ABSTRACT This study presents a distributed control system for a multiagent co‐simulation environment, designed to regulate a direct current (DC) bus voltage in a grid‐connected microgrid (MG). The system adopts a client/server architecture, enabling seamless communication in a network of interconnected components integrated into a MG while implementing adaptive update‐rate communication to optimise data exchange efficiency. A multiagent system (MAS) orchestrates interactions between power converters, ensuring seamless operation of a DC microgrid powered by photovoltaic (PV) arrays, a battery storage system and an inverter/rectifier converter connected to the main grid. The framework integrates Python, TCP/IP sockets and industry‐standard simulators (PLECS, PSIM and RTDS) to create a co‐simulation environment. Key results demonstrate effective DC bus voltage regulation and battery voltage control (used as a proxy for SoC), ensuring system stability under varying operating conditions. The proposed approach enhances system responsiveness through adaptive update rate communication, which dynamically adjusts data transmission among agents and the MAS based on real‐time network conditions. This improvement is evidenced by a reduction of approximately in the average settling time of the secondary control layer, from to , when the communication medium delay varies during system operation under the adaptive update rate, compared with the fixed update rate scenario. These results highlight the superior dynamic performance of the hierarchical control strategy at the supervisory level.

ABSTRACT The current system frequency response (SFR) model that incorporates the wind farm fails to fully account for the operational variations of wind turbine generators (WTGs) across varying wind speeds and different frequency regulation control modes, indicating potential for improvement. To address this issue, this paper first considers the virtual inertia, droop, overspeed de‐loading and pitch angle de‐loading control of the WTG to establish a frequency response model of the WTG. Second, based on the different operating states of WTGs, the equivalent frequency response model of the wind farm is aggregated through unit grouping, and an extended SFR model is constructed by further integrating it with the traditional SFR model. Then, a detailed simulation model of the wind farm is established on the DIgSILENT PowerFactory simulation platform to validate the proposed extended SFR model. The results show that the proposed model can accurately track the frequency response characteristics of the detailed model. Finally, using the proposed SFR model, an analysis is conducted on the impact of the wind farm's frequency regulation parameters and wind speed scenarios on system frequency stability.

ABSTRACT The operation of photovoltaic‐storage‐charging microgrids is increasingly challenged by the volatility of renewable resources and demand patterns driven by dynamic climate conditions. To address these challenges, this paper proposes a climate‐driven low‐carbon dispatch strategy for photovoltaic‐storage‐charging microgrids. First, to accurately capture system uncertainties associated with fine‐scale atmospheric dynamics, a convolutional neural network–long short‐term memory (CNN–LSTM) deep learning model is constructed based on a 15‐min dispatch resolution. Simultaneously, a multifeature long short‐term memory (LSTM) model, integrating temporal periodicity and historical dependencies, is developed to predict weather‐sensitive electric vehicle charging loads. Second, to enhance system resilience against climate‐induced power fluctuations, the operational constraints of the energy storage are modelled explicitly, incorporating a battery degradation cost model to quantify asset lifespan loss. Building on this, a dual‐objective optimisation framework is formulated that internalises dynamic carbon trading costs into the economic dispatch. This model coordinates the power interaction among the photovoltaic system, energy storage and the main grid to maximise total operating revenue while minimising carbon footprints. Simulation results verify that the proposed strategy effectively balances economic efficiency and carbon reduction, achieving a system carbon emission reduction of 39.82%, demonstrating superior adaptability to climate‐driven variations in renewable generation and load demand.

ABSTRACT To achieve the ‘Dual Carbon’ strategic goals, the integration of a high proportion of renewable energy poses significant challenges to the stability and economic efficiency of power systems. This paper proposes a bilevel optimisation method for distributed energy storage configuration that integrates wind‐solar power uncertainty and a carbon trading mechanism. First, a spectral normalisation generative adversarial network (SNGAN) is employed to generate a large number of wind‐solar power output scenarios. These scenarios are then reduced using the K‐means clustering algorithm to obtain typical representative scenarios, significantly improving the quality of scenario generation. Second, a bilevel optimisation model is constructed for distributed energy storage configuration considering a tiered carbon trading scheme. The upper‐level model aims to minimise the total cost, including energy storage investment, grid electricity purchase and carbon trading costs, whereas the lower‐level model focuses on minimising power grid vulnerability indices and active power losses. Finally, an improved Osprey Optimisation Algorithm is developed to solve the bilevel model. Simulation results demonstrate that the proposed method reduces annual carbon emissions by 22.6%, daily active power loss by 22.3% and daily electricity purchase cost by 9.2% compared to the case without ESS and carbon trading while significantly enhancing overall power system performance.

ABSTRACT Large‐scale uncoordinated charging and discharging behaviours of electric vehicles (EVs) exacerbate power grid operational losses and reduce the efficiency of scheduling model solutions. To address this issue, this paper proposes an EVs scheduling method for distribution networks based on DPPO. Firstly, considering the operational conditions and security constraints of the distribution network, an optimisation scheduling model is constructed to minimise the total system operational cost. The objective function includes key parameters such as EV charging/discharging power, branch power losses and curtailment rates of wind and photovoltaic (PV) generation. Secondly, to address the computational challenges posed by the high dimensionality of variables, nonlinear constraints, and discrete variables, the optimisation scheduling model is transformed, and the DPPO algorithm is employed for efficient solution. Finally, simulation results based on the IEEE 33‐node distribution network system demonstrate that the proposed method effectively reduces the total system operational cost and network losses, and significantly enhances the distribution network's capacity to absorb distributed wind and PV generation.

