The coordinated operation of hybrid energy storage systems （HESS) in the power system can make up for the shortcomings of single energy storage in multiple time-scales power and electricity scheduling, but its economic limitations limit its potential to participate in auxiliary grid peak-shaving and enhance new energy consumption in coordination with source load. Therefore, a hybrid energy storage load collaborative optimization scheduling method is proposed based on dynamic time zone partitioning. Firstly, based on the power imbalance between the source and load, a dynamic time zone division strategy is designed by considering the prediction error of new energy output and the demand side response, which guides hybrid energy storage to synergistically enhance the new energy consumption capacity and auxiliary peak shaving effect from the perspectives of electricity quantity and power at different day-ahead-intraday time scales. Secondly, with the goal of minimizing the total daily operating cost of the system, a system day ahead day rolling optimization scheduling model is constructed. Energy based energy storage provides peak shaving power support at the day ahead scale, while power based energy storage smooths out new energy fluctuations at the intraday scale. Rolling optimization is used to reduce system source load prediction errors, and an energy storage cycle life model is embedded to make the system scheduling results closer to actual operating conditions. Finally, the effectiveness and superiority of the proposed method in terms of economy, new energy consumption, and peak shaving effect are verified through numerical examples.

With the high proportion and large-scale distributed photovoltaic grid-connection and the popularity of electric vehicles, how to exert the flexibility of electric vehicles to coordinate friendly distributed photovoltaic and local electric vehicle load flexibility resources in the distribution network is the important issue that needs to be solved. Therefore, distribution network cluster partitioning and operation strategy are proposed considering the collaboration between electric vehicles and distributed photovoltaic. Firstly, an adjustable charging power and flexible aggregation model of electric vehicles is established, and a distributed network cluster partitioning method based on Louvain algorithm with improved modularity index is proposed. Secondly, based on historical data, multi-time scale charging scenarios of electric vehicles are generated, and a distributed cluster collaborative optimization model is proposed considering charging flexibility of electric vehicles. Finally, the algorithm of synchronous alternating direction multiplier method （SADMM） is used to solve distributedly for optimal models of all clusters. The simulation results show that the use of electric vehicles charging flexibility to participate in the collaborative operation of the distribution network can effectively improve the utilization rate of distributed photovoltaic, and ensure the safety of the voltage operation of the distribution network while meeting the charging needs of electric vehicles users.

With the transformation of users from consumers to prosumers, more and more users have demands for electricity storage, thermal storage and cooling storage. However, the high cost of energy storage is a barrier for user-side energy storage. To address this issue, the cloud energy storage mode and the regional integrated energy system with combined cooling heating and power (CCHP) are combined to propose an optimized configuration model for electric/thermal/cold cloud energy storage. Firstly, the structure of the regional integrated energy system with CCHP is built along with its input-output conversion relationships analyzed. Then, the energy charging-discharging behaviors of the users and cloud energy storage provider are analyzed. The two-stage energy storage optimization model is established from the perspectives of the two subjects. In the first stage, the user energy storage demand is optimized with the objective of minimizing the user’s total costs. In the second stage, the energy storage configuration is optimized with the objective of minimizing the cloud energy storage provider’s costs after the cloud energy storage providers integrate the user’s demands. Finally, calculation examples are conducted to verify the advantages of the cloud energy storage mode applied on energy storage configurations. Factors of cold storage and carbon emission existed or not in the system are compared to analyze the influences on the optimization configuration of energy storage.

For the scenario of wind power fluctuation smoothing, taking the wind and energy storage combined power-generation system as the research object, a FIR filter combined with smooth strategy of wind power fluctuation of amplitude reduction link is proposed, and then the optimization dispatching of hybrid energy storage is realized through adaptive reconstruction based on EMD of filtered fluctuation components. In this strategy, a FIR filter that can dynamically adjust the combination of filtering order and cut-off frequency is selected for filtering, and FRA amplitude reduction link is introduced to address high-amplitude data points that FIR filters cannot smooth well, so as to smooth wind power fluctuations. The smoothed and filtered wind power fluctuations are used as hybrid energy storage power for EMD decomposition, and the IMF component is adaptively reconstructed into two groups of high-low frequency power allocated to super capacitors-batteries respectively by an algorithm, so as to obtain the optimal energy storage optimal dispatching parameters. Through the numerical example verification analysis, the FIR filtering added with FRA can adapt to the power characteristics of different frequencies and amplitude, effectively reduce the power fluctuations of 1 min and 10 min scales, and avoid the problem of increasing the energy storage burden caused by excessive smoothing. The adaptive reconstruction of IMF components after EMD decomposition can obtain the power of batteries and super capacitors to compensate for different charge and discharge characteristics, significantly reducing the energy storage output burden and improve dispatching economy.

