Optimal Scheduling of Demand Response-Based AC OPF by Smart Power Grid’ Flexible Loads Considering User Convenience, LSTM-Based Load Forecasting, and DERs Uncertainties

Abstract

The energy storage capabilities of the heating, ventilation, and air conditioning (HVACs) systems, as well as electric vehicles (EVs), render them excellent candidates for demand response. This study proposes a joint coordination of AC optimal power flow and demand response for aggregated EVs and HVACs, in conjunction with the scheduling of large-scale flexible loads. This paper prioritizes user convenience, and ensuring compliance with comfortable temperature boundaries. Additionally, EV capacity and travel distance are estimated using a normal distribution. The primary objective of this study is to minimize the total cost and peak demand while simultaneously maximizing total revenue of participant user in demand response, user convenience, and the integration of renewable energy sources. To achieve an accurate optimal choice, the uncertainties associated with renewables and demand are addressed using scenario-based and long short-term memory (LSTM) methods, respectively. The optimal demand response level is determined through sensitivity analysis, and employing the technique for order preference by similarity to ideal solution (TOPSIS) method. The obtained results demonstrate that by only 20% demand response at large-scale loads and EV and HVAC aggregators, the proposed model reduces peak loads by 12.8% and the total cost by 7.4%.

Keywords

• Smart grid
• demand response
• optimal power flow
• renewable energy
• aggregator
• uncertainty