scholarly journals Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

2022 ◽  
Vol 14 (2) ◽  
pp. 685
Author(s):  
Hussein M. K. Al-Masri ◽  
Abed A. Al-Sharqi ◽  
Sharaf K. Magableh ◽  
Ali Q. Al-Shetwi ◽  
Maher G. M. Abdolrasol ◽  
...  
Keyword(s):  
Optimal Allocation ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Working Hours ◽  
Energy System ◽  
Particle Swarm Algorithm ◽  
Solar Pv ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Enhanced Particle Swarm Optimization

This paper aims to investigate a hybrid photovoltaic (PV) biogas on-grid energy system in Al-Ghabawi territory, Amman, Jordan. The system is accomplished by assessing the system’s reliability and economic viability. Realistic hourly measurements of solar irradiance, ambient temperature, municipal solid waste, and load demand in 2020 were obtained from Jordanian governmental entities. This helps in investigating the proposed system on a real megawatt-scale retrofitting power system. Three case scenarios were performed: loss of power supply probability (LPSP) with total net present cost (TNPC), LPSP with an annualized cost of the system (ACS), and TNPC with the index of reliability (IR). Pareto frontiers were obtained using multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm. The system’s decision variables were the number of PV panels (Npv) and the number of biogas plant working hours per day (tbiogas). Moreover, three non-dominant Pareto frontier solutions are discussed, including reliable, affordable, and best solutions obtained by fuzzy logic. Double-diode (DD) solar PV model was implemented to obtain an accurate sizing of the proposed system. For instance, the best solution of the third case is held at TNPC of 64.504 million USD/yr and IR of 96.048%. These findings were revealed at 33,459 panels and 12.498 h/day. Further, system emissions for each scenario have been tested. Finally, decision makers are invited to adopt to the findings and energy management strategy of this paper to find reliable and cost-effective best solutions.

scholarly journals Multi-objective Parameters Optimization for HEV Based on improved Particle Swarm Algorithm

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Fuel Economy ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Energy Management Strategy ◽  
Multi Objective

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.

Optimal Allocation of Water Resources Based on Multi-Objective Particle Swarm Algorithm and Information Entropy

2014 ◽  
Vol 641-642 ◽  
pp. 75-79 ◽  
Author(s):  
Wei Lin Liu ◽  
Li Na Liu
Keyword(s):  
Information Entropy ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Particle Swarm ◽  
Complex Problem ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Decision Making Problem ◽  
North Water

Water allocation is a very complex problem, involving social, economic, environmental, and political factors. Consequently, it is a multi-objective decision-making problem. This paper presents a multi-objective model for the optimal allocation on multisource water for multiuser under sufficiently considering the harmonious development among economy, society and environment. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to generate a set of Pareto-optimal solutions. At the same time, to facilitate easy implementation for the water allocation operator, information entropy theory is adopted to sort the decision results according to the magnitude of the superiority degrees. As a case study the proposed approach has been applied to the reasonable allocation of water supply and demand in the water-receiving areas of the South-to-North Water Transfer Project in China, in which the maximal benefit of economy, society and environment was regarded as the multi-objectives. The results show that the proposed approach is able to offer the quantifiable benefits or costs among different objectives for the water managers, and is highly professional in making decisions for allocating water among use sectors and different areas.

scholarly journals Enhanced Particle Swarm Optimization-Based Feeder Reconfiguration Considering Uncertain Large Photovoltaic Powers and Demands

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Yi Hong ◽  
Faa-Jeng Lin ◽  
Fu-Yuan Hsu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distribution System ◽  
Urban Areas ◽  
Distribution Systems ◽  
Particle Swarm ◽  
Solar Pv ◽  
Swarm Optimization ◽  
Rural And Urban Areas ◽  
Enhanced Particle Swarm Optimization

The Kyoto protocol recommended that industrialized countries limit their green gas emissions in 2012 to 5.2% below 1990 levels. Photovoltaic (PV) arrays provide clear and sustainable renewable energy to electric power systems. Solar PV arrays can be installed in distribution systems of rural and urban areas, as opposed to wind-turbine generators, which cause noise in surrounding environments. However, a large PV array (several MW) may incur several operation problems, for example, low power quality and reverse power. This work presents a novel method to reconfigure the distribution feeders in order to prevent the injection of reverse power into a substation connected to the transmission level. Moreover, a two-stage algorithm is developed, in which the uncertain bus loads and PV powers are clustered by fuzzy-c-means to gain representative scenarios; optimal reconfiguration is then achieved by a novel mean-variance-based particle swarm optimization. The system loss is minimized while the operational constraints, including reverse power and voltage variation, are satisfied due to the optimal feeder reconfiguration. Simulation results obtained from a 70-bus distribution system with 4 large PV arrays validate the proposed method.

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