Multi-energy systems fast optimization: A new formulation in linear programming for temperatures and magnitudes of thermal power flows in heating systems

Abstract

Geopolitical events and environmental pressures can force regions to speed up their energy transition, as seen in the European Union (EU) shift towards sustainable smart energy systems in response to its current socio-economic and geopolitical situation and to climate change. Among the assets that are envisaged are District Heating Network (DHN), Heat Pump (HP), Thermal Energy Storage (TES), and Photovoltaic Panels (PV).To ensure optimal equipment sizing, the widely adopted method is Mixed Integer Linear Programming (MILP) with a fixed supply temperature. Unfortunately, using MILP to optimize the design and operation considering variable supply temperature is not done in the literature because it is time-consuming. However, this article’s Linear Programming (LP) formulation uses temperature levels to optimize the supply temperature and reaches optimality within 20 min. It improves the Coefficient of Performance (COP) of HP and increases the energetic density of TES. A specific case study examines a residential building with PV connected to a $5^{\text{th}}$ generation DHN, showing that electric self-production can reach 58 %, Seasonal Coefficient of Performance (SCOP) stands at 4.1, and electric self-consumption rate reaches 81 %. This formulation is more complete for optimizing low-temperature DHN, as it accounts for the sensitivity of equipment performance to network temperatures, considering the availability of renewable energy sources.