An urban-level 5G communication network composed of densely distributed 5G base stations (BSs) can provide significant flexibility to support power grid operations. Although existing research has undertaken preliminary explorations, such approaches heavily rely on privacy-involved communication traffic data to model the behavior of 5G BSs. It renders them unsuitable for dispatching 5G BS mega-clusters, thereby failing to enable flexible interaction between urban-level communication networks and power grids. To this end, this paper proposed a novel insight to form synergic operation of 5G network and distribution network with limited information. Firstly, the communication traffic condition of urban 5G network is estimated by applying crowd heatmaps derived from public database. Secondly, by integrating the dynamic sleeping strategy of 5G BSs considering spatial-temporally coupling characteristics, the urban 5G network operation optimization model is constructed to accurately capture operational flexibility. Thirdly, a novel synergic operation framework is formulated, which adopts the distribution location marginal price capable of sensitively reflecting the nodal differentiated operation requirements of the distribution network to spontaneously guide the orderly operation of 5G networks. Case studies validate the efficiency of the proposed method in leveraging the flexibility of urban 5G network to improve the economy and stability of the distribution network.

Performing fast and accurate feasible region characterization (FRC) for aggregated distributed energy resources (DERs) is essential for real-time operational dispatching. Affected by the temporal coupling and the uncertainty associated with DERs, the aggregated feasible regions exhibit high-dimensional coupling and strong stochasticity. However, existing methods cannot quickly perform accurate FRC of the entire operational domain, resulting in preventing real-time updating of FRC based on rolling forecasts of uncertain sources and loads. To this end, an incremental projection-based online full-time-domain FRC method is proposed in this paper, which requires only a local projection of the forecasts to achieve fast and accurate FRC. First, the reversible decoupling equivalent reformulation is developed to decompose the original high-dimensional FRC into several sliced low-dimensional FRCs, while the temporal coupling feature is retained. Then, to shorten the computation time, a universal incremental projection paradigm is constructed, in which the whole FRC is quickly updated by replacing local projections in the complete FRC results obtained offline. Statistical analysis validates that the proposed method can reduce the computation time in the order of 0.55% compared to that of the existing techniques without losing accuracy.

The involvement of multiple control strategies in Voltage-Source-Converter-based multi-terminal DC system (VSC-MTDC) makes its power flow formulas highly nonlinear and multivariate quadratic, exacerbating the difficulty of fundamental power flow analysis. This paper presents a generic power flow method and a corresponding convergence analysis theorem. This method possesses the ability to acquire feasible high-voltage solutions without imposing conservative pre-conditions. First, considering various control strategies for VSC-MTDC, an improved fixed-point type method with a compact and simple iterative structure is developed. Second, a post-condition convergence analysis methodology is constructed, which describes the comprehensive convergent properties of the proposed method associated with a complete DC power flow solvability region. Third, a rigorous analytical proof of the post-condition convergence analysis for the proposed method is conducted, validating the reliability and robustness of the proposed method in obtaining feasible power flow solutions. Computational findings in numerical tests further validate the versatility and accuracy of the proposed method.

The demand response (DR) market, as a vital complement to the electricity spot market, plays a key role in evoking user-side regulation capability to mitigate system-level supply-demand imbalances during extreme events. While the DR market offers the load aggregator (LA) additional profitable opportunities beyond the electricity spot market, it also introduces new trading risks due to the significant uncertainty in users' behaviors. Dispatching energy storage systems (ESSs) is an effective means to enhance the risk management capabilities of LAs; however, coordinating ESS operations with dual-market trading strategies remains an urgent challenge. To this end, this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market, which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market. First, the intrinsic coupling characteristics of the LA participating in the dual market are analyzed, and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed. Second, an uncertain user response model is developed based on price-response mechanisms, and actual market settlement rules accounting for under- and over-responses are employed to calculate trading revenues, where possible revenue losses are quantified via conditional value at risk. Third, by imposing these terms and the capacity allocation mechanism of ESS, the risk-aware stochastic coordinated trading model of the LA is built, where the bidding and pricing strategies in the dual model that trade off risk and profit are derived. The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.

