This paper focuses on grid-connected photovoltaic-energy storage (PV-ESS) systems, targeting active frequency support and multi-mode control. It develops a converter multi-mode strategy integrating Time-of-Use(TOU) electricity prices and active frequency support. The theoretical basis is established by comparing domestic and international control strategies and topologies. A mode adaptive switching scheme is proposed based on electricity prices, frequency, irradiance and storage status. Hardware-in-the-loop(HIL) experiments validate the system’s flexible mode switching and reliable grid frequency support. Moreover, it lowers consumer electricity costs, fully demonstrating the proposed scheme’s feasibility.

Abstract This letter presents a coupling-factor-based nonlinear decoupling method to address transient dynamics in high-order nonlinear voltage source converters (VSCs), a less-explored area compared to their control strategy and small-signal stability. The nonlinear decoupling method divides the complex system into simpler cubic modes using coupling factors, facilitating the application of established transient stability analysis techniques. It applies this approach to analyze transient stability in LCL-filtered VSCs under faults, finding that certain bandwidth adjustments can negatively affect stability.

A directed communication graph based distributed secondary control strategy based on event triggered method is proposed to address the issues of voltage recovery and precise power sharing in DC microgrids. Firstly, a generation model of distributed power sources is established based on droop control, and the influence of droop control are analyzed. Then, to address the problem of secondary control requiring extensive communication to obtain global information, consensus algorithms from graph theory are applied to establish a framework for a directed communication graph based event-triggered distributed secondary control. Subsequently, the convergence of the proposed event-triggered control algorithm is proven using Lyapunov functions. Finally, the feasibility of the proposed control strategy is verified through simulation tests.

Abstract This paper proposes a modified FCS-MPC strategy for voltage source inverters (VSI), which can achieve a better voltage tracking performance than traditional MPC with limited computation burden. Instead of immediately switching at the beginning of the control period, the proposed method will delay the switching action, which actually combines the present state and the next optimal switching state for better tracking performance. As such, the suppression of total harmonic distortion (THD) gets improved. Without additional state traversal, the delaying time computation burden is limited. In simulation test, the efficactiveness is confirmed by the VSI of proposed strategy.

Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents a fault diagnosis method for the distribution network based on the D-S evidence theory Bayesian network. First, the collected relay protection information is divided into two categories, protection information and circuit breaker information; the corresponding Bayesian network model is established based on their respective action logic, and the corresponding component failure probability is obtained by Bayesian backward inference. Second, the fault probabilities obtained from the two Bayesian networks are fused by the D-S evidence theory, and the obtained fault probabilities are used to diagnose the faulty component. Then, using the Bayesian network corresponding to the faulty component to perform Bayesian forward inference, the protection devices and circuit breakers are identified for misoperation or rejection to achieve the fault diagnosis of the distribution network. Finally, the correctness and reliability of the proposed diagnosis method are verified through the analysis of arithmetic cases.

In order to avoid random risks in the power system and rationally allocate power to improve power consumption efficiency, this study designed a power system optimization model based on value-at-risk method, replaced the independent variables of particle swarm optimization algorithm with discrete quantities, and used discrete binary particle swarm optimization algorithm to solve the problem. The experimental results show that compared with other algorithms, DSO has the fastest downward trend, the smallest fluctuation and the best convergence. In the case of distribution power supply and optimization, the node voltage of power network operation is 0.97, the power loss is 0.54, and the system risk value is low. It is indicated that the addition of wind power generation and solar power generation, and the optimization of distribution network can effectively improve the operation of power system. It can be seen that the power system optimization model can effectively predict the risk value, and improve the accuracy of prediction and evaluation, which has certain practical significance and economic value in the field of power grid.

Considering the application requirements of a provincial power grid's dual-active dispatching system, a dual-active system architecture with parallel system characteristics is proposed on the basis of complex parallel control theory. The functions and implementation methods of essential components such as management and control, experimentation and evaluation, learning and training, parallel execution, and feedback are systematically studied. Application practice was carried out in conjunction with the construction of a provincial power grid standby dispatching system, and the construction of a provincial power grid dual-active dispatching system was completed. The proposed design solution can solve the core problems of synchronization, consistency verification and dual-active switching between the main and standby system, improving the stability of dual-active dispatching system operation and switching efficiency. It is of great significance for improving the security and reliability of provincial power grid dispatching and building a dual-active dispatching system with the parallel operation and one-key switching.

