Technical design and optimal energy management of a hybrid photovoltaic biogas energy system using multi‐objective grey wolf optimisation

Al‐Masri, Hussein M. K.; Al-Sharqi, Abed A.

doi:10.1049/iet-rpg.2020.0330

Cited by 12 publications

(9 citation statements)

References 34 publications

(75 reference statements)

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“…It is worth mentioning that Equations ( 16), (19), and ( 20) are computed at each set of solutions of the Pareto frontier in the three aforesaid case scenarios. This is to give more insight regarding system's reliability.…”

Section: Lolp =mentioning

confidence: 99%

“…However, a deficit occurs when reaching the maximum power grid purchasing capacity as in (26) [22]. Finally, after reaching the maximum selling limit, the additional power will be dumped [19]. The equality power Equations of ( 24)-( 26) are actually used in (28) as the equality constraints of the MOFEPSO algorithm.…”

Section: System's Operational Power Strategiesmentioning

confidence: 99%

“…If renewable power is not adequate, the utility grid will provide energy to the load demand within the maximum grid purchase capacity. After that, the optimal configuration of the system parameters is found [19]. This strategy clearly shows the process to reach the best configurations of TNPC versus LPSP in a case, ACS versus LPSP and IR versus TNPC in other cases, which will be considered as Pareto points on the Pareto frontier.…”

Section: Validation Proceduresmentioning

confidence: 99%

“…In [19], the authors developed a detailed mathematical model for PV-biogas hybrid energy system using the multi-objective grey wolf optimization algorithm (MOGWOA). Three different solutions were selected from the Pareto frontier.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Lolp =mentioning

confidence: 99%

Section: System's Operational Power Strategiesmentioning

confidence: 99%

Section: Validation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2022

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“…It has been reported that the most cost-effective HES is PV/diesel/PHS with fixed panels and the most environmentally friendly HES is PV/diesel/battery with double-axis. In [13], a sizing methodol-ogy has been proposed for the design of on-grid and off-grid PV-biogas energy system using the multi-objective grey wolf optimization algorithm. It has been found that the on-grid system is more reliable and cost-effective than the off-grid system.…”

Section: Introductionmentioning

confidence: 99%

A risk‐averse framework for techno‐economic analysis and size optimization of an off‐grid hybrid energy system

Aramoon,

Askarzadeh,

Ghaffari

2023

IET Generation Trans & Dist

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In the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES.

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Recent advances in multi-objective grey wolf optimizer, its versions and applications

Makhadmeh

Alomari

Mirjalili

et al. 2022

Neural Comput & Applic

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Technical design and optimal energy management of a hybrid photovoltaic biogas energy system using multi‐objective grey wolf optimisation

Cited by 12 publications

References 34 publications

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

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

A risk‐averse framework for techno‐economic analysis and size optimization of an off‐grid hybrid energy system

Recent advances in multi-objective grey wolf optimizer, its versions and applications

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