Consensus Control for Reactive Power Sharing Using an Adaptive Virtual Impedance Approach

Reactive power sharing among distributed generators (DGs) in islanded microgrids (MGs) presents control challenges, particularly in the mismatched feeder line condition. Improved droop control methods independently struggle to resolve this issue and centralized secondary control methods exhibit a high risk of collapse for the entire MG system under any failure in the central control. Distributed secondary control methods have been recently proposed to mitigate the reactive power error evident in the presence of mismatched feeder lines. This paper details a mathematical model of an adaptive virtual impedance control that is based on both leaderless and leader-followers consensus controls with a novel triangle mesh communication topology to ensure accurate active and reactive power sharing. The approach balances an enhanced rate of convergence with the anticipated implementation cost. A MATLAB/Simulink model with six DG units validates the proposed control performance under three different communication structures: namely, ring, complete, and triangle mesh topologies. The results suggest that leaderless consensus control is a reliable option with large DG systems, while the leader-followers consensus control is suitable for the small systems. The triangle mesh communication topology provides a compromise approach balancing the rate of convergence and the expected cost. The extensibility and scalability are advantages of this topology over the alternate ring and complete topologies

Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System

Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities (). Although it is difficult to attain these ideal conditions without complex material engineering, the low implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model