Energy-efficient Control of a Smart Grid with Sustainable Homes based on Distributing Risk
Model-based Embedded and Robotic Systems
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The goal of this thesis is to develop a distributed control system for a smart grid with sustainable homes. A central challenge is how to enhance energy efficiency in the presence of uncertainty. A major source of uncertainty in a smart grid is intermittent energy production by renewable energy sources. In the face of global climate change, it is crucial to reduce dependence on fossil fuels and shift to renewable energy sources, such as wind and solar. However, a large-scale introduction of wind and solar generation to an electrical grid poses a significant risk of blackouts since the energy supplied by the renewables is unpredictable and intermittent. The uncertain behavior of renewable energy sources increases the risk of blackouts. Therefore, an important challenge is to develop an intelligent control mechanism for the electrical grid that is both reliable and efficient. Uncertain weather conditions and human behavior pose challenges for a smart home. For example, autonomous room temperature control of a residential building may occasionally make the room environment uncomfortable for residents. Autonomous controllers must be able to take residents' preferences as an input, and to control the indoor environment in an energy-efficient manner while limiting the risk of failure to meet the residents' requirements in the presence of uncertainties. In order to overcome these challenges, we propose a distributed robust control method for a smart grid that includes smart homes as its building components. The proposed method consists of three algorithms: 1) market-based contingent energy dispatcher for an electrical grid, 2) a risk-sensitive plan executive for temperature control of a residential building, and 3) a chance-constrained model-predictive controller with a probabilistic guarantee of constraint satisfaction, which can control continuously operating systems such as an electrical grid and a building. We build the three algorithms upon the chance-constrained programming framework: minimization of a given cost function with chance constraints, which bound the probability of failure to satisfy given state constraints. Although these technologies provide promising capabilities, they cannot contribute to sustainability unless they are accepted by the society. In this thesis we specify policy challenges for a smart grid and a smart home, and discuss policy options that gives economical and regulatory incentives for the society to introduce these technologies on a large scale.