Analysis of microgrid performance, reliability, and resilience (amperre) …

Description (excerpt)

FIELD OF THE TECHNOLOGY The present disclosure relates generally to analyzing and forecasting microgrid performance, reliability, and resilience. BACKGROUND Federal facilities, industrial areas, academic campuses, and communities are working to incorporate greater renewable energy sources and energy storage in their power infrastructure. While renewable sources of energy can—and do—support several facilities, uncertainty still exists about how reliably these sources of energy can support small and/or critical power systems with higher reliability standards such as Army installations, tactical microgrids, remote community grids, and emergency response power systems. Maintaining reliability is already a significant challenge for power grids, and those that have a high proportion of renewable energy face particular challenges due to their intermittent power production. It is important to understand how power resources are most commonly used. Current power systems typically contain one of the following resource configurations: fuel-based dispatchable sources such as generators to supplement utility power; fuel-based dispatchable sources that serve as a primary source; fuel-based dispatchable sources with supporting renewables; fuel-based dispatchable sources with supporting energy storage; or fuel-based dispatchable sources with supporting renewables and energy storage. Conventional power systems commonly prioritize dispatchable, fuel-based resources to provide power to the load. Renewables are treated as supporting sources due to their intermittence. These systems rarely make optimal use of the energy produced by the renewable sources and consume more fuel as a result. Existing power grids may experience barriers to the prioritization of renewable energy sources, so many have not taken this step. Energy storage is often treated as reserve power in the event of adverse conditions rather than as a tool to capture surplus energy from renewable sources. Surplus energy is often wasted, but if it is captured, it minimizes the need to use fuel-based sources. When conventional power projects do choose to implement a control system that prioritizes renewables, these projects face challenges in determining the quantity of renewable resources and energy storage needed to achieve reliability and resilience objectives. The projects often “overbuild,” or compensate for this uncertainty by incorporating an excess quantity of certain renewable resources and energy storage for periods of low power generation. This can prove to be costly and resource intensive, leading many projects to stick to the prioritization of fuel-based sources. It is possible to minimize the need for “overbuilding” through analysis to forecast how well a power grid would reach reliability and resilience objectives for different quantities of renewable sources, energy storage, and supporting dispatchable energy. Current methods to perform such an analysis are underdeveloped. SUMMARY In an embodiment, a discrete, time-domain computational method finds optimal power resources in a microgrid for reliability and resilience. The method involves receiving, by an input module, inputs including microgrid parameters, control conditions, natural resource datasets, and a load profile for the microgrid. The method forecasts, by a forecasting module based on the inputs, forecasted values for a plurality of power grid outcomes related to reliability and resilience, to inform power resource investment decisions. The method generates, by a forecasted results generator, a results report including the forecasted values. The results report is readable by several stakeholders, including Military installations, remote communities, campuses, and institutions. In another embodiment, a non-transitory computer readable medium has instructions stored thereon executable by a processor to perform a discrete, time-domain computation that finds optimal power resources in a microgrid for reliability and resilience. The instructions, when executed, cause the processor to receive, by an input module, inputs including microgrid parameters, control conditions, natural resource datasets, and a load profile for the microgrid. The processor forecasts, by a forecasting module based on the inputs, forecasted values for a plurality of power grid outcomes related to reliability and resilience to inform power resource investment decisions. The processor generates, by a forecasted results generator, a results report including the forecasted values. The results report is readable by several stakeholders, including Military installations, remote communities, campuses, and institutions. BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES FIG. 1 is a diagram of how AMPeRRe models power flow according to an example. FIG. 2 is a diagram illustrating the impact of adde