To address the economic, security, and computational efficiency challenges in transmission-distribution coordinated dispatch with high renewable energy penetration, a multi-period optimization model and an adaptive improved grey wolf optimization (AIGWO) are proposed. First, a three-dimensional objective function framework is established by comprehensively considering economic cost, environmental penalty, and security risk, with complete modeling of unit ramping constraints, energy storage charging-discharging time-coupled constraints, and network security boundary conditions. On this basis, three innovative mechanisms are designed: (1) a nonlinear adaptive convergence factor to dynamically balance global exploration capability, (2) knowledge-guided population initialization to improve solution quality, and (3) hybrid binary-real encoding to cooperatively optimize continuous and discrete variables, thereby overcoming the low convergence efficiency and poor discrete decision space processing of conventional algorithms. Experiments are performed on a modified IEEE 33-bus system for verification. Results show that compared with the standard grey wolf optimization, particle swarm optimization, mixed integer linear programming, and deep Q-network, AIGWO reduces the total cost by 1.72%~5.03%, decreases line overload violations by 89.2%~93.5%, lowers voltage violations by 82.4%~90.3%, and shortens the computation time by 9.32%~94.22%. The proposed algorithm provides an efficient solution for coordinated transmission-distribution resource optimization in high-fluctuation source-load scenarios.
Hydrogen energy,as a core area in global energy transition and low-carbon development,plays a critical role in supporting industrial innovation and talent cultivation through discipline construction.Universities and research institutions worldwide are actively exploring pathways for establishing a hydrogen energy discipline system.Based on a systematic study of hydrogen energy discipline development,this paper reviews the current status in China and conducts practical explorations focusing on talent cultivation and curriculum system design.From the perspectives of disciplinary layout,curriculum structure,research platforms,and faculty development,and by integrating goal-oriented analysis,pedagogical innovation,and strategic resource allocation,the paper presents achievements in cultivating specialized talent,advancing technological innovation,and serving industrial development.Furthermore,it analyzes existing challenges and proposes targeted optimization strategies,aiming to provide theoretical references and practical insights for the high-quality development of hydrogen energy disciplines in China.The findings indicate that current disciplinary construction faces challenges,including insufficient interdisciplinary integration,a shortage of practical resources,and a need for enhanced internationalization.Accordingly,recommendations for subsequent construction and exploration are proposed to facilitate the high-quality development of the hydrogen energy industry.
To address voltage over-limit and instability issues caused by high penetration of distributed photovoltaic in distribution grids,this paper proposes a voltage regulation strategy based on distributed photovoltaic cluster partition.Firstly,a dual-criterion modularity function considering net load and equivalent electrical distance is constructed.A community detection algorithm based on modularity(i.e.fast-unfolding)is applied to dynamically partition photovoltaic clusters.Secondly,differentiated voltage regulation is designed according to the severity of cluster over-limit conditions:intra-cluster reactive power adjustment for mild over-limits,and multi-device hierarchical collaborative control for severe over-limits,with task allocation based on response speed and economic priority.Finally,an optimization model targeting minimization of network losses and voltage deviation is established.The improved multi-organization particle swarm optimization(MPSO)algorithm,combined with niche-based techniques,is employed to determine the optimal regulation sequence and device action levels.Simulation results demonstrate that this method effectively controls voltage fluctuations,reduces network losses,and enhances system stability in both the modified IEEE 33-node system and the IEEE 123-node system.
