We analyze the benefit of storage capacity sharing for a set of consumers in a community (e.g., an apartment building or an industrial park). Each consumer has its own choice of either installing its own storage system or investing in a shared storage system. More precisely, they will decide on the capacity of the storage system to minimize the installation cost subject to a chance constraint for firming (e.g., to limit the exposure risk to the real time market). If the consumers decide to operate a shared storage system, they must also decide on a scheme to allocate the costs. We formulate the problem as a cooperative game and identify an efficient and stable cost allocation rule. In settings where certain statistical information is private, the cooperative storage sharing game becomes embedded with a non-cooperative information reporting game. We show our proposed cost allocation rule induces all consumers to report their private information truthfully.

10aRM11-0061 aWu, Chenye1 aPorter, Jared1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/community-storage-firming01724nas a2200169 4500008003900000245009500039210006900134260003800203300001600241520111500257653001301372100002601385700002101411700002801432700002201460856007201482 2016 d00aA cooperative game for the realized profit of an aggregation of renewable energy producers0 acooperative game for the realized profit of an aggregation of re aLas Vegas, NV, USAbIEEEc12/2016 a5805 - 58123 aThe aggregation of renewable energy has significant potential to mitigate undesirable characteristics such as intermittency and variability and thereby facilitate grid integration. Using cooperative game theory, it has been shown that aggregation is also beneficial for renewable energy producers because they can increase their expected profit by making a coalition, bidding a joint contract that maximizes the expected profit and sharing the profit in a way that keeps the game stable. However, we show that the realized (as opposed to expected) profit of the coalition, using the contract that maximizes the expected profit, cannot be suitably distributed among its members. We propose an alternative coalition contract and prove that it allows for a satisfactory distribution of the realized profit among the coalition members keeping the game stable. We design a new payoff allocation that lies in the core of the game of the realized profit. Finally, we analyze the cost of stabilizing the game by evaluating the loss of expected profit that a coalition incurs by bidding the stabilizing contract.

10aRM11-0061 aChakraborty, Pratyush1 aBaeyens, Enrique1 aKhargonekar, Pramod, P.1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/cooperative-game-realized-profit02252nas a2200169 4500008003900000245006900039210006800108260003500176300001600211520168500227653001301912100002301925700002101948700002201969700002001991856007102011 2016 d00aMechanism design for self-reporting baselines in Demand Response0 aMechanism design for selfreporting baselines in Demand Response aBoston, MA, USAbIEEEc07/2016 a1446 - 14513 aIncentive-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-two01478nas a2200205 4500008003900000022001400039245006300053210006300116260001200179300001400191490000700205520087400212653002801086653001301114100001201127700002601139700002201165700002401187856006101211 2015 d a0885-895000aAggregate Flexibility of Thermostatically Controlled Loads0 aAggregate Flexibility of Thermostatically Controlled Loads c01/2015 a189 - 1980 v303 aIt is widely accepted that thermostatically controlled loads (TCLs) can be used to provide regulation reserve to the grid. We first argue that the aggregate flexibility offered by a collection of TCLs can be succinctly modeled as a stochastic battery with dissipation. We next characterize the power limits and energy capacity of this battery model in terms of TCL parameters and random exogenous variables such as ambient temperature and user-specified set-points. We then describe a direct load control architecture for regulation service provision. Here, we use a priority-stack-based control framework to select which TCLs to control at any time. The control objective is for the aggregate power deviation from baseline to track an automatic generation control signal supplied by the system operator. Simulation studies suggest the practical promise of our methods.10areliability and markets10aRM11-0061 aHao, He1 aSanandaji, Borhan, M.1 aPoolla, Kameshwar1 aVincent, Tyrone, L. uhttps://certs.lbl.gov/publications/aggregate-flexibility01455nas a2200157 4500008003900000245008100039210006900120260003200189520084900221653001301070100001401083700002101097700002201118700001901140856013801159 2015 d00aCoordination of wind power and flexible load through demand response options0 aCoordination of wind power and flexible load through demand resp aOsaka, JapanbIEEEc12/20153 aWe explore the value of demand response (DR) for enhancing wind power integration. This value is derived through load curtailment to manage the variability of wind power. It increases the opportunity to use wind power and brings economic benefits to the aggregation of the load serving entity (LSE) and the flexible energy consumer. DR is provided by the flexible load through call options offers to the LSE. A transaction mechanism is designed to incentivize the DR aggregator by an appropriate selection of the strike price. It is shown that the strike price should be chosen to be the true value of lost load. A multi-stage decision-theoretic formulation is presented to model the interaction between the aggregator and two settlement markets. Simulations reveal that the proposed approach reduces the total costs incurred by the LSE.

