Optimization of a hybrid community district heating system integrated with thermal energy storage system

Evidence from a various research suggests that buildings hold a vital role in climate change by significantly contributing to the global energy consumption and the emission of greenhouse gases. Considering the trend of higher energy consumption in the building sector, it is important to influence this sector by decreasing its energy demand. District generation and cogeneration systems integrated with the energy storage system have been suggested as a potential solution to achieve such planned goals. Unlike the older generation of the DHS, where the focus of the design was on minimizing the system heat loss, in 4th generation DHS, achieving higher system efficiency is made possible by picking the optimal equipment size as well as adopting the appropriate control strategy. Designers have adopted different design methods for selecting the equipment size, however, finding the optimal size is a challenging task. This paper reports the development of a simplified methodology (dynamic optimization) for a hybrid communitydistrict heating system (H-CDHS) integrated with a thermal energy storage system by coupling the simulation and optimization tools together. Two, existing and newly built communities, have been considered and the results of the optimization on the equipment size of both communities have been studied. The results for the newly built community is later compared with the one obtained from the conventional equipment size methods whereas static optimization methods and potential size reduction with the conventional method has been obtained

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

Validation of a district energy system model using field measured data

Load prediction is the first step in designing an efficient community district heating system (CDHS). Even though, several methods have been developed to predict the heating demand profile of buildings, there is a lack of method that can predict this profile for a large-scale community with a numerous user types in a timely manner and with an appropriate level of precision. It, first briefly describes the 4-step procedure developed earlier, utilizing a Multiple Non-Linear Regression (MNLR) method, for predicting the heating demand profile of district, followed by description of the community structure, and its district system. It also reports the field measurement procedure for collecting the data required and the preliminary analysis data. Results obtained from a continuous monitoring of the CDHS over a two-year period is employed to validate the accuracy of the developed model in the predicting the CDHS’s heating load profile. Finally, using the 4-step procedure, the district’s energy demand profile is predicted, and compared with both the measured data and the initial prediction. The outcome shows a less than 11.2% in the mean square root error (MSRE) of the predicted and measured load profiles