南方电网能源发展研究院有限责任公司

Distributed PV inverter(DPVI)has the ability to solve various power quality problems in the distribu-tion network while completing the predetermined active power generation task.However,the small available com-pensation capacity often affects the effectiveness of power quality suppression.This paper studies a method for har-monic and voltage fluctuation management using multiple small-capacity DPVIs cooperatively.Firstly,the system architecture of the comprehensive power quality control scheme for distribution networks based on DPVIs is presen-ted,and the DPVI reference current generation method combining local harmonic control and distributed voltage control is proposed.The node voltage collaborative control strategy under leader-follower mode is designed based on distributed consistency protocol,and the convergence of the proposed distributed control system is proved.Second-ly,the mathematical model of distributed control for the new distribution network is established.Considering the communication delay and uncertain parameters,the system stability and robust stability are analyzed,and stability criteria as well as robust stability criteria are derived.The leader selection optimization model is established with the goal of minimizing the one step convergence factor.The simulation results in various scenarios indicate that the proposed method can effectively achieve comprehensive management of various power quality issues and validate the accuracy of the stability criteria and robust stability criteria considering delay,providing a technology reference for improving the power supply quality of new distribution systems.

[Objective] Improper disposal of end-of-life power batteries may lead to soil and water pollution. Scientific evaluation of the environmental impacts during the regeneration and utilization processes of these batteries and the identification of key environmental impact factors and stages are essential for improving the resource recovery efficiency and promoting the achievement of the “dual carbon” goal. [Methods] Under such circumstances, the life cycle assessment (LCA) method was employed to quantitatively analyze the environmental impacts of two recycling processes for end-of-life batteries: mechanical-physical recovery and wet recovery. This study innovatively specified five treatment stages, and three endpoint and eighteen midpoint environmental impact types were considered. [Results] (1) The environmental impact values of both recycling processes were negative, indicating that the recycling of end-of-life power batteries could effectively mitigate environmental impacts from a life cycle perspective. The mechanical-physical recovery exhibited better environmental performance compared to the wet recovery, with both processes demonstrating the greatest mitigation effects on marine ecotoxicity, accounting for 51.4% and 53.2%, respectively. (2) From the perspective of endpoint environmental impact, the recycling of end-of-life power batteries exhibited markedly higher mitigation effects on resource depletion (97.3%) than on human health (2.6%) and ecosystem (0.1%). (3) The battery crushing and sorting stage and the electrode sheet processing stage were identified as the primary contributors to the environmental impacts, each accounting for about 35.7% of the total impacts. [Conclusion] Sensitivity analysis shows that reducing material inputs and improving the recycling rate of recycled products have the most significant impacts on life cycle environmental effects, while variations in electricity input show relatively minor effects. It is recommended to further strengthen the cascading utilization of end-of-life power batteries and improve the output rate of recycled products to reduce their environmental impacts.

As the proportion of distributed generation, mainly wind turbine generation and photovoltaic, in terminal energy consumption increases, it is of great significance to fully utilize the flexibility resources within power grids and enhance the regulation capability of active distribution networks (ADN). To this end, an ADN collaborative optimization scheduling method is proposed considering dispatchable backup battery of 5G base station (BS) and soft open point. First, an analysis of the power consumption model of 5G BS is conducted, leading to the establishment of a backup battery capacity evaluation model which considers the communication load of 5G BS and the reliability of ADN nodes. Based on this and taking the minimization of the comprehensive operating cost of ADN as the objective function, considering the uncertainty of wind power, photovoltaic output, and load demand, an ADN collaborative optimal scheduling model based on chance constraints is developed. A second-order cone relaxation and a chance-constrained determinization approach based on Latin hypercube sampling are employed to enhance the solution efficiency of the model, which transforms the model into a mixed-integer second-order cone programming problem. Finally, the feasibility and effectiveness of the proposed method are verified by the IEEE 33-bus ADN case.