Targeting the problem whereby electric vehicle charging loads have large temporal randomness, which affects the accuracy of load prediction, an electric vehicle charging load prediction method based on an improved long short-term memory (LSTM) neural network is investigated. The similarity of EV charging load curves is calculated and the data related to EV charging loads are clustered according to the similarity using a spectral clustering algorithm. The principal component analysis method is used to extract the principal components from the clustering results of EV load data. The LSTM neural network takes the main components of EV charging load as inputs, updates the state of the storage unit through the activation function, introduces an attention mechanism to improve the structure of the network, and outputs the prediction results of the EV charging load through the operation of the input gate, forgetting gate, and output gate. The experimental results show that this method can accurately predict the hourly and daily charging loads of electric vehicles and provide support for their orderly charging of electric vehicles.

Employing vast and heterogeneous distributed energy resources (DERs) is crucial for modern distribution systems (MDS). However, existing day-ahead and real-time top-down dispatch methods hardly accommodates long-duration energy storage needs with cross-day dispatch cycles, and fail to use the regulation capabilities of DERs owned by different entities. To this end, a novel multi-spatio-temporal scale coordinated MDS dispatch method is proposed, where the zonal management and the multi-temporal rolling correction with week horizon are utilized to manage DERs locally. First, in spatial dimension, a decentralized management framework based on zonal balancing and coordinated autonomy is proposed, where a self-adaptive zone division method and a hierarchical interactive framework are formed. Second, in temporal dimension, a rolling correction with week horizon mechanism is established, which uses weekly rolling electricity optimization and day-ahead-to-real-time decentralized power scheduling to utilize DERs with differentiated flexibility features. Third, an improved alternating direction multipliers method (ADMM) based solution algorithm is utilized to implement the proposed MDS dispatch method in a practical way. Case study validates the efficiency of the proposed method in improving the local penetration of renewable energy and ensuring operational economy.

With the increasing integration of renewable energy into distribution system, the issue of renewable energy curtailment has become increasingly prominent. Forming a multi-energy complementary and multi-storage coordinated multi-microgrid system is advantageous for the on-site consumption of renewable energy. In this paper, a multi-time scale distributed scheduling strategy is proposed for a multi-microgrid system incorporating wind, solar, hydro, hydrogen and storage, considering source-load uncertainties. Firstly, an energy management framework is established, which achieves global optimization goals while protecting the privacy within microgrids, considering microgrid topology. Secondly, considering the differentiated regulation characteristics of different resources, a scheduling strategy is developed based on rolling optimization, encompassing monthly, weekly, and daily time scales. This strategy effectively utilizes the electric power or energy regulation capabilities of different resources at different time scales. Thirdly, a three-stage electric power and energy balance model is established based on fuzzy chance constraints, and solved by using a variable step-size alternating multiplier method. Finally, a cost allocation method between microgrids and distribution system is achieved based on their respective contributions to the power and energy balance. Case study results demonstrate that the proposed method exhibits good adaptability across different time scales, effectively enhancing the renewable energy absorption capacity.

In order to compensate for the lack of specific quantification methods and processes for the capacity value of hybrid energy storage in existing studies, and the inability to consider the marginal investment benefit of the load supply capacity value of energy storage, this paper proposes a method for evaluating the load supply capacity value of energy storage that considers t he system demand and the capacity confidence level. Firstly, an optimal dispatch model of energy storage based on peak load reduction is established, and a framework for calculating the capacity credit of energy storage considering multiple time sca les is proposed based on the optimization model; secondly, based on the relationship between the load supply capacity value of energy storage and the system capacity adequacy, a calculation method for the marginal benefit of energy storage considering the system adequacy and an assessment framework for the load supply capacity value of energy storage are proposed; and finally, the effectiveness of the methodology is verified by an analysis of the examples proposed in this paper.