ABSTRACT After distributed photovoltaic (PV) systems are connected to the distribution network, the overcurrent problem caused by transient faults instantaneously threatens the safety of PV inverters and other equipment. This paper proposes a comprehensive control method that combines active‐loop adaptive linear active disturbance rejection control (A‐LADRC) with fault voltage adaptation and reactive‐loop smooth switching to address the transient overcurrent issue of inverters under unbalanced faults. Firstly, the low‐voltage ride‐through (LVRT) control principle of distributed PV systems and the mechanism of overcurrent generation under unbalanced faults in the distribution network are elaborated, the phenomenon of grid‐connected current exceeding the rated threshold due to fault voltage is analysed. Secondly, a fault voltage‐adaptive linear active disturbance rejection control method is designed, which is mainly used to address the overcurrent problem at the moment of fault occurrence and recovery. Additionally, to address the overcurrent caused by reactive power control during the fault period, a reactive power outer‐loop smooth switching method is introduced. Finally, a hardware‐in‐the‐loop (HIL) experimental platform based on a real‐time simulator and an actual PV inverter controller is built to verify the effectiveness and superiority of the proposed method in suppressing overcurrent.

ABSTRACT The penetration of plug‐in electric vehicles (PEVs) and distributed energy resources (DERs) is increasing in distribution systems, potentially leading to significant technical and economic challenges. To tackle these challenges, this paper introduces a novel framework for effectively managing DERs and EVs within active distribution systems (ADSs), incorporating time‐varying ZIP load models. A deep reinforcement learning (DRL)‐based control approach is developed that simultaneously optimises both technical and economic objective functions for the efficient operation of ADSs. For this purpose, the PEVs are integrated with different nodes of the ADS through solid‐state transformers (SSTs). Based on available generation, load demand and EV charging profiles, the control algorithm regulates reactive power flow using SSTs and minimises the operational cost as well as power loss of the ADS. The proposed framework is successfully applied and evaluated on standard IEEE systems, demonstrating its efficacy in solving the problem of integrating PEVs and DERs using solid‐state transformers.

ABSTRACT The expansion of global energy systems will require a crucial shift towards durable and flexible technologies. The rapid growth of renewable energy sources demands practical solutions to ensure a stable and reliable power supply within the electrical distribution grid. Microgrid technologies, by integrating various energy sources, offer optimised dispatching to enhance the reliability, resilience, economy, security and sustainability of the grid. However, stationary microgrids, which rely on fixed resources and infrastructure, may encounter challenges such as deployment difficulties and limited flexibility under extreme and special conditions. This paper introduces Mobile Microgrids (MMGs) as an agile solution to augment traditional and stationary microgrid systems, enhancing their flexibility and resilience. MMGs integrate renewable energy sources, energy storage systems and advanced communication technologies to provide reliable and flexible power solutions. We systematically review the key technologies behind MMGs, their business models and applications in diverse critical sectors. This work further discusses the strengths and limitations of existing MMG implementations and provides a critical analysis of current research gaps. We emphasise MMGs' pivotal role in improving grid resilience, offering insights into their prospects and proposing directions for further development in mobile energy systems. By synthesising the existing literature, this review aims to contribute a coherent framework for understanding MMGs and their transformative potential in the global energy landscape.

ABSTRACT Modern energy systems face increasing operational challenges due to the growing penetration of renewables, variability in generation and network congestion, which contribute to curtailment, inefficiencies and avoidable emissions. These issues constrain system flexibility and hinder progress towards net‐zero targets. Digitalisation offers a means to address these challenges by improving system observability, enabling real‐time coordination and supporting data‐driven decision‐making through technologies such as the Internet of Things, artificial intelligence and digital twin. As a result, digitalisation has enhanced the efficiency, reliability and flexibility of energy systems, supporting progress towards net‐zero emissions targets. This paper reviews key technologies that enable energy system digitalisation and examines challenges arising from increased connectivity. Unlike existing studies that consider individual technologies, market mechanisms or policy frameworks in isolation, this work adopts an integrated perspective encompassing enabling technologies, data‐driven applications, data governance and cyber security within digitalised energy systems. This study is guided by a horizon scanning methodology to identify emerging technological and cyber‐physical challenges shaping future energy system design. Additionally, a six‐dimensional framework for energy data governance is used to structure current practices and identify gaps related to data quality, discoverability, sharing, privacy and emerging responsibilities. This paper offers actionable insights for researchers, policymakers and industry stakeholders while identifying areas that require further technical and regulatory development.

ABSTRACT In the context of high‐penetration of photovoltaic (PV) installation, the inherent fluctuation of PV generation leads voltage violations to the distribution system. The traditional voltage adjustment, such as capacitor banks and on‐load tap changer, is dominated by the distribution system operator. With the emergence of microgrid, multiple flexible resources, such as electric vehicle battery swapping stations (EV‐BSSs), can participate in distribution network voltage optimisation by dynamically changing the load time and amplitude characteristics. However, the uncertainty of resources, that is, the volatility of PV power and the randomness of loads, makes it difficult to precisely regulate the voltage. If the charge/discharge pattern of EV‐BSS can compensate for the fluctuations, the voltage regulation would be strongly supported. This paper proposes a collaborative operation framework between distribution networks and microgrid to coordinate voltage regulation in the distribution network, integrating conventional voltage control methods with the EV‐BSS. A PV inverter control strategy is also developed, incorporating both user satisfaction and the factors influencing PV generations. To further enhance system performance, a multiobjective optimisation model complemented by a confidence‐adjustable mechanism is developed to dynamically fine‐tune scheduling robustness under uncertainty. The effectiveness of the model is validated through case studies.