Toward Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets

Transcription

1 Toward Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets Efthymios Karangelos 1 François Bouffard 2 1 Department of Electrical Engineering and Computer Science University of Liège, Liège, Belgium 2 Department of Electrical and Computer Engineering McGill University, Montreal, Canada

2 Background & Motivation Intermittent renewable generation is assuming a key role in modern power systems. Evolving lack of supply-side flexibility highlights the value of demand-side flexibility in maintaining power system security. Gradual deployment of smart meters facilitates the increased utilization of demand-side resources. To realize potential benefits, emphasis has to be placed on establishing an efficient framework for the integration of such resources in competitive market structures.

3 Demand-side Flexibility Generally, Demand Response (DR) deployment has been modeled as a means of reducing demand. But,... To maintain the loads internal energy balance, any reduction may have to be restored base load at a later point in time. energy efficiency demand (MW) Due to efficiency losses the energy to be supplied later may be greater than the earlier reduction. Demand-side resources are not a pure alternative to generation. demand (MW) base load demand response time (%) time (%)

4 Problem Description Provision of spinning reserve from the demand-side in a joint energy and reserve market is analyzed. Co-optimization of power and spinning reserve with multiperiod unit commitment & stochastic security criteria. Stochastic security criteria enforce that the system should be able to withstand a pre-specified set of credible contingencies. Flexible consumers participate in the reserve market by offering to alter their consumption, while maintaining the internal energy balance of their loads.

5 Assumptions Ahead of the operating horizon the future system state is represented through a finite set of scenarios. The failure of any system component at a certain period within the operating horizon has a given probability. The impact of any failure lasts until the end of the operating horizon. Following the occurrence of a failure, unit commitment decisions cannot be altered. In post-failure states reserve capacity is not required, as only single contingencies are considered credible.

6 Market Clearing Objective The optimization objective accounts for the cost of the deterministic unit commitment and the expected cost of operating under every credible system state. Unit Commitment Cost Normal Operation Expected Cost Preventive Actions Cost Corrective Actions Expected Cost Generation, Transmission & Demand Constraints Minimize Expected System Cost Deterministic Variables Unit Commitment Generation Reserve Capacity Demand Reserve Capacity Stochastic Variables Generation Schedule Demand Reduction & Recovery Schedule Involuntary Load Shedding

7 Demand-side Reserve Offers The fact that a load reduction may be followed by load recovery is explicitly acknowledged. Option fee (in /MW) representing the cost of making the loads available. Exercise fee (in /MWh) representing the cost of modifying the demand. Recovery Pattern... Duration of the recovery interval. Efficiency loss of the reduction/recovery cycle.

8 Demand-side Reserve Deployment Demand reduction may be followed by a recovery interval, during which the same consumer cannot sustain another reduction. Demand Reduction Demand Recovery The efficiency loss defines the ratio of demand reduction to demand recovery. Linear Programming ensures that the cost of deploying the resource & the cost of serving the recovery demand are less than the cost of serving the reduced demand.

9 Cost & Revenue Allocation At every system node, the system the summation of the power balance Lagrange multipliers over all credible system states defines the expected nodal price of energy. Under the realized system state every consumer should pay the probability removed Lagrange multiplier for the amount of energy it uses. DP k j (t) = µ k n(t) p k Pdj k (t) n N j Similarly every generator should collect the probability removed Lagrange multiplier for the amount of energy it produces. GR k i (t) = µ k n(t) p k Pgi(t) k n N i

10 Cost & Revenue Allocation A flexible consumer realizes a benefit (cost reduction) through consuming electricity during the recovery periods, which are priced lower than the reduction period. The expected benefit of a flexible consumer is greater than, or at least equal to the value of the offered capacity plus the expected deployment cost. N T N k [ ] µ k n(t) Dj k (t) Sj k (t) = n N j t=1 k=1 N T [cd j + π j (t)] R dj (t) + p k cr dj Dj k (t) t=1 N T N k t=1 k=1

11 Cost & Revenue Allocation As the Lagrange Multipliers integrate the potential effects of network congestion, total demand payments are always greater than, or equal to, the total generator revenues. The expected revenue of a generator is greater than, or at least equal to, the value of the offered reserve capacity plus the expected generation cost. N T N k E [GR i ] = µ k n(t)pgi(t) k n N i t=1 k=0 N T N k = {c gi R gi (t) + c+gi R+gi (t) + p k cp gi Pgi(t) k t=1 + σ i (t) k=0 ] ] [P gi(t) 0 + R + gi (t) σ i (t) [P } gi(t) 0 R gi (t)

12 Demonstrative Example Scheduling of a two bus, 3 generator system over 3 periods. Generator p.u. Generator 2 Generator 3 i cp gi c + gi c gi Demand 1 Demand 2 1 p.u. 1 p.u. Generator 3 can only be switched on at the 3rd period due to unit commitment restrictions. Table : Generation Costs ( per unit) r 2 s 2 c d2 cr d Table : Demand Reserve Offer

13 Demonstrative Example Transmission line failure possible at the start of the 1st or 2nd period. t P 0 g1 (t) P0 g2 (t) P0 g3 (t) Table : Pre-Contingency Power Output (per unit) t R + g2 (t) R+ g3 (t) R d2(t) Table : Reserve Capacity (per unit) Rescheduling demand is only rational between periods 2 and 3. During the 3rd period up-spinning reserve capacity of generator 3 is scheduled to potentially serve the demand recovery.

14 IEEE RTS Case Study 12-hour operating horizon (on-peak hours). The failure of any single generating unit with a capacity greater than 197MW is considered as a credible contingency. 73 potential system operating states. Up to 45% of the demand offered as reserve for a 5/MW option fee and a 10/MWh exercise fee. The impact of a 1-hour recovery interval, with a 10% efficiency loss is assessed.

15 IEEE RTS Case Study The recovery demand has a significant impact on the economic competitiveness of demand-side reserve. Recovery No Recovery Generation Demand Table : Reserve Capacity (MW) Acknowledging the recovery interval results in a decrease of 88.6% in the scheduled demand-side reserve capacity. Due to the efficiency loss, the overall system reserve requirement is increased by 17.8%.

16 IEEE RTS Case Study The additional amount of more expensive reserve increases the total system operating cost. Case C total E [DP] ( ) ( ) Recovery 241, ,068 No Recovery 235, ,387 Table : Solution Summary While the expected demand payment is greater by 52.2% when the payback is acknowledged, the qualitative difference in the operation schedule should not be neglected. Unanticipated load recovery may result in the deployment of potentially more expensive supply-side measures.

17 Conclusions Demand-side resources are not a pure alternative to supply-side resources. The provision of reserve from the demand-side is a means of delaying the deployment of supply-side reserve. The participation of demand-side resources may increase the overall energy and capacity requirements. The true value of the reserve provided by the demand can only be identified through considering the cost of serving the recovery demand.

18 Conclusions (cont d) Further work should concentrate on analyzing the impact of potential load reduction and recovery patterns on the system operating cost. The association of the efficiency loss to the time delay between a reduction and a recovery interval should be identified. For loads with inherent energy storage potential, pre-emptive recovery intervals may also be modeled.

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