In this paper, we first introduce a variational formulation of the Unit Commitment (UC) problem, in which generation and ramping trajectories of the generating units are continuous time signals and the generating units cost depends on the three signals: the binary commitment status of the units as well as their continuous-time generation and ramping trajectories. We assume such bids are piecewise strictly convex time-varying linear functions of these three variables. Based on this problem derive a tractable approximation by constraining the commitment trajectories to switch in a discrete and finite set of points and representing the trajectories in the function space of piece-wise polynomial functions within the intervals, whose discrete coefficients are then the UC problem decision variables. Our judicious choice of the signal space allows us to represent cost and constraints as linear functions of such coefficients, thus, our UC models preserves the MILP formulation of the UC problem. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total dayahead and real-time operation cost, and the number of ramping scarcity events in the real-time operations.

10aRM11-0071 aParvania, Masood1 aScaglione, Anna uhttps://certs.lbl.gov/publications/generation-ramping-valuation-day01890nas a2200193 4500008003900000245009300039210006900132260001200201300001000213520126900223653001301492100002301505700001601528700002201544700002201566700002001588700001701608856007101625 2016 d00aOptimal Pricing to Manage Electric Vehicles in Coupled Power and Transportation Networks0 aOptimal Pricing to Manage Electric Vehicles in Coupled Power and c07/2016 a1 - 13 aWe study the system-level effects of the introduction of large populations of Electric Vehicles on the power and transportation networks. We assume that each EV owner solves a decision problem to pick a cost-minimizing charge and travel plan. This individual decision takes into account traffic congestion in the transportation network, affecting travel times, as well as congestion in the power grid, resulting in spatial variations in electricity prices for battery charging. We show that this decision problem is equivalent to finding the shortest path on an "extended" transportation graph, with virtual arcs that represent charging options. Using this extended graph, we study the collective effects of a large number of EV owners individually solving this path planning problem. We propose a scheme in which independent power and transportation system operators can collaborate to manage each network towards a socially optimum operating point while keeping the operational data of each system private. We further study the optimal reserve capacity requirements for pricing in the absence of such collaboration. We showcase numerically that a lack of attention to interdependencies between the two infrastructures can have adverse operational effects.

10aRM11-0071 aAlizadeh, Mahnoosh1 aWai, Hoi-To1 aChowdhury, Mainak1 aGoldsmith, Andrea1 aScaglione, Anna1 aJavidi, Tara uhttps://certs.lbl.gov/publications/optimal-pricing-manage-electric01242nas a2200157 4500008003900000245010100039210006900140260004200209300001400251520066700265653001300932100002300945700002200968700002000990856007401010 2015 d00aThe perils of dynamic electricity pricing tariffs in the presence of retail market imperfections0 aperils of dynamic electricity pricing tariffs in the presence of aPacific Grove, CA, USAbIEEEc11/2015 a683 - 6883 aIn this paper, we show that the profit maximizing nature of electricity retailers, combined with boundedly rational customer behavior, might induce physical operational problems for the power grid under dynamic retail pricing. This is because, under certain conditions, retailers will be incentivized to design prices that shift load away from supply, further increasing the demand supply gap. We propose an alternative pricing method, referred to as differentiated pricing, which does not suffer from this issue. Our analysis is based on a simple demand model in order to gain useful insights on the repercussions of ignoring retail market imperfections.

