Optimal operation of battery energy storage system (BESS) in the microgrid systems is an effective solution to exploit the efficiency of highly uncertain renewable energy sources. This article presents a model for opt...
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Optimal operation of battery energy storage system (BESS) in the microgrid systems is an effective solution to exploit the efficiency of highly uncertain renewable energy sources. This article presents a model for optimal operation of BESS in the microgrid system using a metaheuristic algorithm to reduce the cost of electricity purchased from the grid. The microgrid model includes grid source, load, photovoltaic system (PVS), wind turbine (WT) and BESS that are built using the Simulink model based on model predictive control. The data of load, radiation, wind speed, and electricity price are updated every 15 min. At each interval during the day, the coyote algorithm (COA) is adjusted to find the optimal power for the BESS. The considered cases include the microgrid without BESS, the microgrid with BESS operated using the heuristic approach based on power difference between generation sources and load demand, and the microgrid with BESS operated to minimize energy costs using the COA and particle swarm optimization (PSO) algorithms. The results show that the proposed model allows to evaluate the optimal BESS operation problem in microgrid with data of load, PVS, WT and BESS updated over each sampled time. In terms of energy cost minimization, the COA-based BESS operation method helps reduce electricity cost by 63.76 % compared to the case without BESS. The electricity cost reduction of COA is 13.22 % and 47.38 % higher than that of PSO and the heuristic algorithm, respectively. In case of environmental parameter variation, the proposed method is also reduced 35.98% of electricity cost compared to the case without BESS and higher 7.60 % and 27.59 % higher than that of the PSO and heuristic methods. The comparison results between the two metaheuristic methods of COA and PSO show that COA often achieves better results than PSO in determining power value for BESS in each interval of the day. Therefore, the optimal BESS operation model in the microgrid based on Simulink m
Enhancing the accuracy of PV power prediction is crucial for guaranteeing secure scheduling and steady power system operation. This research proposes a coyote algorithm (COA) to optimize the prediction model of the lo...
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ISBN:
(纸本)9798400716638
Enhancing the accuracy of PV power prediction is crucial for guaranteeing secure scheduling and steady power system operation. This research proposes a coyote algorithm (COA) to optimize the prediction model of the long-short-term memory network (LSTM). Taking into full consideration of the five factors constraining the output power of PV, and taking PV power generation as the research object, the power generation efficiency under different weather is analyzed, and COA is used to optimize the parameters of the LSTM fully-connected layer, and establish a COA-LSTM combination model to predict the PV power, which has a better convergence speed and solving efficiency, and it can also avoid the local optimal solution effectively. Finally, based on the real-time data of a photovoltaic power station in Xinjiang, simulation is carried out, and the experimental results show that the COA-LSTM is more accurate in predicting the photovoltaic power than the LSTM.
This paper presents a new method based on coyote algorithm (COA) which is inspired from the social life of coyotes for the problem of simultaneous network reconfiguration and distributed generation (DG) placement to r...
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This paper presents a new method based on coyote algorithm (COA) which is inspired from the social life of coyotes for the problem of simultaneous network reconfiguration and distributed generation (DG) placement to reduce real power loss. The effectiveness of the proposed COA method has been evaluated on two distribution systems consisting of 69-node and 119-node systems at two scenarios consisting of reconfiguration only and simultaneous reconfiguration and DG placement. The result analysis has indicated that network reconfiguration combination with optimization of location and size of distributed generation (DGs) is more effective for power loss reduction than network reconfiguration only. About the network reconfiguration only, for the 69-node and 119-node systems COA can search out the optimal solution that reduce power loss by 56.16% and 32.86%, respectively. Meanwhile, the optimal solution obtained by the network reconfiguration combination with optimization of location and size of DGs using COA helps to reduce power loss of two aforementioned systems by 84.37% and 55.31%, respectively. The result comparisons with other methods in the literature have also shown that COA has ability to obtain the better network configuration and location and size of DGs than other methods. Therefore, the proposed COA can be a promising method for the problem of simultaneous reconfiguration and DG placement. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).
