Small-Scale Compressed Air Energy Storage Application for Renewable Energy Integration in a Listed Building

Abstract

Abstract:In the European Union (EU), where architectural heritage is significant, enhancing the energy performance of historical buildings is of great interest. Constraints such as the lack of space, especially within the historical centers and architectural peculiarities, make the application of technologies for renewable energy production and storage a challenging issue. This study presents a prototype system consisting of using the renewable energy from a photovoltaic (PV) array to compress air for a later expansion to produce electricity when needed. The PV-integrated small-scale compressed air energy storage system is designed to address the architectural constraints. It is located in the unoccupied basement of the building. An energy analysis was carried out for assessing the performance of the proposed system. The novelty of this study is to introduce experimental data of a CAES (compressed air energy storage) prototype that is suitable for dwelling applications as well as integration accounting for architectural constraints. The simulation, which was carried out for an average summer day, shows that the compression phase absorbs 32% of the PV energy excess in a vessel of 1.7 m 3 , and the expansion phase covers 21.9% of the dwelling energy demand. The electrical efficiency of a daily cycle is equal to 11.6%. If air is compressed at 225 bar instead of 30 bar, 96.0% of PV energy excess is stored in a volume of 0.25 m 3 , with a production of 1.273 kWh, which is 26.0% of the demand.

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

“…By the literature survey on those topics, in [18,19] the intervention options to improve energy demand flexibility in buildings have been reviewed, including heating, ventilation, and air conditioning (HVAC) systems, energy storage, building heat capacity, appliances, and occupants behaviour. Then, Shakouri et al [20] prosed in their study a multi-objective optimization procedure for demand side management of a residential area; under different scenarios, the advantages of using centralized energy management systems in residential buildings were demonstrated, showing the potential benefits for both consumers and service companies.…”

Analysis on the potential of an energy aggregator for domestic users in the Italian electricity system

“…From a technical point of view, the heating plants transformation, from combustion-based systems to the electrically-driven ones (i.e. HPs or hybrid systems), sometimes entails higher complexity and costs, making it unsuitable depending on the energy scenario boundary conditions; for that reason, the users availability to create the flexibility has to be evaluated [78] choosing also what are the most profitable investments to activate DR strategies [79,80]. Having said, a distinction between different buildings has to be made by the specific energy consumption and the installed technical systems:…”

“…Therefore, the usage characteristics of household appliances strongly influence the electricity off-takes from the grid. An interesting benchmark on the cycles duration for washing machines and dishwashers is given in [80] and it indicates as a reference, 200 minutes for the first ones and 110-160 minutes for the second ones. The average energy consumptions per cycle of those appliances are 0.71.1 kWh and 1.01.6 kWh, respectively.…”

Energy Retrofitting Effects on the Energy Flexibility of Dwellings

“…It implies in building such a mechanism, the crucial role played by probabilistic modelling is beyond the design of the load profile for buildings [14] and beyond the incentive schemes for the market [15]. Dwellings have a small flexible power when considered alone, while a group of them offers the chance to design several pathways of participation to the market, from the local installation of storage [16] to tariff definition [17], from their aggregation as robust and optimized equivalent load [18] up to the neighborhood scale [19], and the sum of their equipment as well [20].…”