Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods

DIba Zia Amirhosseini, Reza Sabzehaar, Mohammad Rasouli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, energy usage and its associated price for a residential smart microgrid are analyzed using three different forecasting methods: Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Polynomial Regression. Energy demand and its price are then forecast while taking into account the effect of demand response. The accuracy of the forecast values are evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) criteria. Numerical results, based on the data acquired for a residential microgrid from San Diego Gas Electric (SDGE), are used for model validation purposes. Such data are employed to assess the performance, demonstrate the effectiveness and verify the reliability of the proposed optimization and forecasting methods. Our analyses show that the ARIMA method is more accurate in forecasting the demand as well as the price of energy for the smart migrogrid compared to other methods.

Original languageEnglish (US)
Title of host publication2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538667057
DOIs
Publication statusPublished - Aug 27 2018
Event9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 - Charlotte, United States
Duration: Jun 25 2018Jun 28 2018

Publication series

Name2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018

Other

Other9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018
CountryUnited States
CityCharlotte
Period6/25/186/28/18

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

Cite this

Amirhosseini, DI. Z., Sabzehaar, R., & Rasouli, M. (2018). Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. In 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 [8447834] (2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PEDG.2018.8447834