Cristina Corchero
Catalonia Institute for Energy Research (IREC)
Head of the Energy System Analytics research group of the Electricity and Power Electronics Department in IREC. PhD on Statistics and Operations Research by Universitat Politècnica de Catalunya in 2011.
Smart buildings are a key element to walk towards smart cities and grids. Nonetheless, there are several degrees of intelligence. A first step is to incorporate commercial self-consumption solutions (kits) in buildings so they can manage the energy from local renewable power generators. Typically, these solutions have a management systems with some programmed scenarios where users configure their preferences. A second step would be to substitute these commercial management algorithms with an Energy Management System (EMS) to optimize the energy dynamic behavior and to reduce the electricity bill. Further, this EMS may contribute to stabilize and improve the quality and emissions of the electricity grid by offering energy flexibility to the electricity system in favor of decentralization. In the latter case, the energy that individual buildings may provide is too low to have a significant impact on the grid, this is when the figure of the Aggregator appears. The Aggregator gathers the energy flexibility of many buildings to respond to the electricity grid needs in a grouped way in constant communications with the EMS.
Buildings having an EMS count on several elements with different energy controllable capabilities; such as solar or wind generators, batteries, heating and cooling systems and electric vehicles among others. The optimization algorithms that control the use of these elements consider several technical restrictions or use conditions but none considers the aging of the battery. In fact, batteries age along time and use, losing capacity, power and efficiency as they grow older.
This study compares the battery aging between buildings that count with an EMS to optimize the electricity bill under three scenarios (Fig.1) in contrast to those that have the commercial management programs (Fig.2). Lithium ion battery lifespan is estimated by means of an electric equivalent battery circuit model that runs on Matlab and simulates its behavior through time.
Moreover, this study evaluates the distribution of the battery costs regarding its use, observing that batteries controlled by commercial self-consumption solutions have longer lifespan because they are underused, ending up in higher calendar ageing costs than the ones that are controlled by optimized EMS (Fig.3).
