Ramon Gumara-Ferret

Bio: Ramon Gumara-Ferret is an academic researcher from Adria Airways. The author has contributed to research in topics: Microgrid & Energy market. The author has an hindex of 1, co-authored 1 publications receiving 189 citations.

A Model Predictive Control Approach to Microgrid Operation Optimization

Abstract: Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

Stochastic Scheduling of Renewable and CHP-Based Microgrids

Abstract: Microgrids (MGs) are considered as a key solution for integrating renewable and distributed energy resources, combined heat and power (CHP) systems, as well as distributed energy-storage systems This paper presents a stochastic programming framework for conducting optimal 24-h scheduling of CHP-based MGs consisting of wind turbine, fuel cell, boiler, a typical power-only unit, and energy storage devices The objective of scheduling is to find the optimal set points of energy resources for profit maximization considering demand response programs and uncertainties The impact of the wind speed, market, and MG load uncertainties on the MG scheduling problem is characterized through a stochastic programming formulation This paper studies three cases to confirm the performance of the proposed model The effect of CHP-based MG scheduling in the islanded and grid-connected modes, as well as the effectiveness of applying the proposed DR program is investigated in the case studies

An Optimal Energy Management System for Islanded Microgrids Based on Multiperiod Artificial Bee Colony Combined With Markov Chain

Abstract: The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority, the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown in comparison with the modified conventional EMS, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show cost reduction (by around 30%), convergence speed increase, and the remarkable improvement of efficiency and accuracy under uncertain conditions. An artificial neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach.