Components design and daily operation optimization of a hybrid system with energy storages. (15th December 2016)

• Record Type: Journal Article
• Authors: Destro, Nicola; Benato, Alberto; Stoppato, Anna; Mirandola, Alberto
• Is Part Of: Energy. Volume 117:Part 2(2016)
• Page Start: 569
• Page End: 577
• Publication Date: 2016-12-15
• Subjects: Particle swarm theory -- Energy storage -- Hybrid system -- Trigeneration system -- PV plant -- Batteries storage system; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.05.097
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The study and the optimization of single devices, plants and integrated systems among producers and users, must be performed considering the possibility of energy storage and conversion among different forms of energy in order to reach the best overall energetic and environmental performance. Small CHP distributed plants are particularly interested in these challenges, both in stand-alone and grid-connected configurations. In this paper, the optimal design and management strategy of a trigeneration system composed by a PV plant, a diesel CHP engine, a reversible heat pump and a boiler is studied. The possibility of hybrid storage by means of a hot and a cold reservoirs, a pack of batteries, and a pumped hydro energy storage is investigated. By applying a model based on the Particle Swarm Optimization, the devices size and the operation strategy are simultaneously optimized. The most suitable devices' hourly-based operation profile and the best management strategy of the energy storages are predicted. The minimization of the overall costs is the problem's optimization target, while the main constrain is the fulfillment of the users' request of electricity, heat, cooling and drinking water. Highlights: An optimization model is applied to a hybrid trigenerative system supplying also water. The model is based on the Particle Swarm Optimization (PSO). The simultaneous optimization of size and hourly load of every device is performed. The optimization target is the minimization of the overall costs. The method has shown strength and relatively low computational costs.