Description
The world is currently facing massive energy- and dramatic environment challenges caused by global warming and increase in energy demand. Through a tight integration of highly intermittent renewable distributed energy resources, the microgrid is the technology of choice to deliver the expected impacts, making clean energy affordable. The focus of this work lies on techno-economic analysis of combined heat and power multi-microgrids (CHP-MMG), a novel distribution system architecture comprising two interconnected microgrids. High computational resources are needed to investigate CHP-MMG. To this aim, a novel two-layer optimization algorithm is proposed to execute techno-economic analysis and find their optimal settings while the energy balance is achieved at minimal operational costs and highest revenues. At a lower level, a sequential least squares programming method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is proposed to search the design, sizing, siting (i.e., setting) that returns the highest internal rate of return (IRR) and the lowest levelized cost of energy (LCOE). The optimization tool is used for a sensitive analysis of hydrogen costs in off-grid and on-grid contests. With CHP-MMG, energy production surplus is converted from electricity to heat and thus, the energy swarm keeps the LCOE lower than 15c€/kWh. The results show that at a hydrogen cost of 3€/kg, the optimal design returns an IRR over 55%. The simulations considering both on-site hydrogen production via plasma decomposition of methane and green hydrogen let to similar high financial outcomes.| Period | 2 Aug 2021 |
|---|---|
| Event title | 9th Global Conference on Global Warming, GCGW 2021 |
| Event type | Conference |
| Conference number | 9 |
| Location | Virtual Conference, CroatiaShow on map |
| Degree of Recognition | International |
Keywords
- Microgrid, Techno-economic analysis, Optimization, Evolutionary Algorithm, SLSQP