Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs

Ali Dargahi, Khezr Sanjani, Morteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Sajjad Tohidi, Mousa Marzband

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
8 Downloads (Pure)


The high penetration rate of renewable energy sources (RESs) in smart energy systems has both threat and opportunity consequences. On the positive side, it is inevitable that RESs are beneficial with respect to conventional energy resources from the environmental aspects. On the negative side, the RESs are a great source of uncertainty, which will make challenges for the system operators to cope with. To tackle the issues of the negative side, there are several methods to deal with intermittent RESs, such as electrical and thermal energy storage systems (TESSs). In fact, pairing RESs to electrical energy storage systems (ESSs) has favorable economic opportunities for the facility owners and power grid operators (PGO), simultaneously. Moreover, the application of demand-side management approaches, such as demand response programs (DRPs) on flexible loads, specifically thermal loads, is an effective solution through the system operation. To this end, in this work, an air conditioning system (A/C system) with a TESS has been studied as a way of volatility compensation of the wind farm forecast-errors (WFFEs). Additionally, the WFFEs are investigated from multiple visions to assist the dispatch of the storage facilities. The operation design is presented for the A/C systems in both day-ahead and real-time operations based on the specifications of WFFEs. Analyzing the output results, the main aims of the work, in terms of applying DRPs and make-up of WFFEs to the scheduling of A/C system and TESS, will be evaluated. The dispatched cooling and base loads show the superiority of the proposed method, which has a smoother curve compared to the original curve. Further, the WFFEs application has proved and demonstrated a way better function than the other uncertainty management techniques by committing and compensating the forecast errors of cooling loads.
Original languageEnglish
Article number7311
Issue number18
Publication statusPublished - 7 Sept 2020


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