Incentive-based Demand Response (DR) is a widely used tool to reduce the demand for electricity at times when the supply is scarce and expensive. In such DR programs, participating consumers are paid for reducing their energy consumption from an established baseline. This baseline is often based on the average historical consumption of a peer group on days that are similar to the upcoming DR event. In essence, baselines are estimates of the counter-factual consumption against which the aggregator measures load reductions and determines payments to the consumers in DR programs. Consumers have an incentive to inflate their baseline to increase the payments they receive. There are celebrated cases of consumers gaming this baseline to derive economic benefit. Several researchers have questioned the fairness of these baseline schemes used in current practice. We propose a novel DR mechanism to address gaming and fairness concerns. In our mechanism, each consumer forecasts their baseline consumption and reports their marginal utility to the aggregator who manages the DR program. Deviations in consumption from the self-reported baseline are penalized, providing an incentive for best-effort truthful estimation of baselines. The aggregator selects a set of consumers for each DR event to meet a load reduction requirement and are paid according to the observed reductions from their reported baseline. We show that truthful reporting of baseline and marginal utility is both incentive compatible and individually rational for every consumer. This establishes the correct baseline and the aggregator is able to meet any random load reduction requirement reliably.

10aRM11-0061 aMuthirayan, Deepan1 aKalathil, Dileep1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/mechanism-design-self-reporting01300nas a2200169 4500008003900000245008600039210006900125260003800194300001600232520072300248653001300971100001800984700001901002700002201021700002001043856006701063 2016 d00aModel and data analysis of two-settlement electricity market with virtual bidding0 aModel and data analysis of twosettlement electricity market with aLas Vegas, NV, USAbIEEEc12/2016 a6645 - 66503 aSystematic nonzero spreads, defined as the differences between day-ahead and real-time prices, are routinely observed in the wholesale electricity markets. Virtual bidding is a financial mechanism which aims to reduce the magnitude of spreads by allowing market participants to arbitrage on the spread. We follow a data-driven approach to develop a two-settlement market model, and consider a game-theoretic setting with virtual bidders as strategic players. We interpret the spread as a measure of the average forecast accuracy of the market and all the virtual bidders. The main results convey the implication that introducing more qualified virtual bidders into the market help the convergence of the spread.

10aRM11-0061 aTang, Wenyuan1 aRajagopal, Ram1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/model-and-data-analysis-two01213nas a2200157 4500008003900000245004800039210004700087260003200134520066000166653001300826100002000839700001600859700002200875700002000897856013800917 2015 d00aEquilibria in two-stage electricity markets0 aEquilibria in twostage electricity markets aOsaka, JapanbIEEEc12/20153 aMost electricity markets have multiple stages, which include one or more forward markets and the spot market. We consider two stages - a day-ahead market and a real-time market. We study equilibrium outcomes in such markets assuming demand to be deterministic. We show via counterexamples that in such two-stage electricity markets, (i) a Nash equilibrium may not exist, or (ii) there may be multiple inefficient Nash equilibria. We also give two sufficient conditions - a "congestion-free" condition and a "monopoly-free" condition - under which a subgame perfect Nash equilibrium exists and yields efficient outcome.

10aRM11-0061 aGupta, Abhishek1 aJain, Rahul1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7403136&refinements%3D4229014380%26filter%3DAND%28p_IS_Number%3A7402066%2901533nas a2200169 4500008003900000245006900039210006700108260003600175300001600211520096200227653001301189100002601202700002401228700002201252700002001274856006901294 2015 d00aLow-dimensional models in spatio-temporal wind speed forecasting0 aLowdimensional models in spatiotemporal wind speed forecasting aChicago, IL, USAbIEEEc07/2015 a4485 - 44903 aIntegrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations. Inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms, we claim that there usually exists an intrinsic low-dimensional structure governing a large collection of stations that should be exploited. We cast the forecasting problem as recovery of a block-sparse signal x from a set of linear equations b = Ax for which we propose novel structure-sparse recovery algorithms. Results of a case study in the east coast show that the proposed Compressive Spatio-Temporal Wind Speed Forecasting (CSTWSF) algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmark models.

