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For a planner of decentralized renewable energy systems with micro-grids, an accurate load prediction is imperative for proper sizing of the system. For an accurate load projection, the following questions need to be answered: How many households actually get connected to the micro-grid? How much electricity do they actually use? What factors affect their consumption level? How does their consumption level change over time? In this study, an attempt has been made to identify answers to these questions by looking into one of the PV micro-grid rural electrification projects in Sundarban area of West Bengal, India. It is found that the number of beneficiary households has been on increasing trends indicating existence of large potential demands. The monthly electricity consumption of the average household is turned out to be within the range of 0.42–0.66 kW h/day thus not showing any trend of increase over time. In order to identify factors that affect consumption level, a regression model has been developed with two explanatory variables, namely, the total number of connections and the average ambient temperature during supply hours (night). Application of the model would be effective in predicting additional load requirement in case grid extension is planned under the same project to accommodate potential demands. Results of this study would also be applicable to optimization of expansion process of micro-grid systems. [ABSTRACT FROM AUTHOR]

An adaptive approach based on a neural network is presented here for the control of a grid interactive solar power generating system. This control approach is based on a summation and a product neuron, which are used to process the non‐linearity of load currents. A sigmoidal and Gaussian functions are used to linearise the response of summation and product neurons. In this control, the product and summation neurons, and sigmoidal and Gaussian functions are within a single layer, which reduces the complexity of control. This control algorithm is superior over artificial neural network (ANN)‐based control algorithm, which has large number of unknown weights and layers. It reduces the computational burden and complexity of the control algorithm. It shows the inherent improved performance at non‐linear balanced and unbalanced loads. The control of net active and reactive components of the power separately improves the efficacy of the control at unbalanced non‐linear load and reduces the voltage transients in the grid voltages. The ANN‐based control also plays an active role in enhancing the power quality of the grid. The reduced complexity of control and direct power feed forward of photovoltaic array (PVA) to the grid improve the dynamic response of the grid‐connected PVA‐based system. [ABSTRACT FROM AUTHOR]

Realistic assessment of utilization potential of solar energy for thermal power generation and identification of niche areas/locations for this purpose is critically important for designing and implementing appropriate policies and promotional measures. This paper presents the results of a detailed analysis undertaken for estimating the potential of solar thermal power generation in India. A comprehensive framework is developed that takes into account (i) the availability of wastelands (ii) Direct Normal Irradiance (DNI) (iii) wastelands that are habitat to endangered species and/or tribal population and/or that is prone to earthquakes and (iv) suitability of wasteland for wind power generation. Finally, using an approach developed for the allocation of wastelands suitable for solar power generation between thermal and photovoltaic routes, the potential of solar thermal power generation is assessed for two threshold values of DNI – 1800 kW h/m 2 and 2000 kW h/m 2 . With all the wastelands having wind speeds of 4 m/s or more allocated for wind power generation, the estimated potential for solar thermal power generation is 756 GW for a threshold DNI value of 1800 kW h/m 2 and 229 GW for a threshold DNI value of 2000 kW h/m 2 . Results obtained can be used for identification of best suited areas for solar thermal power generation in India. [ABSTRACT FROM AUTHOR]