Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model

Economic Analysis of Energy Scheduling and Trading in Multiple-Microgrids Environment

The microgrids (MGs) are considered to be a key component of future power systems. The increased integration of distributed energy resources (DER) has led to critical challenges in energy management (EM) such as uncertainty, pricing and optimal dispatch. The need for control of DER, optimal resource allocation and EM, requires interconnection of MGs, leading to formation of multiple-microgrid (MMG). One of the fundamental objective of MMG is to make optimal use of DER's within various MGs for optimal benefits. At a particular instance, some MGs might have excess energy generated, while some other MGs might buy the excess energy to meet local demands and storage requirements. The MMG facilitates energy trading between the grid and MG and amongst the MGs in order to achieve efficient energy sharing. In this paper, we evolve through different EM and scheduling strategies using visual basic for applications (VBA). The system considered has 3 MGs interconnected forming a MMG with each MG equipped with PV system and energy storage system (ESS). The objective of the strategies is to maximize the profits of individual MG while benefiting the whole MMG in total. The costs of implementing the proposed strategies are calculated and an economic analysis of various strategies is presented

Parameters Extraction of a Photovoltaic Cell Model Using a Co-evolutionary Heterogeneous Hybrid Algorithm

© 2019 IEEE. This paper proposes a new hybrid algorithm with a combination between the wind driven optimization (WDO) algorithm and the differential evolution with integrated mutation per iteration (DEIM) algorithm. The proposed algorithm, a wind driven optimization based on differential evolution with integrated mutation per iteration (WDO-based on DEIM) algorithm, is utilized to extract the unknown parameters in both of a single-diode photovoltaic (PV) cell model and a double-diode PV cell model. To show the effectiveness of the proposed model, its performance is validated internally by comparing the generated current-voltage (I-V) characteristic curves by the proposed algorithm with the actual I-V characteristic curves, and externally with those obtained by the WDO and DEIM algorithms. The results show the superiority of the proposed model. According to the normalized-root-mean-square error (nRMSE), the mean absolute percentage error (MAPE) and the coefficient of determination (R^{2}) of the achieved results, the proposed WDO-based on DEIM algorithm outperforms the aforementioned algorithms. Finally, the average efficiency of the WDO-based on DEIM algorithm is 95.31%, while it is 81.08% for the WDO algorithm and 88.37% for DEIM algorithm in the single-diode PV cell model. While, it is 96.78% based on WDO-based on DEIM algorithm and it is 92.30% for the WDO algorithm and 91.42% for DEIM algorithm in the double-diode PV cell model