مقاله انگلیسی رایگان در مورد ارزیابی استراتژی های سرمایه گذاری برای شبکه های توزیع بر اساس رقابت معیار و DEA – الزویر 2019

1- Introduction

2- Hierarchical evaluation index system of investment strategies in distribution networks

3- Dynamic yardstick evaluation method for investment strategies in distribution networks

4- Case study

5- Conclusions

References

Abstract

The traditional investment for distribution networks is mainly evaluated and conducted from the static perspective. A dynamic comprehensive evaluation method is essential to obtain an efficient investment strategy. Firstly, a hierarchical evaluation index system is constructed to describe the overall performance of the distribution network, which includes six types of criteria such as information level, power supply capacity, asset utilization efficiency and economy, power supply quality and loss, power supply reliability, and sustainable development level. Each criterion is composed of input and output indicators. Then, using projection pursuit method considering subjective weight constraints, a combined weight optimization model is proposed to calculate the weight vector of each criterion. Furthermore, the data envelopment analysis (DEA) based dynamic evaluation approach is set up to compute the investment efficiency defined as ratio of output and input. The time degree theory is also introduced to realize dynamic evaluation. Via yardstick competition among multiple regions and “reward and punishment” rule, a precise investment planning strategy can be achieved while minimizing construction cost and maximizing efficiency of the resource allocation. The numerical results on a practical 10-region distribution system in four planning cycles verify the effectiveness of the proposed method.

Introduction

With the accelerating growth of electricity usage in modern times, the distribution network, playing a significant role in guaranteeing high quality and reliability of power supply for customers, has faced serious challenges. It is a sophisticated and enormous project for distribution networks to achieve precise investment planning [1]. Currently, it is a fact that automation reform of the distribution network is still in its infancy. Meanwhile, the investment scale of the distribution network is gigantic and shows an increasing trend year by year in China. Such being the case, there is no doubt that evaluating the overall investment efficiency comprehensively and scientifically is beneficial to efficaciously avoid deficiency of construction, to strike a balance between technology and economy in the distribution network [2]. In recent years, significant research efforts have been devoted to the optimal investment of modern power distribution systems. A multiobjective optimization considering reliability and costs as two objective functions has been presented in Ref. [3] to make investment strategy for multi-stage smart distribution network expansion planning. Although applying of the proposed model has shown a significant improvement in the area of distribution network planning, contemporary expansion of power system and information infrastructures weren’t considered. Mokryani. G [4] proposed a probabilistic method for active distribution networks (ADN) investment planning with integration of demand response (DR), aiming at simultaneously minimizing the total investment cost and total energy losses of the lines from the point of view of distribution system operators (DSOs). In Ref. [5], regarding DR as virtual distributed resources to cover the effect of uncertain parameters, an aggregated model for planning and reconfiguration of ADN has been proposed, where a bi-level optimization procedure is developed to solve the proposed model. Zeng and Feng [6] presented an optimal integrated investment strategy for supporting growing penetration of electric vehicles in distribution systems. In Ref. [7], by minimizing the total investment and operational costs, a multi-stage expansion planning is proposed to consider dynamic behavior of the system parameters asset management and geographical constraints.