Demand Response Strategy of Energy Prosumer Based on Robust Optimization Through Aggregator

• Published in: IEEE access Vol. 8; pp. 202969 - 202979
• Main Authors: Park, Minsu, Lee, Jihoan, Won, Dong-Jun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregator; Algorithms; demand response; Direct reduction; Distributed generation; Electricity consumption; Energy management; energy prosumer; Energy sources; Error reduction; Load management; Load modeling; logarithmic barrier function; Optimization; Peak load; Prediction algorithms; Predictive models; robust optimization; Robustness; Tariffs; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3034870

In this article, the optimal operation strategy for the aggregator to participate in demand response (DR) market is proposed. First, on the day before the occurrence of DR, Customer Baseline Load (CBL)-based load forecasting is performed using historical load data and a day-ahead scheduling is implemented to minimize electric charges by using peak reduction and arbitrage trading. If the demand response occurs, distributed energy resources (DERs) bid power reduction capacity to the aggregator. In Korea tariff system, demand charges determined by peak load are very expensive. Therefore, DERs should not update their peak load due to demand response market participation. The uncertainty of load prediction is modeled using the average value of mean absolute percentage error (MAPE), and robust optimization (RO) is implemented to determine a bidding capacity, thereby preventing the peak form being updated due to prediction error. Then, the aggregator decides the capacity to participate in DR market by considering the bidding capacity and priority. It presents the method to determine the incentive for participation in DR using a logarithmic barrier function. To evaluate the performance of the proposed algorithm simulation was performed by constructing a scenario for prediction error and mandatory reduction capacity.