An Improved, Negatively Correlated Search for Solving the Unit Commitment Problem’s Integration with Electric Vehicles

Qun Niu,Zhile Yang,Kecheng Jiang

doi:10.3390/su11246945

Abstract

With the rapid development of plug-in electric vehicles (PEVs), the charging of a number of PEVs has already brought huge impact and burden to the power grid, particularly at the medium and low voltage distribution networks. This presents a big challenge for further mass roll-out of electric vehicles. To assess the impact of charging of substantial number of electric vehicles on the grid, a model of 30000 PEVs integrated with unit commitment (UCEV) was investigated in this study. The unit commitment was a large-scale, mixed-integer, nonlinear, NP-Hard (non-deterministic polynomial) optimization problem, while the integration of PEVs further increased the complexity of the model. In this paper, a global best inspired negatively correlated search (GBNCS) method which extends the evolutionary logic of negatively correlated search is proposed to tackle the UCEV problem. In the proposed algorithm, a rounding transfer function in GBNCS, is deployed to convert real-valued variables into binary ones; further, the global best information is combined in the population to improve the efficiency of the algorithm. Numerical results confirmed that the proposed GBNCS can achieve good performance in both a basic IEEE 10 unit commitment problem and the UCEV problem. It was also shown that, among four charging modes, the off-peak charging mode and EPRI (Electric Power Research Institute) charging mode are more economical in PEV charging.

Highlights

Industry revolutions and urbanization have led to a number of intractable environmental and resource problems, such as global warming, due to extensive consumption of fossil fuels [1]
The proposed BNCS and global best inspired binary negatively correlated search (GBNCS) is first tested on the knapsack problem [37], and applied to the basic unit commitment problem (UC) problem and the UC problem integrated with the EV problem
570,032 565,325 565,988 564,162 563,977 563,977 563,977 564,711 564,088 564,168 563,965 564,031 563,980 563,976 563,973 563,964 563,960 563,941 563,937 std According to Table 8, it was found that GBNCS, BNCS, binary gravitational search algorithm (BGSA), hybrid harmony search (HHS), EQA-UC, binary differential evolution algorithm (BDE), and binary symmetric particle swarm optimization (BSPSO)(2–5) were all capable of achieving the best value 563,937 $/day, which was the best result among state-of-the-art UC results GBNCS had the best performance in mean value, and worst value

Summary

Introduction

Industry revolutions and urbanization have led to a number of intractable environmental and resource problems, such as global warming, due to extensive consumption of fossil fuels [1]. To reduce the carbon emissions from the transportation sector is an important part of the global effort to curb global warming To achieve this goal, many governments have introduced a wide range of policies to promote the development of electric vehicles. With the maturity of battery technology, lithium batteries, nickel metal hydride batteries, and lead-acid batteries have been widely used in electric vehicles [4,5,6], and the PEV has become the most popular EV type. Both the research and practice have confirmed the great potentials of PEVs in energy conservation and emission reduction [7,8]

Methods

Results

Conclusion