Two of the most important things you can do for your system are making sure your system is running properly and maximizing your energy savings.
In order to manage demand in a microgrid, each of its elements must belong to a system capable of performing specific and global control, with each layer having its own timescale.
EnergyLink realizes the importance to each of these solutions listed below and offers a non-biased, energy efficient plan for our customers.
In a large microgrid, an Advanced Demand Management System (ADMS), is well suited to be this central control that manages demand in the microgrid. An ADMS is built from different high-level software modules:
With each of these components, ADMS carries out real-time, global grid analysis. DMS manages everything related to energy shortages in the microgrid and the resulting system reconfiguration.
Together with SCADA, it performs traditional remote control functions including fault detection and isolation.
The DSM assesses the actual demand in the microgrid to understand how to integrate different energy resources distributed within the grid. Its goal is to avoid peaks that would overshoot the microgrid’s capacity.
It provides a microgrid manager several options to preserve critical load, to shed demand, and to shift loads in time. This can be done by either postponing the demand or anticipating it by sending a signal to consume when energy is available.
The greater the availability and diversity of distributed energy resources in the microgrid, the greater the opportunity for the DSM to take action under peak consumption. Some loads might be interruptible while others may be flexible, playing on their full range of power use. In-rush current when some equipment starts also creates short peaks that can be managed by controlling batteries in the microgrid.
The ADMS can undertake automated control of demand with the inputs from the forecast and optimization layer.
It sends it an hourly specific management profile for the microgrid for the five rolling days ahead by simultaneously analyzing load forecasts based on historical data, weather forecasts (for loads under the influence of weather such as heating or cooling), and load shedding or shifting capabilities.
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