An illustrative embodiment disclosed herein is method for power distribution optimization. In some embodiments, the method includes determining an efficiency for each power block of a plurality of power blocks of a power distribution optimization system, determining a characteristic for each power block, determining a power to provide, selecting a first percentage of the power that a first power block is to provide and a second percentage of the power that a second power block is to provide at least based on the efficiency for each power block, the characteristic for each power block, and the power to provide, wherein the first percentage of the power is greater than the second percentage of the power, and sending a dispatch command to cause the first power block to provide the first percentage of the power and the second power block to provide the second percentage of the power.

An illustrative embodiment disclosed herein is method for power distribution optimization. In some embodiments, the method includes determining an efficiency for each power block of a plurality of power blocks of a power distribution optimization system, determining a characteristic for each power block, determining a power to provide, selecting a first percentage of the power that a first power block is to provide and a second percentage of the power that a second power block is to provide at least based on the efficiency for each power block, the characteristic for each power block, and the power to provide, wherein the first percentage of the power is greater than the second percentage of the power, and sending a dispatch command to cause the first power block to provide the first percentage of the power and the second power block to provide the second percentage of the power.

The present invention is a method of “Demand Side Management” (DSM) that is intended to manage the increasingly chaotic nature of the power grid, and is designed to adapt to the future impact to the grid patterns caused by the ongoing introduction of renewable power generation sources, battery charging associated with the increasing number of electric vehicles, and future unforeseen developments, by utilizing “Statistical Process Control” (SPC) techniques. SPC is typically a method for controlling manufacturing process variation in a factory, analogously the present invention adapts SPC methods to help monitor the quality of the grid by treating said grid as if said grid were a process with varying levels of quality, to which the present invention can detect, anticipate and respond by making immediate and adaptive future scheduling decisions for the control of device loads, or generation, for the benefit of consumers as well as power companies.

A method for controlling a power distribution system having a number of discretely controllable devices includes processing a system state, defined by observations acquired via measurement signals from a number of meters, using a reinforcement learned control policy including a deep learning model, to output a control action including integer actions for the controllable devices. The integer actions are determined by using learned parameters of the deep learning model to compute logits for a categorical distribution of predicted actions from the system state, that define switchable states of the controllable devices. The logits are processed to reduce the categorical distribution of predicted actions for each controllable device to an integer action for that controllable device. The control action is communicated to the controllable devices for effecting a change of state of one or more of the controllable devices, to regulate voltage and reactive power flow in the power distribution system.

An example operation includes one or more of determining, by a first energy source, that a second energy source configured to provide energy to an area is in need of supplemental energy, providing, by the first energy source, the supplemental energy to at least one location within the area in a prioritized manner, and responsive to a severity of the need and an amount of available energy at the first energy source, notifying, by the first energy source, the at least one transport to provide additional energy to the at least one location in the prioritized manner.

Various embodiments of the teachings herein include a device for controlling energy flows between participants in an energy network which are connected to one another via lines, the device comprising a processor configured to calculate the energy flows in advance for a period of time using an optimization process and to control the energy flows in the period of time on the basis of the result of the calculation. The processor is further configured to include losses that occur in the energy flows in the lines in the calculation using the optimization process.

A method for dispatching a power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power includes: acquiring load data; drawing a load curve; defining a peak load period, a flat load period and a low load period, and calculating average loads of the peak load period, the flat load period and the low load period before a load transfer; determining value ranges of a peak-low load transfer ratio, a peak-flat load transfer ratio and a flat-low load transfer ratio; establishing an objective function considering generation cost of thermal power unit, wind power purchase cost, PV power purchase cost and compensation cost for consumer load transfer; introducing an immune algorithm to calculate grid connection ratio of wind power, grid connection ratio of PV power, peak-low load transfer ratio, peak-flat load transfer ratio and flat-low load ratio corresponding to a minimum operating cost.

A cyber-secure local electrical power market for a power grid with a utility operator transmitting power where a group of participating nodes within the distribution network operate together through respective computers on a blockchain architecture. The participating nodes have controllable resources with controllers in operative communication within the blockchain architecture, such as controllable generators and controllable loads. Decentralized market software operates on computers within the blockchain architecture and shares blockchain datasets that include financial information associated with the controllable resources and operating states of the grid. One or more of the computers in the blockchain architecture calculates Locational Marginal Pricing (LMP) across the participating nodes according to the set of financial information and determines a set of energy service orders corresponding to LMP for the controllable resources to change their operating states. The computers also preferably calculate an energy balance with the transmission system in determining the energy service orders.

Multimode distribution systems and methods are described. A multimode distribution system includes a first source interface for coupling to a first power source, a second source interface for coupling to a second power source, and a first selection device to be coupled via a first connection matrix and the first source interface with the first power source to provide main power to one or more power consumption devices. The multimode distribution system includes a second selection device to be coupled via a second connection matrix and the first source interface with the first power source to provide main power to one or more additional power consumption devices. The second selection device is to be coupled via the second connection matrix and the second source interface with the second power source to provide alternative power to the additional power consumption devices.

A computer program product for recharging a number of battery electric vehicles includes computer usable program code. The computer usable program code is configured to receive, from the number of battery electric vehicles that are to recharge at a number of recharging stations, usage data. The usage data includes a current charge level, a current location, and a planned itinerary that includes a destination. The computer usable program code is configured to determine anticipated electrical loads in the number of sectors of the electrical grid system based on the usage data of the number of battery electric vehicles. The computer usable program code is configured to redistribute the electrical supply on the electrical grid system to at least one recharging station of the number of recharging stations based on the anticipated electrical loads, prior to actual usage defined by the usage data by the number of battery electrical vehicles.