To address the issues of data noise interference,feature scale discrepancy,and insufficient multiscale meteorological pattern modeling in photovoltaic power forecasting,a prediction method based on dynamic data preprocessing and gated dense multiscale convolutional neural network(GDMS-CNN)is proposed.Firstly,an anomaly detection mechanism based on dynamic sliding-window Z-score is established,and missing values are processed via covariance-weighted multivariate interpolation.Secondly,an adaptive piecewise normalization algorithm is adopted to eliminate feature dimensional differences,and cloud-cover correction factor and atmospheric attenuation factor are constructed to enhance physical feature representation.Finally,a GDMS-CNN is designed,wherein the feature extraction efficiency is optimized by depthwise separable convolution modules,densely connected dilated convolution blocks are constructed to capture multiscale spatiotemporal correlation features,and an asymmetric gated channel attention mechanism is embedded to dynamically recalibrate feature weights.Experimental results demonstrate that the proposed method reduces the root mean square error(RMSE)by 16.4%compared with the optimal baseline model genetic algorithm-variational mode decomposition-echo state network(GA-VMD-ESN),and by 43.4%compared with the traditional random forest.The proposed method provides a novel solution for photovoltaic output forecasting and effectively enhances the reliability of power grid dispatching.
To address the practical challenges of poor data quality,strong hyperparameter coupling,and significant peak positioning errors in power system carbon emission peak forecasting,a framework integrating robust data preprocessing with an improved grey-convolution hybrid model is proposed.Firstly,a dynamic quantile boundary-based outlier detection and multi-window weighted robust repair procedure is established,together with a random forest feature importance-based chained multiple imputation method,to suppress outlier disturbances and high-dimensional missingness in non-Gaussian data.Subsequently,an improved convolutional network incorporating variational mode decomposition,dilated convolution,and attention gating is constructed,with an embedded improved grey model for long-term trend extraction;hyperparameter optimization is achieved through a grey relational grade-guided whale optimization algorithm.Experimental results show that compared with genetic algorithm,particle swarm optimization,and grey wolf optimization,the proposed algorithm reduces mean absolute percentage error by 39.7%,32.5%,and 25.4%,and peak prediction time deviation by 77.1%,71.4%,and 60.0%,respectively.Compared with autoregressive integrated moving average(ARIMA),long short-term memory-prophet(LSTM-Prophet),time-series transformer(TST),empirical mode decomposition-LSTM(EMDE-LSTM),and variational mode decomposition-gated recurrent unit(VMD-GRU),the proposed model reduces mean absolute percentage error to 2.89%and peak prediction time deviation to 0.7 h,while improving the inflection point capture rate to 93.8%.This study provides a new technical approach for accurate carbon emission peak prediction and offers data support for power system emission reduction strategy formulation.
To address voltage violations,frequency fluctuations,and other challenges caused by the high-penetration integration of distributed photovoltaic(PV)generation into distribution networks under the"dual carbon"goals and energy transition,as well as the limitations of conventional grid-following energy storage systems due to their passive response characteristics,this paper proposes a grid-forming energy storage-based solution.A bi-level coordinated optimization model integrating site selection,capacity allocation,and control is developed.Scenario analysis is employed to handle PV output uncertainty,and a hybrid optimization method combining an improved particle swarm optimization algorithm with an interior-point method is adopted to solve the model,achieving a multi-objective balance between economic and technical performance.The proposed grid-forming energy storage effectively mitigates reverse power flow from PV systems and significantly improves PV curtailment reduction.Under fault conditions,it enhances the self-healing capability of the distribution network.By integrating virtual synchronous generator control with a multi-objective coordinated optimization strategy,the approach overcomes the technical bottleneck of passive response inherent in traditional energy storage,offering a systematic solution for the secure and stable operation of distribution networks with high renewable penetration.
With the large-scale integration of high-penetration renewable energy into the power grid,there are increasing demands for frequency regulation.To address the issues of high regulation losses and poor economic performance resulting from the frequent ramping of conventional thermal power units,this paper proposes a secondary frequency regulation strategy for a hybrid energy storage system(HESS)that incorporates the response characteristics of both thermal power and compressed air energy storage(CAES).First,the automatic generation control signal is decomposed into high-frequency and low-frequency components using the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and multiscale permutation entropy(MPE)methods.Subsequently,leveraging the similarity between thermal power units and CAES in terms of dynamic response time and regulation inertia,a coordinated control method for a thermal-HESS is developed.This method enables the rational allocation of high-and low-frequency components among different units,thereby enhancing the system's frequency regulation performance while reducing the output variability of the thermal unit.Finally,a dynamic simulation model is built in Matlab/Simulink to validate the regulation performance and economic benefits of the proposed strategy.Simulation results demonstrate that the proposed strategy can fully leverage the analogous response characteristics between thermal power and CAES during secondary frequency regulation,as well as the complementary advantages of the HESS in terms of fast response and large capacity.This coordinated approach effectively reduces and smoothens the output of the thermal power unit,thereby enhancing the overall frequency regulation performance and economic benefits of the thermal-HESS.