With the transformation of the energy structure, distributed photovoltaic (PV) power generation has become increasingly important. However, due to uncertain factors such as weather, equipment, and load demand, the consumption problem is prominent, which restricts the healthy development of the system. It is important to accurately measure the absorptive capacity of distributed PVs, but there are many shortcomings in existing research methods. This paper proposes a comprehensive measurement method to solve this problem and thus conducts a comprehensive metric study of the distributed PV Consumption Capacity considering multiple uncertainties. Based on the output uncertainty and load uncertainty of the distributed PV power generation, a mathematical model of the distributed PV power generation uncertainty is constructed. Based on the distributed PV operation data under various uncertain factors, considering the PV capacity and active power loss connected to the distribution network as objective functions, and setting constraints such as power balance, node voltage, line power flow, and distributed PV output, a comprehensive measurement model of the distributed PV absorption capacity is constructed. A local chaotic search is introduced to improve the firefly algorithm, and the improved firefly algorithm is used to solve the comprehensive measurement model and output the comprehensive measurement results of the absorption capacity. The experimental results show that this method can effectively evaluate the absorptive capacity. In a typical IEEE 32 - node distribution network, the network loss is 30 kW when PV access reaches 534 kW. This method is better than other methods in terms of maximum absorptive capacity, annual PV absorption, and annual network loss, and provides a scientific basis for the planning, operation, and management of distributed PV systems.

As the penetration of renewable energy in power systems continues to increase, the reduction of traditional rotational inertia poses significant challenges to frequency stability. This paper proposes a virtual inertia optimization method for Virtual Power Plants, based on node Rate of Change of Frequency constraints to contribute to the frequency stability of the power system. A bi-level optimization model is developed, considering the frequency dynamic response of both generator and load nodes. The upper-level problem aims at minimizing the VPP operator's cost while the lower-level problem aims at maximizing DERs profit under given price conditions The proposed method is validated through case studies on the IEEE 9-bus 3-generator system and solved by combined Particle Swarm Optimization and Gurobi solver. Results demonstrate that the method effectively enhances frequency support capability and achieves coordinated optimization of economic efficiency and system stability. This research provides theoretical insights and practical guidance for inertia allocation in highrenewable penetration power systems.

Electric vehicles (EVs) are regarded as potential frequency regulation resources owing to their quick response characteristics and low standby costs. The electric vehicle aggregator (EVA) can profit from the regulation market by orderly aggregating the regulation potential of EVs while meeting charging demand. However, existing EVA bidding strategies mostly ignore the non-performance risk due to the uncertainty of EV behavior and frequency regulation signals, which not only results in the EVA failing to deliver the expected profit, but may even face penalties. To this end, a novel risk-averse bidding framework for an EVA coordinating the regulation potential of EVs and energy storage (ES) to participate in the regulation market is proposed. Firstly, the capacity allocation of ES is considered in the bidding process. A portion of ES capacity is used to mitigate the uncertainty of EVs' available regulation capacity upon regulation deployment, and the remaining spare capacity of ES will be leveraged by EVA to participate in the regulation market to obtain extra benefit. Secondly, capacity reservation determined by the maximum energy deviation caused by the awarded regulation capacity per megawatt is also embedded in the bidding process, which avoids EVs and ES from failing to continue following regulation signals due to state-of-charging (SOC) limit violations. Finally, the proposed formulation can be converted into a convex mixed-integer linear program problem, which can be easily solved by the commercial solvers. Numerical results verify the effectiveness of the proposed method in improving EVA profit and preventing not being able to fully follow the regulation signals.

The Interline DC Power Flow Controller (IDCPFC) represents a significant advancement in power distribution and control within Multi-Terminal High Voltage DC (MT-HVDC) systems. Traditional IDCPFC control without external voltage support suffers from coupling issues due to coupled dynamics, which limits transient response in multi-legged converter-based IDCPFC systems. This paper introduces a novel approach that employs vector-based decomposition to address this issue by decoupling current regulation from DC-link voltage regulation, thereby efficiently managing DC power flow across multiple transmission lines in the grid. Furthermore, the concept of zero-sequence control for IDCPFC is introduced. The incorporation of the zero-sequence algorithm increases modulation depth by up to 25%, enhancing DC link voltage utilization and improving system efficiency by reducing switching losses by up to 12%. The proposed method undergoes a detailed analysis encompassing network topology, operational principles, and decoupled control strategies. Simulations and experimental results validate the method’s effectiveness under various grid scenarios.

