During sending-end faults in the hybrid cascaded HVDC (HC-HVDC) system, the transient voltage drop characteristics under the interaction of the AC/DC hybrid system remain unclear, and the reactive power support provided by the HC-HVDC to the sending-end AC system requires further investigation. To address this problem, the reactive power interaction coupling mechanism between the sending-end AC system and the HC-HVDC is revealed, and the transient voltage mathematical model considering fault severity and duration is established. Under the dynamic change of the AC system voltage, the difference between the reactive power provided only by the reactive power compensation devices and by the combined modular multilevel converters (MMC) and reactive power compensation devices is analyzed. It is concluded that using MMC to provide a proportion of reactive power enhances the reactive power support to the AC system during faults. Then, the transient voltage model considering the reactive power support of MMC is established, and the critical reactive power consumption of line commutated converter (LCC) is quantified. It is concluded that the reactive power consumption of LCC exceeding its critical value deteriorates the transient voltage. A coordinated support strategy for the sending-end AC system based on reactive power support of MMC and reactive power regulation of LCC is proposed. It can effectively address the challenge of weakened reactive power support to the AC system due to voltage drop, thereby preventing the unbalanced reactive power from deteriorating the transient voltage, and realizing active support of the transient voltage. Finally, a simulation model is established on the PSCAD/EMTDC platform, and the simulation results validate the effectiveness of the proposed strategy in supporting the transient voltage, under different fault types, durations, severities, and locations.

Aiming at the problem of safe operation caused by the difficulty of new energy consumption and the penetration of high proportion of new energy in the sending and receiving end systems of new energy bases, a security constrained unit commitment model (SCUC) of the sending and receiving end power system considering the cross-region mutual aid of reserve resources is proposed. The active frequency constraint is added to the unit commitment model, which considers the minimum inertia demand of the system, frequency regulation reserve and peak shaving reserve to ensure the active power balance and frequency stability and safety of the sending and receiving end systems. At the same time, the reserve of the unit and energy storage system (ESS) is divided, and the tie-line adjustment space is coupled. Aiming at the total operation economy of the system, the transmission power of the tie line, the off-grid state of the units and energy storage in the sending and receiving system, the power generation level and the operation reserve are optimized, and the daily operation cost of the system is reduced. Finally, the effectiveness and feasibility of the method are verified by the test and analysis of a sendingreceiving two-area system.

The operation friendliness of wind power has a significant impact on the safety and economy of system operation. The scheduling optimization method in the day-ahead stage considering the operation friendliness of wind farms is proposed in this paper. Wind farm prediction accuracy, output fluctuation characteristics, and source-load complementarity characteristics are selected as friendliness indicators to evaluate the generation characteristics of wind farms. The wind power friendliness evaluation method is proposed. The day-ahead hierarchical scheduling model considering evaluation results of operation friendliness is constructed. The upper model formulates the scheduling plan for the wind power and thermal units to minimize the comprehensive cost. The lower model forms power constraints based on the friendliness indicator, and formulates the day-ahead scheduling plan of each wind farm, giving priority to the consumption of high-quality wind power. The numerical analysis verified the effectiveness and feasibility of the proposed method.

To enhance the utilization efficiency of wind and solar renewable energy in industrial parks, reduce operational costs, and optimize the charging experience for electric vehicle (EV) users, this paper proposes a real-time scheduling strategy based on the “Dual Electricity Price Reservation—Surplus Refund Without Additional Charges Mechanism” (DPRSRWAC). The strategy employs a Gaussian Mixture Model (GMM) to analyze EV users’ charging and discharging behaviors within the park, constructing a behavior prediction model. It introduces reservation, penalty, and ticket-grabbing mechanisms, combined with the Interval Optimization Method (IOM) and Particle Swarm Optimization (PSO), to dynamically solve the optimal reservation electricity price at each time step, thereby guiding user behavior effectively. Furthermore, linear programming (LP) is used to optimize the real-time charging and discharging schedules of EVs, incorporating reservation data into the generation-side model. The generation-side optimal charging and discharging behavior, along with real-time electricity prices, is determined using Dynamic Programming (DP). In addition, this study explicitly considers the battery aging cost associated with V2G operations and proposes a benefit model for EV owners in V2G mode, thereby incentivizing user participation and enhancing acceptance. A simulation analysis demonstrates that the proposed strategy effectively reduces park operation costs and user charging costs by 8.0% and 33.1%, respectively, while increasing the utilization efficiency of wind and solar energy by 19.3%. Key performance indicators are significantly improved, indicating the strategy’s economic viability and feasibility. This work provides an effective solution for energy management in smart industrial parks.