To address the challenges in aggregated participation of distributed energy storage stations in electricity energy markets,including insufficient consideration of individual benefits,misalignment between aggregated feasible regions and operational objectives,and weak coupling of bidding strategies in two-stage markets,this paper proposes an individual benefit-driven aggregation method and a two-stage market bidding strategy for distributed energy storage.Firstly,based on Karush-Kuhn-Tucker(KKT)conditions,an optimization model for the aggregated feasible region incorporating individual benefit constraints is constructed,enabling multi-agent resource integration while ensuring that the revenue of each energy storage station is no less than that achieved through independent market participation.Subsequently,a bi-level optimization model for energy storage aggregators participating in day-ahead and real-time two-stage energy markets is established,characterizing strategic bidding behavior in the day-ahead stage and constructing a power adjustment mechanism based on day-ahead schedule deviations in the real-time stage.Based on game theory,the existence and uniqueness of market equilibrium are analyzed,and a two-stage market clearing method is proposed by transforming the bi-level optimization into a single-level KKT system.Simulation results on the Roy Billinton test system demonstrate that the proposed aggregation method increases the total revenue of four aggregators by 15.5%compared to the Minkowski summation method,with revenue improvements reaching 16.7%for aggregators with higher heterogeneity.The total revenue in the two-stage market is 19.0%higher than that achieved by participating only in the day-ahead market.The proposed method achieves energy storage resource integration and revenue enhancement while ensuring individual rationality,effectively coupling day-ahead strategic bidding with real-time flexibility adjustments,thereby providing theoretical foundations and methodological support for distributed energy storage participation in electricity markets.
As an extension of the heat exchanger network, the array-type heat exchangers can effectively enhance the operational capability of advanced adiabatic compressed air energy storage (AA-CAES). However, the complexity of the variable-configuration array-type heat exchanger network exerts a significant influence on the operational capability of the AA-CAES system. To address this gap, this paper proposes a wide-range operational strategy for AA-CAES systems that incorporates array-type heat exchangers. First, a model of the array-type heat exchangers array for AA-CAES is established based on the thermal-electrical analogy theory. Subsequently, a wide-range operation method for AA-CAES is proposed, leveraging the operational characteristics of the array-type heat exchangers. This method determines the number of heat exchanger units participating in power regulation according to the required power output, followed by a multi-objective optimization of the array-type heat exchangers using power deviation and residual thermal energy of the thermal oil as objective functions. Finally, a case study based on the parameters of a commercially operational AA-CAES station is conducted to validate the effectiveness of the proposed method. The results demonstrate that, compared to traditional heat exchangers, the modular heat exchanger array can effectively expand the feasible operating region of the AA-CAES discharging system, reduce power tracking deviation, and increase the utilization rate of thermal energy in the thermal oil. The research will provide the theoretical foundation and technical support for flexible regulation of AA-CAES.
