A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Babol Noshirvani University of Technology
  • Northumbria University
  • University of Beira Interior

Abstract

The inclusion of plug-in electrical vehicles (PEVs) in microgrids not only could bring benefits by reducing the on-peak demand, but could also improve the economic efficiency and increase the environmental sustainability. Therefore, in this paper a two stage energy management strategy for the contribution of PEVs in demand response (DR) programs of commercial building microgrids is addressed. The main contribution of this work is the incorporation of the uncertainty of electricity prices in a model predictive control (MPC) based plan for energy management optimization. First, the optimization problem considers the operation of PEVs and wind power in order to optimize the energy management in the commercial building. Second, the total charged power reference which is computed for PEVs in this stage is sent to the PEVs control section so that it could be allocated to each PEV. Therefore, the power balance can be achieved between the power supply and the load in the proposed microgrid building while the operational cost is minimized. The predicted values for load demand, wind power, and electricity price are forecasted by a seasonal autoregressive integrated moving average (SARIMA) model. In addition, the conditional value at risk (CVaR) is used for the uncertainty in the electricity prices. In the end, the results confirm that the PEVs can effectively contribute in the DR programs for the proposed microgrid model.

Details

Original languageEnglish
Pages (from-to)332-340
Number of pages9
JournalSustainable Cities and Society
Volume41
Publication statusPublished - 1 Aug 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Commercial building microgrids, Conditional value at risk (CVaR), Demand response (DR), Model predictive control (MPC), Plug-in electric vehicles (PEV), Wind power

ID: 25727852