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- From home energy management systems to communities energy managers: the use of an intelligent aggregator in a community in Algarve, PortugalPublication . Gomes, Isaías; Graca Ruano, Maria; Ruano, AntonioThis paper describes the development of community energy management systems (CEMS). A CEMS allows optimal energy sharing within energy communities, as it is a central system that makes the global management of the entire community. The proposed CEMS is based on mixed-integer linear programming (MILP), operating under the receding horizon concept of Model Predictive Control (MPC). A systematic classification of electric appliances, the use of external information such as weather information and energy prices, as well as the use of intelligent forecasting techniques enables the proposed approach to achieve an excellent efficiency. It also allows for an easy installation of as well as a smooth scaling with an increasing number of houses. The system is tested in a real community in Algarve, Portugal. Different simulations are compared to experimental operation and include cases with and without sharing of energy, different resources allocated to the houses considered, and the use of different tariffs. CEMS formulations include sharing of energy without restriction, as well as employing different allocation coefficients strategies. The results show that for the community under study when managed by CEMS such as the one presented in this paper, it would result in significant cost reductions when compared to the case where there is no energy community.
- MILP-based model predictive control for home energy management systems: A real case study in Algarve, PortugalPublication . Gomes, I.L.R.; Ruano, Maria; Ruano, AntonioThis paper addresses the development of an innovative home energy management system (HEMS). The presented HEMS relies on a mixed-integer linear programming (MILP)-based model predictive control. The system takes advantage of the powerful formulation capabilities of a MILP-based mathematical pro-gramming problem with the capabilities of model predictive control to optimize, at each sample instant the HEMS operation using a receding-horizon formulation. The system is designed for a residence located in Algarve, Portugal. The results of the presented system are compared with the real experimental results obtained by a commercial PV-battery management system. Additionally, an analysis of the system???s per-formance is conducted, in terms of operation planning for 2021 market prices compared to 2022 prices, where there was a significant rise of buying price due to the energy world context. In all simulations per-formed, it is verified that the MILP-based model predictive control presents better results, with statistical relevance. CO 2023 Elsevier B.V. All rights reserved.
- Recent techniques used in home energy management systems: a reviewPublication . Gomes, Isaías; Bot, Karol; Ruano, Maria; Ruano, AntonioPower systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.