Integration of sizing and energy management based on economic predictive control for standalone hybrid renewable energy systems. (September 2019)

• Record Type: Journal Article
• Authors: Rullo, P.; Braccia, L.; Luppi, P.; Zumoffen, D.; Feroldi, D.
• Is Part Of: Renewable energy. Volume 140(2019)
• Page Start: 436
• Page End: 451
• Publication Date: 2019-09
• Subjects: Standalone hybrid renewable energy systems -- Sizing and EMS integration -- Economic model predictive control -- Bilevel mixed integer nonlinear problem -- Genetic algorithm; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.03.074
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: An Hybrid Renewable Energy Systems (HRES) can be described as a set of loads, renewable generation and storage units that can operate in standalone mode or connected to the main grid. In order to obtain a good compromise between capital investment and system reliability, an optimum sizing of all HRES components is needed. As power reliability, system cost and operation of the system depend on each other, the sizing methodology must be integrated with the energy management strategy (EMS). This paper presents an optimization methodology for sizing the components of a standalone hybrid wind/PV system (with hydrogen storage and battery storage), which integrates an EMS based on an economic model predictive control (EMPC) approach. The integrated problem to be solved is presented as a bi-level optimization framework composed of an outer loop and an inner loop. The outer loop is in charge of the HRES sizing and it is solved using Genetic Algorithms (GA). The inner loop solves the EMS for each candidate solution as a rolling horizon mixed integer linear problem (MILP). The results have shown an investment saving as well as a reduction of the operation costs with the proposed methodology. Highlights: A stand-alone system with renewable energy sources and energy storage is addressed. The integration of the sizing methodology with an EMS based on EMPC is proposed. The integration is formulated as a bilevel mixed-integer non-linear program-ming. A sequential solution approach based on genetic algorithms is used. A comparison is made with a rule-based EMS using several indicators.