Multi-objective Operation Optimization of Distribution Network with Gravity Energy Storage under Double Carbon Target

doi:10.12204/j.issn.1000-7229.2023.04.006

Abstract

Abstract:

Under the background of “double carbon target ”, the new power system carries the important task of low-carbon transformation of power system. In order to better promote the absorption of new energy and effectively promote the demand response to participate in the operation of power system, this paper proposes a multi-objective optimization model of active distribution network considering gravity energy storage and demand response. Objective function 1 considers the total cost of active distribution network operation and user energy consumption, and objective function 2 is selected as the equivalent carbon-emission cost of active distribution network. Due to the uncertainty of wind power output, the uncertain optimization model is converted to the deterministic optimization model by using the robust optimization theory, and the operation optimization solution method based on NSGA-Ⅱ algorithm is constructed. Finally, the model is solved in three scenarios. The results show that the system equipped with gravity energy storage and the demand response at the same time can not only reduce carbon emissions, but also achieve the purpose of peak-cutting and valley-filling, and the system has better operation economy.

Key words: gravity energy storage, demand response, active distribution network, robust optimization, NSGA-Ⅱ algorithm

CLC Number:

TM732

CUI Wenqian, WEI Junqiang, ZHAO Yunhao, GAO Wei, CHEN Kang. Multi-objective Operation Optimization of Distribution Network with Gravity Energy Storage under Double Carbon Target[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(4): 45-53.

Figures/Tables 13

Fig.1 Basic framework of the active distribution network

Fig.2 Multi-objective optimization model transformation solution flow chart

Fig.3 Schematic diagram of the 21-node case of active distribution network

Table 1 Time-of-use electricity price

Table 2 Scenarios to be optimized

Fig.4 Wind and solar power curves

Fig.5 Load curves in three scenarios

Table 3 Peak load and peak-to-valley difference in each scenario kW

Table 4

Table 5 Utilization of wind and solar power and storage in three scenarios kW·h

Fig.6 Charge and discharge situation of energy storage in Scenario 1

Fig.7 Charge and discharge situation of energy storage in Scenario 2

Fig.8 Charge and discharge situation of energy storage in Scenario 3

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