10aRM11-0071 aAlizadeh, Mahnoosh1 aGoldsmith, Andrea1 aScaglione, Anna uhttps://certs.lbl.gov/publications/perils-dynamic-electricity-pricing01914nas a2200157 4500008003900000022001400039245008200053210006900135260001200204300001100216520140400227653001301631100002101644700002001665856007101685 2015 d a0885-895000aUnit Commitment With Continuous-Time Generation and Ramping Trajectory Models0 aUnit Commitment With ContinuousTime Generation and Ramping Traje c10/2015 a1 - 103 aThere is increasing evidence of shortage of ramping resources in the real-time operation of power systems. To explain and remedy this problem systematically, in this paper we take a novel look at the way the day-ahead unit commitment (UC) problem represents the information about load, generation and ramping constraints. We specifically investigate the approximation error made in mapping of the original problem, that would decide the continuous-time generation and ramping trajectories of the committed generating units, onto the discrete-time problem that is solved in practice. We first show that current practice amounts to approximating the trajectories with linear splines. We then offer a different representation through cubic splines that provides physically feasible schedules and increases the accuracy of the continuous-time generation and ramping trajectories by capturing sub-hourly variations and ramping of load in the day-ahead power system operation. The corresponding day-ahead UC model is formulated as an instance of mixed-integer linear programming (MILP), with the same number of binary variables as the traditional UC formulation. Numerical simulation over real load data from the California ISO demonstrate that the proposed UC model reduces the total day-ahead and real-time operation cost, and the number of events of ramping scarcity in the real-time operations.

10aRM11-0071 aParvania, Masood1 aScaglione, Anna uhttps://certs.lbl.gov/publications/unit-commitment-continuous-time01299nas a2200217 4500008003900000020002200039245005500061210005500116260004000171300001600211520063600227653001000863653002000873653001900893653001300912100002300925700002000948700002200968700002000990856007101010 2014 d a978-1-4799-7746-800aCapturing aggregate flexibility in Demand Response0 aCapturing aggregate flexibility in Demand Response aLos Angeles, CA, USAbIEEEc12/2014 a6439 - 64453 aFlexibility in electric power consumption can be leveraged by Demand Response (DR) programs. The goal of this paper is to systematically capture the inherent aggregate flexibility of a population of heterogenous small appliances in a reduced-order fashion. We do so by clustering individual loads based on their characteristics and service constraints. We highlight the challenges associated with learning the customer response to economic incentives while applying demand side management to heterogeneous appliances. We also develop a framework to quantify customer privacy in cluster-based direct load scheduling programs.

10aCERTS10ademand response10aflexible loads10aRM11-0071 aAlizadeh, Mahnoosh1 aScaglione, Anna1 aGoldsmith, Andrea1 aKesidis, George uhttps://certs.lbl.gov/publications/capturing-aggregate-flexibility02256nas a2200241 4500008003900000022001400039245008200053210006900135260001200204300001600216490000600232520154600238653002301784653001001807653002001817653000901837653001301846100002301859700002001882700002001902700002801922856006401950 2014 d a1932-455300aDynamic Incentive Design for Participation in Direct Load Scheduling Programs0 aDynamic Incentive Design for Participation in Direct Load Schedu c12/2014 a1111 - 11260 v83 aInterruptible Load (IL) programs have long been an accepted measure to intelligently and reliably shed demand in case of contingencies in the power grid. However, the emerging market for Electric Vehicles (EV) and the notion of providing non-emergency ancillary services through the demand side have sparked new interest in designing direct load scheduling programs that manage the consumption of appliances on a day-to-day basis. In this paper, we define a mechanism for a Load Serving Entity (LSE) to strategically compensate customers that allow the LSE to directly schedule their consumption, every time they want to use an eligible appliance. We study how the LSE can compute such incentives by forecasting its profits from shifting the load of recruited appliances to hours when electricity is cheap, or by providing ancillary services, such as regulation and load following. To make the problem scalable and tractable we use a novel clustering approach to describe appliance load and laxity. In our model, customers choose to participate in this program strategically, in response to incentives posted by the LSE in publicly available menus. Since 1) appliances have different levels of demand flexibility; and 2) demand flexibility has a time-varying value to the LSE due to changing wholesale prices, we allow the incentives to vary dynamically with time and appliance cluster. We study the economic effects of the implementation of such program on a population of EVs, using real-world data for vehicle arrival and charge patterns.10aancillary services10aCERTS10aload scheduling10aPEVs10aRM11-0071 aAlizadeh, Mahnoosh1 aXiao, Yuanzhang1 aScaglione, Anna1 avan der Schaar, Mihaela uhttps://certs.lbl.gov/publications/dynamic-incentive-design01887nas a2200217 4500008003900000245007000039210006900109260003900178300001200217520122000229653001001449653000901459653001301468100002301481700001601504700002001520700002201540700001801562700001701580856007201597 2014 d00aOptimized path planning for electric vehicle routing and charging0 aOptimized path planning for electric vehicle routing and chargin aMonticello, IL, USAbIEEEc10/2014 a25 - 323 aWe consider the decision problem of an individual EV owner who needs to pick a travel path including its charging locations and associated charge amount under time-varying traffic conditions as well as dynamic location-based electricity pricing. We show that the problem is equivalent to finding the shortest path on an extended transportation graph. In particular, we extend the original transportation graph through the use of virtual links with negative energy requirements to represent charging options available to the user. Using these extended transportation graphs, we then study the collective effects of a large number of EV owners solving the same type of path planning problem under the following control strategies: 1) a social planner decides the optimal route and charge strategy of all EVs; 2) users reach an equilibrium under locationally-variant electricity prices that are constant over time; 3) the transportation and power systems are separately controlled through marginal pricing strategies, not taking into account their mutual effect on one another. We numerically show that this disjoint type of control can lead to instabilities in the grid as well as inefficient system operation.

