This work proposes a novel approach for the optimal investment planning (multi-step design) of Aggregated Energy Systems (AESs), such as microgrids and multi-energy systems. The method has been developed for applications where the AES design must be revised over time due to the evolution of energy demands, costs, environmental constraints, or the limited lifetime of energy technologies. The core methodology is a detailed Mixed Integer Linear Programming model considering multiple investment stages where energy technologies can be installed/replaced/dismissed, the detailed AES operation on hourly basis, a different lifetime for each technology, the forecasted evolution of energy costs and carbon tax, and (optionally) N-1 reliability, spinning and upward reserve constraints. The model can consider modular and non-modular energy technologies with fixed or variable sizes, as well as cogeneration and co-firing units (e.g., using natural gas and hydrogen). The proposed approach is applied to two case studies: (i) an AES serving a university campus and (ii) an AES serving an off-grid Liquefied Natural Gas production plant with reliability and power reserve requirements. The results indicate that the campus can reduce the CO2 emissions by 45% at the price of increasing the Net Present Cost by only 19.40%, also thanks to the use of co-firing units enabling the transition to green fuels without installing new technologies. For the industrial plant, the results indicate that the optimized reliable AES design allows saving 39% costs compared to the reference (not optimized design) case.
My-AESOPT: A systematic approach for the optimal investment planning and design of aggregated energy systems
Dipierro, Vincenzo;Martelli, Emanuele
2026-01-01
Abstract
This work proposes a novel approach for the optimal investment planning (multi-step design) of Aggregated Energy Systems (AESs), such as microgrids and multi-energy systems. The method has been developed for applications where the AES design must be revised over time due to the evolution of energy demands, costs, environmental constraints, or the limited lifetime of energy technologies. The core methodology is a detailed Mixed Integer Linear Programming model considering multiple investment stages where energy technologies can be installed/replaced/dismissed, the detailed AES operation on hourly basis, a different lifetime for each technology, the forecasted evolution of energy costs and carbon tax, and (optionally) N-1 reliability, spinning and upward reserve constraints. The model can consider modular and non-modular energy technologies with fixed or variable sizes, as well as cogeneration and co-firing units (e.g., using natural gas and hydrogen). The proposed approach is applied to two case studies: (i) an AES serving a university campus and (ii) an AES serving an off-grid Liquefied Natural Gas production plant with reliability and power reserve requirements. The results indicate that the campus can reduce the CO2 emissions by 45% at the price of increasing the Net Present Cost by only 19.40%, also thanks to the use of co-firing units enabling the transition to green fuels without installing new technologies. For the industrial plant, the results indicate that the optimized reliable AES design allows saving 39% costs compared to the reference (not optimized design) case.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


