Abstract
The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.
Highlights
Electricity consumption is increasing rapidly because of the growing population, increased access to electricity, and widespread use of electric devices [1]
The rescheduled power demand contributes to peak reduction, and the shifted load from the peak hours is favorable for the distribution company (DISCO), who is responsible for maintaining the technical stability and reliability of the distribution network
The model is trained by historical data collected from a proposed building energy management system (BEMS), and replicated with a simple yet practical set of linear and nonlinear equations for integration with the constraints of the retail pricing problem of a distribution management system (DMS); 2) An optimal pricing strategy via the artificial neural network (ANN)-based demand response (DR) model considering the peak reduction is formulated in a single level structure, which is simple and practical, and the optimal retail prices are determined by reflecting the decision-making processes of both the DISCO and the end-user; 3) Simulation case studies are performed using real price data in a practical power system
Summary
Electricity consumption is increasing rapidly because of the growing population, increased access to electricity, and widespread use of electric devices [1]. The model is trained by historical data collected from a proposed building energy management system (BEMS), and replicated with a simple yet practical set of linear and nonlinear equations for integration with the constraints of the retail pricing problem of a distribution management system (DMS); 2) An optimal pricing strategy via the ANN-based DR model considering the peak reduction is formulated in a single level structure, which is simple and practical, and the optimal retail prices are determined by reflecting the decision-making processes of both the DISCO and the end-user; 3) Simulation case studies are performed using real price data in a practical power system.
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