A Multi-objective Optimal Scheduling Method for Integrated Energy System of Protected Agricultural Industrial Park Considering Local Consumption Rate of Wind and Solar Engery

doi:10.12204/j.issn.1000-7229.2021.07.003

Abstract

Abstract:

In order to solve the problems of inefficient energy utilization and high energy cost in the production process of protected agricultural industrial park, a multi-objective optimization scheduling method for integrated energy system (IES) of protected agricultural industrial park is proposed, which takes into account the local consumption of wind and solar energy. Firstly, the output model of integrated energy system of protected agricultural industrial park including renewable energy such as wind, solar and biogas power is established. Then, considering various constraints, taking the minimum energy consumption and maximum consumption rate of wind and solar energy as the objectives, the improved multi-objective sine-cosine algorithm based on reverse learning is used to solve the problem. Finally, the integrated energy system of a protected agricultural industrial park is taken as an example. The results show that the optimal scheduling method can effectively reduce the daily operation cost of the park and improve the local consumption rate of wind and solar energy.

Key words: protected agricultural industrial park, integrated energy system(IES), multi-objective optimal, consumption rate, sine-cosine algorithm

CLC Number:

TM73

CHEN Wei, LU Yuan, HE Xin, ZHANG Hailong, YANG Yong, CUI Jianbin. A Multi-objective Optimal Scheduling Method for Integrated Energy System of Protected Agricultural Industrial Park Considering Local Consumption Rate of Wind and Solar Engery[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(7): 20-27.

Figures/Tables 11

Fig.1 IES structure of a protected agricultural industrial park

Fig.2 Flow chart of IES optimal scheduling algorithm

Table 1 Parameters of some equipment in the integrated energy system

Table 2 Operation and maintenance fees of some related equipment

Fig.3 Typical daily curves of cooling, heating and electric loads, and forecasting curves of wind and solar power in summer

Fig.4 Typical daily curves of cooling, heating and electric loads, and forecasting curves of wind and solar power in winter

Fig.5 Optimization results of electricity, heat and cooling load in a typical day in summer

Fig.6 Optimization results of electricity, heat and cooling load in a typical day in winter

Table 3 Comparison of IES optimal dispatching in typical days in summer

Table 4 Comparison of IES optimal dispatching in typical days in winter

Fig.7 Comparison of convergence rates of optimization algorithms

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