Benefits of thermal load forecasts in balancing load fluctuations through thermal storage

Planning and managing the operation of cogenerative plants (CHP) is increasingly becoming an industrial necessity because of the participation of CHP plants in the day-ahead energy market and the need to deal with heat demand fluctuations. Short-term operational planning usually considers power and heat demand forecast, which can widely fluctuate daily and seasonally, to maximise the net revenue and fulfil the heat requirements. In this framework, a Thermal Storage (TS) can balance day-night fluctuations due to outdoor temperature, as well as unexpected energy surplus and deficiencies caused by heat demand forecast errors, thus giving the CHP plant more operational flexibility. In this paper, the capability of a TS to balance thermal load fluctuations and forecast errors is investigated when a Machine Learning (ML) thermal load forecast for short-term predictions up to 48 h is used. The TS is modelled as a layered storage tank with perfectly mixed layers. Weather and consumption data from 2018 to 2020 related to a large greenhouse powered by a CHP plant located in Tuscany were used as a case study to perform the training and validation of the forecast model as well as to analyse the TS capability in balancing fluctuations with volumes ranging from 500 up to 10,000 m3. Several ML algorithms were used and compared against a naive prediction based on load persistency. Support Vector Machine (SVM) resulted as the best-performing algorithm. Using SVM leads to better exploitation of the TS capacity, compared to persistence, leading to a more regular State of Charge (SOC) trend and allowing the system to operate within expected conditions up to 80 % of the year. In contrast, a more naive forecast approach brings to relevant volume size increase to achieve equal performance. Finally, a more accurate forecast reduced the TS size to 50 %, potentially cutting the investment and operational costs compared to the load persistency forecast strategy.

Automated summary

This paper investigates the ability of a Thermal Storage (TS) to balance thermal load fluctuations and forecast errors using a Machine Learning (ML) thermal load forecast model for short-term predictions up to 48 hours. A case study of a large greenhouse powered by a CHP plant in Tuscany is conducted to validate the TS performance at different volumes. The results show that Support Vector Machine (SVM) algorithm performs the best, utilizing the TS capacity more effectively and leading to a more stable trend in State of Charge (SOC) for the system to operate within the expected conditions for up to 80% of the year.