A method and apparatus for predicting power consumption, a device, and a readable storage medium. The method includes: acquiring a reference variable generated in a history time period; and acquiring predicted power consumption in a target time period by inputting a variable characteristic into a power consumption prediction model, the target time period and the history time period having a corresponding relationship, and the power consumption prediction model being obtained by training a sample reference variable marked with sample power consumption. In the method, the reference variable including a discrete reference variable and a continuous reference variable in the history time period is acquired, and a characteristic of the acquired reference variable is acquired; and an extracted variable characteristic is input into a variable prediction model to output the predicted power consumption in the target time period.

A method for predicting the operation state of a power distribution network based on scene analysis is provided, comprising the following steps of step 10) obtaining the network structure and historical operation information of a power distribution system; step 20) extracting representative scene sequence fragments of output of the DGs according to historical output sequences of the DGs; step 30) obtaining a multi-scene prediction result of a future single-time section T.sub.0 through matching the historical similar scenes; step 40) establishing a future multi-time section operation scene tree; and step 50) deeply traversing all scenes in the future multi-time section operation scene tree, performing power distribution network load flow analysis for each scene, calculating the line current out-of-limit risk and the busbar voltage out-of-limit risk of the power distribution network, and obtaining a future operation state variation tendency of the power distribution network with the DGs.

Disclosed herein is a method and system for sharing power or energy across various power supply and control modules. More specifically, disclosed herein are systems and methods for distributing energy. As explained herein, the method discloses receiving, at a microgrid, data from a plurality of data sources. The data is then analyzed to forecast power needs associated with the microgrid. Using the data, the microgrid may determine whether and when to share power with the requesting module.

The present disclosure provides techniques for estimating network states using asynchronous measurements by leveraging network inertia. For example, a device configured in accordance with the techniques of the present disclosure may receive electrical parameter values corresponding to at least one first location within a power network and determine, based on the electrical parameter values and a previous estimated state of the power network, an estimated value of unknown electrical parameters that correspond to a second location within the power network. The device may further cause at least one device within the power network to modify operation based on the estimated value of the unknown electrical parameters. The leveraging of network inertia may obviate the need for probabilistic models or pseudo-measurements.

The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

A rapidly deployable modular microgrid including a plurality of renewable and other energy generation technologies, energy storage technologies, energy distribution networks, and intelligent control systems capable of managing the flow of electrical energy between one or more locations of energy generation, storage, and consumption are disclosed. The aforementioned microgrid may be delivered and rapidly deployed to provide primary or secondary electricity for a variety of purposes; including but not limited to household electrification, commercial or industrial productivity, grid resiliency, water pumping, telecommunication systems, medical facilities, and disaster relief efforts.

Transaction-enabled systems and methods for royalty apportionment and stacking are disclosed. An example system may include a plurality of royalty generating elements (a royalty stack) each related to a corresponding one or more of a plurality of intellectual property (IP) assets (an aggregate stack of IP). The system may further include a royalty apportionment wrapper to interpret IP licensing terms and apportion royalties to a plurality of owning entities corresponding to the aggregate stack of IP in response to the IP licensing terms and a smart contract wrapper. The smart contract wrapper is configured to access a distributed ledger, interpret an IP description value and IP addition request, to add an IP asset to the aggregate stack of IP, and to adjust the royalty stack.

Provided is a control device for controlling a storage battery. The control device includes a communication unit and a control unit. The communication unit communicates with the storage battery in a wired or wireless manner. The control unit controls the communication unit to send a control signal, to the storage battery, that causes the storage battery to operate in a first mode or a second mode. The first mode is a mode in which the width of changes over time in power bought or sold by a power control system connected to the storage battery is controlled to stay within a prescribed range. The second mode is mode in which the width of changes over time in power charged to or discharged from the storage battery is controlled to stay within a prescribed range.

In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.

Provided are an operation limit value management unit managing an operation limit value related to the power flow power of system equipment and a determination unit calculating the power flow state for each set time of the future of a power system based on load dispatching information including a power generation plan value, a predicted output value, and a predicted value of power demand and determining whether or not stable is each power flow state by comparison with the operation limit value. The determination unit sequentially changes the first output power of a first power source defined by the power generation plan value of the first power source and calculates each power flow state based on power including the changed first output power and a predicted value of the output of a second power source.