Optimal scheduling for regional power grid is an important means to increase the consumption of renewable energy, enhance the reliability of power systems, and reduce operating costs. Therefore, focusing on a regional power grid including electric vehicle (EV) charging stations, an auction-based two-stage optimal scheduling method is proposed. In the first stage, the regional power grid is scheduled one day ahead based on the predicted values of local renewable energy generation and consumption. In the second stage, the day-ahead scheduling results are adjusted according to the actual measured values of generation and consumption. For the second stage, an electricity auction method is designed to achieve real-time supply-demand balance of the regional power grid by using EV charging and discharging energies. It is proved that the proposed auction method has the property of incentive compatibility, which ensures that EVs submit true energy demand or supply information in the auction. Simulation results show that the proposed two-stage method can reduce the operating cost of the regional power grid and make EVs obtain the highest utilities only when true information is submitted.

The emergence of fuel-cell hybrid electric vehicles （FCHEV） has effectively promoted the green and low-carbon transformation of the energy-transportation system. At present, the research on FCHEV mainly focuses on energy management and control strategies, with little attention paid to the assessment of flexibility potential. Therefore, a FCHEV cluster flexibility potential assessment method that takes into account difference of owners′ willingness to charge is proposed. Firstly, an urban transportation network model is established to simulate the travel characteristics of FCHEV, and then a single FCHEV charging model is established; Secondly, the analytic hierarchy process is improved and combined with the entropy weight method to establish a comprehensive evaluation system for vehicle owners′ willingness to charge, and the impact of endowment effects and environmental awareness on electric vehicle owners′ willingness is comprehensively considered to respond. Finally, an FCHEV cluster flexibility potential assessment model is established and solved for the potential evaluation results. The simulation results show that the proposed method can reasonably characterize the differences in vehicle owners′ willingness to charge during the assessment process, and effectively improve the flexibility of the system when the cluster participates in optimization scheduling.

With the in-depth implementation of the energy Internet strategy, the participation of renewable energy and microgrid continues to rise．The uncertainty factors in the system have increased significantly, the cooperation and competition among the participating entities have become more and more complicated．A two-layer electric energy trading system with mixed game among power grid, service provider and multi-microgrid is established from vertical and horizontal levels．At the vertical level，the idea of master-slave game is proposed，with service provider as the leader and microgrid as the subordinate．The uncertainty problem is unified in the second stage optimization of the rough and error-free，the segmented robust optimization model of uncertainty stage optimization is constructed，the differential scheduling of uncertainties is realized，and the flexibility of robust optimization is improved．Using Bool-Column and constraint generation(B-C&CG) algorithm to solve the model，and the whole model is divided into the main and sub problems. The main problem optimizes the electricity price uncertainty problem，and the sub-problem optimizes the source load uncertainty problem．In a horizontal level, Nash negotiation model is structured，using the alternating direction method of multipliers(ADMM) algorithm to solve the horizontal level power interaction model between the microgrid．Using the distributed solution method, the trading price strategy is obtained．Then combined with Lagrange multiplier method，each part is optimized alternately and the multiplier is updated．The optimal transaction price of each microgrid is obtained．The simulation results show that the proposed scheme takes into account the robustness，economy and flexibility of the system，reduces the cost of each microgrid and fully protects the privacy of each microgrid．

