Energy AI System™

WRH Power Systems’ Energy AI System™ enables efficient grid management and control through software modules and a central Linux-based controller. The Energy AI System™ is a fully automated supervisory system that monitors and controls power equipment in a grid or microgrid environment. Analytics enable a level of power profitability beyond the capacity of current systems that rely on human operations and intuition. The Energy AI System™ uses data supplied by the entire power system, stored in a local and cloud based historian repository, to make resource usage decisions that result in lower overall operating costs for the power plant. The Energy AI System™ is specifically designed for power grids and microgrids between 250 kW and 100 MW and above.

WRH Power Systems’ Energy AI System™ Software and Control System interfaces with the Supervisory Control and Data Acquisition System at the power generating site to acquire power plant telemetry, including local weather data. Forecasts are continuously run against operating ranges to ensure that systems maintain very high reliability up times. Soft warnings generate alerts in the remote possibility that human involvement is required.

Due to the intermittancy of Solar and Wind power generation, the penetration of these renewable assets to a distributed power system or microgrid is limited, however, renewable penetration is significantly improved using WRH Power Systems’ Energy AI System™. The Energy AI System™ intelligently manages each generation source and predicts future weather, solar irradiation and wind speed, while monitoring site loads and future demand. The Energy AI System™ allows for continuous grid stability while maximizing renewable penetration with minimal thermal generation loading. The power systems designed and operated by WRH Power Systems use battery storage for grid stability, power factor correction, frequency support, voltage regulation and short and longer duration generation source transitions, to further increase the penetration of renewables and reduce the consumption of liquid fuels, such as diesel, natural gas, methane and others, for thermal combustion power generation.

The Energy AI System™ relies on its Monetary Optimization Engine to make critical decisions on what asset to dispatch while minimizing renewable power curtailment by finding an optimal balance between energy storage, major grid assets, and where appropriate, grid imported energy costs and grid exported energy revenues. The Energy AI System™ minimizes the need for plant operators to manage a grid or microgrid on a daily basis by automating energy flow decisions.

The various Energy AI System™ modules are integrated into a top-level supervisor, which is part of the Linux Energy Supervisor Controller that determines how much deferrable load power to bring online, how much power to curtail and whether to buy or sell power from a supporting grid. Buying power takes the form of purchasing kilowatt hours from a connected grid or from attached fossil fuel generators. Selling power takes the form of selling kilowatt hours to a connected grid or deferring power to a type of productive load such as producing desalinated water or other potential uses. Consequently, from an algorithmic perspective, controlling deferrable loads is viewed as exporting power to the grid, which occurs when the supply of energy becomes sufficient to make the deferrable load purchase that energy.