Comprehensive configuration strategy of energy storage allocation and line upgrading for distribution networks considering a high proportion of integrated photovoltaics

Abstract

The rapid development of photovoltaics (PVs) and load caused a significant increase in peak loads and peak‐valley differences in rural distribution networks, which require load peak shifting and line upgrading. Large peak‐valley differences also bring challenges on the safe operation of the utility power grid. Considering the integration of a high proportion of PVs, this study establishes a bilevel comprehensive configuration model for energy storage allocation and line upgrading in distribution networks, which can reduce peak loads and peak‐valley differences. In the upper level, a minimum annual planning cost is obtained by developing the installation capacity of centralised energy storage in transformer stations, the installation location and capacity of decentralised energy storage on lines and a scheme of line upgrading. In the lower level, the minimum total annual operation cost of the distribution network is obtained by developing an optimal scheduling for the centralised energy storage in transformer stations and decentralised energy storage on lines. Simulation results show that compared with the conventional energy storage planning strategy, the configuration investment can be reduced by 467.66 million yuan at least with the proposed strategy.

Cyber-Physical Integrated Planning of Distribution Networks Considering Spatial-Temporal Flexible Resources

“…ij   N denotes the set of communication edges; ( , ) ij indicates that node i can communicate with node j; and i X  denotes the indegree of node i . When SOC variation is used as the control variable, the SOC relationship between different nodes possessing communication connections can be mathematically characterized, provided that the communication topology adheres to a strongly connected graph [36], [37], as follows:…”

“…The control performance of distributed control mainly considers its convergence speed, which is determined by the second largest eigenvalue of the status transition matrix A [36], [37]. A smaller second largest eigenvalue implies a faster convergence speed of distributed control.…”

Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple‐microgrids

“…However, the resilience of recovery was not considered in light of user energy demand. The literature [5, 6] proposes an optimal allocation method for mobile energy storage to enhance the resilience of the distribution network, which effectively improves the ability of the distribution network to withstand disasters. The economy of the distribution network has not been studied further in the literature [5].…”

“…The literature [5, 6] proposes an optimal allocation method for mobile energy storage to enhance the resilience of the distribution network, which effectively improves the ability of the distribution network to withstand disasters. The economy of the distribution network has not been studied further in the literature [5]. The literature [7] proposes an integrated resilience enhancement strategy to achieve resilience enhancement of the distribution network in the presence of climatic hazards.…”

Optimization planning of energy storage based on multi-objective improved particle swarm and droop control algorithm

“…However, if DES is not appropriately configured in the distribution network, with its characteristics of being numerous and scattered, it may increase the volatility and randomness of the power grid. Therefore, it is of great practical significance to establish an optimization model that takes into account both safety and economic efficiency [5][6] .…”