Abstract
In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO) is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO). Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm.
Highlights
In a conventional day-ahead electricity market, in order to obtain the minimum purchase cost, bidding is organized according to the load forecast and security constraints
A unified trading model of the day-ahead and real-time markets based on robust optimization is described considering the uncertainty of the load, wind power, and real-time market price, where the hourly purchase power is regarded as an optimized variable instead of forecast in the day-ahead market
The robust optimization model is adopted by taking into account the uncertainty of the real-time market prices, load, and wind power, and improved quantum-behaved particle swarm algorithm (IQPSO) is proposed to solve the optimization problem
Summary
In a conventional day-ahead electricity market, in order to obtain the minimum purchase cost, bidding is organized according to the load forecast and security constraints. The forecasted real-time prices are uncertain due to the fact that it is difficult to predict real-time prices accurately and obtain their correct probability distribution function since they are always subject to many factors, such as uncontrolled market conditions, balance between supply and demand, flow congestion, and so on [17,18,19] This optimum strategy may be unrealistic if we use fixed prices in the unified trading model. A unified trading model of the day-ahead and real-time markets based on robust optimization is described considering the uncertainty of the load, wind power, and real-time market price, where the hourly purchase power is regarded as an optimized variable instead of forecast in the day-ahead market.
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