With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

In order to accurately and effectively diagnose the transmission line faults of the power system, a genetic-algorithm support vector machine and D-S evidence theory based fault diagnostic model is proposed. Two genetic-algorithm support vector machine based diagnosis boxes separately identify the fault types to get two kinds of preliminary diagnostic results according to the voltage and current phasors data from the phasor measurement units. Then, a deviation coefficient is derived to represent the diagnostic conflicts existing in the two kinds of preliminary diagnostic results. The mass function of the D-S evidence theory is updated by using the deviation coefficient to eliminate the diagnostic conflicts and improve the diagnosis accuracy. Finally, the proposed diagnostic model was applied in an actual power system in Hubei province in China. The simulation results and practical results indicated the feasibility and the effectiveness of the diagnostic model.

To address the problem of DC bus voltage surge caused by load demand fluctuation in an off-grid microgrid, here, an adaptive energy optimization method based on a hybrid energy-storage system to maintain the stability of DC bus voltage is presented. The adaptive energy optimization method consists of three parts: the average filtering algorithm, extracting fluctuating power in demand load; the supercapacitor terminal voltage control, keeping the terminal voltage of the supercapacitor near reference; and the battery pack balance control, adjusting the charge/discharge to balance the state of charge for battery packs. In this proposed method, after extracting the fluctuating power by the low-pass filter when the demand load fluctuates, the battery packs release the power to offset the low-frequency fluctuation load and the supercapacitor to instantaneously compensate the high-frequency fluctuation power, to prolong the service life of batteries and maintain the stability of DC bus voltage. The effectiveness of the proposed adaptive energy optimization method is validated and is confirmed to maintain the stable operation of the off-grid microgrid, extend the cycle life of batteries in off-grid microgrid simulations and experiments.

Aggregating loads and resources on both the supply and demand side of a virtual power plant (VPP) can enhance coordination between distributed generation systems and the power grid, ultimately improving the utilization rate and economic benefits of renewable energy. The energy storage system (ESS) has the added benefit of flexible demand-side resources, which can effectively suppress output uncertainty of distributed units and balance load fluctuations. This paper proposes an improved light robust (ILR) optimization method for the ESS’s demand-side resource, which optimizes the conservatism caused by robust optimization (RO) in solving the VPP optimal scheme, ultimately reducing running costs. By analyzing random factors in the VPP operation process, the ILR dynamically calls ESS backup power to participate in system operation when load fluctuation and output are inaccurate or uncertain, provided operational constraints are met. This approach balances both economy and optimization, ensuring the balance between supply and load fluctuates, improving the utilization rate of ESS backup power, and reducing the total operating cost of VPP operation. The case study show that the model can effectively utilize the reserve energy of the ESS to cope with the load fluctuations during normal operation of the system, improve the economy while ensuring the safe operation of the virtual power plant system, and leave some optimization space for decision-makers by utilizing the demand-side resource characteristics of the energy storage system.

Rural areas, with their vast land and abundant resources, are ripe for the development of distributed energy systems. A two-stage dispatch optimization model has been proposed for a virtual power plant (VPP) in this paper, with the aim of maximizing operational revenue, minimizing costs for villagers, and reducing carbon emissions. This model leverages a benefit allocation strategy based on Pareto optimization, ensuring a balanced approach to conflicting objectives such as financial gain, risk management, and environmental impact. The effectiveness of various allocation strategies is evaluated using the Ideal Point method, which assesses options based on their proximity to an ideal outcome across three critical dimensions: risk, benefit, and carbon emission reduction. This method provides an assessment of each strategy’s impact, ensuring that the chosen strategy is holistic. Case study results have shown that the proposed two-stage model, when combined with the Ideal Point-Pareto optimization method, can effectively utilize dispersed resources in rural areas to enhance operational efficiency and reduce carbon emissions from energy consumption processes. Additionally, with a 47% reduction in computational volume compared to traditional scalar and particle swarm optimization algorithms.

The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks.