With the increasing penetration of wind power, enhancing the renewable energy accommodation rate and reducing the carbon footprint of the IES, this study proposes a comprehensive evaluation method to assess the impact of a novel dynamic Green Certificate Trading (GCT) and Green Hydrogen Certificate Trading (GHCT) joint mechanism. First, considering the integration of the IES into the carbon trading market, a coupled dynamic GCT-GHCT framework is established. This framework links dynamic green electricity certificate revenues with green hydrogen certificate revenues, leveraging cross-subsidization to incentivize renewable energy consumption. Subsequently, an optimal operation model for the IES is formulated with the objective of minimizing comprehensive costs, which encompass energy procurement, green certificates, carbon trading, and wind curtailment penalties. A piecewise linearization approach is applied to transform the optimization model into a Mixed-Integer Linear Programming problem for efficient solving. Furthermore, based on the dispatch results, a multidimensional evaluation index system is constructed, extracting key indicators from economic, technical, and environmental perspectives. To ensure the rationality of the evaluation, a dynamic reward-penalty asymmetric cloud matter-element (ACME) comprehensive evaluation method based on game theory combinatorial weighting is introduced to calculate the index weights and the final comprehensive evaluation value. Finally, multi-scenario simulations are conducted to verify the superiority of the integrated GCT-GHCT trading framework. The results reveal that the proposed approach not only maximizes renewable energy integration but also provides a robust decision-making tool for the low-carbon transition of multi-energy systems.

The five-level switched-capacitor hybrid clamped converter (5L-SCHC) exhibits lower low-frequency voltage ripple in DC-link capacitors and voltages passive self-balancing of switched-capacitors (SCs), offering clear advantages over other multilevel clamped converters. However, the discrete output characteristics of the 5L-SCHC circuit make it difficult to maintain capacitor voltages balancing with traditional modulation methods, leading to suboptimal harmonic performance. To address this issue, this paper introduces an improved carrier redistribution pulse width modulation (ICR-PWM). Through carrier redistribution and variable reference modulation voltage strategy, the optimal distribution of output voltage pulses is achieved within two carrier cycles, thereby enhancing the output performance of the 5L-SCHC converter. On this basis, duty cycle modification is applied to balance the DC-link center capacitor voltage in the first carrier cycle, while the SC voltages are balanced in the other one. Additionally, the upper and lower DC-link capacitor voltages are balanced over the two carrier cycles via optimal zero-sequence voltage (ZSV) injection. Finally, validation is conducted on the experimental platform, experimental results validate that the proposed ICR-PWM method ensures capacitor voltages balance under various conditions, exhibiting remarkably improved harmonic performance over traditional methods.

The hybrid clamped converter cannot suppress low-frequency voltage fluctuations of the dc-link capacitors at low power factors. Rather, it exhibits a slow dynamic response and high output harmonics in such scenarios. To address this limitation, this study presents a novel hybrid modulation method that maintains the natural capacitor voltage balance while combining two basic modulation strategies: one minimizes neutral-point (NP) current to suppress low-frequency fluctuations, and the other maximizes NP current to enhance the dynamic response. The method further bolsters harmonic performance through carrier redistribution. Then, the proposed method dynamically switches between two basic modulation methods based on real-time NP current optimization while calculating the required zero-sequence voltage for active voltage balancing. The proposed hybrid modulation method thus inherits the merits of these basic modulations. Finally, the method is validated under various experimental conditions, with the results highlighting its advantages.

The virtual synchronous compensator (VSCOM) integrates virtual synchronous machine control within a static var generator (SVG), providing active voltage support and improving the adaptability of SVGs to weak grid conditions. However, the interaction between VSCOM, which adopts grid-forming control, and renewable energy grid-connected converters (REGC) based on grid-following control introduces complex transient stability characteristics. This study investigates the effect of VSCOM on the transient synchronous stability of REGC under large grid disturbances. First, a constant voltage current-limiting control strategy for VSCOM is proposed based on its operational characteristics. A mathematical model is then established to assess the enhancement of the static stability limit of REGC by VSCOM. Subsequently, a transient model of the coupled VSCOM-REGC system is developed, considering the short-circuit ratio (SCR), control parameters, and reactive power capacity, to clarify the mechanism by which VSCOM affects the transient synchronous stability of REGC. Finally, an electromagnetic transient simulation model is built using MATLAB/Simulink to verify the theoretical analysis.

Carrier-based modulation strategies with natural capacitor voltage balancing characteristics are widely employed in five-level stacked multicell (SM) converters, owing to their straightforward implementation and reduced capacitor requirements. However, active voltage balancing of DC-link capacitors usually requires zero-sequence voltage (ZSV) or harmonic voltage injection, which introduces significant common-mode voltage (CMV) and degrades system performance. To overcome this limitation, this article proposes a variable reference voltage pulsewidth modulation (PWM) (VRV-PWM) strategy. The proposed method leverages a novel neutral-point (NP) current expression, derived from the SM converter's switching states, to regulate the NP current via duty cycle adjustment, thereby operating without ZSV or harmonic injection. Consequently, this approach maintains an average phase voltage free of low-frequency components while achieving zero average CMV, which significantly suppresses the instantaneous CMV and enhances the output phase voltage performance. An optimal NP current regulation scheme is further derived to achieve independent and decoupled voltage control across all capacitors. Theoretical analysis shows that the proposed strategy complements traditional methods in NP voltage balancing, effectively overcoming their drawback in suppressing low-frequency voltage fluctuations under low power factor conditions. Experimental results confirm the effectiveness of the proposed VRV-PWM, demonstrating its superior performance in CMV reduction and output phase voltage harmonic quality.

With the rapid growth of distributed energy resources (DERs), electrical vehicles (EVs), and energy storage (ES), electricity consumers are transitioning into prosumers. This paper proposes a distributed coordination and value allocation framework for multi-owner heterogeneous resources, addressing key challenges of conflicting interests, poor interoperability, and low utilization. A sharing economy-based mechanism is introduced for peer-to-peer surplus energy exchange without dedicated infrastructure. The mechanism operates through three stages: bidding, winner determination, and settlement. To ensure truthfulness, winner determination incorporates flexible “AND/OR” bid combinations across multiple periods. The Vickrey-Clarke-Groves (VCG) mechanism is further applied in non-trading periods to evaluate individual contributions. This integrated design maximizes social welfare and promotes a growing energy-sharing ecosystem. Numerical experiments based on the IEEE15-bus system and comprehensive performance for funding settlement, individual rationality, budget balance comparison are conducted. The proposed method effectively addresses the dynamic continuity constraints of multi-entity systems, which are neglected by traditional methods. Evaluations show that “OR” and “AND” bidding enhance social welfare/winning rate by 22.3%/5% and the total transferred funds by 146%, respectively.

Tourism-oriented island microgrids face significant operational challenges due to pronounced seasonal fluctuations in electricity, heating, and cooling demands. To address this issue, this paper proposes a unified seasonal dispatch framework that integrates wind power, wave energy, combined cooling, heating, and power units, electric boilers, absorption and electric chillers, and a tri-layer storage system (battery, thermal, and ice). A mixed-integer linear programming model is developed to optimize 24-hour multi-energy flows by coordinating thermal-electric coupling and storage operations. In winter, the strategy prioritizes cascaded waste heat recovery and thermal energy storage to stabilize the heating supply. In summer, it leverages time-of-use pricing and an ice storage system for cooling peak shaving. Validated on a representative Chinese tourist island, the proposed strategy achieves primary energy efficiencies of 81.7% in winter and 88.0% in summer. Compared to conventional baseline modes, it reduces daily operating costs by 38.9% (to 1,591.99 CNY) in winter and by 44.0% (to 2,338.57 CNY) in summer, while simultaneously lowering carbon emissions. These results demonstrate that the coordinated seasonal multi-energy dispatch effectively enhances economic performance, energy utilization, and environmental sustainability for island microgrids.

This paper proposes a method based on interval linear robust optimization to address the potential impacts of multiple uncertainties on the operational security of Regional Integrated Energy Systems (RIESs). The model considers the uncertainty in user loads and renewable energy outputs and determines the value ranges of related parameters through statistical analysis to characterize the boundaries of these uncertainties. To transform the stochastic disturbances into a solvable problem, the model introduces energy balance constraints under the worst-case scenario, ensuring that the system remains feasible under extreme conditions. The research framework integrates Nash bargaining theory, demand response mechanisms, and tiered carbon trading policies, constructing a cooperative game model for RIESs to minimize the overall operation cost of the alliance while providing a reasonable revenue distribution scheme. This approach aims to achieve fairness and sustainability in regional cooperation. Simulation results show that the method can effectively reduce the collaborative operation cost and improve the fairness of revenue distribution. To address potential issues of information misreporting and dishonesty in real-world scenarios, the model introduces an adjustable fraud factor in the revenue distribution process to characterize the strategy deviations of participants. Even under potential fraud risks, the mechanism can maintain an optimal revenue structure and lead the participants toward a stable fraud equilibrium, thereby enhancing the robustness and reliability of the overall collaboration.

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios.

High photovoltaic penetration subjects proton exchange membrane (PEM) electrolyzers in PV–battery–hydrogen microgrids to frequent load changes and prolonged low-load operation, accelerating degradation and increasing long-term operating expenditure. Conventional two-stage planning decouples capacity sizing from intraday dispatch and cannot distinguish degradation among candidate capacity solutions. This study develops a bi-level capacity–dispatch optimization model with an explicit electrolyzer degradation constraint. The lower level expresses start–stop, ramping-induced, and low-load degradation on a common daily basis and minimizes operating cost together with an equivalent degradation cost. Condition-specific daily degradation indices are annualized using the corresponding equivalent days, and the resulting annualized degradation index serves as an upper-level feasibility metric. The upper level minimizes annualized total cost, PV curtailment, and carbon emissions using a modified NSGA-III; entropy-weighted AHP–TOPSIS subsequently ranks the feasible nondominated solutions. Across 30 independent runs, the modified NSGA-III reduces IGD+ by 20.9% and increases the feasible-solution ratio by 10.2% relative to standard NSGA-III. Compared with conventional two-stage planning, the recommended capacity solution reduces the annualized degradation index by 27.7%. The daily degradation index decreases by 26.3%, 28.3%, and 27.5% under the summer, transition-season, and winter conditions, respectively. Under the high-fluctuation condition, the daily degradation index and electrolyzer output fluctuation rate decrease by 35.1% and 47.5%. The model therefore incorporates operating-induced degradation directly into capacity screening while retaining explicit cost, curtailment, and emission trade-offs.

Photovoltaic-storage direct current (DC) flexible (PEDF) systems are susceptible to DC bus voltage disturbances, with the constant power load (CPL) characteristics further exacerbating the risk of system instability. To address these challenges, a collaborative control scheme integrating distributed consensus and demand-side response (DSR) based on a consensus protocol is proposed in this study. A fully distributed control architecture is constructed, wherein the upper layer achieves power coordination through voltage deviation of parallel DC/DC converters and neighborhood interaction, whilst the lower layer dynamically optimizes inter-unit power allocation via the DSR mechanism. Distributed state estimation (DSE) is incorporated to enhance voltage control accuracy. Simulations conducted in the MATLAB (R2022a)/Simulink environment demonstrate that the proposed strategy enables rapid stabilization of bus voltage under load step changes and photovoltaic fluctuation scenarios, with system disturbance rejection capability being effectively enhanced. The effectiveness of the approach in maintaining stable system operation and optimizing power distribution is validated. The results indicate that the voltage deviation of the PEDF system remains below 2% under compound disturbances, with the steady-state error being controlled within 2%. The proposed control strategy, through the integration of the power DSR mechanism, effectively improves the system's anti-disturbance capability. Compared with conventional droop control methods, which typically result in voltage deviations of 3-5%, the proposed strategy achieves a reduction in voltage deviation of over 50%, demonstrating superior voltage regulation performance.

The rapid growth of renewable energy and the inherent volatility of wind power grid integration have imposed stringent requirements on power system security and economic operation. To address this challenge, energy storage systems (ESSs) are widely adopted as flexible regulation tools; however, their high capital costs make the shared energy storage model a more efficient and viable solution. This paper proposes an optimal configuration model for wind farms participating in shared energy storage (SES) based on cooperative game theory. First, integrating wind power output forecasting data and market electricity price information, a wind-storage combined optimization model accounting for wind power uncertainty is first established. Subsequently, a core pricing strategy integrating the core allocation rule with the Vickrey-Clarke-Groves (VCG) auction mechanism is proposed to realize the fair allocation of energy storage resources and effective revenue incentives. Finally, comparative experiments between the proposed core pricing mechanism and the fixed pricing mechanism verify its superiority in terms of social welfare, budget balance, and allocation fairness. The results demonstrate that the proposed mechanism not only enhances the overall social benefits of the wind-storage system but also effectively ensures the incentive compatibility of all participants and the stability of the alliance, providing feasible theoretical and methodological support for the economic dispatch of wind-farm-shared energy storage.

In order to activate the flexible carbon reduction potential of various links in the power generation and consumption sides,while maintaining the original planning of the traditional park,existing resources are integrated and planned for renovation.Firstly,the life cycle assessment(LCA)method is used to measure the carbon emis-sions and absorption processes of the park,and the carbon neutrality planning indicators are proposed after account-ing.Then,a flexible carbon reduction model for the park is established,taking into account uncertainties in source and load,constraints on equipment expansion,and carbon flexibility.Finally,a two-stage robust planning model is constructed with the goal of minimizing the planning cost of the park,and the column and constraint generation al-gorithm is used to obtain the optimal solution to the planning problem.The calculation example shows that the pro-posed model can optimize the energy structure of the park,achieve net zero carbon emissions in the park,and pro-vide scientific support for the transformation and upgrading of traditional parks.

Improving the flexibility of active distribution networks (ADNs) enhances the reliability and economic performance of regional power systems. Common user-side flexible energy resources (FERs), which are numerous but individually limited in capacity, can be aggregated to participate in ADN operations. To accurately and efficiently aggregate heterogeneous user-side FERs, this paper proposes a novel polytope-based aggregation method to construct the aggregated feasible region, which is integrated into a coordinated operation model for ADNs. The proposed method employs an inner approximation using affine transformation to better capture the feasible regions of individual FERs. In addition, a distributionally robust chance-constrained (DRCC) model is integrated to address parameter uncertainties in FERs, and a rolling mechanism is applied to intraday operations for the rolling update of the aggregated feasible region. Finally, a case study of a specific ADN in Northwest China validates the effectiveness of the proposed method and the corresponding improvement in flexibility. The results indicate that the proposed coordinated operation model effectively improves the flexibility of ADNs, and the proposed method reduces the computational burden without compromising economic benefits.

This paper proposes a coordinated optimization framework for multi-island integrated energy systems considering internal carbon trading. Each island operates as an independent energy hub while participating in a shared carbon market, where both internal carbon redistribution and external procurement are explicitly modeled. A unified optimization model is developed to jointly determine energy dispatch and carbon allocation, effectively capturing the coupling between energy operation and carbon emissions. Case studies under different carbon scenarios demonstrate that the proposed approach significantly improves system performance. The results show that internal carbon trading reduces total operational cost by up to 30.4% and decreases external carbon procurement by over 90%. Moreover, the effectiveness of the proposed method becomes more pronounced under carbon-constrained and high carbon price conditions. Overall, the proposed framework provides an effective and scalable solution for the coordinated and low-carbon operation of multi-island energy systems.