This study describes a multi-input power system that is suited for fueling electric automobiles, InterCitys, and airplanes, particularly in situations with significant fluctuating load demand. The dual framework utili...
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This study describes a multi-input power system that is suited for fueling electric automobiles, InterCitys, and airplanes, particularly in situations with significant fluctuating load demand. The dual framework utilizes fuel cells (FC), batteries, and super capacitors (SCs). An energy management system (EMS) remains a critical aspect of lowering overall hydrogen consumption and minimizing the degradation of FC functionality. A novel EMS that has been suggested focused on a novel optimization method known as the coyote optimization algorithm (COA), and it considers the fact that the total load is adequately supplied within the limitations of each power source. To minimize the hydrogen consumption. By maximizing the power generated by the energy storage devices, the energy acquired from the FC is reduced. In comparison to other optimization methods, the COA would be a practical, effective, and relatively straightforward optimizer that only involves a limited number of controlling factors to be set. The framework application MATLAB/Simulink is used to create the proposed method. In order to show the effectiveness of the proposed methodology, a study with several different conventional techniques is performed, which includes the classic proportional-integral control mechanism, the frequency decoupling with state machine (FDSM) controlling technique, the equivalent consumption minimization scheme (ECMS), and the external energy minimization scheme (EEMS). The efficacy of the algorithm and the FC's aggregate H2 usage serve as the focal points for comparison in this work. The outcomes demonstrate that the recommended COA strategy is superior and more effective than the alternative approaches.
Electricity generation from renewable sources is on the rise. The irregular presence of these sources increases the frequency variations induced by the load variance. The penetration of renewable energy sources increa...
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Electricity generation from renewable sources is on the rise. The irregular presence of these sources increases the frequency variations induced by the load variance. The penetration of renewable energy sources increases the instability of the integrated power grid due to decreased system inertia. Therefore, this paper presents an optimal controller design of proportional-derivative with filter cascaded-proportional-integral (PDn-PI) using an innovative coyote optimization algorithm (COA). The modelling of the renewable energy sources (RESs) of photovoltaic (PV) and wind farm interconnection is considered in the load frequency control (LFC). The COA is adopted and integrated with the PI/ PID/ cascaded PDn-PI controllers for the sake of optimally tuning and specifying their gains. Furthermore, the powerful performance of the proposed COA-tuned PDn-PI controller is validated, under all cases studied, not only over PI and PID controllers but also, over the previously reported approaches. To get complete picture of the string of victories gained by the proposed technique, the dynamical uncertainties of load demand and RESs are investigated in both models as third case. The simulation investigation reveals the robustness and superiority dynamic responses of the cascaded PDn-PI with COA in direct contrast to other approaches even under these uncertainties.
The number, layout and configuration of black-start power supplies will directly affect the recovery process of the power system after power outage. A scientific and reasonable black-start deployment optimization sche...
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The number, layout and configuration of black-start power supplies will directly affect the recovery process of the power system after power outage. A scientific and reasonable black-start deployment optimization scheme can greatly accelerate the grid recovery process and reduce the duration of power outage. In this paper, based on the basic principles of power system partition recovery, we divide the power system recovery partition based on improved label propagation algorithm, which is used to guide the deployment optimization of multiple Blackstart power supplies sources. Subsequently, based on the important nodes and key lines of the grid, a mathematical model of multiple black-start power supplies deployment optimization considering the shortest restoration time is established, taking into account various constraints. Finally, the traditional coyote optimization algorithm is improved by improving the coyote growth model, introducing adaptive Le ' vy flight and chaotic optimization perturbation mechanism, and applied to the solution of multi-Black-start power supplies deployment optimization model. The effectiveness of the proposed partition recovery method and the adaptability and superiority of the multi-black-start power distribution optimization method are compared and analyzed by arithmetic examples in Matlab simulation software. The simulation results show that the proposed multi-black start power distribution optimization method with zonal recovery can reduce the outage duration and improve the recovery efficiency.
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