The global climate environment is gradually becoming harsher, meteorological disasters are occurring frequently, and the accident rate of the distribution grid is also growing. Distributed Generation (DG) and mobile energy storage vehicles, as an important part of the new power system, are worth exploring their potential for preserving supply during outage accidents. In this paper, a strategy to consider the participation of mobile energy storage vehicles in dynamic reconfiguration of microgrids under the distribution system is proposed. First, a static configuration model of the microgrid that maximizes the power restoration value is established by considering the formation constraints of the radial microgrid with DG as the power source. Then, considering the maximization of power restoration value in a long time, the scheduling model of mobile storage vehicles is introduced, and the microgrid dynamic reconfiguration strategy considering mobile storage vehicles is proposed. Case studies on IEEE 37 node power system are served for demonstrating the proposed strategy, and the results show that the proposed strategy can enhance the power restoration value in local outage accidents and ensure the continuous power supply of important loads under extreme disasters.

This paper proposes a solution for the challenges faced in predicting the output of distributed solar power stations due to difficulties in obtaining and utilizing data. The proposed method is a multi-scale regional photovoltaic power generation forecasting approach that uses sequence coding reconstruction. Firstly, the reference sites were selected to enhance the data in time domain and frequency domain for the limited site dataset. Then, the forecasting model of sequence coding reconstruction is constructed based on convLSTM, and the attention mechanism is added in the decoding process, so that it can make full use of the spatio-temporal correlation characteristics of regional photovoltaic power and realize the rolling forecasting of regional photovoltaic power. The result shows that the proposed method can maintain a good multi-scale regional photovoltaic power forecasting effect under the condition of limited data amount and low data collection cost, the accuracy of the proposed model is increased compared with the naive forecasting method. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The combined heat and power (CHP) virtual power plant can aggregate distributed resources, allowing them to participate in the electricity market as a whole and to be managed by the grid’s energy scheduling. However, the uncertainties associated with distributed power sources will directly impact the operation stability of virtual power plants. According to the characteristics of various uncertainties, stochastic programming and distribution robust optimization methods are used to improve the accuracy of input data and simplify constraints. On this basis, a two-stage optimal scheduling model is established. The optimization model aims to maximize the CHP-virtual power plant’s profits, and the operation plan of the units is formulated. The safe operation is the goal within the day, and various constraints are comprehensively considered to ensure the feasibility of plan implementation. Finally, an example analysis is carried out in combination with the actual application scenario, which shows that the optimal scheduling method can take into account the operation economy and safety of the CHP-virtual power plant, and has a good application prospect.

Accurate calculation of line loss in distribution networks can better guide the power system on how to optimize the power grid and how to carry out technical loss reduction work. In response to the problems of many electrical parameters, complicated steps and low accuracy of results required for the calculation of theoretical line loss values in traditional distribution networks, this paper proposes a distribution network line loss prediction method based on the light gradient boosting machine (LightGBM) model algorithm. The method uses machine learning models to model key electrical parameters and line loss values to automatically calculate the grid line loss. To address the difficulty of tuning the LightGBM model, we use a Bayesian optimization algorithm to adjust the model parameters. In this paper, grid data from the Kaggle data platform is used for analysis and validation, and the experimental results show that the model proposed in this paper has higher prediction accuracy than the traditional BP model.

Under the background of carbon neutrality and large-scale penetration of renewable energy, the integrated local energy system (ILES) has been extensively recognized as an effective way to reduce carbon emissions and improve renewable energy consumption level due to its high energy utilization and multiple energy complementarity. Hence, an optimal low-carbon scheduling model of ILES considering oxygen-enriched combustion plant (OECP) and generalized energy storages (GESs) is proposed in this paper to reduce the carbon emissions, improve the renewable energy consumption level and reduce the operating costs of ILES. More specifically, the cooperation mechanism between OECP and power to gas (P2G) is established to maintain a high carbon capture level during peak load periods; the GESs under the background of ILES is modelled, which can make full use of the multiple energy complementarity to improve the renewable energy consumption level of ILES. Case studies are performed on an IES that consists of an IEEE 33-bus distribution network, a 44-node district heating network and a 20-node natural gas network to verify the effectiveness and advantages of the proposed model.

