Abstract
Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS).
Highlights
Demand response refers to the demand side management mode in which users transfer or reduce loads in response to electricity prices or incentive signals
As discussed in [7], users are not well prepared to respond to time-varying prices, and TOU pricing is usually only applied to medium and long-term load regulation
The peak membership degree Ui of each time in the peak period is compared with mp, and if Ui ≥ mp, time i belongs to the critical peak period; otherwise, time i belongs to the peak period
Summary
Demand response refers to the demand side management mode in which users transfer or reduce loads in response to electricity prices or incentive signals. Reference [23] introduces the design and implementation of CPP among types of electricity users, while the study presented in [24] develops a critical peak rebate (CPR) strategy in the CPP mechanism and analyzes the load adjustment effects of TOU price and CPP strategy, respectively. Based on fuzzy clustering theory, this paper proposes a PFV period partition model, which can comprehensively analyze the load data of several typical days and add length constraints to the periods. 2. Considering the interests of both grid and demand sides, an optimization model of TOU pricing is proposed and is solved using the SAPSO algorithm, which is developed based on the PSO and SA algorithms to ensure high convergence speed and global convergence. A CPR decision-making model based on power shortage cost is proposed to compensate users’ participation in the CPR strategy and to further improve the stability of the power system
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