Open AccessHybrid‐adaptive differential evolution with decay function applied to transmission network expansion planning with renewable energy resources generation
Author(s) -
Zhang Xuexia,
Wang Xiaomei
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12296
Subject(s) - renewable energy , differential evolution , mathematical optimization , particle swarm optimization , electricity market , grid , computer science , investment (military) , power flow , electric power transmission , electric power system , electricity , power (physics) , algorithm , engineering , mathematics , electrical engineering , physics , geometry , quantum mechanics , politics , law , political science
Abstract As the country implements the reform of the electricity market and a large number of renewable energy resources (RES) are connected to the grid, the model and algorithm of traditional transmission network expansion planning (TNEP) are not suitable. A model of TNEP is built, in which the investment cost of new lines and the market‐based annual congestion surplus are selected as objective functions. A probabilistic DC power flow method is used to describe uncertainties of RES generation. A new algorithm, Hybrid‐Adaptive Differential Evolution with Decay Function (HyDE‐DF), is applied to solve the model of TNEP for the first time. Analysis and comparison in the IEEE 18‐bus system and the 52‐bus system in an area of Sichuan Province prove the feasibility, effectivity, and practicability of the model and solving algorithm. Some comparisons are performed using other variants of Differential Evolution (DE) and a swarm intelligence optimization algorithm to demonstrate the performance superiority of the new algorithm and new application. It is worth mentioning that in the 52‐bus system of an area of Sichuan Province, the investment cost obtained by the new algorithm is at least one or two orders of magnitude lower than other algorithms.