Abstract
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.
Highlights
Despite global efforts to reduce carbon emissions, such as theKyoto Protocol and the Paris Agreement, carbon emissions continue to accelerate
It was difficult to disseminate due to short mileage and high price, but recently, electric vehicle (EV) and infrastructure are rapidly increasing with subsidies and mileage increase because of the improvement of battery technology
We propose a real-time demand response market participation strategy that reduces power consumption according to the independent system operator (ISO)'s instructions when power supply and demand imbalance occurs
Summary
Kyoto Protocol and the Paris Agreement, carbon emissions continue to accelerate. policies on renewable energy source (RES), energy storage system (ESS), and electric vehicle (EV) are being enacted to curb carbon emissions. Virtual power plant (VPP) that operate and control these DERs like a single generator, and aggregator that gather and manage small and medium-size demand resources that cannot directly participate in the market have appeared. Researches applying this in power system have been conducted [6, 7]. Aggregators have modeled demand resources and distributed generators as they participated in the market They did not perform load prediction or take market uncertainties into account in [13,14,15,16,17,18]. The CBL is divided into Max (4/5) and Mid (6/10) for specific details as shown in Table 1 [20,21,22]
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