The construction of a power supply confidence evaluation system can provide a powerful reference for guaranteeing power supply. Given this background, a new confidence evaluation method for confidence in electricity supply preservation based on combined weights and Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) way is employed. First, a power supply confidence evaluation system from four dimensions of power supply and demand relationship, power market stability, energy security, and environmental factors is built. Second, the entropy weight method, Criteria Importance Though Intercriteria Correlation (CRITIC) method, and standard deviation method are combined to verify the combination weight of each index, and the TOPSIS comprehensive evaluation method is presented to assess the confidence of power supply. Finally, a case study on the different operating days for the confidence assessment of power supply in a provincial power system is performed to demonstrate the effectiveness of the adopted method. The analysis results show that the approach put forward in this study can better ensure the confidence of the power supply, and can provide a reference for the power system scheduling, to guarantee the continuous power supply to all power users.

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.

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.

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.

It is a prerequisite for resource characteristics analysis and scheduling optimization research to screen out the representative typical scenarios of wind-solar-hydropower integrated generation systems. Due to the influence of the “dimension effect”, traditional clustering algorithms cannot be directly applied to high-dimensional data clustering, and the existing technical route based on “dimension reduction before clustering” cannot guarantee that lowdimensional features after dimensionality reduction are suitable for clustering tasks, resulting in unstable clustering results. In view of the existing problems, this paper proposes a method for extracting typical output scenarios of windsolar-hydropower based on DEC（deep embedding clustering）. The method can realize high-dimensional output data clustering and avoid that low-dimensional features after dimensionality reduction are not suitable for clustering tasks.First, with the help of the nonlinear representation ability of the stacked autoencoder, the high-dimensional windsolar-hydropower combined output data is deeply represented to achieve data dimensionality reduction. Then, the Kmeans clustering method is used to cluster the deep low-dimensional features, and the stacked encoder is optimized and adjusted at the same time in the clustering process to obtain the low-dimensional wind-solar-hydropower combined output feature suitable for the clustering space. Moreover, the precise division of wind-solar-hydropower combined output scenarios is realized. Finally, the DEC is performed on the wind-solar-hydropower output data of a region in south China. The PCA-K-means algorithm is used to set up a comparison example to verify the effectiveness of the DEC in selecting typical combined output scenarios of wind-solar-hydropower generation.

Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimental results prove the model’s reliability and the model’s high predictability is verified by comparing different prediction models.

Virtual power plant （VPP） technology can realize the effective aggregation and coordinated control of distributed resources to reduce the carbon emissions of power system operation and improve renewable energy consumption. The technology has become an effective way to promote the realization of “dual-carbon” goals. Firstly， the general situation and research status of VPP technology are introduced in terms of intelligent metering， information communication， and coordinated control. Next， given the promotion effect of VPP on the “dual-carbon” goals， the VPP dispatch modes for carbon emission reduction and renewable energy consumption are introduced respectively. Then， the construction status of three different types of VPPs in Shanghai， Jiangsu and a city in Zhejiang are introduced. And the impact of various factors on the key technologies and low-carbon dispatch mode selection of VPPs is analyzed. The results show that VPPs should be further developed in terms of intelligent metering， information communication and coordinated control； key technologies for VPPs and low-carbon dispatch mode should be selected rationally in accordance with load characteristics， resource distribution， and other factors.

The construction of power grid infrastructure lags behind the growth rate of installed power capacity, which makes it difficult to deliver clean energy in some areas, and multiple clean energy sources seize power generation channels. Taking the power system in a certain area of Zhejiang Province as the research object, the day-ahead scheduling mechanism suitable for the joint optimization of multiple power sources under the limitation of transmission section is discussed, and an improved comprehensive power generation benefit evaluation system is established. Firstly, the coefficient matrix of objective function is defined, and a mixed linear integer programming model with variable objective function is established. Then, the transmission channel load rate and transmission power smoothness are defined as the social benefits of power generation in this area. Finally, the optimal cooperation strategy and unit output combination are determined by changing the objective function coefficient matrix. The analysis of an example shows that the comprehensive power generation benefit can be optimized by adopting the strategy of the big alliance cooperative game.

In view of the current situation of China’s power system to make regulation for power generation and load side balance mainly by power plants that are subject to unified scheduling, the existing non-uniformly scheduled small hydropower resources of China can be integrated and utilized effectively when considering the aggregation and control of virtual power plant (VPP) towards distributed resources. Meanwhile, the economic operation of VPP under different seasons of rivers can be achieved, and the consumption of renewable energy such as wind power and photovoltaic (PV) can be promoted. In addition, a certain flexible load reserve (FLR) capacity can be obtained by signing medium and long-term contracts. In this context, a day-ahead and real-time two-stage rolling optimal scheduling strategy for VPP including distributed wind power, PV, and small hydropower is proposed. In the day-ahead stage, aiming at the minimization of operation cost and adopting the multi-scenarios analysis method, the day-ahead scheduling and FLR plan of VPP is formulated. In the real-time stage, with preciser wind and PV prediction, the economic scheduling considering deviation penalty cost can be achieved. The case study of VPP in Jingning County, Lishui City, Zhejiang Province of China verifies the effectiveness and practicability of the proposed method.

To fully mobilize small hydropower resources in the virtual power plants to operate collaboratively， improve operational controllability， and reduce carbon emissions， a low-carbon operation strategy of virtual power plants （VPPs） considering the spatiotemporal coupling characteristics of small hydropower is proposed. Firstly， based on the network theory of node parameter modeling， a matrix collection characterizing the spatiotemporal coupling characteristics among multiple hydropower plants is proposed to facilitate efficient storage and convenient recall of information. Secondly， a low-carbon operation model of VPPs considering the spatiotemporal coupling characteristics is constructed with the objective of their day-ahead equivalent carbon emissions that take account of the regulation factor of the operation mode. The sharing of each hydropower plant’s reservoir capacity regulation is realized through aggregating coordinated scheduling of small hydropower. Finally， a small hydropower plant in a county of Zhejiang is used as an example to verify the effectiveness and practicality of the proposed method.

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.

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.