Open AccessA power balancing method of distributed generation and electric vehicle charging for minimizing operation cost of distribution systems with uncertainties
Author(s) -
Wu Jiekang,
Wu Zhishan,
Wu Fan,
Mao Xiaoming
Publication year - 2017
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.157
Subject(s) - electric vehicle , distributed generation , mathematical optimization , control theory (sociology) , computer science , randomness , electric power system , power (physics) , engineering , renewable energy , electrical engineering , mathematics , statistics , physics , control (management) , quantum mechanics , artificial intelligence
A power balancing method of distributed generation (DG) and electric vehicle charging is presented for minimizing operation costs of distribution systems with uncertainties, which includes the uncertainties in output power of DG and the randomness of charging power of electric vehicles (EV). A probability model is established for the uncertain characteristics of DG and electric vehicle charging. A multi‐state optimization coordination method for DG of renewable energy systems and electric vehicle charging in distribution systems based on quadratic rotation cone programming is presented to minimize the expected generation cost of generators in the main power grid, the expected operation cost of DG systems in distribution systems, and the expected social outage loss. An objective function maximizing the operation efficiency of distribution systems with DG s and EV s is proposed. Using quadratic rotated conic programming, the nonlinear objective function and constraint functions are transformed into a linear form. An IEEE 14‐node distribution system is used as a study example to illustrate adaptability of the proposed model and the feasibility of the proposed method. The simulation results show that the proposed method simplifies the original problem of the optimization problem and makes its solution faster, more stable, and better.