With continuing electricity market reform and expanding multi-region power trading, electricity sales companies (ESCs) must coordinate wholesale procurement, retail package pricing, and demand response decisions under spot price uncertainty and incomplete user information. This study investigates how ESCs can jointly optimize procurement and retail decisions while accounting for user-side strategic interaction and procurement risk. To address this problem, this paper proposes an integrated multi-level game-theoretic framework for ESC strategic decision-making and risk management in a two-level electricity market. The framework combines a leader--follower game between the ESC and end-users, a Bayesian game for user package selection under incomplete information, and a conditional value-at-risk (CVaR)-based model for spot-market procurement risk management. Unlike studies that consider procurement risk, retail pricing, or user response separately, this study links multi-market procurement coordination, risk-aware decision-making, and incomplete-information user interaction within a unified two-level market framework. Case studies show that the ESC achieves the highest profit when all users select the direct-sharing package, with profit increases of 22.6%, 14.5%, and 3.8% compared with the other package-selection cases. In addition, the flexible procurement strategy reduces procurement risk compared with fixed-share procurement strategies, which indicates that dynamic allocation among different market sources can improve the trade-off between expected return and risk exposure. Explicitly modeling user interaction also helps the ESC better coordinate retail package design and user-side response under uncertainty. Overall, the proposed framework strengthens wholesale-retail coordination, improves the profitability and risk-management capability of ESCs, and provides a practical decision-support tool for layered electricity markets.

The “double-high” characteristics of power systems—namely, the high penetration of renewable energy and the widespread use of power electronic devices—have significantly increased operational complexity. This underscores the necessity of adopting coordinated energy storage systems and wind-storage hybrid microgrids to support the black start restoration of thermal power plants. This paper addresses two critical challenges in the black start process of a wind–storage–diesel microgrid: dynamic power coordination and state of charge (SOC) balancing of the energy storage system. A coordinated control strategy is proposed for the entire black start sequence, incorporating SOC equilibrium management. A novel hybrid control architecture is introduced, which effectively integrates grid-forming virtual synchronous generator (VSG)-based energy storage units with grid-following P/Q-controlled storage units, while leveraging the dynamic reactive power support capability of diesel generators. By coordinating SOC balancing among storage units and combining diesel generation with wind power maximum power point tracking (MPPT) control, the strategy enables wind power output to effectively track microgrid load demand. It also ensures reliable reactive power support to prevent black start failure. During periods of power imbalance between wind generation and black start loads, the energy storage system compensates for active power discrepancies. Furthermore, control schemes for both grid-forming and grid-following storage units are enhanced to achieve SOC-based active power distribution, ensuring balanced SOC levels across all units. Finally, a simulation model for the wind–storage–diesel black start is developed in PSCAD/EMTDC, validating the effectiveness and robustness of the proposed control strategy.

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid.

This paper propose a Nash Stackelberg game based trading decision model of joint power market contain frequency/regulation/reserve for day ahead transaction to deal with the challenges brought by the insufficient peak shaving and frequency regulation capacity of a high proportion of renewable energy. This model utilizes Copula-CVaR to quantify the risk of revenue loss caused by the uncertainty of power generation and consumption. The model based double layer game of Nash Stackelberg and considering the total cost of regulation and the profits of multiple types of independent operating entities. So the proposed model is complex with the traditional model because it is not only requires the balance between the upper and lower level entities, but also requires the balance between multiple types of power supplies in the lower level. The rationality and effectiveness of the trading decision model is verified by the measured data of the renewable energy gathering area in northwest China. The calculation results indicate that the trading strategy not only breaks through the limitations of poor flexibility in the power market caused by insufficient grid synchronization machines, but also solves the development bottleneck of long investment payback period and low utilization rate of energy storage stations.