To address the increased load volatility and insufficient interaction stability with the main grid caused by large-scale integration of electric vehicles(EVs)into microgrids,a two-stage optimal scheduling strategy for a PV-storage-EV charging microgrid is proposed,incorporating flexible EV charging and discharging.First,in Stage 1,a piecewise logistic regression model is employed to accurately quantify users'willingness to participate in vehicle-to-grid(V2G)services.A bi-objective optimization model is formulated to minimize both load fluctuations and user charging costs.The zero-sum game strategy is adopted to determine the weighting coefficients of the multiple objectives,thereby fully exploiting the flexible regulation potential of EVs to reduce user costs while smoothing the load profile.Subsequently,based on the results from Stage 1,Stage 2 constructs a model that minimizes both microgrid operating cost and tie-line power standard deviation,optimizing the power dispatch of internal generation units and power exchange with the upstream grid.This stage also investigates microgrid scheduling responses under low EV penetration scenarios.Finally,the mixed-integer programming problem in Stage 1 is solved using Cplex,while the multi-objectivegrey wolf optimizer-enhanced with an improved Tent chaotic map and a state-driven adaptive iterative strategy-is applied to solve the models in both stages.Simulation results demonstrate that,under various EV participation scenarios,the proposed approach enables the microgrid to simultaneously achieve economic benefits for both end-users and the microgrid operator,as well as enhanced grid stability.
To address the poor operational stability and high unit hydrogen production cost caused by strong power fluctuations of wind and photovoltaic (PV) renewable energy, this study investigates an optimal control strategy for a dual-channel hybrid hydrogen production system under wind-PV coupled application scenarios. An optimal control strategy for a dual-channel electrolytic cell system based on ensemble empirical mode decomposition (EEMD) and Petri net-based start-stop correction is proposed. Wind and PV power signals are decomposed using EEMD, and power components at different frequency bands are allocated to alkaline and proton exchange membrane (PEM) electrolytic cells according to their dynamic response characteristics. Meanwhile, a Petri net model is employed to construct start-stop logic for electrolytic cells, effectively suppressing frequent switching under low-load conditions. Furthermore, a multi-objective optimization model is established with the objectives of maximizing system energy conversion efficiency and minimizing the unit hydrogen production cost, which is solved using a multi-objective particle swarm optimization algorithm. Simulation results based on measured wind-PV power output data from the Zhangbei region indicate that the optimized hybrid hydrogen production system achieves an energy conversion efficiency of 58.64% and a unit hydrogen production cost of 2.3958 USD/kg. Compared with conventional single-type hydrogen production schemes, the proposed method improves efficiency by 15.25% and reduces cost by 1.7384 USD/kg, while significantly decreasing the number of start-stop events of electrolytic cells. The results demonstrate that the proposed control strategy effectively enhances system stability and reduces economic cost, providing a practical and feasible optimization approach for the efficient operation of wind-PV hydrogen production systems.
To address the challenges of power fluctuations and ramping demands faced by regional integrated energy systems under high penetration of renewable energy,this paper focuses on the ramping support capability of advanced adiabatic compressed air energy storage(AA-CAES).A multi-timescale optimization dispatch model for regional integrated energy systems incorporating AA-CAES ramping capability is established.First,an operational model of AA-CAES is established to analyze its support capability for thermal power ramping.Second,a multi-timescale optimization dispatch strategy for regional integrated energy systems incorporating AA-CAES ramping capability is proposed.Long-timescale optimization minimizes operational costs while ensuring system power balance,and short-timescale dynamic power correction is achieved using model predictive control.Simulation results demonstrate that multi-timescale scheduling,incorporating AA-CAES ramping capability,effectively enhances the system's resilience to renewable energy fluctuations,reduces thermal power dispatch requirements,lowers operational costs,and improves the integration of renewable energy.This approach provides theoretical guidance for the economic and stable operation of regional integrated energy systems.
To address the issue of rational energy storage configuration in off-grid hydrogen production systems,this paper proposes an optimized configuration method for energy storage in such systems.Firstly,the supporting role of grid-forming energy storage in the voltage and frequency of off-grid systems is analyzed,clarifying the grid-connection approach using grid-forming energy storage as the power source for off-grid system.Secondly,based on the configuration of renewable energy off-grid hydrogen production systems,a control strategy for energy storage to support black start of off-grid systems is formulated.Thirdly,an optimized energy storage configuration model considering the unit cost of hydrogen production and the system's electricity curtailment rate is established,and the particle swarm optimization algorithm is employed to solve the model.Finally,the Datang Duolun Wind and Solar Hydrogen Production Project is selected as the research object.Through economic evaluation and stability analysis of the off-grid hydrogen production system,the effectiveness of the optimized energy storage configuration method for off-grid hydrogen production systems is verified.
