The current operation of China′s highway energy system heavily relies on the external power grid, with low penetration rates of renewable energy and huge carbon emissions. Therefore, a multi-stage planning method for comprehensive energy stations in highway service areas considering hydrogen energy is proposed. Firstly, a comprehensive energy station architecture that includes electricity, cold/hot, gas, and hydrogen energy is constructed to simulate the charging needs of clean energy vehicles within the highway service area. Based on the market ownership of hydrogen fuel cell vehicles in different periods, the planning cycle is divided into multiple stages, with the goal of minimizing total investment and operating costs. A multi-stage planning model for comprehensive energy stations in highway service areas considering hydrogen energy is constructed to determine the capacity configuration schemes for electricity, heating/cooling, gas, and hydrogen energy equipment, as well as operational strategies under different typical daily scenarios. Finally, based on the actual energy load data of a high-speed service area in northern China, it is verified that the proposed multi-stage planning model can effectively improve the economy of comprehensive energy stations and promote the consumption of renewable energy.

In order to fully exploit and utilize the interactive response capability of demand-side resources, optimize the resource allocation of the park and enhance the overall efficiency of the park, this paper proposes a response quantity quota transfer mechanism based on the cooperative and autonomous behaviors of users, and constructs a response quantity accounting model under the framework of master-slave game. In this model, the aggregator as a leader is responsible for setting dynamic quota transfer tariffs and guiding the users to collaborate, and the users as followers adjust their own electricity consumption and response quantity strategies according to the tariff signals released by the aggregator. Through the design and solution of the master-slave game model, this paper explores the optimal allocation strategy that can promote the sharing of response quantity among users, and the model is solved by an iterative algorithm to ensure that the decision-making interaction between the aggregator and users reaches the Stackelberg equilibrium point. The results show that the dual goals of economy and environmental protection of the park energy system can be realized while protecting the interests of users through a reasonable incentive mechanism for the transfer of response quantity quotas and a collaborative autonomy model.

In the global low-carbon energy transition, the integrated energy system （IES）, as the key to achieving the "dual carbon" goal, faces two core challenges: the limitations of traditional carbon accounting methods and dynamic carbon management of multi energy flow coupled systems. Traditional carbon accounting methods are difficult to capture the impact of fluctuations in new energy output and time-varying load characteristics on carbon flow due to static carbon emission factors, resulting in unfair allocation of carbon emission responsibilities. The dynamic carbon management of multi-energy flow coupled systems faces difficulties in tracking cross energy carbon emissions and high complexity of dynamic models. In response to the above challenges, a dynamic carbon flow propagation theoretical framework is proposed, which includes two aspects: 1） A multi-energy flow coupled matrix and time-varying node carbon intensity （TV-NCI） is constructed, and a dynamic carbon index is constructed, and the impact of new energy output fluctuations and time-varying load characteristics on carbon flow changes is analyzed, a new solution is provided for cross energy carbon emission tracking in multi-energy flow coupled systems. 2） By introducing the carbon flow betweenness centrality （CFBC） index, the impact of grid structure on carbon flow paths is analyzed and the key nodes that affect carbon transmission is identified. Through experimental verification of the IEEE 33-node extended system, the proposed method effectively reduces carbon emissions and new energy curtailment rates, and reveals the impact of network topology on carbon propagation paths. The research results provide theoretical support for the low-carbon planning and operation of IES.

With the increasing attention on island development, the issue of renewable energy consumption and integration in island integrated energy systems has become particularly prominent. To address this challenge, liquid air energy storage technology with combined heat and power characteristics is introduced into the existing island integrated energy system framework to promote the accommodation of renewable energy. A multi-objective mathematical model incorporating hybrid energy storage configuration optimization is established and solved using the normal vector constraint method. The model aims to enhance the renewable energy accommodation capacity of the island integrated energy system while improving its economic and low-carbon operation. Case study simulation results demonstrate that the proposed hybrid energy storage-based island integrated energy system model not only significantly improves economic and environmental benefits but also achieves remarkable progress in promoting renewable energy accommodation, effectively verifying the model's practicality and effectiveness.

