Air conditioning loads play a critical role in maintaining the supply-demand balance of building microgrids (BMGs), yet their distributed nature and volatile response may undermine secure and stable operation. This paper proposes a day-ahead and real-time aggregated control strategy for BMG air conditioning loads with user privacy protection. First, an approximate aggregation model is developed based on building heat transfer characteristics, and the aggregated response potential is evaluated by jointly considering user comfort and willingness. Second, without sharing fine-grained user information, a Building Microgrid Operator (BMO)-Load Aggregator (LA) day-ahead distributed-scheduling model is formulated and solved using the alternating direction method of multipliers (ADMM). Finally, to address load fluctuations caused by heterogeneous initial indoor temperature distributions, a real-time control strategy based on State-Queueing (SQ) temperature-state pre-transfer is proposed. Case studies show that, compared with the baseline scheme, the proposed method reduces the system operating cost from CNY 50,694.58 to CNY 47,131.64, a 7% decrease, and decreases load shedding from 1466.35 kWh to 257.31 kWh, an 82% decrease. Meanwhile, the real-time control effectively suppresses power fluctuations in the early control stage, thereby improving both economic performance and response smoothness.

Large-scale disorderly electric vehicle (EV) charging can cause charging-station congestion and aggravate load fluctuations in distribution networks. To address this issue, this paper proposes a spatiotemporal optimization framework for EV charging scheduling in power-transportation coupled networks, considering user bounded rationality. A cumulative prospect theory (CPT)-based charging decision model is developed to characterize deviations from full rationality and generate more realistic charging demand. Based on this demand, a dynamic pricing mechanism is designed to guide EVs toward a more balanced spatial distribution across charging stations by considering real-time traffic conditions and distribution-network operating states. To handle stochastic EV arrivals in real time, a sliding-window scheduling strategy is further introduced to dynamically update charging priorities and optimize charging power allocation within each station. Simulation results show that the proposed framework reduces the average peak-to-valley difference of charging-station loads by 5.56% and decreases the overall load variance by 23.6%, demonstrating its effectiveness in coordinated load regulation.

In Multi-Integrated Energy Systems (MIES), due to the multi-dimensional coupling characteristics of the electrical-thermal-green certificates-carbon market, it makes the multi-level markets collaborative optimization and trusted interactions among cross-subjects face severe challenges. To address this gap, this paper proposes a trusted interaction and multi-market joint optimization management strategy for MIES based on blockchain technology. The strategy establishes a multilevel joint optimization model of electrical-thermal-green certificate-carbon emission based on the renewable energy quota mechanism and stepped carbon price mechanism by considering the dynamic feedback of market, which realizes the dynamic coupling and interaction of the green certificates and carbon quotas. Furthermore, leveraging the consortium blockchain and the Practical Byzantine Fault Tolerance consensus mechanism, a decentralized collaborative framework is designed to support the execution of smart contracts and distributed ledger technology, thereby avoiding the impact of information tampering on the system's security and enabling trusted interactions in the energy management process. Moreover, by integrating the real-time feedback capability of blockchain and the analysis target cascading method, it proposes a collaborative optimization strategy for MIES based on hierarchical decoupling and multidimensional information interaction. The strategy achieves efficient coordination and resource sharing across systems while guaranteeing the autonomy of each park. Simulation results show that the proposed strategy significantly enhances resource allocation efficiency and system security. Compared with the centralized framework, the total operating cost of each IES in MIES is reduced by 5.29%, 27.27%, and 4.19%, respectively, which verifies the effectiveness and applicability of the proposed method in low-carbon cooperative operation.

Real-time state estimation is essential for ensuring the safety and stability of power systems. Although Phasor Measurement Unit (PMU) provides high-accuracy measurements for state estimation, their reliance on GPS time synchronization makes them vulnerable to GPS spoofing attacks (GSAs). These attacks inject falsified GPS signals that introduce phase shifts into PMU measurements, thereby degrading state estimation accuracy. To mitigate this problem, this paper proposes a hybrid method that retains interpretability through a Kalman filter framework while incorporating a Gated Recurrent Unit (GRU) network to adaptively learn the Kalman gain for identifying and correcting attack angles. Extensive simulations on IEEE 14-bus and 118-bus systems confirm the effectiveness of the proposed method. The results show that the method consistently outperforms existing techniques under diverse GSA scenarios.