10aRM11-0061 aSanandaji, Borhan, M.1 aTascikaraoglu, Akin1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/low-dimensional-models-spatio01162nas a2200181 4500008003900000245006800039210006000107260003600167300001600203520056900219653002800788653001300816100002100829700002100850700002200871700002000893856006700913 2015 d00aThe real value of load flexibility — congestion free dispatch0 areal value of load flexibility congestion free dispatch aChicago, IL, USAbIEEEc07/2015 a5002 - 50093 aIn this paper we present a new value proposition for load flexibility. This value is derived through enabling a congestion free dispatch, which brings economic benefits to the market participants (loads and generators), subject to certain conditions on the network. If participant classes are considered as collectives, then no class of participants is economically disadvantaged. We show that load flexibility increases the opportunity for congestion free dispatch. The economic implications of this new paradigm are studied using a simple two bus example.

10areliability and markets10aRM11-0061 aMather, Jonathan1 aBaeyens, Enrique1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/real-value-load-flexibility02400nas a2200193 4500008003900000020002200039245007000061210006900131260004000200300001600240520176500256653001002021653001302031100002102044700002902065700002202094700002002116856007002136 2014 d a978-1-4799-7746-800aDuration-differentiated energy services with a continuum of loads0 aDurationdifferentiated energy services with a continuum of loads aLos Angeles, CA, USAbIEEEc12/2014 a1714 - 17193 aThe problem of balancing supply and demand in the power grid becomes more challenging with the integration of uncertain and intermittent renewable supply. The usual scheme of supply following load may not be appropriate for large penetration levels of renewable supply. The reason is the increased level of reserves required to maintain a reliable grid, which affects both operational costs (reserves are expensive) and the environmental benefits of renewables (on-line reserves might increase CO2 emissions). An alternative paradigm is to use demand side flexibility for power balance. In this paper, we focus on one particular way of exploiting the demand side flexibility. We consider a group of loads with each load requiring a constant power level for a specified duration within an operational period. The loads are differentiated in terms of the duration of service they require. The flexibility of a load resides in the fact that the power delivery may occur at any subset of the total operational period. We consider the problems of scheduling, control and market implementation for a continuum of these loads. If the loads and the available power are known in advance, we find conditions under which the available power can service all the loads, and we describe an algorithm that constructs an appropriate allocation. In the event the available supply is inadequate, we characterize the minimum amount of power that must be purchased to service the loads. In addition, we investigate the implementation of a forward market in which consumers can purchase duration differentiated services. We first characterize the social welfare maximization problem and then show the existence of an efficient competitive equilibrium in this forward market.

10aCERTS10aRM11-0061 aNayyar, Ashutosh1 aNegrete-Pincetic, Matias1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/duration-differentiated-energy01798nas a2200229 4500008003900000020002200039245005100061210005100112260002700163300001600190520110800206653001801314653002001332653002801352653001301380100002101393700001701414700002301431700002201454700002001476856007201496 2013 d a978-1-4673-5714-200aAggregate flexibility of a collection of loads0 aAggregate flexibility of a collection of loads aFirenzebIEEEc12/2013 a5600 - 56073 aWe consider a collection of flexible loads. Each load is modeled as requiring energy E on a service interval [a; d] at a maximum rate of m. The collection is serviced by available generation g(t) which must be allocated causally to the various tasks. Our objective is to characterize the aggregate flexibility offered by this collection. In the absence of rate limits, we offer necessary and sufficient conditions for the generation g(t) to service the loads under causal scheduling without surplus or deficit. Our results show that the flexibility in the collection can be modeled as electricity storage. The capacity Q(t) and maximum charge/discharge rate m(t) of the equivalent storage can be computed in real time. Ex ante, these parameters must be estimated based on arrival/departure statistics and charging needs. Thus, the collection is equivalent a stochastic time-varying electricity storage. We next consider the case with charging rate limits. Here, we offer bounds on the capacity and rate of the equivalent electricity storage. We offer synthetic examples to illustrate our results.

