Enhancing demand-side flexibility for Heat Booster Substations in Ultra-Low Temperature District Heating systems

Abstract

Ultra-low temperature district heating (ULTDH) systems improve the heat distribution efficiency by reducing thermal network losses and can provide an easier access to Renewable Energy Sources (RES) by utilizing them as heat sources through Power-to-Heat solutions. In this paper, a data-driven seasonal-ARIMA model is developed to forecast Domestic Hot Water (DHW) demand at different time resolutions ranging from 10 to 60 minutes and a control-oriented non-linear model of a Heat Booster Substation (HBS) is developed, with main components including a Hot Water Storage Tank (HWST), two electric-driven heat pumps and a heat exchanger. Further, an optimal control strategy using Genetic Algorithm is developed for DHW preparation in ULTDH systems with the dual objective of generating load-shift flexibility on the demand side and reducing energy costs. The developed control strategy is successfully implemented to utilize the thermal energy storage capability of the HBS to optimally schedule the charging of the HWST based on electricity price signals. In comparison to the current rule-based control strategy, the developed control strategy demonstrates significant demand side flexibility and achieves energy cost savings of more than 70%. With increased DHW usage and greater volatility in electrical prices due to higher penetration of RES, greater potential for demand side flexibility is demonstrated and increased energy cost savings (more than 100%) are realized.