As the participation of photovoltaic-storage systems (PVSS) in the energy and frequency regulation ancillary service markets continues to increase, the market risks caused by photovoltaic output uncertainty will directly affect photovoltaic integration efficiency and the provision of system flexibility, thereby having a significant impact on the sustainable development of power systems. Therefore, studying the risk decision-making of PVSS in the energy and frequency regulation markets is of great importance for supporting the sustainable development of power systems. First, to address the issue where the existing studies regard PVSS as a price taker and fail to reflect the impact of bids on clearing prices and awarded quantities, this paper constructs a market bidding framework in which PVSS acts as a price-maker. Second, in response to the revenue volatility and tail risk caused by PV uncertainty, and the fact that existing CVaR-based bidding studies focus mainly on a single energy market, this paper introduces CVaR into the price-maker (Stackelberg) bidding framework and constructs a two-stage bi-level risk decision model for PVSS. Finally, using the Karush-Kuhn-Tucker (KKT) conditions and the strong duality theorem, the bi-level nonlinear optimization model is transformed into a solvable single-level mixed-integer linear programming (MILP) problem. A simulation study based on data from a PV-storage power generation system in Northwestern China shows that compared to PV systems participating only in the energy market and PVSS participating only in the energy market, PVSS participation in both the energy and frequency regulation joint markets results in an expected net revenue increase of approximately 45.9% and 26.3%, respectively. When the risk aversion coefficient, beta, increases from 0 to 20, the expected net revenue decreases slightly by about 0.4%, while CVaR increases by about 3.4%, effectively measuring the revenue at different risk levels.

With the rapid increase in the number of electric vehicles, the strong time-variability and behavioral diversity of charging loads have posed significant challenges to the stable operation and planning of urban distribution networks. To address this issue, this paper proposes a short-term load forecasting method that combines a Newton–Raphson optimized K-means clustering algorithm with an integrated deep learning model. First, charging stations are adaptively clustered according to their load profiles, improving the segmentation accuracy under different charging behaviors. Then, for each cluster, personalized prediction models are developed by integrating multi-source data such as historical load, meteorological information, and electricity price, utilizing a combination of convolutional neural networks (CNN), long short-term memory networks (LSTM), and attention mechanisms (AM). Finally, the effectiveness of the proposed approach is validated with real-world regional data, and the results demonstrate significant improvements in both forecasting accuracy and generalization capability.

Distribution grids with high photovoltaic and wind penetration face alternating high-net-load shortages and renewable-surplus periods. Existing integrated-energy virtual power plant (VPP) studies often optimize VPP economics under fixed flexibility assumptions, leaving bidirectional grid-service requirements, source-load uncertainty, and cross-carrier allocation weakly coordinated. To address this gap, this paper develops a scenario-based robust mixed-integer linear programming (MILP) framework for an electricity-hydrogen-heat-cooling VPP. The framework translates grid states into shortage-support or surplus-absorption requests, then jointly selects scenario-invariant day-ahead interaction and available-flexibility limits with scenario-dependent multi-energy dispatch under nested budgeted stress scenarios. The resulting schedules are assessed using a 24-h mechanism benchmark,56 public California source-load days split into 2019 calibration and disjoint 2020 out-of-sample sets, and post-dispatch IEEE 33- and 69-bus AC screening. On the out-of-sample set, the robust model lowers common operating cost and renewable curtailment by 21.96% and 21.09%, respectively, relative to a fixed-parameter robust benchmark, while remaining comparable with the deterministic optimized model. All 360 selected IEEE 69-bus hourly power flows converge under two independent methods; however, the 0.89878 p.u.minimum voltage shows that convergence does not guarantee voltage-limit feasibility. The results demonstrate that coordinating usable flexibility and multi-energy recourse improves economics and renewable utilization while exposing network bottlenecks.

With the rapid increase in electric vehicle (EV) ownership, charging loads have an increasingly significant impact on the operation of urban distribution networks. Due to the high randomness, volatility, and heterogeneity of EV charging behavior, traditional load modeling methods struggle to fully capture its characteristics. To address this issue, this paper proposes an EV charging load behavior analysis method based on the integration of ISODATA clustering and feature selection. First, to overcome the limitation of requiring a predefined number of clusters in traditional K-means, an adaptive Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm is introduced to automatically identify typical charging behavior patterns. Then, a multi-dimensional feature indicator system is constructed, and the Spearman correlation coefficient method is employed to select the optimal subset of features. Finally, quantitative user profiling is performed based on the selected features, and visualized using radar charts. Case studies demonstrate that the proposed method effectively improves clustering accuracy, feature representativeness, and profile interpretability, providing a valuable reference for EV load modeling, classification forecasting, and differentiated control strategies.

