title: Planning of Community-level Decentralised Power Grids Based on the Complex Network Theory
Creators: name: Wu, Qigang
Publisher: name: University of Liverpool Repository
Year: 2026

description: An ambitious target has been established for reducing emissions and reaching a carbon-neutrality net-zero world in many countries. To this proposal, more distributed energy resources, especially intermittent Renewable Energy Sources (RESs), are integrated into the power grid, causing more uncertain fluctuations and power mismatches. Meanwhile, the power grid size and capacity increased rapidly, leading to the higher complexity of controlling the power system. Therefore, decentralising the power grid is becoming an urgent issue recently. In this context, this thesis focuses on designing a community-level decentralised power network by allocating distributed resources and finding the boundaries of clustered networks by the complex network theorem. Firstly, a new complex network-based metric is proposed for assessing the significance of the Decentralised Energy Storage System (DESS) inside a power grid. It aids the optimal location selections by improving grids' net-ability structurally. An auxiliary Genetic Algorithm (GA) sizing strategy is also deployed for deciding the optimal capacity of each DESS with the minimum daily operating and investment costs. The result shows that the DESS improves the rate of cost reduction within an equivalent 24h daily operation. Moreover, this methodology finds quasi-optimal solutions with better feasibility and efficiency. The improvement of network performance by the DESS depends on its original structure. The result shows that with the assistance of siting plan by a complex network theory, the calculation efficiency improves and performs better in larger power grids. In the IEEE-30 test system, our solution is about 1/3 calculation time as the GA search. The quasi-optimal costs 1.8% more than the optimal searched by the GA. Meanwhile, the DESS can save more cost for networks with higher network-wide ESP value. In the IEEE-118 and IEEE-300 test systems, only the proposed hybrid-GA search can find a solution within a limited calculation time. Therefore, it could be promising in solving siting issues in the planning of smart grids. Then, this thesis proposes a new approach for dividing a large power grid into clusters using the complex network theorem. The approach integrates power flow dynamic, line impedance, generator-load relations and power generator cost-efficiency into a single static weighted adjacency matrix. Meanwhile, a multi-objective Genetic Algorithm planning structure is also denoted for transforming a CDN to VMs with mutual interaction between partition and DER allocation. The proposed metric is tested in both transmission and distribution networks. The 118-bus system test shows that even with a higher value of the proposed indicator, there are fewer power exchanges between sub-networks. Meanwhile, in the 69-bus radial system test, the GA-based co-planning method outperforms previous methods in forming more self-sufficient and efficient interconnected VMs. An intermediate solution is suggested by implementing a trade-off between inter-VM power exchange and the operation cost. The solution uses only a 6.4% cost increase and get less inter-communities power exchange. Finally, inspired by damages from the reversed direction of power flows in the distribution, the partitioning algorithm is updated to consider power flow directionality with another complex-network approach for clustering the power grid. Meanwhile, former topological grid visualisation methods cannot comprehensively evaluate power flow dynamic features, including its generation cost, possible strength and directions. Hence, a new metric is proposed in this chapter, innovated by the electrical betweenness concept. The simulation result shows that considering power flow directionalities, the clustered network is more adequate internally with fewer exchanges between sub-networks. Meanwhile, the new metric can substitute the deterministic power flow for estimating the power flow dynamic.