Mechanism Design for Demand Response Programs

Demand Response (DR) programs serve to reduce the consumption of electricity at times when the supply is scarce and expensive. The utility informs the aggregator of an anticipated DR event. The aggregator calls on a subset of its pool of recruited agents to reduce their electricity use. Agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates of the energy an agent would have consumed if they were not participating in the DR program. Baselines are used to determine payments to agents. This creates an incentive for agents to inflate their baselines. We propose a novel self-reported baseline mechanism (SRBM) where each agent reports its baseline and marginal utility. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects or calls on agents to meet the load reduction target. Called agents are paid for observed reductions from their self-reported baselines. Agents who are not called face penalties for consumption shortfalls below their baselines. The mechanism is specified by the probability with which agents are called, reward prices for called agents, and penalty prices for agents who are not called. Under SRBM, we show that truthful reporting of baseline consumption and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is assured to meet the load reduction target. SRBM is also nearly efficient since it selects agents with the smallest marginal utilities, and each called agent contributes maximally to the load reduction target. Finally, we show that SRBM is almost optimal in the metric of average cost of DR provision faced by the aggregator

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

Integrated retail and wholesale power system operation with smart-grid functionality

Our research team is developing an agent-based test bed for the integrated study of retail and wholesale power markets operating over transmission and distribution networks with smart-grid functionality. This test bed seams together two existing test beds, the AMES Wholesale Power Market Test Bed and the GridLAB-D distribution platform. As a first step, we have designed an integrated retail/wholesale market module specifically based on the ERCOT (Texas) energy region, and we are using simplified versions of this module to study potential retail consumer response to real-time-pricing contracts supported by advanced metering. This study reports on the latter work

Learning Dynamical Demand Response Model in Real-Time Pricing Program

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.Comment: Accepted to IEEE ISGT NA 201

Effects of price-responsive residential demand on retail and wholesale power market operations

This paper describes a computational platform for studying the effects of price-responsive residential demand for air-conditioning (A/C) on integrated retail and wholesale power market operations. The physical operations of the A/C system are represented by means of the physics-based equivalent thermal parameter model. Residential A/C energy usage levels are determined by means of a stochastic dynamic-programming optimization in which the daily comfort attained by the resident is optimally traded off against his daily energy costs, conditional on retail energy prices, environmental conditions, and A/C operational constraints. An example is provided to illustrate the dynamic feedback loop connecting residential A/C load, the energy prices determined at wholesale conditional on A/C load, and the retail energy prices offered to residential A/C consumers by wholesale energy buyers