With the widespread adoption of micro synchronous phasor measurement unit ( $\mu $ -PMU) in distribution networks, the deep integration of multisource measurement data enhances state estimation accuracy while simultaneously confronting data security challenges posed by false data injection attacks (FDIAs). To this end, this article proposes a collaborative enhancement mechanism that integrates a multisource measurement fusion scheme with an FDIA detection method using adaptive-threshold Kullback-Leibler distance (KLD). First, data from the supervisory control and data acquisition (SCADA) system and $\mu $ -PMU measurements are unified through a linear transformation model. Subsequently, high-precision pseudo-measurement data are generated using a backpropagation-gray wolf optimizer particle swarm optimization (BP-GWOPSO) hybrid intelligent algorithm for multisource measurement fusion, thereby compensating for missing SCADA data. Next, the KLD algorithm dynamically analyzes discrepancies in the probability distribution of state variables. An adaptive detection threshold is established through a time-sliding window mechanism to perform FDIA detection. Simulation results demonstrate that, compared to fixed-confidence-interval-threshold detection methods, the proposed adaptive threshold detection approach based on multisource measurement fusion exhibits superior performance across diverse attack scenarios and intensities. This method significantly enhances the discrimination accuracy and real-time responsiveness of FDIA detection whilst maintaining state estimation precision.

With the increasing penetration of renewable energy into integrated energy systems (IES), the intensifying source-load mismatch has led to real-time power imbalance in off-grid IES clusters, decrease in system energy utilization efficiency and power supply reliability, thereby affecting the safe and stable operation of IES clusters. To address this gap, by fully utilizing the information transmission and energy complementarity between IES, a two-stage distributed collaborative optimization strategy for IES clusters is proposed in this paper. In the first stage, the load capacity of IES is evaluated based on the load margin, and a power allocation model based on a consensus algorithm is established. The real-time power of each controllable unit of IES is obtained through iterative processes, the reasonable output and minimum adjustment cost of multiple IES devices can be achieved, and the total power command of the electric-hydrogen hybrid energy storage system (HESS) is transmitted to the second stage. In the second stage, a HESS energy management strategy is developed considering the operational characteristics of alkaline electrolyzers. Furthermore, dynamic adjustment rule for charging and discharging power weighting factors is designed based on a logistic function, and a multi-mode real-time power allocation strategy for HESS is proposed, adjusting the output of two types of energy storage in real time. Finally, numerical simulation verified the feasibility and superiority of the proposed power allocation strategy of IES clusters.

As a flexible regulatory resource, hybrid energy storage system (HESS) is capable of providing multiple reliable ancillary services, which improves the adaptability of the distribution system to large-scale grid connection of the distributed generation (DG) and alleviate the pressure of peak load and frequency response. In this context, this paper proposes an optimal dispatch strategy of a HESS for DG electricity production and multiple auxiliary service markets to create stackable benefits for HESS operators. Firstly, different types of energy storage system (ESS) (energy-based and power-based) are unified to the joint optimal framework of peak shaving (PS), frequency containment reserves (FCR), and secondary frequency regulation (SFR). By constructing a virtual ESS model based on the idle-time reuse response, an optimal bidding strategy for HESS under this revenue combination is proposed to achieve the optimal utilization of resource allocation. Furthermore, an "hourly-minute-secondly" progressive time series is introduced, and a multi-timescale hierarchical dispatch model named "daily baseline-regulation basepoint-real-time regulation" is constructed. Specifically, the PS capacity is allocated day-ahead and a two-stage capacity allocation method for FCR and SFR is proposed in the intraday, which realizes the parallel optimal of HESS at the scale of full clearance in the auxiliary service markets. Results show that compared to the combined benefits of two types of ESS, the proposed method achieved the comprehensive income increased by 4.87 % and the auxiliary services income increased by 15.2 %. Under this revenue combination, the SFR market can create an additional 60 %-90 % economic value for HESS operators. The proposed hierarchical optimal model can better adapt to the trading rules of different auxiliary service markets and provide guidance for HESS and DG to further participate in the electricity market.

