Abstract
Compared to the step tariff, the real-time pricing (RTP) could be more stimulated for household consumers to change their electricity consumption behaviors. It can reduce the reserve capacity, peak load, and of course the electricity bill, which could achieve the purpose of saving energy. This paper proposes a coordinated optimization algorithm and data-driven RTP strategy in electricity market. First, the electricity price is divided into two parts, basic electricity price and fluctuating price. When the electricity consumption is equal to the average daily electricity consumption, the price is defined as the basic electricity price, which is the clearing electricity price. The consumer electricity data are analyzed. A random forest algorithm is adopted to predict the load data. Optimal adjustment parameters are obtained and the load fluctuation and the fluctuation of the electricity price are further quantified. Secondly, the appliances are modeled. The operation priority is established based on the preferences of customers and the Monte Carlo method is used to form the power load curve. Then, the smart energy planning unit is proposed to optimize the appliances on/off time and running time of residential electrical appliances. An incentive mechanism is used to further standardize the temporary electricity consumption. An improved multiobjective particle swarm optimization (IMOPSO) algorithm is adopted, which adopts the linear weighted evaluation function method to maximize the consumer’s social welfare while minimizing the electricity bill. The simulation proves that the stability of the power grid is improved while obtaining the best power strategy.
Highlights
Power consumers have coincident peak load, resulting in a large peak-valley difference
The final electricity price At at time t consists of two parts: the basic electricity price A0 and the fluctuating electricity priceAd
The primary industry in this region currently accounts for 1.8% of the society, and the secondary industry is about 71.8% of the electricity consumption in the Northeast power grid
Summary
Power consumers have coincident peak load, resulting in a large peak-valley difference. In order to reduce the peak-valley difference and enhance the stability of power supply, optimization methods were proposed from the power generation side and the load side, such as time-of-use electricity price (TOU) and Demand Response (DR). According to the equipment operation priority set by the consumers, a load forecasting model is proposed, and the Monte Carlo method is used to form a load curve On this basis, incentive mechanisms are used to further regulate temporary electricity consumption. The final electricity price At at time t consists of two parts: the basic electricity price A0 and the fluctuating electricity priceAd. The electricity fluctuating price Ad is the power function of the load fluctuation valueAi. The greater the deviation of actual power consumption from the daily average load, the greater the peak-valley difference and the greater the reserve capacity.
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