production)

29. Welfare maximisation vs. Cost minimisation Discontinuous inverse demand function - in fig. 3a, welfare contributions of DSM would cancel, so it is not used, while in 3b, it improves welfare (source: own production).