LAUSR

Multienergy systems (MESs), as coupling of various energy sectors, can offer appropriate solutions to the operation of energy systems. The flexibility introduced by energy diversity can leverage the operation of MESs in supplying different energy demands, e.g., electricity, heat, etc. This article presents a continuous-time optimization framework for the day-ahead operation of MESs, where a function space solution method is proposed to reduce the dimensionality of the proposed model. The proposed methodology projects the day-ahead model into a finite-dimensional function space spanned by Bernstein polynomials and converts the problem with an indefinite dimension into a finite-order mixed-integer linear programming problem that can be easily solved with commercial solvers. The main purpose of the proposed framework is to model the parameter and decision trajectories of MESs in a way to reduce the day-ahead energy load approximation errors and minimize the costs associated with net-load deviations in real-time operation. In this way, the flexibility introduced by the operation of different energy units in MESs can be efficiently captured to further improve the performance of such systems. The proposed optimization framework is studied regarding four test MESs and the results for multiple cases are presented and compared. The results demonstrate the efficiency of the proposed methodology in capturing the load approximation errors, as it gives a better solution to the cost-efficient operation of MESs at a reasonable computational time.