Scheduling Distributed Energy Resource Operation and Daily Power Consumption for a Smart Building to Optimize Economic and Environmental Parameters

Zahra Pooranian,Mauro Conti,Vinod P,Jemal Abawajy

doi:10.3390/en11061348

Zahra Pooranian, Mauro Conti + Show 2 more

Open Access

https://doi.org/10.3390/en11061348

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Journal: Energies	Publication Date: May 25, 2018
Citations: 41	License type: CC BY 4.0

Affiliation: University of Padua, Deakin University

Abstract

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO2 emissions and the daily energy cost.

Highlights

With the steadily rising electricity costs coupled with its impact on the environment, smart home energy efficiency has become one of the major issues that is necessary to improve the overall efficiency of power consumption in smart buildings
Based on the numerical tests carried out, we have determined that: (i) the electricity cost decreases using the solver for the optimization scenarios, and this rate is even higher with more components (Figure 3d) as the microgrid scenario is applied; and (ii) the solution can respond to the electricity demand using its storage, and even the rate of electricity that is sold is significant compared to the consumer demand
We have peaked during these time intervals, which can be deduced from Figure 3a,c

Summary

Introduction

With the steadily rising electricity costs coupled with its impact on the environment, smart home energy efficiency has become one of the major issues that is necessary to improve the overall efficiency of power consumption in smart buildings. A microgrid (MG) encompasses a portion of an electric power distribution system that is located downstream of the distribution substation. It includes a variety of distributed energy resource (DER) units (such as solar panel and wind turbine [2]) and serves different types of end users of electricity and/or heat [3,4]. Each microgrid is able to supply power to local residents in a small geographical area in a cost-effective manner, by integrating a networked group of renewable generators and storage systems. A smart building with its own DER can be considered a microgrid because DERs provide local energy for the smart home residents

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Results

Conclusion