10aRM11-0061 aWang, Dai1 aKalathil, Dileep1 aPoolla, Kameshwar1 aGuan, Xiaohong uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7403359&refinements%3D4229014380%26filter%3DAND%28p_IS_Number%3A7402066%2901213nas 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-spatio01562nas a2200205 4500008003900000022001300039245008700052210006900139260001200208300001400220490000700234520091400241653002801155653001301183100001201196700002601208700002201234700002401256856007601280 2015 d a0301421500aPotentials and economics of residential thermal loads providing regulation reserve0 aPotentials and economics of residential thermal loads providing c04/2015 a115 - 1260 v793 aResidential Thermostatically Controlled Load (TCLs) such as Air Conditioners (ACs), heat pumps, water heaters, and refrigerators have an enormous thermal storage potential for providing regulation reserve to the grid. In this paper, we study the potential resource and economic analysis of TCLs providing frequency regulation service. In particular, we show that the potential resource of TCLs in California is more than enough for both current and predicted near-future regulation requirements for the California power system. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements, recommended policy changes, and participation incentive methods, and compare TCLs with other energy storage technologies. We show that TCLs are potentially more cost-effective than other energy storage technologies such as flywheels, Li-ion, advanced lead acid, and Zinc Bromide batteries.10areliability and markets10aRM11-0061 aHao, He1 aSanandaji, Borhan, M.1 aPoolla, Kameshwar1 aVincent, Tyrone, L. uhttps://certs.lbl.gov/publications/potentials-and-economics-residential01206nas a2200169 4500008003900000022001400039245005500053210005500108260001200163300001100175520068900186653001300875100002600888700002400914700002200938856007600960 2015 d a1949-302900aRamping Rate Flexibility of Residential HVAC Loads0 aRamping Rate Flexibility of Residential HVAC Loads c12/2015 a1 - 103 aResidential air conditioners (ACs), refrigeration units, and forced air heating loads are candidates for providing ancillary services to the grid. Motivated by the need for resources with high ramping rate capability, we investigate the ramping rate flexibility of such loads and show that a collection of residential heating, ventilation, and air conditioning (HVAC) loads can provide regulating reserve service with certain ramping rate bound that is a result of enforcing a no-short-cycling requirement. A load is called short-cycled if it is switched on and off quicker than a certain allowed time. We support our proposed bounds and theorems with illustrative simulations.

