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Aliasghar Ghadimi

Aliasghar Ghadimi

Academic rank: Associate Professor
ORCID: https://orcid.org/0000-0001-7276-2221
Education: PhD.
ScopusId: 56678490500
Faculty: Engineering
Address: Arak University
Phone: 08632625620

Research

Title
A four-stage framework for optimal scheduling strategy of smart prosumers with vehicle-to-home capability under real time pricing based on interval optimization
Type
JournalPaper
Keywords
optimal scheduling, smart prosumers, vehicle-to-home, interval optimization
Year
2023
Journal IET Generation, Transmission & Distribution
DOI
Researchers Marcos Tostado Veliz ، Aliasghar Ghadimi ، Mohammad Reza Miveh ، Ra’Ed Nahar Myyas ، Francisco Jurado

Abstract

With the emergence of the Smart Grid concept, utility companies require more active participation of home users in the power sector. This changing paradigm is enabled by the wide deployment of multiple home assets such as small renewable-based generators or storage facilities. In this context, consumers are no longer conceived as pure loads but also active agents that can exchange energy with the grid. To promote this active participation, utility companies promote different price-based demand response programs to change the consumer patterns on pursuing a more efficient and economic system operation. In this regard, home energy management programs are becoming an essential tool for efficiently managing the different home users while addressing multiple demand response goals at minimum cost. In essence, a home energy management system is a computational optimization tool, which has to handle multiple uncertainties brought by weather forecast or energy pricing. This paper tackles this issue by developing a novel robust home energy management program based on interval optimization. In contrast to other related approaches, the proposal avoids the explicit use of interval arithmetic. Instead, the different uncertain parameters are sequentially incorporated into the scheduling task through different stages and interval-based formulation. The developed methodology incorporates weather, load, energy pricing and plug-in electric vehicle related uncertainties. A benchmark case study in a smart prosumer layout serves to prove the effectiveness of the new approach.