With increasing utilizations of distributed energy resources and smart energy apparatuses, power distribution systems are undergoing a transition from passive to active networks, which challenges distribution system operators in many aspects of energy management and system control. In this paper, a short-term control strategy for active power distribution systems is proposed to regulate voltages into statutory ranges. The voltage control strategy is modeled as a convex optimization problem by applying a second-order cone relaxation. After the network decomposition, the alternating direction method of multipliers is employed to solve the established optimization model in a decentralized mode. Finally, the IEEE 33-bus and 123-bus test systems are employed to demonstrate the performance of the proposed voltage control strategy in active power distribution systems.

With the increasing data privacy concerns raised by not only organizations but also individuals in distribution systems, traditional centralized data-driven forecasting approaches for short-term load forecasting (STLF) in distribution transformer supply zones are confronted with the predicament of isolated data island. To this end, a federated model-agnostic meta learning (FMAML) based STLF method is proposed. On the basis of federated learning(FL), model agnostic meta learning (MAML) is employed to build high-quality personalized models for clients, thereby significantly enhancing the personalization and compatibility of the Federated Learning FL, while easing data privacy concerns leveraging the feature of FL. The stochastic controlled averaging (SCA) algorithm is integrated as the federated aggregation algorithm to mitigate the impacts of client-drift (CD) phenomenon that causes slow convergence and even divergence during the training process, especially when the data is highly heterogeneous. Finally, numerical results verify the high accuracy and strong robustness to data heterogeneity and packet dropout of the proposed method.

In this study, a multi‐stage long‐term expansion planning model for an active distribution network (ADN) is presented, with the aim of minimising the investment and operation cost in a coordinated manner over an established horizon. The planning model optimises the following alternatives: upgrading the capacities of substations, reinforcing and/or constructing cable circuits, placing voltage regulators (VRs) and/or static VAR generators, and determining the connection points for distributed generators (DGs). The investment decisions are optimised over the entire planning horizon which can be further divided into multiple periods, and the operation strategies, e.g. active management of DG as well as ADN topology reconfiguration, are determined according to the profiles of representative scenarios. To relieve the computational burden, the original model is properly simplified as a mixed‐integer quadratic constrained programming problem through linearisation and approximation techniques, and the solution optimality is guaranteed after invoking the off‐the‐shell solver. A 24‐node test system is employed to validate the effectiveness of the proposed model.

This paper proposes a Stackelberg game‐based optimal energy management model for a microgrid with commercial buildings (CBs), which include a cluster of flexible loads, such as a heating, ventilation, and air conditioning (HVAC) system and a lighting system. In particular, the microgrid operator (MGO) determines the optimal energy management scheme while the CBs enjoy a dynamic pricing tariff to adjust their consumption patterns for cost saving. The interactions between MGO and CBs are formulated as a bi‐level optimisation problem where the MGO behaves as a leader and CBs act as followers. The proposed model is transformed into a mixed integer linear programming (MILP) problem by jointly using the Karush–Kuhn–Tucker (KKT) condition and the strong duality theory. Besides, the effects of correlated solar irradiance and solar illuminance uncertainties on the load profile are taken into account through invoking the Nataf transformation‐based 2 M   + 1 point estimate method (PEM). Finally, case studies are served for demonstrating the feasibility and efficiency of the proposed method.

With the extensive installation of intelligent electronic devices with bi-directional fault monitoring capabilities, richer fault direction information can be collected and utilized to achieve an accurate fault diagnosis. In this paper, we consider the fault diagnosis problem in active distribution systems with distributed generators connected, such as rotating electrical machine power sources and centralized inverter interfaced renewable energy resources. The fault diagnosis problem is modeled as a linear integer programming problem with an objective to minimize the numbers of fault zones and false alarms. A novel functional form is derived to capture the expected alarms sent by bi-directional fault monitoring devices to be compared with the actual alarms received by the dispatch center. Uncertainties in both the monitoring and communication stages are considered in the model by formulating the numbers of false alarms in the objective function. Three types of suspected false alarms can be detected: “missing alarms”, “distorted alarms”, and “reverse alarms”. By solving the developed optimization model for fault diagnosis, false alarms and suspected fault zones can be found. Case studies in a modified IEEE 33-bus system and a 55-bus system in Guangzhou, China are carried out in several different scenarios with multiple faults to demonstrate the performance of the proposed model. Numerical tests show that the proposed approach is superior in computational time such that it can be used for real-time fault diagnosis in active distribution systems.

As China progresses with its electricity market reforms in pursuit of “carbon peak and carbon neutrality” objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China’s electricity financial market and proposes derivative instruments tailored to China’s market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China’s electricity financial market, aligning with global strategies and addressing the unique challenges of China’s energy transition.