Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms

Kang, Qi; Feng, ShuWei; Zhou, MengChu; Ammari, Ahmed Chiheb; Sedraoui, Khaled

doi:10.1109/tits.2016.2638898

Cited by 179 publications

(56 citation statements)

References 27 publications

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“…Since 2005 [51], it has been successfully used to handle many complicated optimization problems [52][53][54][55][56][57][58]. It has been compared with differential evolution (DE) [59,60], GA [25,53,61], particle swarm optimization (PSO) [62,63] and evolutionary algorithm (EA) [64] for multi-dimensional numeric problems. Its performance is better than or similar to these algorithms.…”

Section: Improved Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Liu

Tang

et al. 2019

Energies

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Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences.

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Section: Improved Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Liu

Tang

et al. 2019

Energies

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“…However, the optimal controller developed in [11] will not necessarily lead to a stable system. The authors of [12] formulate the load scheduling of PHEVs as a multi-objective constrained optimization problem with the stability of the power system considered, and a weight aggregation multi-objective particle swarm optimization (WA-MOPSO) is presented to reach the optimal solution in a smart grid scenario. However, the algorithm they propose requires each vehicle to report its local information, such as battery levels and exit times, to a central optimal controller, so its implementation requires high computation capability and may not be feasible in a large-scale network.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

Lin

Cai

et al. 2019

Energies

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With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), the coordinated charging of PHEVs has become an important issue in power distribution systems. This paper employs a multi-objective optimization model for coordinated charging of PHEVs in the system, in which the problem of valley filling and total cost minimization are both investigated under the system’s technical constraints. To this end, a hierarchical optimal algorithm combining the water-filling-based algorithm with the consensus-based method is proposed to solve the constrained optimization problem. Moreover, a moving horizon approach is adopted to deal with the case where PHEVs arrive and leave randomly. We show that the proposed algorithm not only enhances the stability of the power load but also achieves the global minimization of vehicle owners charging costs, and its implementation is convenient in the multi-level power distribution system integrating the physical power grid with a heterogeneous information network. Numerical simulations are presented to show the desirable performance of the proposed algorithm.

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“…In [13], the adjustable direct current OPF is presented and the objective function is taken as the total generation cost of units. And at the same time, the power flow characteristics of a modern power system are becoming increasingly complex due to the growing penetration of distributed generations [14][15][16][17] and the deployments of novel power electronic loads [18][19][20][21][22]. In this context, multi-objective OPF (MOPF) has received the extensive attention of researchers in the field of OPF [23][24][25][26][27][28], since it can coordinate different-weight or even conflicting multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

Processes

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To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

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Optimal Load Scheduling of Plug-In Hybrid Electric Vehicles via Weight-Aggregation Multi-Objective Evolutionary Algorithms

Cited by 179 publications

References 27 publications

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

A Dual-Objective Substation Energy Consumption Optimization Problem in Subway Systems

Multi-Objective Optimal Charging Control of Plug-In Hybrid Electric Vehicles in Power Distribution Systems

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

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