Economic and Environmental Analysis of DER Integration in Smart Homes with Development of RTP and DLC

The problem of distributed energy resources (DER) integration in smart homes is evaluated in this paper. This has been done by considering the energy cost savings of smart home and environmental pollution reduction. The DER utilized in the study are photovoltaic (PV) panel, wind turbine (WT) and energy storage system (ESS). The Pareto optimality is employed to solve the two-objective optimization problem. In addition, demand response (DR) programs including real-time pricing (RTP) and direct load control (DLC) are applied in this research. In other words, smart home residents respond to price signals as well as designed incentives. Loads are classified into two groups based on their responsibility. The percentage of loads that respond to these signals and incentives are responsive loads. It was found that, regarding environmental issues, DER integration would be cost-effective. Besides, DR programs reduce energy costs and environmental pollution and increase the cash flow of smart home residents.

[1]  Yael Kovo NASA's Data Portal , 2016 .

[2]  G. Krajačić,et al.  Integration of renewable energy and demand response technologies in interconnected energy systems , 2018, Energy.

[3]  Salman Kahrobaee,et al.  Optimum sizing of distributed generation and storage capacity in smart households , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[4]  José L. Bernal-Agustín,et al.  Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology , 2017 .

[5]  S.M.T. Bathaee,et al.  Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response , 2017 .

[6]  G. Scelba,et al.  Multicriteria Optimal Sizing of Photovoltaic-Wind Turbine Grid Connected Systems , 2013, IEEE Transactions on Energy Conversion.

[7]  Mohsen A. Jafari,et al.  Integration of Demand Dynamics and Investment Decisions on Distributed Energy Resources , 2016, IEEE Transactions on Smart Grid.

[8]  Seung Ho Hong,et al.  User-expected price-based demand response algorithm for a home-to-grid system , 2014 .

[9]  G.R. Yousefi,et al.  A MADM-based support system for DR programs , 2008, 2008 43rd International Universities Power Engineering Conference.

[10]  Ashu Verma,et al.  Analysis of techno-economic viability with demand response strategy of a grid-connected microgrid model for enhanced rural electrification in Uttar Pradesh state, India , 2019, Energy.

[11]  Seyed Hossein Hosseinian,et al.  Optimal Investment on DG and ESS for Smart Homes under Demand Response , 2019, 2019 27th Iranian Conference on Electrical Engineering (ICEE).

[12]  Giovanni Manassero Junior,et al.  Analysis of demand response and photovoltaic distributed generation as resources for power utility planning , 2018 .

[13]  Qiang Yang,et al.  Demand response under real-time pricing for domestic households with renewable DGs and storage , 2017 .

[14]  Saeed Mohajeryami,et al.  A novel economic model for price-based demand response , 2016 .

[15]  Gevork B. Gharehpetian,et al.  Optimal Energy Dispatch of Smart Home Equipped with PV, WT, and ESS Using Load Control under RTP , 2018, 2018 Smart Grid Conference (SGC).

[16]  Angel A. Bayod-Rújula,et al.  Future development of the electricity systems with distributed generation , 2009 .

[17]  M. P. Moghaddam,et al.  Flexible demand response programs modeling in competitive electricity markets , 2011 .

[18]  Alireza Soroudi,et al.  Power System Optimization Modeling in GAMS , 2017 .

[19]  Salman Kahrobaee,et al.  Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households , 2013, IEEE Transactions on Smart Grid.