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In this work a novel metaheuristic technique is used to obtain the optimal set point of the different sources used in the proposed system of three interconnected multi–MGs. the metaheuristic technique is one to one based optimizer (OOBO), that can tackle with different optimization difficulties and can give more adequate, acceptable, and satisfied results compared with other optimization techniques22,23. The OOBO is used to obtain the optimal set point of the two batteries in both MG1 and MG2. While the Lagrange multiplier approach. is used to obtain the optimal set point of the Diesel generators of MG1, MG2, and micro gas turbine of MG3 as indicated in the proposed model in the following section during both modes of operation, Grid connected mode and autonomous mode.
The main contributions of this work can be listed as:
proposing an effective day-ahead scheduling for three-interconnected multi-MGs based on a cutting-edge meta-heuristic technique, OOBO, with the goal of minimizing daily operating costs by calculating the optimal set points of the different energy sources. The optimal set-points for diesel generators and MGT are obtained using the Lagrange multiplier approach.
Applying a load-shifting technique-based load management approach to reduce the operational costs of a multi-interconnected microgrid during both grid-connected and islanded operation modes.
The remainder of the paper is organized as follows: Section "Multi-microgrid system" illustrates the components of the proposed three- interconnected MGs system. Section "The energy management system (EMS)" elaborates the optimization technique used to obtain the optimal set point of the proposed system. Section "One to one based optimizer (OOBO)" elaborates the EMS for the proposed system for the most efficient technical operation including optimal set points, EMS cost function, and restrictions of the different components of the proposed system. The obtained simulation results are reviewed and assessed in section "Simulation results". The conclusion from this work is discussed in the last section "Conclusion".
Proposed model of three interconnected MGs.
MG''s operating considerations vary depending on the mode of operation. The most effective system operation strategies prioritize cost-effectiveness, dependability, and low emissions. The optimal dispatch challenge of DG units in MGs is to minimize generation costs and reduce environmental pollution. An MG''s generation expenses include fuel, emissions, operation, maintenance, and purchasing/selling costs. To assess the cost of MG generation, the proposed methodology took into account the power production of renewable energy DGs and the fuel cost of non-renewable energy DGs24,25.
The PV array consists of numerous PV modules connected in parallel and series. This turns the incident solar irradiance into photovoltaic current. The extracted power from PV module is illustrated in the following equation25:
Wind turbines convert wind''s kinetic energy (KE) into mechanical energy. The power extracted from wind turbine depend on wind speed as indicated in the following equation25:
The operating cost of diesel generator can be stated as a quadratic polynomial function of its actual output power.as indicated in the following equation25:
A fuel cell is an electrochemical device that uses chemical energy to produce electrical energy. Fuel cells are very efficient and emit no hazardous gases, making them a possible alternative source to conventional combustion engines. The fuel used in this system is hydrogen with cost can be expressed by the following equation25:
The state of charge (SoC) of battery in case of charging and discharging mode can be calculated by the Eqs. (6) and (7) as indicated by the following25,26,27:
The cost of battery appeared as quick discharging and charging, is deleterious to battery life, the cost is determined by Eq. (8) as following:
The planned EMS operates on a day-ahead basis, with the goal of meeting the required load demand at the lowest possible operational cost based on 24-hour prediction data. Day-ahead scheduling can be achieved through two phases, the first phase is day-ahead unit commitment, and second phase is day-ahead load management.
The EMS''s input data include predicted load demand, extracted power from PV, WT, micro gas turbines, diesel generators, grid tariff, and system restrictions. The EMS''s output data is the scheduled power for the following day, calculated using the lowest energy consumption cost. The suggested economic operating approach involves managing both the electricity generated by DERs in MGs and the power traded among three MGs and the main utility grid28.
The second phase is load shifting technique-based demand side management, in which controllable loads are moved from high-cost to low-cost periods to minimize the MG''s energy consumption costs. The desired load curve is oppositely proportional to the MG''s operating cost, which changes depending on the time of usage. The desired result or desired load can be determined as follows:
The suggested single objective function for the optimization problem of load shifting approach can be expressed as follows:
where PLoad (t) is actual load after load management at time interval t, PL (t) is required MG load at time interval t, conn (t) is connected load at time interval t, and disconn (t) is disconnected load at time interval t at load management period. In this work, the controlled load is considered to be 10% of overall load29.
Calculating the set-points of the different sources for the next day, day-ahead scheduling, using previous data. A novel meta-heuristic algorithm OOBO is suggested to find the optimal operating points of batteries and the transferred electricity with the main grid. It determines the ideal time for charging or discharging the batteries and the ideal time for purchasing or selling power from or to the utility grid in order to reduce day-ahead energy consumption costs. Battery banks can reduce daily running costs by discharging mode during high power consumption and charging during low power consumption periods, when the electricity rates are lowest. While the optimal operating set point for both diesel generators and MGT are obtained using the Lagrange multiplier approach.
The single objective function of the optimization problem of day-ahead unit commitment is minimizing the overall operational cost per day as in Eq. (13) that indicated the overall cost during grid connected mode as following:
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