10aRM11-0061 aSanandaji, Borhan, M.1 aVincent, Tyrone, L.1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/ramping-rate-flexibility-residential01162nas 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-flexibility02091nas a2200169 4500008003900000245006800039210006800107260003600175300001400211520153200225653001301757100002701770700001301797700001301810700002201823856007601845 2015 d00aToken based scheduling of HVAC Services in commercial buildings0 aToken based scheduling of HVAC Services in commercial buildings aChicago, IL, USAbIEEEc07/2015 a262 - 2693 aThis paper proposes a novel distributed architecture for controlling Heating, Ventilation and Air conditioning (HVAC) systems in commercial buildings. Zone Modules use local models and measurements to compute requests for HVAC service over various future time windows. These requests are expressed in terms of the heating/cooling service required which we can conceptually regard as tokens. A Central Scheduler balances token requests and allocates tokens to each zone for the next time slot. This allocation attempts to minimize total energy consumption while respecting operational constraints. Zone modules update their local models based on the measured thermal responses resulting from allocated tokens, and re-compute forward token requests. This proposed token based architecture is inspired by medium access control protocols in communication networks. It offers several advantages in the context of HVAC systems. The architecture is scalable to realistic buildings with 200-500 thermal zones, it is robust relative to non-stationary environmental conditions and unanticipated changes in user needs, and it is modular enabling low-cost deployment without requiring expensive custom thermal modeling of buildings. We develop the zone module algorithms for computing token requests and central scheduler algorithms to allocate tokens. Using simulation studies, we demonstrate that the performance loss of our token based scheduling strategy is modest in comparison to a fully centralized nonlinear optimal control scheme.10aRM11-0061 aRadhakrishnan, Nikitha1 aSu, Yang1 aSu, Rong1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/token-based-scheduling-hvac-services01673nas a2200169 4500008003900000245007400039210006900113260002900182520101900211653001901230653002801249653001301277100001701290700003601307700002201343856013801365 2015 d00aVirtual Battery Models for Load Flexibility from Commercial Buildings0 aVirtual Battery Models for Load Flexibility from Commercial Buil aKauai, HIbIEEEc01/20153 aFrequency regulation is becoming increasingly important with deeper penetration of variable generation resources. Flexible loads have been proposed as a low-cost provider of frequency regulation. For example, the flexibility of loads with inherent thermal energy storage resides in their ability to vary their electricity consumption without compromising their end function. In this context, the aggregate flexibility of a collection of diverse residential air-conditioning loads has previously been shown to be well modeled as a virtual battery using first principles load models. This analytical method will not scale to more complex flexible loads such as commercial HVAC systems. This paper presents a method to identify virtual battery model parameters for these more complex flexible loads. The method extracts the parameters of the virtual battery model by stress-testing a detailed software model of the physical system. Synthetic examples reveal the effectiveness of the proposed identification technique.10aflexible loads10areliability and markets10aRM11-0061 aHughes, J.T.1 aDominguez-Garcia, Alejandro, D.1 aPoolla, Kameshwar uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070131&refinements%3D4254602198%26filter%3DAND%28p_IS_Number%3A7069647%2902400nas 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-energy02094nas a2200205 4500008003900000245009100039210006900130260003200199300001600231520138800247653002301635653001801658653002801676653001301704653003801717100002601755700001201781700002201793856007301815 2014 d00aFast Regulation Service Provision via Aggregation of Thermostatically Controlled Loads0 aFast Regulation Service Provision via Aggregation of Thermostati aWaikoloa, HIbIEEEc01/2014 a2388 - 23973 aFederal Energy Regulatory Commission (FERC) Order 755 requires scheduling coordinators to procure and compensate more for regulation resources with faster ramping rates. Thermostatically Controlled Loads (TCLs) are a tremendous demand-side resource for providing fast regulation service due to their population size and their ability of being turned ON or OFF simultaneously. In this paper, we consider modeling and control of a collection of TCLs to provide such regulation service. We first develop a non-uniform bin state transition model for aggregate modeling of a collection of TCLs. The non-uniform model presents a potential for more accurate prediction while requiring fewer number of bins (reducing the complexity of the model) than the existing uniform bin models. We also propose a randomized priority control strategy to manipulate the power consumption of TCLs to track a regulation signal, while preventing short cycling, and reducing wear and tear on the equipment. The proposed control strategy is decentralized in the sense that each TCL makes its own decision solely based on a common broadcast command signal. This framework reduces the communication and computational efforts required for implementing the control strategy. We provide illustrative simulations to show the accuracy of the developed non-uniform model and efficacy of the proposed control strategy.10aancillary services10aload modeling10areliability and markets10aRM11-00610aThermostatically controlled loads1 aSanandaji, Borhan, M.1 aHao, He1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/fast-regulation-service-provision01885nas a2200169 4500008003900000245006200039210006100101260003600162300001000198520132800208653001901536653002801555653001301583100002601596700001801622856007501640 2014 d00aFinancial storage rights: Definition and basic properties0 aFinancial storage rights Definition and basic properties aPullman, WA, USAbIEEEc09/2014 a1 - 63 aThe decreasing cost of energy storage technologies coupled with their potential to bring significant benefits to electric power networks have kindled research efforts to design both market and regulatory frameworks to facilitate the efficient integration of such technologies. The primary challenge resides in designing market systems that provide the correct incentives to deploy and operate storage systems efficiently in both the short and long-run. In the following paper, we propose an open access approach to the integration of storage in which storage is treated as a communal asset centrally operated by the System Operator (SO) to maximize social welfare; not unlike the operation of the transmission network today. Concomitantly, we propose a novel electricity derivative, which we refer to as financial storage rights (FSRs), to enable the redistribution of the additional merchandising surplus (attributable to storage) collected by the SO. FSRs do not interfere with the socially optimal operation of storage, and their definition as a sequence of nodal power injections facilitates their use by market participants to mitigate the cost and/or risk of meeting contractual commitments. Moreover, the revenue collected by the SO through the sale of FSRs can be used to remunerate capital expenditures in storage.10aenergy storage10areliability and markets10aRM11-0061 aMunoz-Alvarez, Daniel1 aBitar, Eilyan uhttps://certs.lbl.gov/publications/financial-storage-rights-definition01646nas a2200217 4500008003900000020002200039245008700061210006900148260003700217300001200254520091800266653001901184653002501203653002801228653001301256100001201269700002601281700002201307700002401329856007501353 2014 d a978-1-4799-3272-600aFrequency regulation from flexible loads: Potential, economics, and implementation0 aFrequency regulation from flexible loads Potential economics and aPortland, OR, USAbIEEEc06/2014 a65 - 723 aThermostatically Controlled Loads (TCLs) such as air conditioners, heat pumps, water heaters and refrigerators have a great potential for providing regulation reserve to the grid. This paper aims to provide a foundation for a practical method of enabling TCLs to provide regulation service. We study the economic, regulatory, and practical aspects to realize such a vision. We show that the potential of TCLs in California is more than enough for both current and predicted near-future regulation requirements. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements and participation incentive methods, and present a practical control framework for TCLs to provide regulation service. Numerical experiments are provided to illustrate the efficacy of our methods in addressing practical issues such as short cycling of units, communication latency, and dynamics modeling errors.10aflexible loads10afrequency regulation10areliability and markets10aRM11-0061 aHao, He1 aSanandaji, Borhan, M.1 aPoolla, Kameshwar1 aVincent, Tyrone, L. uhttps://certs.lbl.gov/publications/frequency-regulation-flexible-loads01491nas a2200193 4500008003900000020002200039245010800061210006900169260003700238300001200275520080000287653001301087653003801100100002601138700001201164700002201176700002401198856007501222 2014 d a978-1-4799-3272-600aImproved battery models of an aggregation of Thermostatically Controlled Loads for frequency regulation0 aImproved battery models of an aggregation of Thermostatically Co aPortland, OR, USAbIEEEc06/2014 a38 - 453 aRecently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results. 10aRM11-00610aThermostatically controlled loads1 aSanandaji, Borhan, M.1 aHao, He1 aPoolla, Kameshwar1 aVincent, Tyrone, L. uhttps://certs.lbl.gov/publications/improved-battery-models-aggregation01744nas a2200205 4500008003900000020002200039245009600061210006900157260003700226300001600263520102300279653002801302653001301330653001501343100002001358700002601378700003701404700002201441856007501463 2014 d a978-1-4799-3272-600aModel Predictive Control of regulation services from commercial buildings to the smart grid0 aModel Predictive Control of regulation services from commercial aPortland, OR, USAbIEEEc06/2014 a2226 - 22333 aWe first demonstrate that the demand-side flexibility of the Heating Ventilation and Air Conditioning (HVAC) system of a typical commercial building can be exploited for providing frequency regulation service to the power grid using at-scale experiments. We then show how this flexibility in power consumption of building HVAC system can be leveraged for providing regulation service. To this end, we consider a simplified model of the power grid with uncertain demand and generation. We present a Model Predictive Control (MPC) scheme to direct the ancillary service power flow from buildings to improve upon the classical Automatic Generation Control (AGC) practice. We show how constraints such as slow and fast ramping rates for various ancillary service providers, and short-term load forecast information can be integrated into the proposed MPC framework. Finally, we provide extensive simulation results to illustrate the effectiveness of the proposed methodology for enhancing grid frequency regulation.

