Abstract:To address the high operating costs caused by response delays of flexible loads and differences among parks during the coordinated operation of multi-park microgrids, a low-carbon economic operation method considering the spatiotemporally asynchronous responses of flexible loads is proposed. Firstly, a coordinated operation model of a multi-park microgrid cluster is constructed, in which wind power, photovoltaic generation, energy storage, gas turbines, and flexible loads are incorporated. Secondly, flexible loads are classified into shiftable, transferable, and curtailable loads, and their actual response delays are characterized using stochastic variables. On this basis, a low-carbon economic optimization model is established by incorporating inter-park power exchange, electricity purchase and sale with the main grid, and carbon trading. Finally, the proposed method is evaluated through a case study involving three parks. It is demonstrated that renewable energy accommodation and inter-park energy sharing can be effectively improved, while electricity purchase costs, energy storage operating costs, and gas turbine operating costs can be reduced. Compared with the scheme in which the spatiotemporally asynchronous responses of flexible loads are not considered, the total operating cost is reduced by 8.24%, and carbon emissions are further decreased.

Abstract:Under the current carbon peaking and carbon neutrality goals, the high energy consumption and high emissions of copper smelting enterprises have become more pronounced. How to fully tap the potential of waste heat recovery in the production processes while leveraging the synergistic mechanism of the carbon market and green certificate market has gradually become a key factor in the low-carbon transformation of this industry. To address these problems, a low-carbon optimization strategy for copper smelting enterprises involving waste heat recovery and carbon-green certificate joint trading is proposed. First, the differential process regulation characteristics of typical copper smelting processes are classified, and a waste heat recovery model is constructed to fully exploit the energy-saving and carbon-reduction potential of waste heat resources; Second, full-process carbon emissions of copper smelting are quantified, integrating a stepped carbon trading mechanism with green certificate trading to establish a carbon-green certificate joint trading framework; Finally, a full-process low-carbon optimization strategy is formulated considering actual production constraints of enterprises. The results of the case study show that the proposed strategy can achieve synergistic optimization of economic and low-carbon benefits on the premise of ensuring production safety, providing a reference for the low-carbon transformation of energy-extensive industries.

Abstract:To address correlated source-load forecast errors and delayed reliability feedback for flexible load dispatch in high-penetration renewable distribution systems, a flexible load dispatch method embedding analytical reliability indices is proposed. Firstly, critical contingencies are screened using uniform design, and forecast-error correlations are represented through Cholesky decomposition. Analytical minimum-load-shedding functions are constructed by combining the modified stochastic response surface method with polynomial chaos expansion (mSRSM-PCE). Then, load shifting and shedding decisions are mapped to expected demand not supplied (EDNS), and a comprehensive model considering reliability risk, regulation cost, user comfort loss, and flexible-load capacity cost is formulated. Lastly, the method is validated using 50 source-load scenarios generated by a generative adversarial network. Compared with conventional PCE, mSRSM-PCE reduces the mean absolute error and root mean square error of EDNS by 30.38% and 11.82%, respectively, while limiting the absolute relative error of mean EDNS to 0.0329%. The recommended flexible-load capacity ratio is 8% under the baseline condition, reducing EDNS by 10.30%~80.52% across four typical scenarios. Sensitivity analyses confirm reliable performance under different objective weights and forecast-error correlations.

Abstract:A regional common market model featuring electricity-green certificate-carbon synergies is proposed for high-penetration renewable energy sending-end regions, where inelastic intra-provincial demand and lower regional market concentration lead to cost shifting. This model integrates power, green certificate, and carbon trading across inter-and intra-provincial markets, eliminating generators' speculative arbitrage arising from cost inversion under decoupled two-tier markets. A generalized Nash game is introduced to allocate the environmental value of green power, ensuring equitable benefit sharing across regions. Case studies demonstrate that this coordinated multi-energy and multi-rights trading mechanism enhances intra-provincial demand price elasticity, reduces system costs and carbon emissions, and significantly improves renewable energy utilization, offering a market-oriented and enduring solution for green power development.

