In microgrids, forecasting solar power output is crucial for optimizing operation and reducing the impact of uncertainty. To forecast solar power output, it is essential to forecast solar irradiance, which typically requires historical solar irradiance data. These data are often unavailable for residential and commercial microgrids that incorporate solar photovoltaic. In this study, we propose an hourly day-ahead solar irradiance forecasting model that does not depend on the historical solar irradiance data; it uses only widely available weather data, namely, dry-bulb temperature, dew-point temperature, and relative humidity. The model was developed using a deep, long short-term memory recurrent neural network (LSTM-RNN). We compare this approach with a feedforward neural network (FFNN), which is a method with a proven record of accomplishment in solar irradiance forecasting. To provide a comprehensive evaluation of this approach, we performed six experiments using measurement data from weather stations in Germany, U.S.A, Switzerland, and South Korea, which all have distinct climate types. Experiment results show that the proposed approach is more accurate than FFNN, and achieves the accuracy of up to 60.31 W/m2 in terms of root-mean-square error (RMSE). Moreover, compared with the persistence model, the proposed model achieves average forecast skill of 50.90% and up to 68.89% in some datasets. In addition, to demonstrate the effect of using a particular forecasting model on the microgrid operation optimization, we simulate a one-year operation of a commercial building microgrid. Results show that the proposed approach is more accurate, and leads to a 2% rise in annual energy savings compared with FFNN.

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The popularity of microgrids is increasing considerably because of their environmental and technical advantages. However, the major challenge in microgrid integration is its financial feasibility due to high capital costs. To address this obstacle, renewable energy incentive programs, which are the motivation of this study, have been proposed in many countries. This paper provides a comprehensive evaluation of the technical and financial feasibility of a campus microgrid based on a techno-economic analysis using the Microgrid Decision Support Tool, which was implemented to support decision-making in the context of microgrid project investment. A method for microgrid design aiming to maximize system profitability is presented. The optimal microgrid configuration is selected depending on financial indices of the project, which directly address the returns on an investment. Most importantly, this analysis captures all the benefits of financial incentives for microgrid projects in California, U.S., which presents a key difference between the California market and other markets. The impact of incentives and uncertain financial parameters on the project investment is verified by sensitivity analysis. The outcomes show that the optimal configuration generates significant electricity savings, and the incentives strongly determine the financial feasibility and the optimal design of a microgrid.

Throughout the developing world, most remote and isolated communities are still without reliable electricity in the twenty-first century, and this is primarily due to the high cost of grid extensions. In communities that do have electricity, they usually rely on diesel generators, though these have high operating and maintenance costs, while also polluting the environment. A more sustainable approach is to deploy microgrids, however, microgrids have a high upfront cost, which is a major obstacle, especially in rural areas of developing countries. This study aims to investigate the parameters that can be influenced to make microgrids more economical for rural electrification. Through sensitivity analyses, five key policy and technology parameters were identified. They include real discount rates, diesel prices, grants, battery chemistry, and operating strategies. The system was then redesigned using scenarios formulated by varying these parameters. Results show that the parameters affect the configuration, levelized cost of energy (LCOE), renewable energy penetration (REP), and pollutant emissions. The study uses three remote communities in the Beni Department of Bolivia as case studies. MDSTool was used as a modeling framework to design the microgrids. The unique insights and lessons learned during the design process are discussed at length because these may be valuable for future microgrid designs for remote communities.

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