Implementing coordinated dispatching for distribution system operator (DSO) and microgrids (MGs) is an effective approach to enhancing the utilization of distributed energy resources (DERs) and promoting local consumption of renewable energy. However, existing centralized optimization methods face privacy protection issues, making practical application challenging. Iterative distributed optimization methods suffer from low computational efficiency and convergence issues, while current non-iterative algorithms struggle to handle high-dimensional operational regions with time-coupling constraints. To this end, a novel non-iterative coordinated optimal dispatching method of distribution system and MG considering time-coupling constraints is proposed, effectively incorporating the time-coupling characteristics of the operational region. First, a coordinated dispatching model for distribution system and MGs that accounts for MG operational costs is constructed based on a non-iterative distributed coordination framework. Then, the original high-dimensional operational region is decoupled into lower-dimensional regions through a priority decision for the MG, with the progressive vertex enumerations (PVE) algorithm employed to solve the operational region. The case study results validate the effectiveness and feasibility of the proposed method. The proposed non-iterative algorithm achieves a computational time of merely 1.28% of that required by iterative algorithms.

Developing a coordinated optimal dispatch (COD) strategy for distribution systems (DS) and accessed microgrids (MGs) is one of the solutions to enhance the flexible operation of power systems. Under the premise of the operational autonomy of MGs, existing COD studies can be classified into iterative methods that allow for independent decision-making in DS and MGs, and non-iterative methods based on the operational region (OR) modeling of MGs. However, the iterative methods hardly guarantee computational convergence and efficiency, while non-iterative methods struggle to solve equivalent projections with high-dimensional ORs. To this end, a fast approximation progressive vertex enumerations (FAPVE) algorithm-based noniterative COD approach is proposed, where the high-dimensional OR considering time coupling characteristics can be formed effectively. First, a reliable outer normal vectors calculation method along with a vertices and normal vectors count limiting mechanism are introduced to enhance the computational efficiency and stability of the FAPVE algorithm. Second, a timedecoupling method for day-ahead MGs OR based on initial MG decisions is presented to ensure the applicability of the proposed algorithm to day-ahead COD problem. Statistical analysis verifies the computational efficiency and accuracy of the FAPVE algorithm, and demonstrates the effectiveness of the proposed time-decoupling method based on non-iterative framework.

Accurate estimation of the charging demand of electric vehicle (EV) users has a profound impact on the profitability of the EV aggregator (EVA). Existing studies concerning the market strategy of EVA normally assume that EV users are equipped with complete rationality and simplify the real interactive process among EVAs and EV users, making the derived strategy unable to adapt to the practical situations. To address this bottleneck, a novel competitive bidding and pricing strategy for EVA considering both the bounded rationality of EV users and various game relationships among multiple agents is proposed. Firstly, the evolutionary game theory is utilized to simulate the boundedly rational charging behaviors of EV users under differentiated charging prices, while their demand elasticity and uncertainty of driving pattern is also taken into account. Secondly, the complex interactions among EVAs and EV users are modeled, where the competition between EVAs is constructed by a non-cooperative game with incomplete information and the interplay between EVAs and EV users is built as a Stackelberg game. Finally, numerical results validate the effectiveness of the proposed method and demonstrate the pricing strategy preferences of EVAs based on the diverse price elastic coefficients of EV users.

The increasing penetration of renewable energy in the electricity market suppresses marginal prices, posing profitability challenges for wind power producers. To address this, effective medium-to-long-term (MLT) rolling transactions can hedge against spot market price risks and improve profitability. However, conventional bidding approaches often fail to capture the intricate uncertainties associated with wind generation and trading dynamics over extended periods. This paper introduces a bi-level multi-agent deep reinforcement learning (DRL) approach specifically designed for optimizing wind energy MLT rolling transactions. The proposed method innovatively integrates the Black-Scholes model with the Hamiltonian function to structure an optimal decision-making framework that balances short-term bidding efficiency with long-term strategic positioning. By separately optimizing transaction quantities and prices, the model prevents conflicts between these variables and ensures more accurate and effective decision-making. Additionally, the approach leverages advanced spatiotemporal modeling capabilities through the TimesNet-Latent-GNN framework, enabling it to capture complex market dependencies and achieve superior performance in managing price risks and maximizing profitability. Validation using real-world transaction data from the Shanxi electricity market demonstrates that the proposed method significantly outperforms traditional risk-averse strategies in terms of profitability and risk mitigation.