Underwater compressed air energy storage(UW-CAES),which utilizes flexible underwater air bags to enable constant-pressure charge and discharge,has emerged as a compelling solution for renewable energy accommodation.However,there remains a distinct lack of research focused on parameter optimization to simultaneously reduce the capital costs of UW-CAES and enhance the operational economics of the plant.To address this critical gap,this paper proposes an optimal configuration method for UW-CAES based on distributionally robust chance constraints(DRCC).First,a comprehensive UW-CAES system model is established,explicitly accounting for the impact of pipeline pressure losses on system dynamics.Subsequently,an optimal configuration framework incorporating these pressure losses is formulated to optimize key system parameters,with the dual objectives of minimizing investment costs and maximizing operational revenues.Furthermore,the DRCC approach is employed to reformulate the stochastic chance constraints into tractable linear constraints.This mathematical transformation not only ensures computational efficiency but also facilitates a flexible trade-off between economic optimality and robustness.Case studies demonstrate the efficacy of the proposed methodology:the optimized system maintains a rated discharge power of 60 MW while reducing the required rated charge power to 53.2 MW-an 8.75%decrease compared to the original baseline-thereby significantly improving overall system efficiency.Finally,sensitivity analyses reveal that systematically calibrating the confidence level and Wasserstein radius within the DRCC framework effectively navigates the equilibrium between economic performance and system conservatism.
To address the challenges posed by time-varying system inertia and the insufficient adaptability of conventional thermal-storage frequency regulation strategies under high renewable penetration,this paper proposes a coordinated thermal-storage frequency control strategy based on online inertia estimation and adaptive deadband optimization.The strategy employs a hierarchical coordination mechanism:under small disturbances,energy storage systems—acting as fast,distributed flexible resources—are prioritized for response through a reduced deadband setting,thereby avoiding frequent cycling and wear of thermal units;under large disturbances,the equivalent system inertia is identified via inversion of the frequency response,enabling adaptive adjustment of the storage's virtual inertia and droop coefficient to dynamically compensate system damping.Furthermore,a full-lifecycle cost model incorporating cycle-life degradation is established to quantify the economic benefits of the proposed strategy.Simulation results demonstrate that the approach effectively mitigates system oscillations while significantly reducing overall frequency regulation costs,offering a technically and economically viable solution for distributed energy storage participation in grid ancillary services and frequency stability management in low-inertia power systems.
To address the insufficient resilience of distribution networks under high penetration of renewable energy,this paper proposes a resilience enhancement strategy incorporating advanced adiabatic compressed air energy storage(AA-CAES).A dispatch model is formulated in which AA-CAES participates in grid contingency response,and the uncertainty of renewable generation is characterized using distributionally robust chance constraints based on the Wasserstein distance.Simulation tests are conducted on a modified IEEE 33-node system to validate the effectiveness of the proposed strategy.Results show that,with AA-CAES deployed,the loss-of-load rate during extreme events is significantly reduced-decreasing by 3.84%compared to the scenario without AA-CAES,at the cost of only a 1.17%increase in dispatch cost.The study concludes that the proposed strategy effectively enhances the power supply capability of distribution networks under disaster-induced disturbances,achieving coordinated optimization between operational economy and resilience through a modest cost increment and substantial reliability improvement.