To fully unleash the emission reduction potential of virtual power plant （VPP） and tap into the potential profits of clean energy, a source-load low-carbon economic dispatching model based on the secondary carbon market and green certificate allocation system is proposed. Firstly, aiming at the hub that aggregates various energy devices within VPP, the optimization objective is to minimize the comprehensive operation and maintenance costs. Secondly, a secondary carbon market trading system based on carbon quota trading and auction markets is established to simulate the incomplete information game involving multiple enterprises on the source side. Thirdly, based on carbon flow theory, carbon emissions and carbon flow density are combined into a carbon indicator, and a green certificate allocation method for the load side is established to improve the economic benefits of users purchasing green certificates, thereby enhancing the value of green certificates and benefiting clean energy enterprises that provide them. Finally, a multi-region VPP model is used as an example to simulate typical days in summer and winter. The results of the case study verify that the secondary carbon market and green certificate allocation system can effectively utilize market transmission mechanisms to increase the revenue of environmentally friendly enterprises and encourage high-carbon emitting enterprises to transition towards low-carbon operations.

Aiming at the problems of AC/DC hybrid distribution network node voltage instability, high rate of abandoned wind and photovoltaic power and high system operation and maintenance cost caused by source-load volatility, a low-carbon optimization operation method of AC/DC hybrid distribution network taking into account network reconfiguration and demand response is proposed. Firstly, a stepped carbon trading model for AC/DC hybrid distribution networks is established. Secondly, a low-carbon optimization operation model for AC/DC hybrid distribution networks is developed with the optimization objectives of system economy and low-carbon, taking into account the constraints of network reconfiguration, demand response, capacitor switching, access to distributed power sources, and charging/discharging of energy storage. Then, the nonconvex distribution network optimization model is transformed into a mixed-integer convex planning model and solved by introducing intermediate variables and a second-order cone relaxation method. Finally, the proposed method is simulated and analyzed by the improved IEEE 33-node AC/DC hybrid distribution system, and the results show that the proposed method can significantly reduce the carbon emissions and the rate of wind and photovoltaic power abandonment generated by the system operation, and improve the system voltage stability and dispatch economy.

Aiming at the "Dual-Carbon" emission reduction target and the peak shaving problem of power system brought by the large-scale grid connection of new energy sources, a cascade hydro-thermal-wind-photovoltaic-storage multi-energy joint optimal scheduling model considering the stepped carbon trading is proposed. Firstly, hierarchical optimal scheduling is adopted, and the minimum load fluctuation is taken as the objective function in the upper model, using a modified sand cat swarm optimization(MSCSO)algorithm that considers Circle mapping strategy, triangular migration strategy, Levy flight migration strategy and elite reverse learning strategy to optimize the output of cascade hydropower. Thus, the equivalent load curve after smoothing is obtained. In the lower model, the minimum comprehensive cost is taken as the objective function to solve its piecewise linearization. Finally, a multi-scenario simulation is carried out in the modified IEEE 30 nodes, and the results show that the proposed model can effectively reduce carbon emissions, give full play to the deep peak shaving capacity of thermal power units, effectively improve the new energy consumption capacity and the low-carbon and economic operation of the system, which verifies the validity of the model.

The participation of flexible load in the optimal scheduling of new power system has a significant effect on improving the consumption capacity of new energy, but the potential of flexible load has not been fully explored. To solve this problem, this paper presents a day-ahead and intra-day optimal scheduling method based on source-load prediction. Firstly, the sparrow search algorithm is used to optimize the convolutional long-term and short-term memory neural network ( SSA-CNN-LSTM ) for day-ahead and intra-day power prediction of new energy and load. Secondly, according to the characteristics of flexible load and the flexibility of demand response, the load is divided into different types, such as shiftable, transferable and reducible load. The day-ahead and intra-day two-stage low-carbon environmental economic dispatch model of source-load interaction is constructed with the goal of optimizing the system operation cost and pollutant gas emission considering the staged carbon transaction cost. Finally, the improved multi-objective grey wolf algorithm (MOGWO) is used to solve the model. The example analysis shows that the total cost of the flexible load classification is reduced by 8.6 %, the pollutant emission is reduced by 4.1 %, and the new energy consumption capacity is increased by 4.2 % compared with the traditional method. The uncertainty of new energy and load response is significantly reduced in multiple time scales and the low-carbon environmental and economic comprehensive benefits of the new power system are improved.

