Published December 21, 2022 | Version v1
Conference paper Open

Optimal Contract Power and Battery Energy Storage System Capacity for Smart Buildings

  • 1. GECAD Research Group Polytechnic of Porto, School of Engineering (ISEP), Porto, Portugal
  • 2. Polytechnic of Porto, School of Engineering (ISEP), Porto, Portugal

Description

This paper proposed a Mixed Binary Linear Programming (MBLP) approach to find the optimal size of some components of a Smart Building (SB) attempting to reduce the overall cost. The considered SB is equipped with local resources such as Photovoltaic (PV) panels, Electrical Vehicles (EVs), and the Battery Energy Storage System (BESS). Moreover, the SB is only connected with the grid by an Energy Management System (EMS) in which the whole SB has a single Contract Power (CP) such that EMS manages the power flow among external grid, local resources, apartments, and common services, for the goal of reducing the electricity bill. Hence, the wrong choice of CP and BESS capacity will impose unnecessary charges on the electricity bill. As a results, EMS has played a crucial role in SB in determining the best CP and BESS values. The obtained results of this work show the efficiency of the model in which by finding the optimal capacity of CP and BESS, the electricity bill improves by a 34% reduction

Notes

This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project BENEFICE-PTDC/EEI-EEE/29070/2017 and UIDB/00760/2020 under CEECIND/02814/2017 grant.

Files

Author Zhara_Optimal Contract Power.pdf

Files (698.7 kB)

Name Size Download all
md5:90fe5f43ee8addf41888eec8522418b1
698.7 kB Preview Download

Additional details

Funding

Fundação para a Ciência e Tecnologia
PTDC/EEI-EEE/29070/2017 - Building Resources Management towards flexible Contracted Power PTDC/EEI-EEE/29070/2017
Fundação para a Ciência e Tecnologia
CEECIND/02814/2017/CP1417/CT0002 - Not available CEECIND/02814/2017/CP1417/CT0002
Fundação para a Ciência e Tecnologia
UIDB/00760/2020 - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development UIDB/00760/2020