Optimal Scheduling Strategy for Limited Electricity in Multiple Parks in the Absence of Power Supply from Grid

doi:10.12204/j.issn.1000-7229.2021.01.012

Abstract

Abstract:

In the absence of power supply from grid, due to the limitation of the power supply capacity of the isolated microgrid and the uncertainty of the renewable energy output, the situation of insufficient power supply will occur. Making a reasonable power dispatch strategy can improve the electricity economy and customer satisfaction. This article focuses on the situation of limited electricity in the absence of power supply from grid. Taking multiple parks for power sharing as the research object, the grid reasonably allocates electricity to different types of loads in different parks, adjusts the working time of the loads, and establishes an optimal scheduling model of limited electricity for multiple parks to minimize power outage loss, configuration costs of energy storage, and customer dissatisfaction. Then, the non-dominated sorting genetic algorithm based on the elite strategy (NSGA-II) is applied to solve the model, and fuzzy membership degree method is used for the Pareto solution set to obtain the optimal solution. Finally, taking multiple parks in a certain area of Shanghai as an example, the scheme comparison verifies that the optimization strategy proposed in this paper can effectively improve the utilization rate of electric energy, reduce the loss of power outage and the cost of energy storage, and improve customer satisfaction as well.

Key words: absence of power supply from grid, multiple parks, power sharing, optimal scheduling for limited electricity

CLC Number:

TM73

JIANG Youhua, YANG Jinwan, ZHAO Le, WANG Chunji, CAO Yilong. Optimal Scheduling Strategy for Limited Electricity in Multiple Parks in the Absence of Power Supply from Grid[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 105-116.

Figures/Tables 15

Fig.1 Schematic diagram of energy flow and information flow in multiple parks

Fig.2 Flow chart for solving the optimal scheduling model

Fig.3 Curves of PV power generation and load operation

Table 1 Situations of electricity generation and power consumption in multiple parks

Table 2 Parameters of the three objective functions

Fig.4 Pareto solution maps under different algorithms

Fig.5 Optimal results of the three objective functions at different iterations under different algorithms

Table 3 Total objective function values of scheme 1 and 2 in multiple parks

Table 4 Proportion of electricity allocated to each park under scheme 1 and 2

Fig.6 Actual situation of electricity storage and charge and discharge power under scheme 1 and 2

Fig.7 Objective function values of each park under scheme 2 and 3

Table 5 Proportion of electricity allocated to load in each park under scheme 2 and 3

Fig.8 Proportion of electricity allocated to various types of load under scheme 2 and 3

Fig.9 PV + DE power generation and load operation under scheme 2 and 3

Fig.10 Actual situation of electricity storage and charge and discharge power under scheme 2 and 3

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