Abstract:Against the backdrop of accelerating global carbon neutrality and increasingly complex urban decarbonization systems, existing urban energy models still have limitations in system-boundary representation and flexible carbon quota adjustment. A decarbonization pathway optimization method for metropolitan areas based on dynamic carbon quota allocation is proposed. First, multisource data are integrated, and the LEAP model is employed to simulate the evolution of urban energy demand and carbon emissions under different policy scenarios. Second, a government-industry Stackelberg game is formulated, in which the urban carbon emission constraint is embedded into the industry quota adjustment process, and the quota adjustment coefficient and optimal output are derived analytically. Third, emission-reduction cost perturbation scenarios are established to analyze changes in sectoral emission-reduction contributions, timing, and technology substitution intensity. Finally, a case study of Tianjin is conducted. The results show that the decarbonization pathways of key urban sectors can be identified by the proposed method. Compared with static allocation, the dynamic quota mechanism achieves greater emission reductions while balancing enterprise profit and government social welfare.

Abstract:In order to improve the photovoltaic capacity of microgrid and tap the flexible adjustment potential of load side new balance resources, a differentiated aggregation method based on improved isomorphic polytopes is proposed. The operation constraints of flexible resources such as energy storage, temperature-controlled loads and so on are uniformly represented as linear polytopes. After rule-based grouping, the inner approximation aggregation is realized through the scaling and translation of the benchmark isomorphic polytopes; According to the characteristics of photovoltaic high noon power generation, the time-differentiated weights are introduced to enhance the regulatory capability of core time periods, and the day ahead optimization model of microgrid with photovoltaic consumption as the guidance and minimum operation cost as the goal is constructed. The simulation results show that the proposed method can accurately characterize the aggregation flexibility, significantly improve the PV consumption level, stabilize the output fluctuation, and the scheduling feasibility and calculation efficiency meet the engineering requirements, providing an efficient solution for the large-scale load side resource support of new energy consumption in the microgrid.

Abstract:In recent years, a huge impact is caused on the active distribution network by the increasing number of new energy vehicles based on electric vehicles and hydrogen vehicles and the large-scale access of new energy. An economic dispatch method of active distribution network considering the low-carbon economic benefits of new energy vehicles is proposed. Firstly, the operation framework of active distribution network with new energy vehicle access is constructed, and the operation model of multi-energy conversion equipment is established. Secondly, considering the schedulability of the multi-energy load demand on the load side of the distribution network, a response model is established to assist the grid-connected consumption of new energy. Then, by introducing new energy vehicles to replace fuel vehicles to solve the problems of new energy output difficulties and environmental pollution in the active distribution network, a new energy vehicle participation model in the carbon-green certificate market transaction is constructed to improve the economic benefits of the active distribution network. Finally, taking the minimum daily average total operating cost of active distribution network as the goal, the optimal scheduling model is constructed. Through the comparative analysis of different schemes, the positive effect of the proposed method on improving the economy, new energy consumption and low-carbon operation of active distribution network is verified.

Abstract:In the face of global climate change challenges, the Dual Carbon goals are proposed by China, and the low-carbon operation of the energy system is urgently required. As a critical component of the power system, demand-side resources are recognized as playing a critical role in achieving these strategic objectives through optimized operation and flexible interaction. However, several issues are found in the current technical standards system, including a lack of essential supporting standards, incompatibility between existing standards and emerging technologies, and poor coordination among standards. The potential benefits of demand-side resources in the context of the Dual Carbon strategy are severely hindered by these challenges. To address these issues, a thorough analysis of the shortcomings of existing standards is conducted, and based on this, a comprehensive and well-coordinated standards system is developed. The primary goal is defined as providing detailed technical guidance and a clear direction for the standardization of the flexible and green interaction of demand-side resources under the Dual Carbon strategy, thereby effectively supporting the achievement of the Dual Carbon goals.

Abstract:Oscillation instability is frequently induced by grid-following (GFL) converters under weak grid conditions. However, system stability can be significantly enhanced by the integration of a certain proportion of grid-forming (GFM) converters within renewable energy stations. Consequently, the capacity ratio of GFM to GFL converters is regarded as a critical factor governing system stability. To address this issue, an analytical capacity allocation model based on the critical short-circuit ratio (CSCR) is proposed. Initially, positive-and negative-sequence impedance models for both GFL and GFM converters are established to facilitate the analysis of sequence impedance characteristics. Subsequently, a three-port equivalent model of the hybrid grid-connected system is constructed, and the dominant control parameters affecting the interactive stability are accurately identified through sensitivity analysis. Furthermore, by integrating the generalized Nyquist stability criterion with the CSCR boundary conditions, an analytical model for the minimum GFM integration ratio, which strictly ensures the small-signal stability of the system, is rigorously derived. Finally, the validity of the theoretical analysis is verified through transient simulations. As demonstrated by the results, the critical GFM capacity can be accurately calculated by the proposed analytical model, whereby the system's impedance crossover frequency is successfully driven rightward into the stable region. Through this configuration, the phase margin of the system is significantly improved, and the broadband interactive instability under weak grid conditions is effectively suppressed.