10areliability and markets10aRM11-00610asmart grid1 aMaasoumy, Mehdi1 aSanandaji, Borhan, M.1 aSangiovanni-Vincentelli, Alberto1 aPoolla, Kameshwar uhttps://certs.lbl.gov/publications/model-predictive-control-regulation01755nas a2200193 4500008003900000020002200039245006800061210006800129260004000197300001600237520110400253653001901357653003001376653002801406653001301434100002101447700001801468856007501486 2014 d a978-1-4799-7746-800aVariability and the Locational Marginal Value of Energy Storage0 aVariability and the Locational Marginal Value of Energy Storage aLos Angeles, CA, USAbIEEEc12/2014 a3259 - 32653 aGiven a stochastic net demand process evolving over a transmission-constrained power network, we consider the system operator's problem of minimizing the expected cost of generator dispatch, when it has access to spatially distributed energy storage resources. We show that the expected benefit of storage derived under the optimal dispatch policy is concave and non-decreasing in the vector of energy storage capacities. Thus, the greatest marginal value of storage is derived at small installed capacities. For such capacities, we provide an upper bound on the locational (nodal) marginal value of storage in terms of the variation of the shadow prices of electricity at each node. In addition, we prove that this upper bound is tight, when the cost of generation is spatially uniform and the network topology is acyclic. These formulae not only shed light on the correct measure of statistical variation in quantifying the value of storage, but also provide computationally tractable tools to empirically calculate the locational marginal value of storage from net demand time series data.