The introduction of hydrogen energy utilization will further highlight the high dimensionality, nonlinearity, and uncertainty of integrated energy systems, thereby increasing the difficulty of their scheduling strategy formulation. In this paper, a method based on multi-agent deep reinforcement learning for optimizing the scheduling of integrated energy systems is proposed. Firstly, a comprehensive energy system model was established, integrating wind, solar, and hydrogen coupling to provide heating, cooling, and electricity. Subsequently, flexible model for proton exchange membrane fuel cells was developed, and its electrical and thermal output characteristics were analyzed. Four scheduling performance evaluation indicators were defined, and a reward function for deep reinforcement learning was constructed based on the Technique for Order of Preference by Similarity to Ideal Solution approach. Combining the strategy of representing time series segment states and decision variable grouping training, the multi-agent twin delayed deep deterministic policy gradients algorithm was improved to establish a scheduling strategy learning model. Finally, a case study analysis is conducted to compare the proposed method with the baseline approach, confirming that the proposed approach significantly enhances the flexibility of scheduling strategies, ensures a balance between low-carbon emissions and economic efficiency, and ultimately improves the overall scheduling performance.

With the rapid rise of the electric vehicle market and its increasingly significant role in power systems, the clustering analysis of high-dimensional electric vehicle charging data has emerged as an important research direction in load forecasting, demand response, and intelligent dispatch. However, the classical K-Means algorithms often misclassify a large portion of data into “noisy clusters” in high-dimensional settings due to the limitations of the Euclidean distance metric. Thereby the interpretability and practical value of the clustering results are diminished. In this paper, the improved quantum K-Means algorithm is developed upon the principles of normalization and inner product similarity from quantum computing. By converting the Euclidean distance metric into a quantum similarity-based metric, the improved algorithm may capture the subtle differences among the data effectively. Take the electric vehicle charging dataset as an example, the results show that the improved quantum K-Means algorithm surpasses the traditional method in reducing the within-cluster sum of squares and enhancing the silhouette coefficient, which significantly improves the stability and discriminability of the clustering outcomes. This study provides a unique way to identify the electric vehicle charging behaviors accurately for the optimal dispatch of the power system. Additionally, it validates the potential of quantum clustering methods in high-dimensional data processing.

Model-free deep reinforcement learning has emerged as a promising method for addressing the scheduling challenges in integrated energy systems. However, uncertainty in system states continues to hinder optimization efforts. This paper proposes a hybrid framework that integrates deep learning prediction models with deep reinforcement learning scheduling models. Initially, Gaussian process regression is employed to extract interval information from stochastic variables, organizing the input data into structured time series segments. Subsequently, a hybrid prediction model, combining Transformer and long short-term memory networks, is constructed for multi-step interval prediction of these stochastic variables. Finally, a synchronized training mechanism couples the twin delayed deep deterministic policy gradient method with the hybrid prediction model, fully exploiting trends in system state changes to enhance scheduling performance. Experiments are conducted to validate the effectiveness of the proposed framework using open-source data to analyze the influence of different prediction methods on scheduling. Results show that the proposed approach improves overall performance by 22.4% compared to the baseline method

Multistage expansion planning for distribution networks (DMEP) must jointly address uncertainty and reliability, while coordinating the scheduling, siting, and scaling of diverse distributed energy resources (DER). To overcome the limitations of traditional long-term planning in adapting to dynamic operational demands, this paper proposes a bilevel planning model incorporating an adaptive short-term correction mechanism. In the upper level, probability distributions of source-load growth rates are modeled to determine long-term expansion schemes for substations, feeders, and distributed generations. In the lower level, a short-term correction mechanism is introduced to address source-load fluctuations and support flexible DER operation, enabling stageby-stage rolling deployment of energy storage (ES). Distinct from fixed phase boundaries, an adaptive phase partitioning strategy is developed to dynamically identify the optimal expansion timing, avoiding premature investment and excessive operational burdens. Furthermore, reliability constraints are incorporated into DMEP, with ES siting optimized through key node assessment and network expansion designed using a graph-theoretic approach, enhancing both nodal supply security and regional power balance. Case studies demonstrate that the proposed method effectively addresses source-load uncertainties and operational risks of vulnerable nodes and branches, reducing the total planning cost by 7.64 %. Validation on a 54-node system further confirms its scalability and practical value.