[Objective] This study addresses the challenges faced by wind farms， exhibiting varying construction costs， in their participation in electricity markets. It seeks to facilitate a seamless transition from a fixed-price procurement model to a competitive spot market model for wind power while accounting for the inherent uncertainty of wind power generation. Consequently， designing a robust transition mechanism is essential for wind power participation in electricity markets. [Methods] A medium- and long-term contract mechanism is proposed to adjust wind farm revenue by modifying contract coverage. A bi-level programming is employed to model participation in both the medium- and long-term and spot markets. The upper-level problem is to determine the optimal medium- and long-term contract coverage of wind power， with the objective of minimizing government subsidy costs and improving the fairness index of unit generation profits across all types of wind farms. The lower-level problem is framed as a joint clearing model for energy and reserve markets， accounting for the uncertainty of wind power output， which is represented by typical scenarios. The bi-level model is transformed into a single-level optimization model using the Karush-Kuhn-Tucker condition substitution and the big M method. Additionally， the corresponding linearization methods are proposed to handle the product terms involving price， continuous variables， and absolute value terms in the upper objective， eventually transforming the single-level optimization model into a mixed-integer linear programming model. To enhance computational efficiency， the check-add method is applied to address the capacity constraints of the transmission lines when solving the bi-level programming model. A simulation analysis of a real 44-unit， 1560-bus system containing two wind farms was conducted to validate the effectiveness of the proposed method. [Results] The contract hedging effect enables the wind farm to mitigate the risk of spot price fluctuations. Within the decision cycle， the subsidy amount is reduced by 1.34 million yuan， and the unit profit gap narrows by 0.015 yuan/kWh. Additionally， decision-makers can effectively adjust the policy impact by tuning the weighting factors. [Conclusions] The results indicate that By determining the optimal contract coverage ratio， the proposed approach effectively reduced government subsidy costs and narrowed the per-unit profit gap between wind farms， achieving a smooth transition to market participation.

With distributed photovoltaics access massively, which is characterized by stochasticity and volatility, has posed new technical challenges to the safe and reliable operation of the distribution network. To evaluate and improve the hosting capacity of distributed photovoltaics in distribution network, the article proposed a robust optimization method for the hosting capacity of distributed photovoltaics in the distribution network, which considers adjustable characteristics of 5G base stations. Firstly, a 5G base station adjustable characteristics model is constructed, which considers the communication load migration and the dynamic power backup of the energy storage. Secondly, a robust optimization model of the maximum hosting capacity of distributed photovoltaics in the distribution network with 5G base stations is established. Then, the model is solved using an increasingly tight linear cut algorithm combined with a column-and-constraint generation algorithm. Finally, the effectiveness of the model is verified on an improved IEEE 33-node distribution network, and the effects of source-load uncertainty, 5G base station communication load migration, and dynamic energy storage backup on the maximum access capacity of distributed photovoltaics in distribution network are analyzed.

The output of wind power and photovoltaic has the characteristics of randomness, volatility, and intermittency. Direct grid connection will lead to a lower power generation income of the power station, a greater volatility of grid connection of electric energy, and more wind and photovoltaic power discards, resulting in lower carbon emission reductions. The addition of pumped storage power plants effectively reduces the above impacts. Therefore, this paper studies the application scenario of wind photovoltaic and pumped storage combined power generation, establishes a multi-objective optimization model that comprehensively considers the three objectives of maximizing the economic benefits of the combined system, minimizing the system power fluctuation, and maximizing carbon emission reduction, and converts the multi-objective problem into a single objective problem for solution by normalization. In this paper, the gray wolf algorithm, which can realize the adaptive adjustment of local search and global search, is used to simulate and optimize the grid connected power of wind power, photovoltaic, and pumped storage. The optimization results show that the established model can effectively improve the economic benefits of the system and greatly reduce the fluctuation of power grid connection. In addition, the efficient use of new energy also greatly improves the carbon emission reduction capacity of the joint system, which proves that the model has high feasibility.

Accurate prediction of transmission line project cost is of great significance to construction quality and cost control.Since the feature dimension in the traditional transmission line project cost prediction is too high and a single prediction model is difficult to fit the complex cost data,a transmission line project cost prediction method is proposed based on embedding dimensionality reduction and ensemble learning.Firstly,the features are sorted with the embedding method and the XGBoost model to screen out the features that have a significant impact on the cost,achieving the data dimensionality reduction.Then the XGBoost,random forest,SVM and other models are integrated to form a two-layer ensemble learning model.Finally,a case study is carried out based on the data of real transmission line projects,and the proposed method is compared with the XGBoost,random forest,SVM,ELM,and BP neural network models.The rusults show that the mean absolute percentage error of the proposed method is within 4%,which is superior to other single model,and is of great value to the research of transmission line project cost control.

With the rapid development of various load side resources such as adjustable loads and electric vehicles,how to accurately regulate them has become an important research point.In order to give full play to the regulation ability of flexible loads in distribution networks,a hierarchical cluster regulation method for flexible loads in distribution networks based on improved alternating direction multiplier method is proposed.Firstly,the flexible loads are clustered hierarchically using the BIRCH clustering algorithm.Secondly,based on Nash negotiation theory,the original problem is decomposed into two sub problems:cost minimization and revenue allocation,and a flexible load cluster regulation model for distribution networks is established.Then,an improved alternating direction multiplier method is proposed by introducing an adaptive variable parameter acceleration factor.Finally,a simulation example is used to verify the effectiveness of the proposed method.The results show that the proposed method can effectively achieve cluster regulation under access of large-scale flexible loads,and its convergence performance is better than the conventional methods.

