Abstract-This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers.Index Terms-Demand response provider, scenario approach, stochastic economic dispatch NOMENCLATURE ISO independent system opeartor LSE load serving entity DRP demand response provider P G,i power generation of a generator P
Demand Response (DR) provides both operational and financial benefits to a variety of stakeholders in the power system. For example, in the deregulated market operated by the Electric Reliability Council of Texas (ERCOT), load serving entities (LSEs) usually purchase electricity from the wholesale market (either in day-ahead or real-time market) and sign fixed retail price contracts with their end-consumers. Therefore, incentivizing end-consumers’ load shift from peak to off-peak hours could benefit the LSE in terms of reducing its purchase of electricity under high prices from the real-time market. As the first-of-its-kind implementation of Coupon Incentive-based Demand Response (CIDR), the EnergyCoupon project provides end-consumers with dynamic time-of-use DR event announcements, individualized load reduction targets with EnergyCoupons as the incentive for meeting these targets, as well as periodic lotteries using these coupons as lottery tickets for winning dollar-value gifts. A number of methodologies are developed for this special type of DR program including price/baseline prediction, individualized target setting and a lottery mechanism. This paper summarizes the methodologies, design, critical findings, as well as the potential generalization of such an experiment. Comparison of the EnergyCoupon with a conventional Time-of-Use (TOU) price-based DR program is also conducted. Experimental results in the year 2017 show that by combining dynamic coupon offers with periodic lotteries, the effective cost for demand response providers in EnergyCoupon can be substantially reduced, while achieving a similar level of demand reduction as conventional DR programs.
Summary
Demand response (DR) is rapidly gaining attention as a solution to enhance the grid reliability with deep renewable energy penetration. Although studies have demonstrated the benefits of DR in mitigating price volatility, there is limited work considering the choice of locations for DR for maximal impact. We reveal that very small load reductions at a handful of targeted locations can lead to a significant decrease in price volatility and grid congestion levels based on a synthetic Texas grid model. We achieve this through exploiting the highly nonlinear nature of congestion dynamics and by strategically selecting DR locations. We demonstrate that we can similarly place energy storage to achieve an equivalent impact. Our findings suggest that targeted DR at specific locations, rather than across-the-board DR, can have substantial benefits to the grid. These findings can inform energy policy makers and grid operators how to target DR initiatives for improving grid reliability.
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