Optimal Aggregator Bidding Strategies for Vehicle-To-Grid Energy and the Environment Seminar By Eric Sortomme PhD Candidate, University of Washington October 7, 2010 1
Outline Introduction State of the Art in the Field Research Areas Preliminary Results 2
Introduction Electric vehicles (EVs) are poised to receive mass acceptance from the general public Reduced environmental impacts Energy Independence Mass adoption of EVs are not without challenges EVs are more expensive than traditional vehicles Mass, uncoordinated EV charging can cause energy supply issues and distribution overloads One proposed way to address these challenges is through Vehicle-to-Grid (V2G), the provision of energy and ancillary services to the grid from an EV Power flow can be either unidirectional or bidirectional Allows EVs to participate in most energy markets 3
Introduction V2G offers benefits to all participants EV owners can generate revenue and receive lower energy prices Utilities can increase system flexibility for increased system control Significant to the operation of V2G are aggregators which bid combined capacities of many EVs into the appropriate markets Base load Peak Energy Spinning Reserves Regulation Non-Spinning Reserves Bidding is not without its challenges EVs can disconnect from the grid whenever the owner has need Owners are not primarily concerned with power market participation and may not do so if there is much inconvenience 4
5 Source: NREL
Research Area The intent of this research is to develop optimal bidding strategies for aggregators which maximize their profits and the benefits to customers and utilities. If structured correctly, maximum aggregator profits will come from maximum sales of ancillary services which will generate higher revenues for customers and more useful services for utilities. 6
Potential Impacts of Research General Welfare Aid in hastening the adoption of EVs Reduced emissions Energy independence Reduced foreign intervention (Mid East Oil Countries) Aggregator Development Provides useful algorithms for different types of controllable loads Utility planning Algorithms can be used by utilities to forecast EV charging impacts and benefits Can determine what load and feeder constraints will need to be imposed 7
State of the Art on V2G Early work was feasibility and proof of concept Other recent studies looked at optimizing buying and selling base load and peak energy for single EV The necessity of aggregators to V2G was explored in several recent works. Optimal aggregator bidding of regulation was considered in a recent study 8
Unidirectional V2G Most studies considered only bidirectional V2G. Bidirectional V2G has the challenges of: Additional aftermarket hardware for the EVs Interconnection studies and anti-islanding protection at the point of connection Increased cycling wear on the batteries Customer resistance against the idea of letting the utility “drain their batteries” Manufacturer resistance to utilities degrading their batteries while under warranty. Because of these issues a logical first step is to begin with unidirectional V2G. No aftermarket EV hardware required No interconnection studies nor anti-islanding protection required Battery cycling is not an issue Customer concerns are assuaged 9
State of the Art: Unidirectional V2G One study looked at the general concept Involved modulating the charging around the Preferred Operating Point (POP) POP is a market term to describe the operating point scheduled by the aggregator or generator with the system Another study proposed a unidirectional regulation algorithm Dispatches each EV as all on or all off to modulate the group of EVs around the POP, “bang-bang charging” POP is set to follow renewable energy output levels 10
Deficiencies in the State of the Art Ancillary Services Algorithms: Only one regulation algorithm has been proposed Aggregator Bidding Strategies No optimal bidding strategies have been explored Setting the POP has only been lightly touched on 11
Research Areas Unidirectional ancillary services algorithms Aggregator profit maximization algorithms for bidding ancillary services and setting the POP for unidirectional V2G Minimization of distribution feeder losses and voltage variations Algorithms for bidirectional V2G 12
Unidirectional Regulation Varies each individual EV around its POP to respond to the regulation signal from the aggregator Aggregator capacity is the summation of all individual capacities. Advantages over bang-bang charging are: Reduced the energy losses in the feeders Can operate within a heavily loaded feeder’s constraint without overloading the feeder Reduced transients on the distribution system 13
Unidirectional Spinning Reserves Similar to regulation but only reduces charging from the POP Can be bid combined with regulation The capacity of regulation up can be broken into regulation capacity and spinning reserves capacity 14
Unidirectional Regulation Algorithm RS >= 0 No Yes ( RS/R U ) * MnAP i + POP i ( RS/R D ) * MxAP i + POP i < CRi/Ef i < CRi/Ef i No Yes No Yes PD i = ( RS/R D )* PD i = ( RS/R U )* PD i = CR i /Ef i PD i = CR i /Ef i MxAP i + POP i MnAPi + POPi MnAP i Where: is the minimum additional power draw of the i th EV RS is the system regulation signal provided to PD i is the power draw of the battery of the i th the aggregator R U EV is the regulation up capacity of the CR i aggregator is the charge remaining to be supplied to the i t h EV R D is the regulation down capacity of the MP i is the maximum possible power draw of i th aggregator POP i is the preferred operating point of the i th EV M C,i is the maximum charge capacity of the i th EV battery MxAP i EV is the maximum additional power draw of the i th Ef i is the efficiency of the i th EV’s battery EV 15 charger SOC i is the state of charge of the i th EV battery
Unidirectional Spinning Reserves Algorithm RRS >= 0 ( RSS/R R ) * RsRP i + PD i < CR i /Ef i Yes No FP i = ( RRS/R R )* FP i = CR i /Ef i RsRP i + PD i Where: RRS is the responsive reserve signal provided to the aggregator R R is the responsive reserve capacity of the aggregator RsRP i is the reduction in power draw available for spinning reserves of the i th EV FP i is the final power draw of the i th EV combining the effects of regulation and responsive reserves 16
Graphical Description Battery Power Draw (kW) Maximum Power Draw Reg. Down Cap. Actual Power Draw POP Reg. Up Cap. Final Power Draw Spin. Res. Spin. Res. Signal Cap. Time (min) 17
18 Graphical Depiction of Variables
Aggregator Profit Maximization Any optimization of V2G assets will be done by the aggregator Since the aggregator is a market participant, it is assumed that the aggregator will strive for its own best interests If the markets and regulations are structured properly, this will also lead to maximum benefits for the utilities and customers A fixed percentage of ancillary services revenues and a fixed mark up on energy delivered to the customer accomplishes this objective 19
POP Selection The POP will determine how fast the EVs charge and how much regulation capacity can be bid, therefore its selection is very important Two types of POP selection algorithms are considered: Smart Charging Optimal Analogues of the Smart Charging algorithms 20
Smart Charging POP Selection Smart charging algorithms considered Price based Load based Considers the net of load and uncontrollable renewables Maximum Regulation Participation (MaxReg) Keeps a near constant POP so as to bid regulation for the entire charging period Does not consider spinning reserves 21
Price Based Formulation 250 Max 200 Day-ahead Price $/MWh 150 Actual 100 50 Min 0 0 5 10 15 20 25 Time (hours) Max-Actual POP t MP ( ) Max-Min 22
Load Based Formulation 4 x 10 2.3 Max 2.2 2.1 Day-ahead Net Load (MW) 2 1.9 1.8 Actual 1.7 1.6 1.5 Min 1.4 1.3 0 5 10 15 20 25 Time (hours) Max-Actual POP t MP ( ) Max-Min 23
24 MaxReg Based Formulation Time CR POP t ( )
Shortcomings of Smart POP Selection Do not consider price of ancillary services Do not account for aggregator profits Do not view each hour’s schedule in terms of the entire charging period 25
Optimal POP Selection Maximize the aggregator profits Income comes from a fixed percentage of regulation revenues and a fixed markup on energy Costs are assumed constant since unidirectional V2G has no incremental costs Subject to Maximum and minimum power draw of EV chargers Battery capacities 26
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