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A framework to estimate the potential of solar cooking for institutional level applications is presented. Using the weather data of a given location (such as solar radiation, ambient temperature, and wind speed), the proposed framework estimates the number of days in a year when solar cooking is feasible and consequently the amount of useful energy saved. Around 57% of the annual useful energy required for cooking (5475 TJ/year out of 9562 TJ/year) in the institution-types considered in this study can be met with solar energy. The adoption of solar cooking in these institution-types is expected to save 197 thousand tonnes of LPG annually. The corresponding annual amount of CO2 emissions mitigation is estimated at 592 thousand tonnes. Amongst the six institution-types considered in the study, cooking of mid-day meal (MDM) in government schools of India has the maximum potential of solar cooking. The annual potential of useful energy savings with the use of solar energy for cooking of MDM is estimated at 3881 TJ out of a gross requirement of 6960 TJ/year. The framework developed and approach used in the study can be used for assessment of potential of institutional solar cooking as well as identification of niche areas for immediate intervention in any other country as well. [ABSTRACT FROM AUTHOR]

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]