Abstract:To address the lack of a unified metric for heterogeneous reactive-power resources in weak renewable-energy grids and the absence of a voltage-security boundary when flexible resources are called for demand response, a coordinated configuration method is proposed. Transient voltage support, inertia contribution, and life-cycle cost are jointly considered. A multidimensional equivalent support-efficiency factor is first defined for a synchronous condenser (SC), grid-forming energy storage (GFMES), and a static var generator (SVG). The factor is then introduced as a virtual short-circuit-capacity weight to construct a generalized equivalent dynamic short-circuit ratio (GED-SCR). The sum of the corrected critical short-circuit ratio (CSCR) and a case-specific security margin is imposed as the strength constraint. Device locations and capacities are determined by a quantum genetic algorithm (QGA) and differential evolution (DE). A modified IEEE 33-bus equivalent system is employed to evaluate the configuration. The GED-SCR of the critical buses increases from approximately 1.1-1.6 to the range of 2.5-3.5; for Bus 25, the minimum voltage during the fault is significantly elevated, and the voltage recovery time is reduced to the millisecond scale.A security boundary for checking the available reactive-power capability of flexible demand-side resources before response activation is thereby provided;Demand-response scheduling itself is outside the scope of the configuration model.

Abstract:Considering the demand response mechanism and the dynamic coupling characteristics of multi loads in integrated energy systems, a multi load forecasting model based on an AGCN-TimeXer framework that incorporates integrated demand response is proposed. Firstly, drawing on consumer psychology theories, the electricity demand response signals are modeled and their uncertainty is quantified. Comprehensive demand response signals are then constructed by calculating cooling and heating load responses using the coupling response principle. Secondly, an adaptive graph convolutional network (AGCN) is employed to construct a dynamic adaptive graph structure, thereby facilitating the extraction of dynamic coupling relationships among multi loads. Finally, the self-attention mechanism of TimeXer is utilized to explore the temporal dependence characteristics of load data, and the cross-attention mechanism is adopted to analyze the relationships between multivariate loads and external influencing factors, including integrated demand response signals, dynamic coupling relationships among multivariate loads, and meteorological factors. Simulation results demonstrate that the proposed model significantly outperforms traditional forecasting methods in terms of prediction accuracy.

Abstract:Aiming at the multi-index collaborative energy dispatch problem of grid-connected wind, solar and storage microgrids under the condition of high proportion of renewable energy access, an optimal dispatch method based on deep deterministic policy gradient (DDPG) is proposed. First, a microgrid dispatching model consisting of wind power, photovoltaic, energy storage, user load and upper-level power grid is established; Second, a composite reward function is constructed that takes into account operating income, power balance, new energy consumption and energy storage operation constraints, and power imbalance, non-consumption of new energy and energy storage state-of-charge out-of-bounds are set as penalty items; Finally, DDPG is used to continuously adjust the energy storage charging and discharging power and grid interactive power, and adaptive attenuation noise is introduced to take into account early exploration of training and later stable convergence. The proposed method can significantly improve the training convergence speed and online decision-making efficiency while ensuring dispatching economy, and is suitable for microgrid energy dispatching under conditions of wind and solar output, load and electricity price fluctuations.

Abstract:As a typical flexible and adjustable resource, multiple constraints such as user comfort, policy norms, and market income need to be taken into account when air conditioning loads are utilized for power grid peak shaving. Therefore, the regulation preference and differen-tial compensation mechanism are introduced, and a multi-objective optimization decision-making method for peak shaving of air condition-ing clusters is proposed, in which the sensitivity of user regulation is considered. Firstly, an air conditioning cluster load model is construct-ed, where the characteristics of regulation sensitivity and the equivalent thermal parameter model are integrated. Then, the peak-shaving framework of air-conditioning clusters with both simulation accuracy and regulation duration is built: a differential compensation mecha-nism, in which temperature regulation amplitude and regulation duration are considered, is established in the economic dimension, and a quantitative characterization method of user satisfaction based on an S-function is proposed in the user behavior dimension. Finally, with the goal of maximizing the economic benefits of aggregators and achieving the highest customer satisfaction, an optimal decision-making model for peak shaving of air-conditioning clusters is established, which is solved by combining the Pareto frontier solution and the knee-point decision-making method. It is demonstrated by the simulation results that the profit of the aggregator's peak adjustment can be signifi-cantly improved by the proposed method under the premise that the user's comfort is ensured, and at the same time, the reasonable distribu-tion of the user-side subsidy is realized.