High-voltage direct current (HVDC) sending systems have been the main means of renewable power cross-regional sharing and consumption. However, the transient overvoltage problems restrict the transmission capacity and renewable energy accommodation. The allocation of wind–solar–thermal storage capacity has become an important factor affecting the safety and stability of renewable energy sending. A capacity planning method is proposed for a wind–solar–thermal-storage bundled HVDC sending system considering transient overvoltage constraints. Firstly, based on quantile regression analysis and Gaussian mixture modeling, the typical scenario generation method is proposed to depict the uncertainty of renewable energy. Then, the transient overvoltage characteristics of the integrated HVDC transmission system are analyzed. The relationship between the power output of power sources and the system short-circuit capacity is derived. Meanwhile, the calculation method of the minimum short-circuit capacity of the HVDC system is proposed. Based on the calculation method, the transient overvoltage constraint corresponding to the voltage support strength is constructed. Finally, considering the transient overvoltage constraints, the capacity planning model of the wind–solar–thermal storage is established. The upper-layer model optimizes the configuration scheme of the wind–solar–thermal storage to minimize the total system cost. The lower-layer model optimizes the operation scheduling under the typical operation scenarios of renewable energy and delivery load. The optimal capacity planning scheme for the wind–solar–thermal storage is determined through the coordinated optimization of the two-layer model. The feasibility and effectiveness of the proposed method are verified through a case analysis. The results show that the proposed planning method can effectively maintain a higher short-circuit ratio and improve the voltage support strength under the premise of completing the sending plan.

With the increasing penetration of renewable energy in power systems, it is vital to adopt methods to enhance the acceptance capacity of renewable energy. Energy-intensive loads have excellent potential for regulating the utilization of renewable energy. Existing studies have often overlooked the regulatory potential of energy-intensive industrial loads. The coordinated optimization of source, load, and storage can improve the matching degree between power supply and load demand and achieve on-site consumption of renewable energy. This paper proposes a coordinated optimization method for source–load–storage integrated systems, utilizing for regulation energy-intensive industrial loads such as electrolytic aluminum load and polysilicon load. The operational characteristics and regulatory ability of electrolytic aluminum load and polysilicon load were analyzed in the production process. Operation models of energy-intensive loads are proposed. A coordinated operation model of a source–load–storage integrated system is established. The operation schemes of thermal units, energy storage, and energy-intensive loads are jointly optimized to guarantee power supply capacity and renewable energy consumption. In addition, power purchase from the bulk power system and the time-of-use electricity price are considered to ensure a reliable power supply for energy-intensive loads. The case results showed that on the premise of ensuring that the production meets the requirements, the flexibility and economy of system operation were effectively improved. Reasonably rated power and capacity for the energy storage system can improve the regulation ability and reduce the operating costs of regional systems.

Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper proposes a multi-objective bi-level sustainability planning method for IES considering the bilateral response of supply and demand and hydrogen utilization. Firstly, the multi-energy flow in the IES is analyzed, constructing the system energy flow framework, studying the support ability of hydrogen utilization and the bilateral response of supply and demand to system energy conservation, emission reduction and sustainable development. Secondly, a multi-objective bi-level planning model for IES is constructed with the purpose of optimizing economy, RE consumption, and carbon emission. The non-dominated sorting genetic algorithm II (NSGA-II) and commercial solver Gurobi are used to solve the model and, through the simulation, verify the model’s effectiveness. Finally, the planning results show that after introducing the hydrogen fuel cells, hydrogen storage tank, and bilateral response, the total costs and carbon emissions decreased by 29.17% and 77.12%, while the RE consumption rate increased by 16.75%. After introducing the multi-objective planning method considering the system economy, RE consumption, and carbon emissions, the system total cost increased by 0.34%, the consumption rate of RE increased by 0.6%, and the carbon emissions decreased by 43.61t, which effectively provides reference for resource planning and sustainable development of IES.

This paper addresses the problem of underestimated temperature measurements in practical proton exchange membrane (PEM) electrolyzer engineering due to heat losses by enhancing the existing second-order RC equivalent circuit model of PEM electrolyzers. We present a novel engineering circuit model for PEM electrolyzers, incorporating the effects of heat losses from gases and pipelines. The objective is to enhance the model’s ability to predict electrolyzer performance and align control strategies with the realities of engineering practice. However, the PEM electrolyzer model is complex, being time-varying and nonlinear due to multi-physics field coupling. The parameters of the equivalent circuit are changed by the electrical energy input and its own state. To tackle the problem of parameter variation, firstly, a recursive identification algorithm is employed to estimate the internal equivalent circuit parameters of the engineering model. Then, the additional resistance is fitted according to the relationship between heat loss and current to complete the engineering circuit model identification. Finally, using MATLAB to construct an engineering model and validate the effectiveness of the proposed identification algorithm.

