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

Abstract

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.

Optimal Location and Sizing of Distributed Generators in Power System Network with Power Quality Enhancement Using Fuzzy Logic Controlled D-STATCOM

“…Heuristic pdf method Ant-lion optimization [42] coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45] From Tables 2-7, it is observed that the selection of DGs location is better found under GA for CPLF and CILF in comparison to other methods. By comparing the real power loss, sensitivity, and accuracy, it can be concluded that bus 17 is the best location for the placement of DG for both CPLF and CILF.…”

“…Ant-lion optimization [42] Coyote optimizer [43] Modified sine-cosine algorithm [44] Particle swarm optimization [45]…”

“…It is also observed that the proposed technique gives better results in comparison to existing techniques presented in literature [4,9,12,15,17,20,28,30,32,[35][36][37][38][39][40][41][42][43][44][45]. However, to analyze the efficacy of the proposed work in this paper, the results achieved are compared with the existing techniques such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The GA seems to have the main advantage of utilizing entity definition, i.e., strings, instead of handling such entities themselves.…”

“…Figures 6 and 7 provide the performance parameter comparison of GA with the heuristic method at different buses for CPLF and CILF. Moreover, in order to have comprehensive analysis and comparison for the DG location with GA and heuristic pdf, the results are compared with those obtained with the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45]. The performance parameter evaluation for the estimation of DG location by using the ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] is shown in Tables 4-7 for CPLF and CILF type of load.…”

“…In this article, the heuristic probability distribution method and GA are applied for finding the best location and sizing of DG in the IEEE 33 bus system for the constant power load model (CPLF) and the constant impedance load model (CILF). The performance parameters show the superiority of GA over the heuristic probability distribution method and other existing methods such as ant-lion optimization algorithm [42], coyote optimizer [43], modified sine-cosine algorithm [44], and particle swarm optimization [45] in the IEEE 33 standard bus system. By considering lower real power losses, improved accuracy, and sensitivity, selection criteria of three DGs location are decided, and after that, size of DG is decided in terms of real and reactive power.…”