Abstract:The large-scale integration of electric vehicles (EVs) has caused significant spatial and temporal fluctuations in distribution network loads, posing challenges to power system operation security and charging infrastructure planning. Traditional time-series and statistical models fail to capture the influence of traffic behavior on the formation of charging loads. To address this issue, a hybrid EV charging load forecasting method combining traffic equilibrium theory and deep neural networks (DNN) is proposed. First, a traffic equilibrium model considering energy constraints is established to simulate traffic flow and charging behavior under different travel demand scenarios, thereby generating network operation data. Then, correlation analysis and SHAP methods are applied to identify key influencing features and determine the primary factors affecting charging loads. Finally, a multi-layer fully connected neural network is constructed to predict charging loads at multiple charging station nodes. It is demonstrated that the proposed model exhibits good convergence and stability, and that charging loads can be accurately forecast while the coupling relationship between traffic behavior and power demand is effectively revealed. Technical support is provided for charging infrastructure planning and the optimization of modern distribution systems.

Abstract:A distribution network reconstruction strategy is proposed to address the issues of high proportion of new energy integration into the power grid, increase network active power loss, and severe wind and solar power curtailment. First, to fully leverage the regulatory capacity of the load side, a price demand response model based on load classification is established; Second, considering the cost of active power loss, wind and light curtailment, as well as switch operation costs, a distribution network reconstruction model with the goal of minimizing energy loss cost is proposed. Then, as by processing the model, the possible effects of zero injection isolated nodes, branch disconnection, and convex processing of non-convex form problems are solved. The original model is transformed into a mixed integer second-order cone programming model; Finally, the effectiveness of the proposed method is verified through numerical analysis using an IEEE 33 node system with distributed power sources and energy storage.

Abstract:China's power spot market construction has been accelerated, with market operation support platforms preliminarily established in pilot regions. These platforms have been tailored to current needs by integrating existing regulations with the actual operational conditions of the power system, thereby laying a solid foundation for the long-term stable operation of the power trading market. As the national dual-carbon strategy deepens, the integration of high-proportion renewable energy has become a routine aspect of power dispatching, rendering existing technical support systems inadequate to meet future market development demands. Drawing on domestic and international research experiences and practical applications, the characteristics and operational challenges of the power trading market under high-proportion renewable energy integration have been thoroughly investigated. Comprehensive evaluations have been conducted across multiple aspects—including system model construction, clearing strategies, risk analysis, trading mechanism selection, and simulation—to identify key technical features and development pathways. Certain technical approaches have already been proven reliable through practical implementation, providing crucial support for the safe and efficient operation of the power spot market under high-proportion renewable energy scenarios.

Abstract:As a new energy infrastructure, the photovoltaic-storage integrated charging station (PSICS) not only enables orderly EV charging but also promotes the green economic transition of power systems. First, topological structure and operational mechanism of PSICS are systematically elaborated; Then, a PSICS-EV collaborative optimization framework based on Stackelberg game theory is established. By coordinating the multi-agent interest demands of distribution network operators, charging station operators, and EV users, a bi-level scheduling model considering multi-timescale characteristics is developed. A hybrid solution strategy combining heuristic algorithms with commercial solvers is employed for model resolution. The results show that the proposed model can effectively improve the operational economy of the PSICS, reduce the charging costs for EVs, and alleviate the power supply pressure on the distribution network.

Abstract:To quantitatively evaluate the comprehensive potential of a virtual power plant (VPP) in grid interaction, a multi-dimensional statistical indicator-based assessment method is proposed for VPPs under an electricity market-oriented environment. First, a hierarchical VPP control architecture consisting of the resource layer, user layer, and operation layer is established, within which key components—such as renewable energy generation, thermostatically controlled loads, and distributed energy storage systems—are modeled in a detailed manner. On this basis, a VPP optimal scheduling model is formulated with the objective of minimizing total operating cost, and a distributed solution framework based on the alternating direction method of multipliers (ADMM) is adopted to solve the problem. Furthermore, a multi-dimensional statistical evaluation framework for VPP is established from six dimensions, namely regulation capacity, ramp rate, response time, duration, operating cost, and response reliability. Simulation case studies demonstrate that the proposed evaluation system can effectively quantify the regulation capability, response performance, and operational economics of VPP under different dispatching requirements, thereby providing theoretical support and methodological guidance for the precise participation of VPP in grid interaction and the assessment of its aggregated value.