scholarly journals Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

2019 ◽  
Author(s):  
Sitong Lv ◽  
Jianguo Li ◽  
Yongxin Guo ◽  
Zhong Shi
Keyword(s):  
Distributed Generation ◽  
Scenario Analysis ◽  
Optimal Allocation ◽  
Reduction Method ◽  
Programming Model ◽  
Distribution Network ◽  
Active Management ◽  
Scenario Reduction ◽  
Active Distribution Network ◽  
Primal Dual

In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

scholarly journals Optimal Allocation of Intermittent Distributed Generation under Active Management

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2608 ◽  
Author(s):  
Zhong Shi ◽  
Zhijie Wang ◽  
Yue Jin ◽  
Nengling Tai ◽  
Xiuchen Jiang ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Optimal Allocation ◽  
Programming Model ◽  
Distribution Network ◽  
Distribution Networks ◽  
Active Management ◽  
Management Mode ◽  
Distributed Power ◽  
Primal Dual ◽  
Upper Level

In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.

scholarly journals A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4262 ◽  
Author(s):  
Sitong Lv ◽  
Jianguo Li ◽  
Yongxin Guo ◽  
Zhong Shi
Keyword(s):  
Cluster Analysis ◽  
Distributed Generation ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Optimal Allocation ◽  
Reduction Method ◽  
Scene Analysis ◽  
Analysis Method ◽  
Scenario Reduction ◽  
Cluster Analysis Method

In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system.

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

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