Abstract
This paper presents a novel approach to multi-type distributed generation (DG) planning based on the analysis of investment and income brought by grid-connected DG. Firstly, the timing characteristics of loads and DG outputs, as well as the environmental benefits of DG are analyzed. Then, on the basis of the classification of daily load sequences, the typical daily load sequence and the typical daily output sequence of DG per unit capacity can be computed. The proposed planning model takes the location, capacity and types of DG into account as optimization variables. An improved adaptive genetic algorithm is proposed to solve the model. Case studies have been carried out on the IEEE 14-node distribution system to verify the feasibility and effectiveness of the proposed method and model.
Highlights
The increasingly critical energy crisis and environmental pollution are promoting the rapid development of distributed generation (DG) technology
A method for multi-type DG planning is proposed in this paper to provide a detailed analysis of the income items and investment items brought by grid-connected DG
The approach can measure the economic benefits of DG objectively and help all parties make decisions on the planning scheme according to their own interests
Summary
The increasingly critical energy crisis and environmental pollution are promoting the rapid development of distributed generation (DG) technology. In [17], a DG allocation optimization model to minimize the operation cost of DG and the network loss and to maximize the environmental benefits of DG was presented In both [16] and [17], the authors did not consider the timing characteristics of loads and DG outputs. In this paper, based on the consideration of the timing characteristics of loads and DG outputs, combined with the power generation costs of traditional power plants, through a comprehensive analysis of the income items and investment items of grid-connected DG and from such aspects, as loss reduction, delaying the upgrade of lines, environmental protection, saving fuels, electricity trading and subsidies and the fixed investment and maintenance of DG, a multi-objective optimization model for the site selection, type selection and capacity determination of DG has been established. An improved adaptive genetic algorithm is proposed to analyze the IEEE 14-node distribution system, which verifies the feasibility and effectiveness of the proposed method and model
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