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

12 Pages Posted: 16 Aug 2022

See all articles by Kanato Tamashiro

Kanato Tamashiro

University of the Ryukyus

Eitaro Omine

affiliation not provided to SSRN

Narayanan Krishna

affiliation not provided to SSRN

Alexey Mikhaylov

Financial University under the Government of the Russian Federation

Ashraf Mohamed Hemeida

Aswan University

Tomonobu Senjyu

University of the Ryukyus - Graduate School of Engineering and Science

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

Suggested Citation

Tamashiro, Kanato and Omine, Eitaro and Krishna, Narayanan and Mikhaylov, Alexey and Hemeida, Ashraf Mohamed and Senjyu, Tomonobu, Optimal Components Capacity Based Multi-Objective Optimization and Optimal Scheduling Based Mpc-Optimization Algorithm in Smart Apartment Buildings. Available at SSRN: https://ssrn.com/abstract=4191920

Kanato Tamashiro (Contact Author)

University of the Ryukyus ( email )

Okinawa
Japan

Eitaro Omine

affiliation not provided to SSRN ( email )

No Address Available

Narayanan Krishna

affiliation not provided to SSRN ( email )

No Address Available

Alexey Mikhaylov

Financial University under the Government of the Russian Federation

Ashraf Mohamed Hemeida

Aswan University ( email )

Egypt, Aswan
Aswan, 81524
Egypt

Tomonobu Senjyu

University of the Ryukyus - Graduate School of Engineering and Science ( email )

Okinawa
Japan

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