Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Zhang, Qian; Ding, Jinjin; Shen, Weixiang; Ma, Jie; Li, Guoli

doi:10.1155/2020/5695917

Cited by 13 publications

(10 citation statements)

References 38 publications

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“…Meta-heuristic methods such as particle swarm optimization, differential evolution, evolutionary algorithms, and nature-inspired techniques such as ant-lion optimization and gray-wolf optimization are used in various research areas of electrical engineering [12][13][14][15][16]. Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a nature-inspired algorithm inspired by the collective behavior of birds in search of food.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Nuvvula

Elangovan

Kishore³

et al. 2021

Energies

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Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Nuvvula

Elangovan

Kishore³

et al. 2021

Energies

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“…A multiobjective PSO (MOPSO) algorithm was developed on the basis of the Pareto front to solve the multiobjective optimization problems [55,56], and its efficiency in solving the multiobjective routing optimization problems has well been demonstrated [57]. When dealing with such problems, the related issues should be analyzed as follows: (1) how to obtain nondominated solutions and find Pareto optimal solutions; (2) how to update the particles through the personal and social best selection mechanisms; and (3) how to avoid local optimal conditions and maintain the convergence and diversity of solutions [58][59][60]. In this study, an IMOPSO algorithm integrating the archive-based selection mechanism is developed to improve the diversity and convergence of optimal solutions.…”

Section: Imopspmentioning

confidence: 99%

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Wang

Guan

et al. 2020

Sustainability

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In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

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“…Stochastic and robust optimization-based collaborative operation approaches for microgrids are formulated to derive the energy scheduling scheme, whose objective is to minimize the total microgrids' operation costs under uncertain factors [19,24]. Multiobjective optimization dispatch for microgrids with EV charging is studied to achieve minimized operation cost, greenhouse gas emission reductions, and enhanced reliability of services [25]. Tese operation decision models mainly focus on maximizing collective benefts by aggregating all the entities into the overall microgrid system as one unit, while the beneft of individual microgrid is not considered.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Model for Microgrids Integrating Electric Vehicles to Grid and Building Based on Interest Balance

Chu

Wang²,

Yang

2022

Mathematical Problems in Engineering

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Microgrids allow energy exchange among multiple interconnected microgrids for greater energy efficiency and collective economic interest. However, in some cases, the benefit of some microgrids within the network may not be uncertain. In view of the increasing development of electric vehicles (EVs), a multiobjective model is proposed to improve the performance of microgrids by integrating electric vehicles-to-grid (V2G) and vehicles-to-building (V2B) based on global and individual benefit balance. Two reference models are built to verify the validity of the proposed method, and models are formulated as mixed integer linear programming formats solved by the weighting method. A set of parameters of microgrids are adopted to model the driver behaviors (e.g., available hours of EV), energy transactions (e.g., electricity), performance factor (e.g., emission factor), distributed energy (e.g., solar panel), and energy load of five commercial buildings (e.g., hotel) located in Shanghai. Simulation results demonstrate the effectiveness of the operation decision models in the energy management of microgrids under neutral, proeconomic, proenvironmental, and proenergy weighting scenarios. The case study results specify that the proposed method can achieve operational cost, CO2 emission, and primary energy consumption reductions for each microgrid, with total benefits declining slightly.

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Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Cited by 13 publications

References 38 publications

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Multiobjective Model for Microgrids Integrating Electric Vehicles to Grid and Building Based on Interest Balance

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