Development of an optimization algorithm for the energy management of an industrial Smart User

Highlights: • Development of a genetic optimization algorithm for the management of a Smart User. • Algorithm tested with experimental data collected in an actual industrial Smart User. • Different management strategies tested. • Performance comparison among conventional and developed strategies. - Abstract: The growth of world energy demand combined with global warming and climate change is one of the most urgent global challenges and induced policy measures to foster the use of renewable energy sources. In order to cope with the intrinsic variability of solar and wind, active management of distribution networks and customers is required, if the creation of the so called Smart Grid is desired. This paper focuses on the strategies to enable prosumers (i.e. customers able to self-generate all or part of their energy needs) to optimally manage their generation and loads in order to minimize their energy bill and, at the same time, support the distribution grid stability by responding flexibly to its requirements in terms of active load management. In this study an industrial prosumer equipped with solar and wind generation as well as with a co-generation unit with absorption chiller and heat/cold storage was considered. The work presents an optimization algorithm that was developed and applied to this Smart User to manage operations of the CHP in order to optimize the power generation and the usage depending on internal and external inputs as loads, weather forecast and price from the electricity and natural gas market. The proposed algorithm was tested with real experimental inputs of different typical days and its performance was compared with three common scenarios, i.e. traditional supply, electric load following and thermal load following operation of the CHP. Results compare the different control strategies of the CHP (i.e. thermal and electric load following) and shows economic advantages allowed by means of the optimization algorithm, which appears to be an effective instrument to prepare prosumers to the smart grid of the future.