10aload modeling10aload regulation10areliability and markets10aRM11-0061 aNayyar, Ashutosh1 aTaylor, Josh1 aSubramanian, Anand1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/aggregate-flexibility-collection01545nas a2200277 4500008003900000022001400039245005000053210004900103260001200152300001600164490000600180520074400186653001000930653003900940653001800979653001700997653002801014653002701042653001301069100002301082700002301105700002501128700002201153700002001175856007201195 2013 d a1949-305300aReal-Time Scheduling of Distributed Resources0 aRealTime Scheduling of Distributed Resources c12/2013 a2122 - 21300 v43 aWe develop and analyze real-time scheduling algorithms for coordinated aggregation of deferrable loads and storage. These distributed resources offer flexibility that can enable the integration of renewable generation by reducing reserve costs. We present three scheduling policies: earliest deadline first (EDF), least laxity first (LLF), and receding horizon control (RHC). We offer a novel cost metric for RHC-based scheduling that explicitly accounts for reserve costs. We study the performance of these algorithms in the metrics of reserve energy and capacity through simulation studies. We conclude that the benefits of coordinated aggregation can be realized from modest levels of both deferrable load participation and flexibility.10aCERTS10adistributed energy resources (der)10aload modeling10aoptimization10areliability and markets10arenewables integration10aRM11-0061 aSubramanian, Anand1 aGarcia, Manuel, J.1 aCallaway, Duncan, S.1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/real-time-scheduling-distributed01902nas a2200241 4500008003900000022001300039245005900052210005800111260001200169300001400181490000700195520121500202653001001417653002801427653002701455653002001482653001301502100001901515700001801534700002001552700001401572856007401586 2013 d a0142061500aRisk-limiting dispatch for integrating renewable power0 aRisklimiting dispatch for integrating renewable power c01/2013 a615 - 6280 v443 aRisk-limiting dispatch or RLD is formulated as the optimal solution to a multi-stage, stochastic decision problem. At each stage, the system operator (SO) purchases forward energy and reserve capacity over a block or interval of time. The blocks get shorter as operations approach real time. Each decision is based on the most recent available information, including demand, renewable power, weather forecasts. The accumulated energy blocks must at each time t match the net demand D(t) = L(t) − W(t). The load L and renewable power W are both random processes. The expected cost of a dispatch is the sum of the costs of the energy and reserve capacity and the penalty or risk from mismatch between net demand and energy supply. The paper derives computable ‘closed-form’ formulas for RLD. Numerical examples demonstrate that the minimum expected cost can be substantially reduced by recognizing that risk from current decisions can be mitigated by future decisions; by additional intra-day energy and reserve capacity markets; and by better forecasts. These reductions are quantified and can be used to explore changes in the SO’s decision structure, forecasting technology, and renewable penetration.10aCERTS10areliability and markets10arenewables integration10areserve markets10aRM11-0061 aRajagopal, Ram1 aBitar, Eilyan1 aVaraiya, Pravin1 aWu, Felix uhttps://certs.lbl.gov/publications/risk-limiting-dispatch-integrating01993nas a2200181 4500008003900000245010000039210006900139260003900208520135000247653002401597653002801621653001501649653001301664100002101677700002201698700002001720856007101740 2013 d00aA statistically robust payment sharing mechanism for an aggregate of renewable energy producers0 astatistically robust payment sharing mechanism for an aggregate aZurich, SwitzerlandbIEEEc07/20133 aVariability of supply is a fundamental difficulty associated with renewable resources in the electricity market. One way of mitigating this difficulty is to aggregate a diverse collection of resources in order to exploit the negative correlations that may exist among them. We consider an aggregation scheme where individual renewable energy producers offer day-ahead contracts to an aggregate manager which in turn participates in a two stage electricity market. The net payment received by the aggregate manager from the market has to be fairly distributed among the participants in the aggregate. Since the actual power supplied by the aggregate is random, the net payment it receives is also random. The problem of sharing this random payment is complicated by the fact that different participants may have different statistical models for the payment because they have different statistical models for their and other producers' net generation. We propose a simple payment sharing mechanism that is independent of the statistical models of the participants. We show that our payment sharing mechanism ensures that individual producers are better off in the aggregate than on their own. Further, under certain conditions, aggregation provides the social benefit of increasing the amount of renewable energy available in the day-ahead market.10aelectricity markets10areliability and markets10arenewables10aRM11-0061 aNayyar, Ashutosh1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6669463&tag=101437nas a2200265 4500008003900000020002200039245008200061210006900143260003300212300001400245520059000259653002000849653001800869653002700887653002800914653001500942653001300957100002300970700001900993700001801012700002501030700002201055700002001077856007401097 2012 d a978-1-4673-2065-800aOptimal power and reserve capacity procurement policies with deferrable loads0 aOptimal power and reserve capacity procurement policies with def aMaui, HI, USAbIEEEc12/2012 a450 - 4563 aDeferrable loads can be used to mitigate the variability associated with renewable generation. In this paper, we study the impact of deferrable loads on forward market operations. Specifically, we compute cost-minimizing ex-ante bulk power and reserve capacity procurement policies in the cases of fully deferrable and non-deferrable loads. For non-deferrable loads, we analytically express this policy on a partition of procurement prices. We also formulate a threshold policy for deferrable load scheduling in the face of uncertain supply, that minimizes grid operating costs.

