DOMES: A general optimization method for the integrated design of energy conversion, storage and networks in multi-energy systems

A realistic pursuit of decarbonization targets requires planning and designing new configurations of “multi-energy systems” to identify the optimal number, type, location and size of the energy conversion and storage units and their interconnections with the end users of different forms of energy. The common approach in the literature is to treat the optimization problem of energy conversion and storage separately from that of energy networks, and the few attempts to address the two problems simultaneously have led to oversimplifications due to the very large number of decision variables involved. To fill this gap, this study introduces “ DOMES ” ( Design Of Multi-Energy Systems ), a general optimization method for the integrated synthesis, design and operation of a multi-energy system in its entirety. With the goal of minimizing costs and reducing carbon emissions, DOMES can simultaneously find the location, type, size and operation of the energy conversion and storage units, as well as the topology and capacity of the energy networks, to meet the energy demand of the end users. To make the problem computationally solvable while ensuring sufficiently good accuracy of the solution, mathematical techniques such as linearization, problem decomposition and time series aggregation have been applied. DOMES is capable of finding the global optimum of the problem either while planning new systems from scratch or when starting from existing systems. Considering a densely populated urban district, the investment costs of renewable conversion plants outweigh those of the district heating network and electric microgrid, which together account for less than 10 % of the total. A much higher economic impact of energy networks is expected when considering larger, less densely populated areas. • Optimal synthesis, design and operation of multi-energy systems including networks. • Development of a two-level evolutionary algorithm with a MILP sub-problem. • Decomposition of the topological optimization from the design and operation problem. • Design from scratch of new multi-energy systems or retrofit of existing ones. • Single or multi-objective optimization to minimize total costs and carbon emissions.