Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm

Abstract

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.

Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved Cuckoo Algorithm

“…The parameters of the energy storage devices are shown in Table 2. The microgrid used different amounts of electricity according to the different time periods [49][50][51][52]. The specifications of the wind turbines, the PV cell, and the batteries are 1000 kW/unit, 400 W/pc, and 1000 A•h/2 V/block, respectively.…”

A Harmony Search Switching Matrix Algorithm for Enhanced Performance of Solar PV Arrays During Non-Uniform Irradiance Scenarios

“…So, in this paper, the above techniques have been considered to compare the performance of the proposed technique under partial shading along with the conventional SP and TCT connections. Additionally, various dynamic techniques such as genetic algorithm (GA) [39], particle swarm optimization (PSO) [40], African vultures optimization (AVO) [41], modified Harris hawks optimizer (MHHO) [42], dragonfly (DF) [43], honey badger (HB) [44], Munkres assignment (MA) [45], grey wolf optimization (GWO) [46] and firefly algorithm (FF) [47] have been used for the comparison with 9x9 PV arrays. Also, FER [34] has been used for the comparison with the proposed technique in a 9x5 array along with SP and TCT.…”

“…During case B5 (FIGURE 10 (e)), the maximum power output of HSR has been noted as higher i.e., 15.19kW with single power peak in the P-V curve than SP (10.23kW with 6 peaks), TCT (12.47kW with 5 peaks), ER (11.96kW with 7 peaks), AC (14.48kW with 3 peaks), Sudoku (13.72kW with 4 peaks), MOS (15.26kW with 4 peaks), HS (14.12kW with 5 peaks) and CM (10.81kW with 7 peaks). Later on, the power generation and peaks of the HSR are compared with GA [39], PSO [40], AVO [41], MHHO [42], DF [43], HB [44], MA [45], GWO [46] and FF [47] reconfiguration techniques under the above shading cases. It has been found that HSR generated nearly equal power output with a single peak compared to the other dynamic techniques during all the cases.…”

“…Also, this methodology is highly flexible and responsive as compared to static methods allowing better adaption to varying and unpredictable partial shading scenarios. An improved Pelican optimization algorithm for switching matrix is proposed in [41] that improved the power generation of PV array by up to 30% during short and wide shading cases. A dynamic leader based collective intelligence reconfiguration algorithm has been proposed in [42] which is found to be effective in enhancing the power output of arrays and reducing the peaks in the power-voltage (P-V) curves during partial shading.…”

“…The proposed HS technique has been tested using three array sizes of 5x5, 9x9 and 9x5 under numerous partial shading cases and compared with series-parallel (SP), totalcross-tied (TCT), Sudoku [23], Futoshiki [24], Magic Square (MS) [25], Competence Square (CS) [26], SD-PAR [27], Odd-Even (OE) [28], electrical reconfiguration (ER) [29], ancient Chinese (AC) [30], modified Sudoku (MOS) [31], hyper Sudoku (HS) [32], Chaos Map (CM) [33] and FER [34] in MATLAB platform. Later on, comparison of the proposed technique with various dynamic algorithms such as genetic algorithm (GA) [30], particle swarm optimization (PSO) [40], African vultures optimization (AVO) [41], modified Harris hawks optimizer (MHHO) [42], dragonfly (DF) [43], honey badger (HB) [44], Munkres assignment (MA) [45], grey wolf optimization (GWO) [46] and firefly (FF) [47] techniques has been carried out under different shading cases. The comparison is done using generated P-V curves, power output, loss, efficiency, enhancement, and available power deviation.…”