Abstract
Recently, there have been frequent fluctuations in the wholesale prices of electricity following the increased penetration of renewable energy sources. Therefore, retailers face price risks caused by differences between wholesale prices and retail rates. As a hedging against price risk, retailers can utilize critical peak pricing (CPP) in a price-based program. This study proposes a novel multi-stage stochastic programming (MSSP) model for a retailer with self-generation photovoltaic facility to optimize both its bidding strategy and the CPP operation, in the face of several uncertainties. Using MSSP, decisions can be determined sequentially with realization of the uncertainties over time. Furthermore, to ensure a global optimum, a mixed integer non-linear programming is transformed into mixed integer linear programming through three linearization steps. In a numerical simulation, the effectiveness of the proposed MSSP model is compared with that of a mean-value deterministic model based on a rolling horizon method. We also investigate the optimal strategy of a retailer by changing various input parameters and perform a sensitivity analysis to assess the impacts of different uncertain parameters on the retailer’s profit. Finally, the effect of the energy storage system on the proposed optimization problem is investigated.
Highlights
Renewable energy sources (RESs) have been introduced to the power system of several countries, supported by political investment
Based on the rolling horizon (RH) method, we identify the effectiveness of the proposed multi-stage stochastic programming (MSSP) model compared with that of a mean-value deterministic model
This paper proposes an optimal critical peak pricing (CPP) operation for a retailer with a self-generation PV facility
Summary
Renewable energy sources (RESs) have been introduced to the power system of several countries, supported by political investment. Zhang et al [20] proposed a decision model to determine critical events by considering the interests of both customers and retailers This model is deterministically re-calculated daily based on day-ahead price updates for the remaining scheduling horizon. The contributions and novelty of the proposed method can be summarized as follows: (1) We suggest an optimization problem to determine the bidding strategy and schedule critical events to maximize the expected profit of retailers considering balancing costs; (2) we develop an MSSP for decisions to be taken recursively with the realization of uncertainties; (3) we linearize the non-linear problem with a binary variable; (4) we analyze the impact of various parameters on the retailer’s optimal strategy and profit.
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