In recent years, with the global warming, distributed photovoltaic and energy storage have developed rapidly under the background of carbon peak and carbon neutralization. Photovoltaic can be added to the microgrid as a distributed energy, but the volatility and randomness of photovoltaic itself will reduce the stability of the entire electric system, and even affect the operation of the microgrid. The introduction of energy storage media brings a new chapter to improve the stability of power system, improving the income of microgrid, and reducing the volatility caused by photovoltaic power generation. With the gradual implementation of the real-time electricity price policy, the use of energy storage system to obtain economic benefits will be an economic direction of the future micro grid. Therefore, based on the analysis of the operation principle of photovoltaic and energy storage, this paper uses MATLAB to establish the simulation control model of photovoltaic and storage DC bus grid-connected, and verifies the rationality of photovoltaic and storage grid-connected. Secondly, the multi-objective mathematical modeling was carried out by minimizing the total cost, pollutant emission and energy storage utilization of microgrid. Finally, an improved multi-objective PSO algorithm is proposed, which combines the fast optimization ability of particle swarm optimization algorithm with the strong global search ability of artificial fish swarm algorithm, optimizes the economic operation of photovoltaic storage microgrid, and improves the solution performance of the algorithm.

New energy stations and energy storage power stations are working in closer cooperation in the context of a high proportion of new energy access with high uncertainty. Given the characteristics of distributed autonomous decision-making between new energy stations and energy storage power stations， a decomposition collaborative mechanism based on the alternating direction method of multipliers （ADMM） is used to establish a distributed collaborative optimization model of two operators considering the uncertainty of new energy. The optimal energy storage charging and discharging strategy under the distributed framework is obtained through few information iterations. The effectiveness and applicability of the proposed algorithm are verified by comparing the computational results of the proposed algorithm with those of the centralized algorithm using data from a small integrated energy system in North China.

With more and more distributed microgrid such as the photovoltaic and energy storage connected to the isolated microgrid cluster, the power flow of the traditional microgrid are deeply integrated, which reduces the control ability of the isolated microgrid and makes the voltage and frequency fluctuation more and more frequent. The paper first puts forward the distributed cooperative control theory based on microgrid, then applies the distributed secondary frequency control method of the distributed microgrid cluster with the photovoltaic and energy storage. The simulation experiment of the distributed microgrids distributed control shows that the frequency secondary control method of distributed microgrid proposed in this paper is effective and practical.

城市配电网线路大多采用电缆线路,电缆线路的电容特性相比于同等电压等级的架空线路更加明显,更易造成配电网线路电压越限,造成一定程度的电气设备损害.本文首先通过对配电网电缆线路产生的电容效应,导致电压升高进行机理分析,首次给出了考虑电缆充电特性含虚拟电容充电的配电网模型,然后通过电力系统仿真软件ETAP搭建某城区某110kV变电站110kV～10kV配网系统模型,并在所带某条10kV电压越限线路各处可行位置配置电抗器进行无功补偿,利用仿真对比的方法,得到最优布点位置.

电缆线路在城市配电网中应用越来越频繁,但其特定的电容充电功率较同等电压等级的架空线路更大,尤其在负荷低谷期或者线路轻载时,时常出现某些10kV配电网线路电压越限、无功倒送问题.本文首先给出集中补偿电抗器容量的计算方式,然后通过电力系统仿真软件ETAP搭建某城区某110kV变电站110kV～10kV配网系统模型,并在所带某条10kV电压越限线路末端位置配置电抗器进行无功补偿.仿真结果表明该条电压越限线路在安装所求容量的电抗器后,能够将线路各处节点电压控制在要求的范围内.

为解决110 kV电网存在大量的T接点与T接线等问题,将优化布局布线技术应用到T接站改为T接点处理中.开展了统一的T接点与T接线模型和数据管理分析,建立了T接点、T接线之间的关系,提出了T接站通过特殊布线算法还原为T接点的方法.在成熟的省级电网调度大屏潮流图的布局布线算法生成相应的潮流图基础上,对地级电网潮流图的需求进行了评价,进行了地级电网调度大屏潮流图自动生成系统的实验.研究结果表明,该系统运行稳定可靠,在多个地级电网调度中心得到了实际应用.