MGFF-based resilience assessment of a hybrid energy system for water treatment plants under frequent grid blackouts

Abstract Grid unreliability remains a major constraint to sustainable energy delivery for critical infrastructure in developing economies. This study investigates the techno-economic performance, environmental impact, and resilience of an optimized hybrid energy system integrating photovoltaic (PV), biogas generation, and battery storage under stochastic grid failure conditions. Using HOMER Pro-based optimization and Monte Carlo simulation, system configurations were evaluated under varying dispatch strategies, financing structures, and Main Grid Failure Frequency (MGFF) scenarios. Results identify the PV/Biogas/Battery configuration as the most cost-effective, achieving a baseline cost of energy (COE) of $0.258/kWh and net present cost (NPC) of $1.86 million under commercial financing conditions. Under concessional financing (10% discount rate), COE decreases to $0.210/kWh, highlighting the critical role of capital structure in renewable deployment. As grid failure frequency increases (MGFF = 100-1,000), the hybrid system demonstrates strong economic resilience, with COE rising moderately to $0.243/kWh, while a conventional grid-dependent system experiences sharp cost escalation exceeding $0.50/kWh. The hybrid configuration maintains a resilience index above 0.95 in over 90% of stochastic simulations and achieves substantial emission reductions under severe blackout conditions. Statistical validation confirms that outage occurrences follow a Poisson process, supporting the robustness of the stochastic modelling framework.

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Publication Details

Journal
Discover Energy
Published
2026-05-12
DOI
https://doi.org/10.1007/s43937-026-00152-5
Primary Topic
Hybrid Renewable Energy Systems
Type
article
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article

MGFF-based resilience assessment of a hybrid energy system for water treatment plants under frequent grid blackouts

Paul Kusi, Patrick N. Ayambire, Albert K. Awopone
Discover Energy
Hybrid Renewable Energy Systems
article

MGFF-based resilience assessment of a hybrid energy system for water treatment plants under frequent grid blackouts

Paul Kusi, Patrick N. Ayambire, Albert K. Awopone
article en

Abstract

Abstract Grid unreliability remains a major constraint to sustainable energy delivery for critical infrastructure in developing economies. This study investigates the techno-economic performance, environmental impact, and resilience of an optimized hybrid energy system integrating photovoltaic (PV), biogas generation, and battery storage under stochastic grid failure conditions. Using HOMER Pro-based optimization and Monte Carlo simulation, system configurations were evaluated under varying dispatch strategies, financing structures, and Main Grid Failure Frequency (MGFF) scenarios. Results identify the PV/Biogas/Battery configuration as the most cost-effective, achieving a baseline cost of energy (COE) of $0.258/kWh and net present cost (NPC) of $1.86 million under commercial financing conditions. Under concessional financing (10% discount rate), COE decreases to $0.210/kWh, highlighting the critical role of capital structure in renewable deployment. As grid failure frequency increases (MGFF = 100-1,000), the hybrid system demonstrates strong economic resilience, with COE rising moderately to $0.243/kWh, while a conventional grid-dependent system experiences sharp cost escalation exceeding $0.50/kWh. The hybrid configuration maintains a resilience index above 0.95 in over 90% of stochastic simulations and achieves substantial emission reductions under severe blackout conditions. Statistical validation confirms that outage occurrences follow a Poisson process, supporting the robustness of the stochastic modelling framework.

Discover Energy
Kumasi Technical University (GH)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Hybrid Renewable Energy Systems
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MGFF-based resilience assessment of a hybrid energy system for water treatment plants under frequent grid blackouts — Paul Kusi, Patrick N. Ayambire, et al. · Discover Energy (2026) | TGRS Research Map | TGRS