Carrying Capacity Evaluation of Distribution Network for Distributed Photovoltaic Cluster Considering User-Side Demand Response

doi:10.12204/j.issn.1000-7229.2023.02.012

Abstract

Abstract:

Aiming at the problem that photovoltaic clusters are not considered in the traditional evaluation method of carrying capacity of distribution network for distributed photovoltaic power, a two-layer model of carrying capacity with photovoltaic clusters as the evaluation unit is constructed. In the outer layer of the model, PSO algorithm is used to describe the change of load demand through price elasticity coefficient, and the time-of-use price is optimized with the goal of maximizing the income of E-seller. In the inner layer, taking no reverse power transmission to the main network as the index, considering the voltage deviation and equipment thermal stability constraints, a sensitivity ranking method is proposed to calculate the carrying capacity for photovoltaic power. In the actual distribution network of a county in China, the impact of a single photovoltaic cluster on the carrying capacity of the distribution network is calculated, and the carrying capacity of the distribution network for the photovoltaic cluster is calculated to verify the effectiveness of the proposed model and algorithm.

Key words: distribution network, distributed photovoltaic power, demand response, carring capacity, cluster

CLC Number:

TM721

YU Haozheng, ZHAO Hanjie, LI Ke, ZHU Xingxu, HUANGFU Xiaowen, FAN Jiangchuan, LI Cuiping, LI Junhui. Carrying Capacity Evaluation of Distribution Network for Distributed Photovoltaic Cluster Considering User-Side Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(2): 122-130.

Figures/Tables 20

Fig.1 Structure schematic of power grid with high proportion of distributed photovoltaic power access

Fig.2 Influencing factors of carrying capacity for distributed photovoltaic power

Fig.3 Two-layer model of carrying capacity for distributed photovoltaic power

Fig.4 Flow chart of two-layer model of carrying capacity for distributed photovoltaic power

Fig.5 Flow chart of TOU price optimization based on PSO algorithm

Fig.6 Flow chart of ultimate carrying capacity

Fig.7 Topology of example power grid

Table 1 Time-of-use price parameter

Table 2 Parameter table of price elasticity matrix

Fig.8 Load before and after TOU price optimization

Table 3 Income comparison statement

Fig.9 Process for sensitivity sorting adjustment

Fig.10 Carrying capacity of each PV cluster before and after TOU optimization

Fig.11 Load carrying evaluation results under different electricity price subsidies

Fig.12 Voltage amplitudes in different scenarios

Fig.13 Reverse load rate of each scenario

Fig.14 Sensitivity of photovoltaic cluster to distribution network carrying capacity

Table 4 Sensitivity table key clusters

Fig.15 Power perturbation of critical and non-critical clusters

Fig.16 Carrying capacity sensitivity of Inter cluster

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