Distributed algorithms have significant advantages in communication, reliability, flexibility, and other aspects, demonstrating their competitiveness in the economic dispatch of power grids. For this purpose, a multi-period economic dispatch model for microgrids is constructed, which includes transferable loads and purchasing electricity from the large power grid. A dynamic power constraint generation strategy based on the Karush-Kuhn-Tucker （KKT） condition is designed to address the issue that current distributed consensus dispatch algorithms cannot solve the problem of dispatch resource cost functions being non strictly convex. This strategy can dynamically modify the power constraints in the local optimization process, avoiding the centralized participation of transferable loads and purchasing electricity in response, and eliminating the impact of non strictly convex scheduling resource cost functions that prevent convergence of results. Finally, simulation examples verify the correctness and effectiveness of the proposed algorithm.

Aiming at the insufficient new energy consumption capacity in actual power grid operation, an intelligent generation method is proposed for operating mode samples of optimal new energy consumption based on generative adversarial networks (GAN) and reinforcement learning (RL). The method is used to establish generator and discriminator models through deep neural networks with a small sample set of actual power grid operation modes. And based on the objective function of GAN, the generator and discriminator can be trained for maximum and minimum adversarial training to generate false operating mode samples, distribution characteristics of which approach the real samples. During the training process of the generator, a reward and punishment feedback function is defined for new energy consumption to provide a quantitative good or bad response to the “generation action”. On this basis, the reinforcement learning method is introduced. Following the optimization principle of accumulating the most rewards, the generator parameters are continuously optimized in real-time by the reinforcement learning method with policy gradients. Operating mode samples are intelligently generated with optimal new energy consumption characteristics. The proposed method is tested in the actual power grid of a certain region. The results show that it can effectively generate operation mode samples that meet the optimal new energy consumption, and provide data support for improving the current operation scheduling decision of insufficient new energy consumption.

In the optimization of unit commitment, it can provide a safer and more economical system operation scheme considering short-circuit current constraints and branch switching. However, its solution faces challenges due to the large scale of the problems. A heuristic decomposition algorithm is proposed based on analytical target cascade and large neighborhood search. First of all, the joint optimization model of unit commitment and branch switching incorporating short-circuit current constraints is established and hence it is transformed into a mixed integer linear programming model with a separable structure. Then, the analytical target cascade method is used to decompose the converted model into upper level coordination master problem, lower level integer programming and linear programming subproblems. Moreover, the large neighborhood search is introduced in solving the lower level subproblems. Finally, the simulations on IEEE 118-bus and 54-unit systems and an actual power system show that the proposed algorithm can quickly converge to feasible solutions, whereas not affecting solution quality.

In order to promote new energy consumption and low-carbon economic operation in microgrids, a multi-energy microgrid cluster model with shared energy storage power stations is constructed. Firstly, a multi-energy microgrid model with electricity, heat and gas is established, and multiple microgrids form a cooperative alliance to jointly establish shared energy storage. Secondly, acknowledging the intricate correlation and inherent unpredictability of wind and solar energy outputs, a refined multi-objective robust optimization scheduling model for the multi-energy microgrid cluster is formulated. This approach integrates Copula-multi-scenario confidence gap decision theory to attain the dual objectives of cost minimization and carbon emission reduction. Finally, considering the energy interaction contribution, risk level, and carbon reduction contribution of the members in the alliance, the benefit of the improved Shapely value method is used to quantify the contribution of each alliance subject and equitably distribute the cooperation surplus. The example results show that the proposed model reduces the total cost by 18.64% and carbon emission by 30.24% compared with the independent allocation of energy storage, and it realizes the precise matching between the comprehensive contribution of each subject to the alliance and the distribution of the cooperation surplus, so as to enhance the enthusiasm of the multi-energy microgrids to participate in the cooperation.

To achieve energy efficiency and emissions reduction in data centers, integrating data centers into integrated energy systems for collaborative optimization is an effective approach to achieve this goal. Firstly, the principles of a hybrid data-model driven strategy are explained, then the applications of model-driven and data-driven approaches in integrated energy systems is reviewed separately. Secondly, the load forecasting model of the data center and the current status of the application of a hybrid data-model driven method in integrated energy systems with data centers are introduced in detail. On this Basis, a framework for scheduling strategies in integrated energy systems with data centers utilizing a data-model hybrid drive is proposed. Finally, the current research issues are discussed, and an outlook on future development directions is provided, providing a reference for researchers in this field.