Accurately describing the error characteristics of wind power output prediction is helpful for the rational allocation of system reserve capacity and the optimization of day-ahead scheduling plans.This paper proposes a day-ahead optimization scheduling method for power systems considering wind power ramping reserve requirements.First,based on the wind power ramping segment,the ramping characteristics are extracted,and a two-dimensional interval of ramping amplitude-predicted power is established.The adaptive kernel density estimation method is used to fit the probability distribution of wind power prediction errors.Then,based on the distribution of wind power prediction errors,the system reserve requirements are determined.Looking for minimal comprehensive operating costs of reserve costs and risk costs,a continuous-time day-ahead optimization scheduling model is established.Next,the Bernstein polynomial interpolation solution space transform is adopted to complete model conversion,thereby optimizing reserve capacity,unit combination,and output plans.Finally,a case study verifies that the established wind power prediction error distribution model can accurately describe the stochastic characteristics of wind power.The proposed day-ahead scheduling method can effectively allocate system reserve capacity,ensuring operational safety and economic efficiency.

The monopole blocking of the LCC-HVDC system can lead to transient overvoltage at the sending-end AC system. Hence, this paper first investigates the power interaction characteristic between AC and DC systems under monopole blocking. Furthermore, by decoupling DC current from AC voltage, the sound pole commutation failure criterion after monopole blocking considering the commutation bus voltages at both ends is derived. Then, an optimal DC current control strategy for suppressing transient overvoltage is proposed. By adjusting the DC current order adaptively, the proposed strategy could have the ability to suppress the sending-end transient overvoltage and sound pole commutation failure effectively. Finally, the effectiveness of the proposed strategy is verified in the CIGRE HVDC bipolar system model in PSCAD/EMTDC.

The energy-shared alliance model with multiple players is one of the feasible and optimal pathways to improve resource utilization without requiring costly and time-consuming infrastructure investments. However, the traditional energy trading method is not suitable for the Shared Alliance model due to it limits the large-scale progress of energy trading among multiple participants for CO-governance and restricts the willingness of more players to enter the Shared Alliance. This paper studies the energy trading strategy for the Shared Alliance multiplayer based on Nash Negotiation. An energy-sharing framework is established which contains energy storage, multi-microgrid groups, and the superior power grid. The cooperative and competitive energy market transaction mechanism of multiple suppliers and buyers is constructed by Nash negotiation theory, the surplus electricity of each participant is shared through the transaction mechanism. The numerical simulation is carried out to verify the effectiveness of the proposed method. The results show that the proposed model can give full play to the initiative of the demand side and improve the utilization of energy storage. Such research delivers abundant technical support for energy complementarity among multi-participants and provides sustainable development of energy-sharing mechanisms.

Considering risk and reserve safety constraints, an optimization scheduling method considering the wind power ramp-up reserve demand is proposed. Based on the wind power ramp-up segment, the ramp-up amplitude-forecast power two-dimensional interval is established, and the adaptive kernel density estimation method is used to fit the probability distribution of wind power forecast errors. According to the wind power forecast error distribution, the system reserve demand is determined. With the objective of minimizing the comprehensive operating cost of the system, including reserve cost and risk cost, the reserve capacity, unit combination, and output plan are optimized. The case study verifies that the established wind power forecast error distribution model can more accurately describe the random fluctuation characteristics of wind power. The proposed day-ahead scheduling method can reasonably allocate system reserve capacity, ensuring the safety and economy of operation.

With the intensification of the energy crisis and the global greenhouse effect, it is particularly necessary to develop renewable energy generation and reduce carbon emissions. Therefore, a low-carbon economic dispatch model of the electricity-gas-heat integrated energy system (IES) considering ladder-type carbon trading mechanism (LCTM), vehicles charging, and multimode utilization of hydrogen is proposed. A multimode utilization of hydrogen model in the IES is established and the model energy flow is analyzed. The vehicles charging demand is simulated by Monte Carlo, which provides a model to calculate the carbon emissions of vehicles. In addition, the ladder-type carbon trading mechanism and integrated demand response are introduced to improve the system economy and low carbon performance. The simulation results demonstrate that the proposed model can significantly reduce carbon emissions and improve the economy of the IES.

