Abstract
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.
Highlights
With the increasing demand for electricity, the deepening of the traditional energy shortage situation, and increasingly prominent environmental problems, the development of distributed generation (DG), especially renewable energy generation technology, has been widely supported.DG has the advantages of high energy efficiency, clean environmental protection, flexible installation position, and so on [1,2,3]
Compared to other planning methods, this paper focuses on the active characteristics of the active distribution network, and through a large number of comparative analyses shows that active management mode may affect distributed power planning
Where Pn is the scenario probability of the n scenario, CWTG,i and CPVG,i are the operation and maintenance costs of the wind power and the photovoltaic unit electricity received by the i node, and EWTG,in (t) and EPVG,in (t) are the wind power received by the i node and the photovoltaic unit electricity generated during the t period of the n typical day, respectively
Summary
With the increasing demand for electricity, the deepening of the traditional energy shortage situation, and increasingly prominent environmental problems, the development of distributed generation (DG), especially renewable energy generation technology, has been widely supported. On the basis of considering uncertainty, this paper considers that the distributed generation planning method based on multiscenario technology can reasonably transform the uncertain model into the deterministic programming model, which can effectively reduce the difficulty of modeling and solving, and can fully consider its time on the basis of uncertain modeling of distributed generation. Based on the above analysis, the uncertainties of wind power and photovoltaic system are firstly established considering the characteristics of distributed generation and load timing. The quantum evolutionary algorithm and primal dual interior point method are used to solve the upper and lower layers of the proposed model It is verified by the Institute of Electrical and Electronics Engineers (IEEE) 33-bus distribution network system. The results show that the two-layer model is reasonable and the algorithm is effective
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