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Planning the multi-objective operation of micro-grids in the presence of thermal loads and the charging and discharging of thermal storage devices using the evolutionary training and learning algorithm (TLBO)
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Rohollah Homayoun   , Bahman Bahmani Firouzi * , Taher Niknam |
| Department of Electrical Engineering, Faculty of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran |
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Abstract: (340 Views) |
The use of distributed generation based on the simultaneous production of electricity and heat is one of the important steps in the division of distribution networks into micro-grids as building blocks of intelligent systems. Therefore, it is necessary to study and evaluate the performance of distributed generation along with the units of simultaneous production of electricity and heat in micro-grids and operation of micro-grids with regard to electrical energy storage and heat storage. In this paper, modeling the behavior of CHP units and thermal energy storage and formulating the problem of optimal multi-objective utilization of micro-grids by considering heat and energy simultaneous production units with heat storage using an evolutionary training and learning algorithm (TLBO) is provided. The object functions include the costs of operating the micro-grid, the amount of network losses and the amount of deviation of the bus voltage from the nominal value. To solve the optimization problem, the evolutionary algorithm TLBO is used, which is a powerful and effective algorithm in this field. The study network is a 69-bus network that includes a number of distributed generation units and a number of simultaneous sources of electricity and heat. The results show the effective planning of multi-purpose operation of micro-grids in the presence of thermal loads by using MATLAB. |
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| Keywords: Combined Heat and Power (CHP), Heat Storage, Distributed Generation (DG), Multi-objective operation planning, Training and Learning Algorithm (TLBO) |
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Full-Text [PDF 1463 kb]
(149 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2020/12/19 | Accepted: 2021/06/15 | Published: 2021/06/15
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