为缓解风电自身有功调节能力差的问题,提出一种用于提升风电场有功调节能力的风储系统多时间尺度运行策略.首先,为减少储能充放电状态转换次数,将储能单元划分为充、放电组,分别承担充、放电需求,根据储能单元荷电状态,采用储能单元动态分组机制更新充、放电组中的储能单元.其次,基于模型预测控制方法构建风储系统多时间尺度运行模型,用于提升风电场不同时间尺度的有功调节能力.提出储能单元功率分配策略,精细化管理储能单元有序动作,优化储能出力在各单元间的功率分配.最后以新疆某风电场构建算例,结果表明,所提策略能有效提升风储系统有功调节能力并减少储能充放电状态转换次数.

The construction of energy storage power stations in wind power gathering areas can ensure the primary frequency modulation capability of the whole area and improve the economy at the same time. A storage capacity allocation method based on operational strategy is proposed for building storage power stations in wind power gathering areas. Energy storage stations use an independent operation mechanism to participate in power market transactions on the premise of ensuring the primary frequency modulation capacity of the region, so as to maximize the utilization rate and efficiency of energy storage. A two-tier model for collaborative optimization of operation configuration of energy storage power stations is proposed. The outer layer configures energy storage with the maximum return on investment in the whole life cycle as the target. The inner layer optimizes operation strategy with the maximum expected profit in daily operation as the target considering energy storage cycle life. The outer and inner layers are optimized to achieve comprehensive optimization, so as to get the result of energy storage configuration. An improved sparrow search algorithm is presented to improve the convergence rate and analyze the two-level configuration model. An example shows that the proposed method can improve the economy of energy storage while ensuring the primary frequency modulation capability of the region.

Energy storage (ES) can mitigate the pressure of peak shaving and frequency regulation in power systems with high penetration of renewable energy (RE) caused by uncertainty and inflexibility. However, the demand for ES capacity to enhance the peak shaving and frequency regulation capability of power systems with high penetration of RE has not been clarified at present. In this context, this study provides an approach to analyzing the ES demand capacity for peak shaving and frequency regulation. Firstly, to portray the uncertainty of the net load, a scenario set generation method is proposed based on the quantile regression analysis and Gaussian mixture model clustering. Then, a multi-scenario and multi-time scale optimal operation model is established to handle the uncertainty of net load, and the power correction model for ES operations is established to accommodate the balance of ES charging/discharging and optimization of system operation cost. Finally, based on the solution results of the above models, the method for determining the system's demand for ES capacity is proposed, and the relationship between the penetration of RE, ES power and capacity, and the confidence level of meeting demand is obtained. Numerical studies show that with a confidence level of 90% for satisfying demand, the 49.5% RE penetration system (the maximum load is 9896.42 MW) needs ES power and capacity of 1358 MW and 4122 MWh for peaking and ES power and capacity of 478 MW and 47 MWh for frequency regulation. Further, as the penetration of RE increases, the proportion of ES demand power to the system's power supply capacity and duration demand of ES also increase.

The quasi-three-phase tripping strategy of the double-circuit transmission line can improve the reliability of power supply compared with the traditional automatic reclosing strategy, but some complex types of faults will still interrupt the power supply by tripping the whole phase, as well as reclosing without distinguishing the nature of the fault by rectifying the time, a secondary impact will be caused to the system if reclosing in permanent faults. To address the above problems, a smart tripping and closing strategy for line-to-line un-grounding faults of the wind farm double-circuit transmission line based on the fault phase voltage characteristics is proposed in this paper. Firstly, the inter-phase coupling characteristics of faults in quasi-three-phase operation mode is analyzed, and a new tripping strategy based on quasi-three-phase operation mode is proposed, ensuring continuous power supply while building inter-phase coupling circuit; secondly, to establish the equivalent circuit of permanent and transient faults, the fault nature criteria is proposed based on the two fault phase voltage difference characteristics. Then a smart tripping and closing strategy for the wind farm double-circuit transmission line line-to-line un-grounding faults is proposed. Finally, the simulation results show the correctness of the strategy.

针对新能源高渗透系统灵活性需求激增的问题,文中提出一种新能源高渗透系统灵活性供给能力评价方法.首先,基于净负荷时序波动特性建立新能源高渗透系统灵活性需求模型,根据灵活性改造火电机组、需求响应和储能的运行特性,构建源荷储侧灵活性资源供给能力模型,精确计算新能源高渗透系统的灵活性需求量;其次,采用节点运行灵活性的思想建立灵活性资源供给能力评价指标;然后,基于协同优化的思想构建新能源高渗透系统灵活性评价指标计算模型,并通过Yalmip调用CPLEX对模型进行求解;最后,基于改进的IEEE 39节点系统进行仿真算例分析.结果表明,所提出的灵活性评价方法通过源、荷、储灵活性资源协调优化,能够在实现系统整体灵活性供给能力优化的同时使系统经济性更优.