Abstract
The objective of this study was to create a tool that will enable renewable energy microgrid (REμG) facility users to make informed decisions on the utilization of electrical power output from a building integrated REμG connected to a smart grid. A decision support tool for renewable energy microgrids (DSTREM) capable of predicting photovoltaic array and wind turbine power outputs was developed. The tool simulated users’ daily electricity consumption costs, avoided CO2 emissions and incurred monetary income relative to the usage of the building integrated REμG connected to the national electricity smart grid. DSTREM forecasted climate variables, which were used to predict REμG power output over a period of seven days. Control logic was used to prioritize supply of electricity to consumers from the renewable energy sources and the national smart grid. Across the evaluated REμG electricity supply options and during working days, electricity exported by the REμG to the national smart grid ranged from 0% to 61% of total daily generation. The results demonstrated that both monetary saving and CO2 offsets can be substantially improved through the application of DSTREM to a REμG connected to a building.
Highlights
The use of building integrated renewable energy microgrids (REμG) has in recent years become an effective means of providing renewable energy while offsetting greenhouse gas emissions for residential and commercial consumers
While previous studies have focused on the operations and analysis of specific renewable energy systems, localized weather forecasting, demand side management and multiple tariff based smart grids, this study focuses on developing a holistic decision support tool that may be applied to any building in any location with a standard REμG and grid connection
The decoded European Centre for Medium-Range Weather Forecasts (ECMWF) operational forecast data were used as explanatory variables in the linear regression models and RegARIMA for the analyzed climate variables
Summary
The use of building integrated renewable energy microgrids (REμG) has in recent years become an effective means of providing renewable energy while offsetting greenhouse gas emissions for residential and commercial consumers. Optimizing the energy utilization from REμGs has become an area of significant research interest. The creation of a decision support tool would be useful for REμG users to optimize their energy utilization from renewable sources, taking into account REμG power output, building electricity consumption, electricity tariff (ET) structures, feed-in tariff (FIT) structures, and CO2 emissions. One important consideration in the creation of such a decision support tool is the fact that accurate predictions of climatic variables are required to simulate REμG power output. Several studies have been carried out regarding the prediction of climatic variables and simulation of renewable energy output power. REμG electricity consumption, electricity pricing structures, FIT structures and associated CO2 emissions have been modeled and analyzed in previous research. Li et al [2]
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