Optimal Components Capacity Based Multi-Objective Optimization and Optimal Scheduling Based Mpc-Optimization Algorithm in Smart Apartment Buildings

Abstract

Changes in the global energy landscape have increased the importance of research on energy management methods in the power grid. In particular, Demand Side Management (DSM) at consumers is attracting worldwide attention. Distributed generation in intelligent consumers, such as Smart Houses (SH), contributes to the introduction of new distributed generation from the demand side by determining optimal scheduling. However, Fuel-cells (FC) and Battery Energy Storage Systems (BESS) are expensive, and their installation is a significant burden for the demand side. Therefore, this paper proposes a Smart Apartment Building (SAB) model in which multiple distributed power sources are shared by multiple consumers to reduce operation costs and carbon emissions through the implementation of highly efficient operation methods. An important aspect of such a study is to understand the characteristics of the demand side and to propose an operating method that takes into account the preferences of the demand side. In this paper, to provide a variety of options, a Pareto front is generated through multi-objective optimization to reduce the total cost and carbon dioxide emissions of the model. The optimal component capacity is also considered at the same time. A Model Predictive Control (MPC)-based optimization algorithm is then developed to achieve highly efficient operation, thus contributing to the reduction of the two objectives. As a result, in a compromise between the two conflicting objectives, the MPC algorithm successfully reduces operation costs by 44.4% and carbon dioxide emissions by 54.7% compared to the original case.

Keywords: Multi-Objective Optimization Problem, model predictive control, Distributed Generation, Battery Energy Storage System, MILP