In order to achieve low-carbon hydrogen production under diversified demands, a comprehensive energy management method based on hydrogen energy hub is proposed. Firstly, a comprehensive energy system management framework is constructed considering new energy units, electrochemical energy storage, gas generator units, hydrogen energy hubs and hydrogen loads. Then, based on the oxygen complementary utilization principle of hydrogen production by water electrolysis and rural biomass gasification, the design of hydrogen hub is introduced and the optimal operation model of hydrogen hub is established. Based on the energy management framework of the integrated energy system and the hydrogen energy hub model, the energy optimization management model of the integrated energy system is established, considering the constraints of hydrogen supply and demand balance, power balance, and operating characteristics of each equipment, in order to achieve efficient, economical, and environmentally friendly hydrogen production and energy management under diversified hydrogen demand. Finally, the numerical simulation of an integrated energy system with hydrogen energy hub verifies the effectiveness of the proposed method.

Constructing a zero-carbon island microgrid with a water and power supply system is an effective means to promote the sustainable development of independent islands. However, energy storage aging significantly impacts its storage power and operating power over time. To improve the reliability of island microgrid planning results, a collaborative electricity-water planning method for zero-carbon microgrids on an independent island considering the dynamic aging characteristics of energy storage is proposed. Firstly, refined physical models of electrochemical energy storage and hydrogen energy storage are established to analyze aging effects on operating efficiency. Secondly, a freshwater preparation system is introduced to establish a zero-carbon microgrid electricity-water cooperative planning model for independent islands in order to minimize the total investment operation and maintenance cost of the microgrid. To enhance the model solution speed, the planning problem is divided into two layers. The upper layer optimizes the installed capacity of the energy storage system with minimum investment cost based on the capacity of renewable energy, and the lower layer replans renewable energy capacity based on the upper layer′s results. Then, the ADMM method is used to solve iteratively until the total investment cost converges to obtain the optimal island planning scheme. Finally, the simulation analysis of a Fujian island verifies the method′s rationality and effectiveness.

Against the backdrop of global climate change and energy transition, the power system is facing multiple challenges, including economic viability. To ensure the good economic benefits of the new power system and implement the low-carbon development strategy, this paper proposes an optimal scheduling method for gas power generation system under various carbon emission constraints, including carbon emission quota constraints, carbon emission intensity constraints, and the addition of carbon capture and storage technology constraints. Firstly, system economy and new energy consumption are taken as optimization objectives, and the carbon emission constraint proposed in this paper is introduced on top of the basic operational constraints. Secondly, the relevant parameters are determined, and multi-objective particle swarm optimization algorithm is used to solve the model. Finally, the optimization results are compared and analyzed under multiple constraints and scenarios. The experimental results show that this method has significant effects on reducing system costs and improving the capacity of new energy consumption, which is of great practical significance to achieve low-carbon transformation of the power system.

The decision variables for power allocation of large-capacity battery energy storage power station are numerous, and their strategies need to consider multiple optimization objectives and the uncertainty of automatically adapting to the scenario. Therefore, this paper proposes a power allocation decision-making method for battery energy storage power stations based on multi-agent deep reinforcement learning（MADRL）. Firstly, based on the structure and power allocation characteristics of large-capacity battery energy storage power stations, a power allocation decision framework based on MADRL is constructed. Each energy storage unit is equipped with a power allocation intelligent agent, and multiple agents form a cooperative relationship. Then, a multi-agent DRL model for power allocation is designed, considering the optimization objectives of active power loss, state of charge （SOC） consistency, and state of health loss of battery energy storage power stations. The deep deterministic policy gradient （DDPG） algorithm is used to decentralize the training of network parameters for each agent. After the algorithm converges, the charging and discharging power values of the energy storage subsystem are obtained. Finally, the effectiveness of the proposed method is verified by the example, which can effectively improve the SOC balance of the energy storage subsystem while reducing active power loss, state of health loss, and charging and discharging switching times.

To address the demand for improving operational economy in electric hydrogen systems, a capacity optimization configuration method of electric hydrogen systems is proposed with hierarchical control of decommissioned power batteries considering time-sharing tariff. The remaining energy decay rate of decommissioned power batteries is analyzed and a hierarchical control strategy is presented to extend their service lives. On this basis, an operational control strategy is designed that prioritizes electric load satisfaction and powers the electrolyzer using decommissioned batteries. A capacity optimization configuration model of the electric hydrogen system incorporating the time-sharing tariff mechanism is established. Case study results demonstrate that under consideration of time-sharing tariff and hierarchical control strategies, the operational cost reaches approximately \mathrm{\yen } 239 800. This configuration exhibits superior economic performance and applicability compared to both hierarchical control strategies with new batteries and integrated control strategies with decommissioned batteries. Moreover, the decay rate of decommissioned batteries under this strategy measures about 52% of that observed in integrated control strategies.

The construction of a high proportion of new energy power systems is accompanied by a reduction in inertia/primary frequency regulation(PFR) resources with conventional synchronous machines. In order to address this, it is necessary to consider the construction of ancillary service market with multiple type resources to provide inertia and primary frequency regulation. In this paper, a two-stage joint clearing mechanism considering multiple type resources electrical energy and inertia/PFR ancillary service is proposed, and a joint clearing optimization model including energy storage, wind power, photovoltaic and frequency-supported loads is established. The capacity of all kinds of ancillary service resources required by the system and the start and stop of each resource are solved in the security constrained unit commitment(SCUC) pre-clearing model considering the frequency security constraints. In the formal clearing model, the simple inertia and the demand constraint of PFR ancillary service capacity are considered, and the market pricing is simpler and more transparent taking into account frequency security. The simulation results of the improved IEEE 39-node system demonstrate the effectiveness of the proposed two-stage market clearing mechanism.

To address the issue of insufficient frequency regulation resources in power systems caused by high penetration of renewable energy, a two-stage optimization method is proposed based on virtual power plant （VPP） technology. This method aggregates flexible resources within buildings to jointly participate in multi-energy markets and frequency regulation ancillary service markets. Firstly, a gas-electric VPP model is constructed with smart buildings as units. Secondly, considering the high-quality frequency regulation potential of electric energy storage and electric vehicles within buildings, a day-ahead and real-time two-stage optimization strategy is designed for VPP-assisted grid frequency regulation. In the day-ahead stage, based on forecasts such as wind and solar generation, the mixed-integer linear programming method is applied to obtain the global optimal day-ahead scheduling scheme, aiming to minimize the total operating cost of the VPP. In the real-time stage, model predictive control combined with mixed-integer quadratic programming is used for rolling optimization based on real-time measurements such as frequency regulation signals, reducing prediction errors. Finally, simulation results under different scenarios demonstrate that the proposed method enables the VPP to fully exploit the flexibility potential of electric energy storage and electric vehicles for grid frequency regulation, achieving optimal resource allocation in both energy and frequency regulation markets while significantly improving the overall operational efficiency of the VPP.

With the promotion of new power system construction and new energy comprehensive participation in market trading, the source-load bilateral stochasticity causes increasing system balance problems. By comparing the existing system balance risk response mechanism at home and abroad, it is found that the market construction of flexibility resources in China is relatively backward. Therefore, the intraday multiple flexible resource matching transaction based on the deviation linkage of generator-user alliance is proposed. Firstly, the main body of balance responsibility of the “generator-user alliance” deviation linkage is proposed, and the market framework of the intraday multiple flexible resource matching transaction based on the deviation linkage of generator-user alliance is constructed. Then, the intraday generator-user alliance deviation linkage resources matching transaction model is established. Finally, the IEEE 18-node system example is used to prove that the proposed transaction can effectively incentivize the system regulation potential, reduce the system balancing cost, mitigate the deviation price risk of the operating entities to a certain extent, and at the same time, enhance the flexible resource cost recovery ability.