10aload management10aload modeling10apower system economics10areliability and markets10arenewables10aRM11-0061 aSubramanian, Anand1 aTaylor, J., A.1 aBitar, Eilyan1 aCallaway, Duncan, S.1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/optimal-power-and-reserve-capacity02310nas a2200253 4500008003900000020002200039245005400061210005300115260003200168300001600200520149400216653003901710653000901749653002701758653001301785653003801798100002301836700002301859700003601882700002501918700002201943700002001965856007101985 2012 d a978-1-4577-1095-700aReal-time scheduling of deferrable electric loads0 aRealtime scheduling of deferrable electric loads aMontreal, QCbIEEEc06/2012 a3643 - 36503 aWe consider a collection of distributed energy resources [DERs] such as electric vehicles and thermostatically controlled loads. These resources are flexible: they require delivery of a certain total energy over a specified service interval. This flexibility can facilitate the integration of renewable generation by absorbing variability, and reducing the reserve capacity and reserve energy requirements. We first model the energy needs of these resources as tasks, parameterized by arrival time, departure time, energy requirement, and maximum allowable servicing power. We consider the problem of servicing these resources by allocating available power using real-time scheduling policies. The available generation consists of a mix of renewable energy [from utility-scale wind-farms or distributed rooftop photovoltaics], and load-following reserves. Reserve capacity is purchased in advance, but reserve energy use must be scheduled in real-time to meet the energy requirements of the resources. We show that there does not exist a causal optimal scheduling policy that respects servicing power constraints. We then present three heuristic causal scheduling policies: Earliest Deadline First [EDF], Least Laxity First [LLF], and Receding Horizon Control [RHC]. We show that EDF is optimal in the absence of power constraints. We explore, via simulation studies, the performance of these three scheduling policies in the metrics of required reserve energy and reserve capacity.

10adistributed energy resources (der)10aPEVs10arenewables integration10aRM11-00610aThermostatically controlled loads1 aSubramanian, Anand1 aGarcia, Manuel, J.1 aDominguez-Garcia, Alejandro, D.1 aCallaway, Duncan, S.1 aPoolla, Kameshwar1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/real-time-scheduling-deferrable01487nas a2200241 4500008003900000020002200039245004100061210004100102260003200143300001600175520079400191653001000985653002800995653002301023653002701046653001301073653001501086100001901101700001801120700001401138700002001152856007301172 2012 d a978-1-4577-1095-700aRisk limiting dispatch of wind power0 aRisk limiting dispatch of wind power aMontreal, QCbIEEEc06/2012 a4417 - 44223 aIntegrating wind and solar power into the grid requires dispatching various types of reserve generation to compensate for the randomness of renewable power. The dispatch is usually determined by a system operator (SO) or an aggregator who `firms' variable energy by bundling it with conventional power. The optimal dispatch is formulated as the solution to a stochastic control problem and shown to have a closed form that can be quickly computed. Different objectives and risk constraints can be included in the formulation and trade-offs can be evaluated. In particular one can quantify the influence of sequential forecasts on the total integration cost and the choice of dispatched generation. When the forecast error is Gaussian, the optimal dispatch policy can be precomputed.

10aCERTS10areliability and markets10areserve generation10arisk-limiting dispatch10aRM11-00610awind power1 aRajagopal, Ram1 aBitar, Eilyan1 aWu, Felix1 aVaraiya, Pravin uhttps://certs.lbl.gov/publications/risk-limiting-dispatch-wind-power01409nas a2200265 4500008003900000020002200039245002400061210002400085260003300109300001600142520068700158653001000845653002400855653001600879653002800895653002700923653001300950100001800963700002200981700002801003700001901031700002001050700001401070856005901084 2012 d a978-1-4577-1925-700aSelling Random Wind0 aSelling Random Wind aMaui, HI, USAbIEEEc01/2012 a1931 - 19373 aWind power is inherently random, but we are used to 100 percent reliable or 'firm' electricity, so reserves are used to convert random wind power into firm electricity. The cost of these reserves is frequently a hidden subsidy to wind power producers. We propose an alternative: package random wind power into electricity with different levels of reliability and sell them at different prices. This variable-reliability market is more efficient than the current firm-electricity market, and may require lower subsidy. However, we have to think of electricity differently. We also explore interesting differences between the variable-reliability and related real-time markets.

10aCERTS10aelectricity markets10areliability10areliability and markets10arenewables integration10aRM11-0061 aBitar, Eilyan1 aPoolla, Kameshwar1 aKhargonekar, Pramod, P.1 aRajagopal, Ram1 aVaraiya, Pravin1 aWu, Felix uhttps://certs.lbl.gov/publications/selling-random-wind