Techno-economic modelling of hybrid energy system to overcome the load shedding problem: A case study of Pakistan

Bakht, Muhammad Paend; Salam, Zainal; Bhatti, Abdul Rauf; Sheikh, Usman Ullah; Khan, Nuzhat; Anjum, Waqas

doi:10.1371/journal.pone.0266660

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…However, this work considered an off-grid scenario where the system was entirely dependent on renewables, and the utility grid was not considered. Whereas, in [14,15] author uses the state flow method to resolve the problem of load shedding while the author does not consider consumer satisfaction. An algorithm for demand satisfaction was developed to achieve maximum consumer comfort at a minimum price for various budget scenarios [16].…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

et al. 2023

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Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

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Section: Introductionmentioning

confidence: 99%

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

et al. 2023

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“…A PV-hydro-diesel generator based HES is proposed in Iraq by using HOMER software [30]. In [31], the authors have used particle swarm optimization in MATLAB to propose the optimal HES which consists of PV and battery storage system along with standby diesel generator to minimize the load shedding in Quetta, Pakistan. The optimal size of PV modules, wind turbines, and battery storage systems along with diesel generators as a backup power source is proposed by using particle swarm optimization to minimize per unit of cost of energy in Saudi Arabia [32].…”

Section: Introductionmentioning

confidence: 99%

The intelligent modelling and optimization of an economic and ecosystem-friendly model for grid connected prosumer community

et al. 2023

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The establishment of grid-connected prosumer communities to bridge the demand-supply gap in developing nations, especially in rural areas will assist to minimize the use of carbon enriched fossil fuels and the resulting economic pressure. In the promoted study, an economic and ecosystem-friendly hybrid energy model is proposed for grid-connected prosumer community of 147 houses in district Kotli, AJK. The grid search algorithm-based HOMER software is used to simulate and analyze the load demand and biomass sources-based onsite collected data through a survey for an optimal proposed design. The research objectives are to minimize the net present cost (USD) of design, the per unit cost of energy (USD/kWh), and the carbon emissions (kgs/year). A sensitivity analysis based on photovoltaic module lifetime is also performed. The simulations show that the per unit cost of energy is reduced from 0.1 USD/kWh to 0.001 USD/kWh for the annual energy demand (kWh/year) of the community. The number of carbon emissions is also minimized from 122056 kgs/year to 1628 kgs/year through the proposed optimal energy model.

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A multi-stage optimisation-based decision-making framework for sustainable hybrid energy system in the residential sector

Mehmood,

Zhang,

Ren

2023

Sustainable Futures

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Techno-economic modelling of hybrid energy system to overcome the load shedding problem: A case study of Pakistan

Cited by 11 publications

References 43 publications

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

The intelligent modelling and optimization of an economic and ecosystem-friendly model for grid connected prosumer community

A multi-stage optimisation-based decision-making framework for sustainable hybrid energy system in the residential sector

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