Under the low-carbon development goal, energy storage allocation is the key measure to ensure the safe and economic operation of low-carbon parks, and to reduce carbon emissions. To solve the problems of inaccurate carbon emission calculation and insufficient utilization of equivalent energy storage resources in low-carbon parks, this paper proposes a dynamic emission factor calculation method based on the carbon emission flow theory, which realizes the accurate measurement of indirect carbon emissions from park electricity consumption. Then, taking into account the available equivalent energy storage resources in the park, it proposes an energy storage capacity optimization allocation model considering the equivalent energy storage characteristics of thermal system, and uses the big M method to equivalently transform the nonlinear constraints in the model. Finally, it conducts simulation analysis based on a case system to verify the correctness and effectiveness of the proposed model.

A distribution network flexibility evaluation method considering collaborative interaction of flexible resources is proposed.Firstly,a flexibility evaluation indicator system is constructed,which includes three aspects:collaborative regulation capacity,collaborative efficiency quality,and power grid reliability.Secondly,a Gaussian mixture model is used to construct typical operating scenarios,and the subjective and objective weights of evaluation indicators are determined using Analytic Hierarchy Process and Random Forest Model,respectively.Through a double-layer evaluation process,the evaluation indicator values for each typical scenario are calculated.Then,based on the probability of typical scenarios appearing,the comprehensive flexibility evaluation index values of each flexible resource access scheme are weighted.Finally,the feasibility and effectiveness of the proposed method are verified through the case study on a 54-bus distribution network.

Electric power is vital for the national security, economy, and people's livelihood of a country. Ensuring the stable and secure supply of electric power is crucial for achieving carbon peaking and carbon neutrality. Therefore, it is imperative to analyze the weaknesses and challenges of power supply security in China and construct a power supply guarantee system that adapts to the new era and facilitate high-quality economic development. Herein, the research progress of power security supply is reviewed, the current status of power supply in China is summarized, and the trend in power security supply in China during the 14th Five-Year period and for the medium and long terms is analyzed. Moreover, considering the recent power rationing incidents, the problems and challenges for power supply in China are summarized and analyzed. On this basis, the basic principles of adhering to security first, a low-carbon path, market-oriented reforms, and technological innovations are proposed, and a three-step roadmap for constructing a new power supply guarantee system is investigated. Furthermore, we propose the following suggestions: (1) enhancing China's power supply guarantee capabilities to solidify its foundation for power supply security; (2) improving the intrinsic security of power supply by focusing on the demand side; (3) establishing a new-generation technical system for guaranteeing power supply security; and (4) optimizing the market system to construct a power security ecology participated by all.

A method for evaluate the maximum hosting capacity of distributed photovoltaic for distribution network considering the schedulable potential of 5G base station is proposed.Firstly,construct a power load demand model for 5G base station and analyze the schedulable potential of 5G base station's own energy storage;Then,establish the distributed photovoltaic maximum hosting capacity evaluation model of distribution network considering the dispatchable potential of 5G base station;Subsequently,auxiliary variables are introduced and the model is subjected to second-order cone relaxation to construct a linearized model for distributed PV maximum hosting capacity assessment.Finally,the improved IEEE 33 bus distribution network is used to evaluate the distributed PV maximum hosting capacity of distribution network under different scenarios.The results show that considering the dispatchable potential of 5G base station can effectively improve the maximum hosting capacity of distributed new energy in distribution network.

A new energy supportability assessment method of distribution network considering demand-side management and network reconfiguration is proposed.Firstly,a demand-side management and network reconfiguration model suitable for new energy carrying capacity assessment of distribution network is constructed.Secondly,with the goal of maximizing the supportability of distributed new energy,a new energy supportability assessment model of distribution network considering demand-side management and network reconfiguration is established.And then,the model is solved using second-order cone relaxation method.Finally,an improved IEEE 33-bus distribution network is used for simulation,and a comparative analysis of the new energy supportability of the system distribution under different scenarios is carried out.The results show that considering the demand-side management and network reconfiguration can effectively improve the distributed new energy supportability of distribution network.

In order to comprehensively and effectively analyze the influence of equipment fault on power system operation under the random fluctuation of renewable energy and load power,a comprehensive evaluation model of fault severity considering source-load power uncertainty is proposed. Considering the security,stability and economy of power grid operation and different time scale processes of steady and transient states,a comprehensive evaluation index system of fault severity is constructed from multiple spatiotemporal perspectives. The stochastic response surface method is used to deal with the uncertain factors and calculate the probability density distribution of indicators,and the expected normalized decision matrix is obtained.The G1-entropy weight method is adopted to assign indicator weights,and the technique for order preference by similarity to an ideal solution is improved to realize the solution of comprehensive evaluation model of fault severity. The simulative results of IEEE 39-bus system show that the proposed model can comprehensively and objectively realize fault screening and sorting,and has certain flexibility and universality,which is convenient for decision makers to identify critical faults from global and local perspectives.