In order to effectively apply the detailed geographic and load information provided by digital technology, this paper proposes a practical double-Q planning model for large-scale medium voltage distribution network. Meanwhile, a coding method and an elite Ant-Q algorithm(EAQ) suitable for solving the model in this paper are proposed. Based on the basic characteristics of the medium voltage distribution network, the components in the distribution network are abstracted into nodes and branches in the graph theory. A variety of practical issue, such as cost parameters (investments, maintenance, reliability) and technical constraints (feeder capacity constraints, number constraints of substation feeder), as well as road network constraints and connection mode constraints are taken into consideration. In addition, the storage of road network matrix information, reliability evaluation algorithm and model solving algorithm are suitable for large-scale distribution network. Finally, the proposed model and algorithm are applied to a business area to be planned in Guangzhou, which verifies the effectiveness of the proposed model and algorithm.

Smart switches such as remote-controlled switches (RCSs) and soft open points (SOPs) provide powerful fault isolation and reconfiguration ability for flexible distribution networks (FDNs) to enhance resilience. However, resilience-oriented optimal deployment of smart switches in FDNs encounters two major challenges: (1) Multiple heterogeneous faults can hardly be incorporated. (2) Lack of explicit modeling for the topological coupling relationship between pre-disaster operation strategies and post-disaster multi-phase fault management procedures. To address these challenges, an optimal smart switch deployment method is proposed by constructing a tri-layer synergized resilience enhancement model. This model addresses multiple heterogeneous faults, including line failures, substation transformer issues, and switch malfunctions. And the topological coupling relationship between RCSs and SOPs deployment, pre-disaster proactive islanding, post-disaster fault propagation, fault isolation, and service restoration is meticulously constructed. Subsequently, based on resilience metrics and fault topology metrics, a two-stage K-medoids clustering method is proposed to select critical scenarios, which balances computational feasibility and precision. Then, the proposed model is transformed into a scenario-based two-stage distributionally robust optimization formulation. Finally, case studies on 40-node and 126-node urban distribution networks validate the approach, highlighting the impact of heterogeneous faults and the effectiveness of proactive islanding and SOPs in enhancing FDN resilience.

The existing reliability models of the cyber physical distribution systems, which are mostly based on some simplified assumptions, cannot accurately evaluate the reliability of complex cases in practical engineering applications. To solve this problem, an elaborate reliability evaluation method considering the whole process of fault location, isolation and supply restoration is proposed. This paper establishes reliability models of components and the two systems, summarizes the mapping relationship between various cyber system failures and the physical fault handling into several laws, proposes the reliability evaluation procedure in the framework of Monte Carlo method, and verifies the feasibility and effectiveness of the method in an actual distribution network cyber physical system. In the proposed method, the multiple component characteristics, complex topological structure, coupling relationship of the cyber-physical distribution systems, and the actual human-computer joint participation are considered in the analysis of fault location, isolation and supply restoration process, which provides a effective and accurate theory for the application of reliability evaluation in the actual distribution network and prosumer energy management system.

In order to solve the placement problem of three kinds of feeder terminal units (FTU) in the distribution network, this paper proposes a novel mathematical model. The model considers economic cost and electricity supply reliability from the perspective of life cycle cost. The reliability algorithm in this model is established for the distribution network configured with centralized feeder automation. Different evaluation indices of reliability and the importance of several kinds of customers are also considered in this model. Aiming at the reliability evaluation in this model, this paper puts forward the reliability analysis method for the distribution network with three kinds of FTUs. In view of the difficulty to express the reliability of distribution network in a formula with decision variables, and the non-deterministic polynomial hard (NP-hard) nature of this model, a variety of intelligent algorithms are applied to solve the model. The feasibility and effectiveness of the model and methods for FTUs placement optimization problem are verified by a case study of the Roy Billinton test system (RBTS) Bus 5 system.

Energy sharing can activate prosumer flexibility to support power system operations. However, in practice, communication delays are inevitable and seriously threaten the real-time energy sharing equilibrium. While many asynchronous methods aim to address this, they ignore the learnability of prosumer’s response and often struggle to balance convergence speed and stability. To tackle this issue, this paper proposes a novel asynchronous algorithm based on best response (BR) learning. First, we demonstrate that the prosumer’s best response in ADMM-based energy sharing can be represented as the piecewise linear function. Besides, we propose a batch-fitting method to learn the BR function with high accuracy and low computational cost. Building on this, we develop an asynchronous ADMM algorithm with a practical error criterion to ensure strict convergence of energy sharing under severe delays. Numerical studies indicate that the proposed algorithm can achieve faster convergence with fewer iterations and greater reliability across various energy sharing scales and asynchronous rates than existing asynchronous algorithms.