To achieve coordinated scheduling of economic efficiency and reliability in active distribution grids, this paper constructs an integrated optimization scheduling model that combines distributed power sources, load aggregators, and energy storage systems. The model introduces a load aggregator response mechanism and constructs a multi-objective optimization model based on two dimensions, operational reliability and economic efficiency, to minimize system volatility indicators and operating costs. To coordinate the conflicting relationships between multiple optimization objectives, the weighted Tchebycheff method is used to convert the multi-objective problem into a single-objective problem, and the differential evolution algorithm is introduced for global optimization. Simulation based on an IEEE-33 node distribution system, designing three typical scenarios to compare and analyze the optimization effects under different resource allocation strategies. The results show that the “source-load-storage” coordinated optimization strategy can effectively smooth the load curve, reduce system operating costs, improve renewable energy utilization and system regulation flexibility, and verify the practicality and robustness of the proposed method.

Extreme natural disasters can easily cause large-scale power outages in distribution networks (DN), and energy storage system (ESS) contributes to an essential part of integrated solutions to this problem owing to its flexible regulation and rapid response characteristics. A two-stage robust optimization model for ESS that considers the resilience enhancement of a DN under extreme weather conditions is proposed. First, the impacts of secondary hazards on the component failure rates were quantified, and a time-varying matrix of distribution line failures was constructed. Second, an overall recovery index of the DN and an important load recovery index were proposed. Finally, a two-stage robust optimization model for the ESS is established to improve DN resilience with the objective of minimizing the comprehensive economic cost of the ESS and the annual comprehensive weighted load loss, which is solved using the column-and-constraint generation algorithm (C&CG). Furthermore, numerous simulations were performed on the IEEE 33-node system, and it showed that the proposed method can not only ensure the optimal comprehensive economics of the ESS and fully tap the support potential of the ESS, but also maximize the resilience of the DN. Compared to the DN without energy storage system, the proposed method improves the overall resilience and important load recovery of the DN by about 15.9% and 4.3%, respectively.

After large-scale electric vehicles are connected to the power distribution network, the disorderly charging behavior of users with significant uncertainty seriously affects the power quality and stable operation of the local distribution grids.Due to the short running time and the lack of collection devices, it is difficult to obtain a large amount of historical data required for electric vehicles to access the distribution grids to optimize the dispatching operation, so the generation of large-scale electric vehicle charging data based on existing small samples has become an important research direction. In this paper, we propose a data sampling method based on the K-Means-GMM-MLP mixed model. Firstly, the historical charging data was transformed, and the starting charging time data was sampled by K-Means clustering and Gaussian Mixture Model (GMM), and then the potential relationship between the starting SOC, the end SOC, the total battery capacity and the starting charging time in the historical EV charging data was learned through the Multilayer Perceptron (MLP) to obtain the MLP model, and finally, the sampled starting charging time data was used to guide the trained MLP model to obtain the remaining EV charging information. This method can generate a large amount of data through small-sample single-dimensional data sampling and the learning and training of its potential relationship with multi-dimensional data. Experimental results show that the model has good convergence and can obtain a large number of sampling data that conform to the actual distribution law and retain the latent logical relationship between each dimension.

To address the uncertainties introduced by the integration of large-scale distributed generation （DG） and loads in flexible interconnected distribution networks， this paper proposes a multi-timescale optimal scheduling model incorporating multi-scenario analysis while considering the output characteristics of diverse resource types. First， multi-scenario analysis is employed to simulate and analyze the uncertainties associated with wind and PV power generation and load. For the day-ahead scheduling stage， a two-stage distributionally robust optimization （DRO） model is established， accounting for the charging/discharging characteristics of energy storage， the start-stop behavior of micro gas turbines， and the switching states of capacitor banks. For the intraday scheduling stage， a multi-objective rolling optimization （MORO） model is formulated. Subsequently， the 1-norm and ∞-norm are employed to identify the worst-case probability distribution， while the column-and-constraint generation （C&CG） algorithm and multi-objective particle swarm optimization （MOPSO） are applied to solve the day-ahead and intraday scheduling models， respectively. Finally， simulation tests on a modified IEEE 28-node test system demonstrate that the proposed scheduling model effectively reduces operational costs under extreme conditions while enhancing power supply reliability in flexible distribution networks.