Optimal Scheduling of Demand Response-Based AC OPF by Smart Power Grid' Flexible Loads Considering User Convenience, LSTM-Based Load Forecasting, and DERs Uncertainties

Alireza Zarei; Navid Ghaffarzadeh; Farhad Shahnia; Miadreza Shafie-Khah

doi:10.1109/ACCESS.2024.3498598

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Optimal Scheduling of Demand Response-Based AC OPF by Smart Power Grid' Flexible Loads Considering User Convenience, LSTM-Based Load Forecasting, and DERs Uncertainties

Journal article

Open access

Peer reviewed

Optimal Scheduling of Demand Response-Based AC OPF by Smart Power Grid' Flexible Loads Considering User Convenience, LSTM-Based Load Forecasting, and DERs Uncertainties

Alireza Zarei, Navid Ghaffarzadeh, Farhad Shahnia and Miadreza Shafie-Khah

IEEE access, Vol.12, pp.171617-171633

2024

DOI: https://doi.org/10.1109/ACCESS.2024.3498598

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Abstract

aggregator

Costs

Demand response

HVAC

Load flow

Load modeling

Long short term memory

optimal power flow

Reactive power

renewable energy

Renewable energy sources

Smart grid

Smart grids

Uncertainty

The energy storage capabilities of the heating, ventilation, and air conditioning (HVACs) systems, as well as electric vehicles (EVs), render them excellent candidates for demand response. This study proposes a joint coordination of AC optimal power flow and demand response for aggregated EVs and HVACs, in conjunction with the scheduling of large-scale flexible loads. This paper prioritizes user convenience, and ensuring compliance with comfortable temperature boundaries. Additionally, EV capacity and travel distance are estimated using a normal distribution. The primary objective of this study is to minimize the total cost and peak demand while simultaneously maximizing total revenue of participant user in demand response, user convenience, and the integration of renewable energy sources. To achieve an accurate optimal choice, the uncertainties associated with renewables and demand are addressed using scenario-based and long short-term memory (LSTM) methods, respectively. The optimal demand response level is determined through sensitivity analysis, and employing the technique for order preference by similarity to ideal solution (TOPSIS) method. The obtained results demonstrate that by only 20% demand response at large-scale loads and EV and HVAC aggregators, the proposed model reduces peak loads by 12.8% and the total cost by 7.4%.

Details

Title: Optimal Scheduling of Demand Response-Based AC OPF by Smart Power Grid' Flexible Loads Considering User Convenience, LSTM-Based Load Forecasting, and DERs Uncertainties
Authors/Creators: Alireza Zarei - Imam Khomeini International University
Navid Ghaffarzadeh - Imam Khomeini International University
Farhad Shahnia - Murdoch University, Centre for Water, Energy and Waste
Miadreza Shafie-Khah - University of Vaasa
Publication Details: IEEE access, Vol.12, pp.171617-171633
Publisher: IEEE
Number of pages: 17
Identifiers: 991005720668907891
Murdoch Affiliation: School of Engineering and Energy
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.18 Power Systems & Electric Vehicles; 4.18.204 Smart Grid Optimization
Web Of Science research areas: Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
ESI research areas: Engineering