To address the interconnected challenges of bus voltage limit violations,reverse power flow overloading,and deteriorated power supply reliability caused by high-penetration distributed renewable energy integration,this paper proposes an energy storage optimal planning method considering generation-storage coordination for local consumption and power supply reliability.An energy storage optimal planning model is established,aiming to minimize the annualized comprehensive cost(including energy storage investment and renewable curtailment penalties)while optimizing voltage fluctuation and net load fluctuation.The non-convex nonlinear model is solved using an improved multi-objective particle swarm optimization algorithm.By incorporating an adaptive inertia weight mechanism and a dynamic crowding distance-based non-dominated solution set update strategy,the algorithm effectively avoids premature convergence and local optima traps.Simulation results based on the IEEE 33-bus distribution network demonstrate that the"storage configuration+reasonable curtailment of renewable energy"scheme increases renewable energy local utilization by 12%and reduces annualized comprehensive cost by 5.6%compared to the"reasonable curtailment of renewable energy"scheme,while achieving a 7.5%cost reduction compared to the"storage configuration"scheme alone.
In response to the challenges where large-scale renewable energy integration leads to intricate source-network-load-storage elements and surging complexity in new power systems,rendering traditional balancing architectures and hierarchical analysis methods inadequate,theoretical achievements and research technologies regarding hierarchical and partitioned balance architectures are comprehensively reviewed.The adaptability requirements of new power systems for such architectures are elucidated,followed by a summary and comparative analysis of existing hierarchical control and partitioning strategies.Furthermore,layer-zone fusion mechanisms are explored,and existing technical limitations are analyzed from data and modeling perspectives.The results indicate that while renewable energy control pressures can be alleviated by existing strategies,deficiencies remain in handling massive heterogeneous data fusion and precise modeling of complex systems;moreover,high dynamic balance demands are difficult to be met by current layer-zone coordination mechanisms.Future hierarchical and partitioned balance architectures are identified as a critical direction for supporting the operation of new power systems.Notably,a novel technical pathway for achieving safe and efficient operation under the"carbon neutralization and carbon peaking"goals is offered by the introduction of large models and artificial intelligence technologies.
The increasing penetration of renewable energy and growing electricity demand in islanded microgrids have intensified the uncertainties on both the generation and load sides, posing severe challenges to their secure, stable, and economic operation. Traditional robust optimization methods, which over-emphasize extreme system conditions, often compromise operational economy. This paper employs fuzzy theory to generate stochastic optimization scenarios for the system. Based on the probability of scenario occurrence and the minimum hybrid energy storage system capacity required for each scenario, a scenario reduction process is conducted. A stochastic optimization-based dispatch method for islanded microgrids is proposed. The method involves establishing uncertainty models for renewable energy and load to generate stochastic scenarios, formulating a mathematical model, performing demand response dispatch under each scenario, and finally filtering out extreme scenarios. Based on the proposed method, experimental verification is carried out in an island microgrid case. The proposed method reduces the system operating cost by 20.17% compared to the traditional robust optimization approach. The results verify the effectiveness and superiority of the proposed method.
Against the risk of frequency instability arising from reduced system inertia due to the integration of high-proportion new energy into the power grid, this paper proposes a power system safety forewarning and auxiliary decision-making method considering minimum inertia constraints. Firstly, a dual-constraint critical inertia evaluation model is established, which calculates the critical inertia by integrating the rate of change of frequency constraint and the minimum frequency constraint, thereby improving the accuracy of the inertia safety boundary. Secondly, an equivalent inertia calculation framework for the source-grid-load-storage system is constructed to accurately calculate the total system inertia level and quantify the specific contributions of virtual inertia from conventional units and new energy sources, load inertia, and dynamic inertia from energy storage. Once the actual system inertia falls below the critical inertia threshold, a forewarning is activated and the inertia deficit is quantified; meanwhile, to minimize the system operating cost, a multi-resource optimal dispatch model incorporating inertia security constraints is developed. By coordinately adjusting the output of conventional units, the frequency regulation strategies of new energy sources, and the charging-discharging strategies of energy storage, the proposed method achieves inertia safety forewarning and auxiliary decision-making for power systems. Simulation results demonstrate that: the proposed dual-constraint inertia safety model effectively avoids the risk of missed judgment inherent in traditional single-constraint models; the proposed forewarning mechanism enables advance identification of inertia shortages and quantifies the deficit; the proposed auxiliary decision-making scheme significantly reduces system operating costs while ensuring frequency security.