The traditional approach based on human experience has limited diversity in operating scenarios, making it difficult to fully characterize the complex operating boundaries of high proportion new energy power systems. Operation model arranging based on this approach may result in safety risk blind spots in the system. This article proposes a typical operation mode extraction method based on convolutional neural networks and self-attention mechanisms. Firstly, an autoencoder model is constructed using convolutional neural networks to intelligently extract complex spatial coupling relationships between variables in the operation of high proportion new energy grids. Secondly, based on the extracted operational characteristics of the power grid, a feature clustering layer is introduced, which is jointly optimized with an autoencoder model to achieve clustering. Then, the clustering results of the operation mode are characterized by the proposed indicators of the new energy-load-traditional energy combination mode and the clustering effect evaluation indicators. Finally, the sample set represented is expanded by class centers and static security assessments are conducted. The calculation results show that this method can effectively explore the spatial correlation and complex combination patterns of high-dimensional operating variables in the power grid. The safety verification carried out on this basis helps to differentiate and characterize the safety risks of high proportion new energy power systems between different modes, providing strong support for the formulation of typical operating modes of new power systems.

Aiming at the existing clustering algorithms′ lack of non-convex cluster identification ability and parameter sensitivity in extracting typical load curves, a typical load curve extraction method based on the improved density peak clustering (DPC) algorithm is proposed. Firstly, an adaptive cluster centre selection method based on local density and relative distance is proposed to solve the subjective uncertainty problem of artificially selecting cluster centres in the traditional DPC algorithm. Secondly, two new parameters of cluster cross-density and cluster boundary density are defined, and an initial cluster correction strategy is proposed to effectively solve the problem of assigning cascading errors to non-cluster centre points. Comparison experiments with six 2D datasets, four multidimensional datasets and one actual REFIT electrical load measurement dataset show that the proposed improved DPC algorithm outperforms the traditional DPC, K-means and DBSCAN algorithms in three evaluation indexes, namely, accuracy (ACC), adjustment of rand index (ARI) and Fowlkes-Mallows Index (FMI), where they are better than the traditional DPC, K-means and DBSCAN algorithms. Among them, ACC, ARI and FMI are improved by 25.40 %, 46.92% and 21.83 % on average. The results show that the typical load curve extracted by the proposed improved DPC algorithm is more representative, which can provide more accurate data support for the optimal regulation of power system flexibility resources.

Black start path optimization is a key step in the process of power grid restoration. To solve the problems of large search space, high computational complexity, and slow convergence speed in black start path optimization of new energy power grids, an improved A * algorithm based black start path optimization strategy for new energy power grids is proposed. Firstly, the uncertainty of the output power of new energy stations is analyzed, and a BP neural network is used for ultra short term power prediction. Secondly, taking the restoration time of transmission lines as the objective function, a black start restoration optimization model is constructed considering constraints such as line operation status and starting power. Based on this, the traditional A * algorithm is improved by introducing weight coefficients and path smoothing strategies to obtain the optimal restoration path for black start of power grids containing new energy. Finally, a MAT/AB simulation example is used to compare the performance of different path optimization strategies during the black start process. The results show that this algorithm has higher recovery efficiency compared to other strategies, and improves the available active power and total power generation during the black start process, effectively ensuring the reliability and speed of the power supply scheme.

The large-scale access of renewable energy to the power grid has intermittent and strong random characteristics, which is easy to cause significant frequency fluctuations. For multi-area power grids, the existing multi-agent cooperative algorithms based on neural networks often have unsatisfactory control performance due to gradient explosion and gradient disappearance in the face of strong random disturbances. To this end, this paper proposes an adaptive weight residual proximal policy optimization method for automatic power generation control. This method connects the layers of the agent policy neural network through residual connections, and adds a trainable adaptive weight to the residual connection to achieve the optimal residual ratio and alleviate the problem of gradient explosion and gradient disappearance of the neural network, so that the training process of the deep network is more stable and efficient, and then the multi-area cooperative optimal solution under strong random disturbance is obtained faster to eliminate the frequency fluctuation caused by strong random disturbance. The effectiveness of the proposed algorithm is verified by simulation tests on IEEE two-area and three-area load frequency control systems. Compared with various reinforcement learning algorithms, the proposed algorithm has faster convergence and stronger stability, higher frequency stability and control performance.