To enhance the computability of scheduling issue for the distribution system integrated with multiple microgrids (MG), existing studies have proposed compressed projection methods to dimensionally reduce the feasible domain of massive distributed energy resources (DER). However, these studies generally ignored the MG’s decision-making preferences, leading to ineffective application of DERs in MG. In this study, a novel feasible domain projection method considering MG’s decision-making preferences is proposed. First, we propose a scheduling prioritization classification principle to classify DERs within a MG into internal and redundant resources by determining whether the Distribution System Operator (DSO) is available for direct scheduling. Among them, internal resources are prioritized to ensure MG’s resource utilization rate, and redundant resources are utilized for additional economic benefits. Secondly, we employed the progressive vertex enumeration algorithm with MGs’ decision-making process to solve the feasible domain of internal resources, and, the feasible domain of redundant resources is represented by the virtual battery model to address the high-dimensional complexity of the feasible domain morphology caused by several temporally coupled constraints. Finally, the total feasible domain for MG is the sum of two feasible domains. The results indicate that proposed method can effectively aggregate DERs while taking MGs’ decision preferences into account.

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model’s adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method.

With the proliferation of electric vehicles (EVs), the EV aggregator (EVA) operating multiple EV charging stations, has emerged to provide charging services to EV users by setting differentiated charging prices to respond to time-varying electricity prices. Existing studies on the profitable strategy of EVA mostly focus on the formulation of bidding or pricing schemes or the optimization of single time-period coordination, failing to fully combine these sequential strategies and exploit the spatial-temporal shifting characteristics of EVs. To address this bottleneck, a novel multi-period joint bidding and pricing strategy for EVA considering the interactions with distribution system operator (DSO) and EV users is formulated. Firstly, a stochastic bi-level bidding and pricing joint optimization model is established, where the market clearing process considering baseline load uncertainties is simulated at the lower level. Secondly, to estimate the dynamic charging behaviors of EV users under differentiated charging prices, a robust semi-dynamic traffic assignment (SDTA) model is constructed to derive EV charging loads considering their coupling effect under traffic restrictions. Finally, an iterative method based on fixed-point theory is designed to obtain the optimal bidding and pricing strategy of EVA, which derives the equilibrium solution by sequentially solving the stochastic bi-level bidding and pricing subproblem and the robust SDTA subproblem. Numerical results verify the effectiveness of the proposed method in obtaining a profitable and stable strategy of EVA when embedded with the practical decision-making process of DSO and EV users.

The power flow (PF) calculation for AC/DC hybrid systems based on voltage source converter (VSC) plays a crucial role in the operational analysis of the new energy system. The fast and flexible holomorphic embedding (FFHE) PF method, with its non-iterative format founded on complex analysis theory, exhibits superior numerical performance compared with traditional iterative methods. This paper aims to extend the FF-HE method to the PF problem in the VSC-based AC/DC hybrid system. To form the AC/DC FFHE PF method, an AC/DC FF-HE model with its solution scheme and a sequential AC/DC PF calculation framework are proposed. The AC/DC FFHE model is established with a more flexible form to incorporate multiple control strategies of VSC while preserving the constructive and deterministic properties of original FFHE to reliably obtain operable AC/DC solutions from various initializations. A solution scheme for the proposed model is provided with specific recursive solution processes and accelerated Padé Approximant. To achieve the overall convergence of AC/DC PF, the AC/DC FFHE model is integrated into the sequential calculation framework with well-designed data exchange and control mode switching mechanisms. The proposed method demonstrates significant efficiency improvements, especially in handling scenarios involving control mode switching and multiple recalculations. In numerical tests, the superiority of the proposed method is confirmed through comparisons of accuracy and efficiency with existing methods, as well as the impact analyses of different initializations.