Techno-economic-environmental optimization of grid-connected hybrid microgrid integrating renewable and geothermal resources in Naama, Algeria

This paper presents a techno-economic and environmental optimization of a grid-connected hybrid microgrid (MG) designed to meet the combined electrical and thermal loads of Ain Ouarka and the neighboring town of Assela in Naama, Algeria, with surplus electricity exported to the Sonelgaz/SKTM national grid. The proposed MG integrates photovoltaic (PV) power generation, wind turbines, a geothermal cogeneration heat and power (CHP) unit, a diesel generator (DG), a boiler, lithium-ion battery storage, and a bidirectional converter. To ensure realistic operation, hourly real electrical and thermal load profiles were simulated over a full year. While HOMER Pro was used for component sizing and feasibility analysis, its standard dispatch strategies, load following (LF) and cycle charging (CC), rely on a rule-based search space that cannot simultaneously optimize electrical, thermal, and storage decisions in multi-energy systems. LF minimizes fuel use but cannot coordinate CHP thermal flows or battery SOC trajectories, whereas the CC dispatch strategy forces generators to operate at rated power even when this increases fuel consumption and operational costs, often trapping the optimization in locally optimal solutions. To address these limitations, a MATLAB Link–based Mixed-Integer Linear Programming (ML-MILP) controller was incorporated to perform single-objective hourly dispatch optimization aimed at minimizing instantaneous operating cost. HOMER then evaluates these optimized dispatch schedules and outcomes through long-term multi-objective optimization, minimizing the net present cost (NPC), levelized cost of energy (LCOE), and annual operating cost while maximizing renewable fraction (RF) and reducing emissions. Three system scenarios were examined. Scenario A, based solely on DG, grid electricity, and a boiler, resulted in an NPC of 17.6 M€, an LCOE of 0.373 €/kWh, and an operating cost of 942,420 €/year. Scenario B, a renewable–fossil hybrid without CHP, reduced costs to an NPC of 12.0 M€ and an LCOE of 0.254 €/kWh. The hybrid with complete integration, Scenario C, optimized with ML-MILP and involving geothermal-based CHP and thermal coupling, performed best with an NPC of 4.19 M€, LCOE of 0.0854 €/kWh, and a yearly operating cost of 408,050 €, which translated to a saving of 76.2 %, 72.9 %, and 56.7 % over Scenario A, with a further 27.5 % reduction in emissions of CO 2 and grid dependency reduced from 100 % to 28.6 %. Sensitivity analysis performed for fuel costs, total investment costs, and discount rate further validates the proposed approach applied to the optimal MG configuration. These results confirm that the incorporation of geothermal-based MGs with solar-wind generators, under MILP-based dispatch strategy, significantly enhances the sustainability, techno-economic performance, and operational robustness against uncertainties in hybrid MGs in semi-arid regions.