10aenergy storage10aLocational marginal value10areliability and markets10aRM11-0061 aBose, Subhonmesh1 aBitar, Eilyan uhttps://certs.lbl.gov/publications/variability-and-locational-marginal01798nas 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-collection01519nas a2200205 4500008003900000245011700039210006900156260003400225300001400259520079600273653002301069653002001092653002801112653001301140100001201153700002601165700002201191700002401213856007601237 2013 d00aA generalized battery model of a collection of Thermostatically Controlled Loads for providing ancillary service0 ageneralized battery model of a collection of Thermostatically Co aMonticello, ILbIEEEc10/2013 a551 - 5583 aThe thermal storage potential of Thermostatically Controlled Loads (TCLs) is a tremendous flexible resource for providing various ancillary services to the grid. In this work, we study aggregate modeling, characterization, and control of TCLs for frequency regulation service provision. We propose a generalized battery model for aggregating flexibility of a collection of TCLs. A theoretical characterization of the aggregate power limits and energy capacity of TCLs is provided. Moreover, we propose a priority-stack-based control strategy to manipulate the power consumption of TCLs for frequency regulation, while preventing short cycling on the units. Numerical experiments are provided to show the accuracy of the proposed model and the efficacy of the developed control method.

10aancillary services10aload regulation10areliability and markets10aRM11-0061 aHao, He1 aSanandaji, Borhan, M.1 aPoolla, Kameshwar1 aVincent, Tyrone, L. uhttps://certs.lbl.gov/publications/generalized-battery-model-collection01902nas a2200193 4500008003900000020002200039245007600061210006900137260003400206300001600240520125400256653000901510653002801519653001301547100002401560700001701584700003601601856007101637 2013 d a978-1-4799-0177-700aPrice-based distributed control for networked plug-in electric vehicles0 aPricebased distributed control for networked plugin electric veh aWashington, DCbIEEEc06/2013 a5086 - 50913 aWe introduce a framework for controlling the charging and discharging processes of plug-in electric vehicles (PEVs) via pricing strategies. Our framework consists of a hierarchical decision-making setting with two layers, which we refer to as aggregator layer and retail market layer. In the aggregator layer, there is a set of aggregators that are requested (and will be compensated for) to provide certain amount of energy over a period of time. In the retail market layer, the aggregator offers some price for the energy that PEVs may provide; the objective is to choose a pricing strategy to incentivize the PEVs so as they collectively provide the amount of energy that the aggregator has been asked for. The focus of this paper is on the decision-making process that takes places in the retail market layer, where we assume that each individual PEV is a price-anticipating decision-maker. We cast this decision-making process as a game, and provide conditions on the pricing strategy of the aggregator under which this game has a unique Nash equilibrium. We propose a distributed consensus-based iterative algorithm through which the PEVs can seek for this Nash equilibrium. Numerical simulations are included to illustrate our results.

10aPEVs10areliability and markets10aRM11-0061 aGharesifard, Bahman1 aBasar, Tamer1 aDominguez-Garcia, Alejandro, D. uhttps://certs.lbl.gov/publications/price-based-distributed-control01545nas 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=101563nas a2200193 4500008003900000020002200039245006700061210006700128260003300195300001600228520088300244653003901127653002801166653001301194100003601207700002201243700003401265856007001299 2012 d a978-1-4673-2065-800aDecentralized optimal dispatch of distributed energy resources0 aDecentralized optimal dispatch of distributed energy resources aMaui, HI, USAbIEEEc12/2012 a3688 - 36933 aIn this paper, we address the problem of optimally dispatching a set of distributed energy resources (DERs) without relying on a centralized decision maker. We consider a scenario where each DER can provide a certain resource (e.g., active or reactive power) at some cost (namely, quadratic in the amount of resource), with the additional constraint that the amount of resource that each DER provides is upper and lower bounded by its capacity limits. We propose a low-complexity iterative algorithm for DER optimal dispatch that relies, at each iteration, on simple computations using local information acquired through exchange of information with neighboring DERs. We show convergence of the proposed algorithm to the (unique) optimal solution of the DER dispatch problem. We also describe a wireless testbed we developed for testing the performance of the algorithms.

10adistributed energy resources (der)10areliability and markets10aRM11-0061 aDominguez-Garcia, Alejandro, D.1 aCady, Stanton, T.1 aHadjicostis, Christoforos, N. uhttps://certs.lbl.gov/publications/decentralized-optimal-dispatch01437nas 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