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.
Regression methods fit a model to the historical data which is used to predict the baseline @cite_4 @cite_3 . They can potentially overcome biases incurred by averaging methods @cite_1 . They often require considerable historical data for acceptable accuracy, and the models may be too simple to capture the complex behavior of individual agents.
@cite_1 @cite_4 @cite_3
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Demand response is increasing in popularity and many utilities are developing demand response programs. However, there exists many challenges to the deployment of demand response. One of the main barriers to widespread rollout is the uncertainty surrounding the value of demand response. In this regard, there is a real and pressing need to evaluate demand response if its full potential is to be realized. This paper presents a comprehensive review of the literature and identifies some of the key barriers to the deployment and the challenges to the evaluation of demand response and provides some recommendations on evaluation methodologies. To improve forecasting accuracy for baseline load and load impact from demand response resources, this paper develops three innovative statistical models. These models are regression spline fixed effect model, fixed effect change point model and mixed effect change point model. The models developed are applied to forecast baseline load and load impact from air conditioning cycling demand response program in Southern California. All three forecasting models yield accurate forecasts for baseline load and load impact from demand response events. Noticeable rebound effect from demand response events are observed from the dataset. We present methods for analyzing commercial and industrial facility 15-min-interval electric load data. These methods allow building managers to better understand their facility's electricity consumption over time and to compare it to other buildings, helping them to “ask the right questions” to discover opportunities for demand response, energy efficiency, electricity waste elimination, and peak load management. We primarily focus on demand response. Methods discussed include graphical representations of electric load data, a regression-based electricity load model that uses a time-of-week indicator variable and a piecewise linear and continuous outdoor air temperature dependence and the definition of various parameters that characterize facility electricity loads and demand response behavior. In the future, these methods could be translated into easy-to-use tools for building managers.