Gerenciamento e controle pelo lado da demanda de uma microrrede residencial

Abstract

Demand-side management (DSM) is an important factor contributing to achieve a balance between electricity production and demand in smart grids. DSM plays a key role in Home Energy Management (HEM), allowing to improve load management and grid reliability, reducing energy demand during peak hours, and minimizing energy consumption in response to price increases. This thesis proposes two approaches to address DSM applied to residential microgrids consisting of an access point to the local utility grid, Renewable Energy Sources (RES) (photovoltaic and wind), Energy Storage System (ESS) composed of batteries (lead-acid and lithium-ion), and loads commonly found in consumers’ homes. The first approach results in a Mixed Integer Linear Programming (MILP) optimization problem that aims to manage the energy flow between the equipment present in a computational residential microgrid by allocating the residential loads in an optimal way from the available resources, seeking to minimize the consumption of electric energy from the utility grid in order to reduce the cost paid by the prosumer. The second approach proposes a model predictive control strategy to manage and control the energy resources of an experimental residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, these proposals encompass issues of ESS degradation, the cost of electricity, RES and the management of electrical loads. Computational and experimental results for different scenarios combining supply/storage under different climatic conditions are presented to demonstrate and verify the effectiveness of the proposed strategies. Thus, the main contributions of this thesis are the development of strategies based on MILP and MPC for the planning, management and control of the use of household appliances in residential microgrids, maximizing the economic and energy benefits, minimizing the electricity consumption of the utility grid, and fulfilling with technical operational constraints defined a priori.