Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm

Zhang, Xizheng; Wang, Zeyu; Lu, Zhangyu

doi:10.1016/j.apenergy.2021.118018

Cited by 168 publications

(40 citation statements)

References 31 publications

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“…To demonstrate the advantages of this study over general optimization algorithms, we introduce a particle swarm optimization algorithm as a comparison for verification. Numerous scholars have developed applications of particle swarm optimization algorithms in different multi-objective optimization problems to demonstrate their reliability, such as, Zhang et al used particle swarm optimization algorithm to establish a multi-objective optimization model of vehicle charging and discharging and load scheduling of microgrid to realize load scheduling of microgrid for electric vehicles [38]. Wang et al combined BP neural network and multi-objective particle swarm algorithm to build an intelligent design model for product imagery modeling, and realized the personalization of product modeling driven by multiple imageries [26].…”

Section: Setting the Parameters Of The Game Modelmentioning

confidence: 99%

Optimal Design of Product Form for Aesthetics and Ergonomics

Zhou¹,

Ma²,

Zhang³

et al. 2022

CADandA

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The traditional product form design research usually starts from a single aspect such as aesthetics, ergonomics and Kansei engineering, and lacks the comprehensive consideration of multiple constraints. To solve this problem, a product form optimization design method oriented to aesthetics and ergonomics is proposed. First, based on the theory of computational aesthetics, a quantitative product form aesthetic index system is established. Using the entropy method to achieve the comprehensive evaluation of product form aesthetics. The ergonomic evaluation is carried out by measuring the difference between the standard values and the actual values of the design parameters. Then, taking the aesthetic and ergonomic evaluation as game players, a noncooperative game model is established, and the Nash equilibrium is solved to achieve product form optimization. Finally, We verify by experiments that this method can better realize the multifactor fusion design of the product.

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Section: Setting the Parameters Of The Game Modelmentioning

confidence: 99%

Optimal Design of Product Form for Aesthetics and Ergonomics

Zhou¹,

Ma²,

Zhang³

et al. 2022

CADandA

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show abstract

“…After many generations, animals and weak foraging methods are eliminated or transformed into better forms. E. coli that lives in the human intestine has a four-stage foraging method: chemotactic, swarming, reproduction, elimination, and dispersal [30].…”

Section: Bacterial Foraging Algorithmmentioning

confidence: 99%

Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

Hao¹,

Su²,

Lei³

2022

Energy Engineering

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Renewable energy sources (RES) such as wind turbines (WT) and solar cells have attracted the attention of power system operators and users alike, thanks to their lack of environmental pollution, independence of fossil fuels, and meager marginal costs. With the introduction of RES, challenges have faced the unit commitment (UC) problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units' inputs and outputs, and specifying the optimal generation of each unit. The output power of RES such as WT and solar cells depends on natural factors such as wind speed and solar irradiation that are riddled with uncertainty. As a result, the UC problem in the presence of RES faces uncertainties. The grid consumed load is not always equal to and is randomly different from the predicted values, which also contributes to uncertainty in solving the aforementioned problem. The current study proposes a novel two-stage optimization model with load and wind farm power generation uncertainties for the security-constrained UC to overcome this problem. The new model is adopted to solve the wind-generated power uncertainty, and energy storage systems (ESSs) are included in the problem for further management. The problem is written as an uncertain optimization model which are the stochastic nature with security-constrains which included undispatchable power resources and storage units. To solve the UC programming model, a hybrid honey bee mating and bacterial foraging algorithm is employed to reduce problem complexity and achieve optimal results.

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“…The aggregated sum technique was adopted to solve the MOO problem of optimal allocating of DG and DSTATCOM units (Thangaraj and Kuppan, 2017) with distribution systems in order to minimize the power loss, TVD, and maximize the stability index using the Lightning Search Algorithm (LSA). A combination of Modified Gravitational Search and PSO Algorithm (MGSA-PSO) (Zhang et al, 2022) was proposed for electric vehicle demand dispatch in a microgrid for different charging and discharging scenarios to reduce operating costs cost, pollutant treatment cost, and reduce the load variance of the power grid. The authors used the aggregated sum to transfer the MOO problem into an SOO one.…”

Section: Introductionmentioning

confidence: 99%

A Comparison Study of Multi-Objective Bonobo Optimizers for Optimal Integration of Distributed Generation in Distribution Systems

et al. 2022

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In this paper, the three newly published Multi-Objective Bonobo Optimizer (MOBO) variants are assessed and evaluated using statistical analysis for solving the multi-objective optimization of Distributed Generation (DG) into distribution systems. The main objectives of the study are to minimize system loss and enhance voltage profile. While the first variant, MOBO1, depends on the sort and grid-index approach, the second variant, MOBO2, relies on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm technique. The last variant, MOBO3, is inspired by the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The three MOBO algorithms are compared to themselves and to other algorithms solving the same optimization problem. These algorithms include the MOJAYA, Multi-Objective Artificial Ecosystem-Based Algorithm (MOAEO), Multi-Objective Gravitational Search Algorithm (MOGSA), and Multi-Objective Particle Swarm Optimization (MOPSO). The 33-bus and 85-bus radial distribution systems are used test systems for solving the optimal allocation of single- and three-DG units operating at unity power factor. In order to find the best compromise solution, the Pareto Optimal front method is adopted with the help of a fuzzy-based function. The obtained results show the effectiveness of the MOBO variants compared with other algorithms in terms of different statistical parameters and multi-objective performance metrics such as diversity, hypervolume, spacing, and set coverage. While the MOBO algorithm reduces power loss and TVD by 39.59 and 68.31% for a single DG, they are reduced to 58.13 and 88.44% for three DG units allocated to the 33-bus distribution system, respectively. On the other hand, the MOBO algorithm reduces power loss and TVD by 37.28 and 66.84% for a single DG, respectively, they are decreased to 46.35 and 82.53% for three DG units assigned to the 85-bus distribution system. Among the three MOBO variants, it is found that the MOBO1 is superior for a single-DG allocation, while the MOBO3 is the best for the allocation of three-DG units.

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Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm

Cited by 168 publications

References 31 publications

Optimal Design of Product Form for Aesthetics and Ergonomics

Optimal Design of Product Form for Aesthetics and Ergonomics

Optimal Intelligence Planning of Wind Power Plants and Power System Storage Devices in Power Station Unit Commitment Based

A Comparison Study of Multi-Objective Bonobo Optimizers for Optimal Integration of Distributed Generation in Distribution Systems

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