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Abstract
Reducing loss and providing a secure power supply for customers have always been the main goal of distribution systems planners. Recently, the development of competitive electricity markets and responsive loads participation in such markets together with the integration of regional energy networks (RENs) have resulted in creating some challenges for RENs' owners to increase operating efficiency, and reduce investment costs. In this paper, to overcome these problems, a new modeling for coordinated planning of distribution networks and RENs in the presence of responsive loads is proposed and to achieve the optimal solution of the problem, the genetic algorithm is used. Operation uncertainties due to the installation of wind turbines and photovoltaic resources in RENs are considered. The probability-tree method is used to generate and model operation scenarios and their variable production, respectively. To validate and confirm the efficiency of the proposed model, numerical studies were applied on a 25-bus Institute of Electrical and Electronics Engineers standard distribution network, including two RENs equipped with responsive loads. The results show the fact that the flexibility of the demand side in the electrical sector due to the activity of responsive loads causes to increase in the installation capacity of energy resources and storages.
Original language | English |
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Journal | Energy Science & Engineering |
Volume | 11 |
Issue number | 10 |
Pages (from-to) | 3344-3363 |
Number of pages | 20 |
ISSN | 2050-0505 |
DOIs | |
Publication status | Published - Oct 2023 |
Keywords
- distribution system planning
- multiagent systems
- private ownership
- regional energy networks
- responsive loads
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Dive into the research topics of 'Coordinated Planning of the Distribution System and Regional Energy Network in The Presence of Responsive Loads'. Together they form a unique fingerprint.Projects
- 1 Finished
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Long-term Distribution System Planning in Presence of Regional Energy Networks
Dehghani Sanij, M., Mirzaei, A. & Anvari-Moghaddam, A.
01/09/2018 → 30/04/2022
Project: PhD Project