Abstract:
A method and system restores power in a power distribution network. The network includes a plurality of power sources, a plurality of loading zones, a plurality of switching devices interconnected between the plurality of power sources and the plurality of loading zones, and an intelligent electronic device associated with each of the plurality of switching devices to control the switching devices. A base network state is defined and a power restoration logic is created for the base network state. A simulation is run for the power restoration logic and then the power restoration logic is transmitted to a power restoration controller which thereafter monitors and controls the intelligent electronic devices.
Abstract:
A method and system restores power in a power distribution network. The network includes a plurality of power sources, a plurality of loading zones, a plurality of switching devices interconnected between the plurality of power sources and the plurality of loading zones, and an intelligent electronic device associated with each of the plurality of switching devices to control the switching devices. A base network state is defined and a power restoration logic is created for the base network state. A simulation is run for the power restoration logic and then the power restoration logic is transmitted to a power restoration controller which thereafter monitors and controls the intelligent electronic devices.
Abstract:
In one aspect of the teachings herein, demand responsive loads are selected for involvement in a given DR event using an advantageous approach to selection that is based on using a mathematical network model to evaluate power loss in a power distribution network as a function of different combinations of demand responsive load selections and corresponding load reduction values. The mathematical network model comprises a mathematical representation of the power distribution network as a multi-phase unbalanced distribution network, including mathematical representations of the physical components in the power distribution network and the connecting relationships of those components. As overall power loss in the system is a function of different combinations of demand responsive load selections, the mathematical network model is used to evaluate system power loss under different demand response load selections, in a manner that automatically accommodates mesh networks and other complex network topologies, distributed generation sources, etc.
Abstract:
In one aspect of the teachings herein, demand responsive loads are selected for involvement in a given DR event using an advantageous approach to selection that is based on using a mathematical network model to evaluate power loss in a power distribution network as a function of different combinations of demand responsive load selections and corresponding load reduction values. The mathematical network model comprises a mathematical representation of the power distribution network as a multi-phase unbalanced distribution network, including mathematical representations of the physical components in the power distribution network and the connecting relationships of those components. As overall power loss in the system is a function of different combinations of demand responsive load selections, the mathematical network model is used to evaluate system power loss under different demand response load selections, in a manner that automatically accommodates mesh networks and other complex network topologies, distributed generation sources, etc.