10aCERTS10aPEVs10aRM11-0071 aAlizadeh, Mahnoosh1 aWai, Hoi-To1 aScaglione, Anna1 aGoldsmith, Andrea1 aFan, Yue, Yue1 aJavidi, Tara uhttps://certs.lbl.gov/publications/optimized-path-planning-electric01813nas a2200265 4500008003900000022001400039245007500053210006900128260001200197300001100209490000700220520105800227653001001285653002101295653002001316653001801336653000901354653001301363100002301376700002001399700002001419700002001439700001701459856007101476 2014 d a0885-895000aReduced-Order Load Models for Large Populations of Flexible Appliances0 aReducedOrder Load Models for Large Populations of Flexible Appli c09/2014 a1 - 170 vPP3 aTo respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances.

10aCERTS10adeferrable loads10aload management10aload modeling10aPEVs10aRM11-0071 aAlizadeh, Mahnoosh1 aScaglione, Anna1 aApplebaum, Andy1 aKesidis, George1 aLevitt, Karl uhttps://certs.lbl.gov/publications/reduced-order-load-models-large01505nas a2200241 4500008003900000022001400039245009900053210006900152260001200221300001400233490000600247520076600253653001001019653002001029653002101049653001801070653001301088100002301101700002001124700001801144700002401162856007701186 2014 d a1949-305300aA Scalable Stochastic Model for the Electricity Demand of Electric and Plug-In Hybrid Vehicles0 aScalable Stochastic Model for the Electricity Demand of Electric c03/2014 a848 - 8600 v53 aIn this paper we propose a stochastic model, based on queueing theory, for electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) charging demand. Compared to previous studies, our model can provide 1) more accurate forecasts of the load using real-time sub-metering data, along with the level of uncertainty that accompanies these forecasts; 2) a mathematical description of load, along with the level of demand flexibility that accompanies this load, at the wholesale level. This can be useful when designing demand response and dynamic pricing schemes. Our numerical experiments tune the proposed statistics on real PHEV charging data and demonstrate that the forecasting method we propose is more accurate than standard load prediction techniques.10aCERTS10ademand response10aload forecasting10aload modeling10aRM11-0071 aAlizadeh, Mahnoosh1 aScaglione, Anna1 aDavies, Jamie1 aKurani, Kenneth, S. uhttps://certs.lbl.gov/publications/scalable-stochastic-model-electricity01434nas a2200181 4500008003900000245009500039210006900134260003500203300001400238520081300252653001001065653002001075653003201095653001301127100002301140700002001163856006901183 2013 d00aLeast laxity first scheduling of thermostatically controlled loads for regulation services0 aLeast laxity first scheduling of thermostatically controlled loa aAustin, TX, USAbIEEEc12/2013 a503 - 5063 aWe propose a least laxity first (LLF) scheduling algorithm for a heterogeneous population of thermostatically controlled loads (TCL), aimed at providing regulation services for the power grid. TCLs periodically switch between on and off states in order to keep their monitored temperature in a certain comfort band. In our scheme, TCLs inform a central controller of their anticipated deadlines to switch states, allowing for their switching events to be scheduled. An LLF policy schedules these transitions to provide regulation with minimum deviation from the autonomous evolution of the TCLs. To manage large populations, we bundle requests with similar laxity values in a limited number of clusters, considerably reducing computational and communication costs, and preserving the privacy of participants.10aCERTS10aload regulation10apower generation scheduling10aRM11-0071 aAlizadeh, Mahnoosh1 aScaglione, Anna uhttps://certs.lbl.gov/publications/least-laxity-first-scheduling01938nas a2200241 4500008003900000022001400039245008300053210006900136260001200205300001600217490000600233520122700239653001001466653002101476653002001497653002001517653001701537653001301554100002101567700002301588700002001611856006501631 2013 d a1949-305300aReal-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling0 aRealTime Power Balancing Via Decentralized Coordinated Home Ener c09/2013 a1490 - 15040 v43 aIt is anticipated that an uncoordinated operation of individual home energy management (HEM) systems in a neighborhood would have a rebound effect on the aggregate demand profile. To address this issue, this paper proposes a coordinated home energy management (CoHEM) architecture in which distributed HEM units collaborate with each other in order to keep the demand and supply balanced in their neighborhood. Assuming the energy requests by customers are random in time, we formulate the proposed CoHEM design as a multi-stage stochastic optimization problem. We propose novel models to describe the deferrable appliance load [e.g., plug-in (hybrid) electric vehicles (PHEV)], and apply approximation and decomposition techniques to handle the considered design problem in a decentralized fashion. The developed decentralized CoHEM algorithm allow the customers to locally compute their scheduling solutions using domestic user information and with message exchange between their neighbors only. Extensive simulation results demonstrate that the proposed CoHEM architecture can effectively improve real-time power balancing. Extensions to joint power procurement and real-time CoHEM scheduling are also presented.

