This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on‐load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non‐linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second‐order cone programming model through distflow equations and constraints relaxation. A modified 50‐bus application example is used to verify the proposed model.

The traditional distributed generation (DG) locating and sizing method is based on a fixed network structure, which is assumed to be unchangeable during the planning period. The flexible distribution network structure is not considered. This paper proposes a novel method for DG locating and sizing in active distribution network (ADN), which mixes DG locating and sizing problem and distribution network reconfiguration problem together. The objective is to minimize the total costs of the distribution network in the planning period. DG locating and sizing considering network reconfiguration is a complex mixed-integer non-linear programming problem. This paper convert the dual-level planning model to the second-order cone programming model, which can be solved with commercial solver GUROBI. The proposed model and method are testified with the modified IEEE 33-bus system.

Power-to-ammonia (P2A) technology presents an effective strategy for facilitating the low-carbon transition of integrated energy system (IES), effectively addressing the stochastic and intermittent nature of renewable energy generation. In response to the challenges posed by the increased penetration of renewable energy, this paper develops an optimization scheduling model of IES based on chance constraints within an electricity–hydrogen–ammonia coupled architecture. The model integrates P2A technology, hydrogen blending in gas turbine (GT) and gas boiler (GB), and coal–ammonia co-combustion, while further modeling the dynamic characteristics of the electrolyzer. To account for renewable energy uncertainties, the probability reserve is formulated using chance-constrained programming (CCP). The model is solved using the sequence operation theory, which converts the CCP into a deterministic equivalent model to balance the expected and stochastic outputs of renewable generation. Case studies on four scenarios demonstrate that the proposed model achieves a 5.82% reduction in carbon emissions and a renewable energy utilization rate of 98.3%, outperforming traditional scheduling approaches. Additionally, the optimal system performance is achieved when hydrogen blending ratios reach 16% for GT and 18% for GB. These results underscore the model's effectiveness in enhancing low-carbon operation, maximizing renewable energy utilization, and improving the economic efficiency of IES.

The increasing complexity of the distribution system makes the practical distribution network operation much difficult. This paper presents an ordinal optimisation (OO) approach to solve the distribution network reconfiguration, which is an NP‐ hard problem with discrete control variables. OO uses crude and computationally fast model to reduce the search space. The spirit of OO is to seek the good enough solution instead of the best with high probability. The proposed approach is validated with two practical distribution systems, TPC 84 node test system. The results are compared with Optimal Flow Pattern (OFP), Common Genetic Algorithm (CGA), Partheno Genetic Algorithm with Tree Structure Encoding technology (TSE‐PGA) and Second‐Order Cone Programming (SOCP).