南方电网能源发展研究院有限责任公司

[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.

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.

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.