10aCERTS10adeferrable loads10ademand response10aload management10aoptimization10aRM11-0071 aChang, Tsung-Hui1 aAlizadeh, Mahnoosh1 aScaglione, Anna uhttps://certs.lbl.gov/publications/real-time-power-balancing01469nas a2200265 4500008003900000022001400039245009000053210006900143260001200212300001200224490000700236520067700243653001000920653002700930653002400957653002800981653001301009653001501022100002301037700001301060700001801073700002001091700001901111856007301130 2012 d a1053-588800aDemand-Side Management in the Smart Grid: Information Processing for the Power Switch0 aDemandSide Management in the Smart Grid Information Processing f c09/2012 a55 - 670 v293 aOver the course of several decades after their introduction, power systems merged into large interconnected grids to introduce redundancy and to leverage on a wider pool of generation resources and reserves. As the system grew in size and complexity, a cyberphysical infrastructure was progressively developed to manage it. Traditionally, general-purpose computing and communication resources have been used in power systems, specifically to serve two needs: 1) that of monitoring the safe operation of the grid and logistics of power delivery, and 2) that of gathering information required to dispatch the generation optimally and, later on, to operate the energy market.10aCERTS10ademand-side management10aelectricity markets10apower system monitoring10aRM11-00710asmart grid1 aAlizadeh, Mahnoosh1 aLi, Xiao1 aWang, Zhifang1 aScaglione, Anna1 aMelton, Ronald uhttps://certs.lbl.gov/publications/demand-side-management-smart-grid02181nas a2200253 4500008003900000022001400039245006700053210006600120260001200186300001600198490000700214520143600221653001001657653002701667653002401694653001801718653002001736653001301756653001501769100002301784700002001807700002301827856007701850 2012 d a0733-871600aFrom Packet to Power Switching: Digital Direct Load Scheduling0 aFrom Packet to Power Switching Digital Direct Load Scheduling c07/2012 a1027 - 10360 v303 aAt present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.10aCERTS10ademand-side management10aelectricity markets10aload modeling10aload scheduling10aRM11-00710asmart grid1 aAlizadeh, Mahnoosh1 aScaglione, Anna1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/packet-power-switching-digital-direct