Systems and method are described herein for determining scheduling of electrical power system loads and generation resources. A load scheduling module determines load scheduling characteristics of the electrical power system loads and the generation resources based on power system constraints including power system flow, voltage constraints associated with each electrical system loads or each generation resource, or energy storage capacity of an energy storage device coupled to at least one of the electrical power system loads or the generation resources. The load scheduling module initiates a first signal that curtails power of a subset of the electrical power system loads based on the load scheduling characteristics. The load scheduling module initiates a second signal that adjusts power of at least a portion of remaining loads of the electrical power system loads to accommodate for the subset of the plurality of the electrical power system loads having curtailed power.

An energy dispatch system, apparatus, and method are disclosed. The energy dispatch system includes a server and multiple site controllers respectively corresponding to multiple users. Each user corresponds to an energy storage device, and each energy storage device has a stage of charge. The server determines a scheduled power consumption target of each scheduling period for each user according to a total support power, multiple power consumption references of the users, and the stages of charge. The server transmits each scheduled power consumption target corresponding to a first scheduling period of the scheduling periods to the corresponding site controller. Each site controller controls the corresponding energy storage device to charge or discharge according to the corresponding actual load and the corresponding state of charge in the first scheduling period so that the power consumption of the corresponding user during the first scheduling period meets the corresponding scheduled power consumption target.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a load forecast by predicting load measurements at future time points, wherein a load measurement at a time point characterizes a cumulative measure of energy consumption by a population of energy consuming devices at the time point. In one aspect, a method comprises: generating an input load array that includes a respective load value for each of a plurality of time points, wherein: for a plurality of previous time points, the input load array includes load values for the previous time points that define load measurements at the previous time points; and for a plurality of future time points, the input load array includes load values for the future time points that are masked values; and processing the input load array using a load forecasting model to unmask the masked values corresponding to the future time points, comprising generating an output that defines, for the plurality of future time points, predicted load measurements at the future time points.

Provided is a capacity design method and apparatus for performing solar power generation of a residential complex that may calculate a capacity of a solar power generator and an energy storage device to achieve energy independence of a residential complex including a plurality of housings and reduce burden affecting a grid.

A system and method to join distributed energy resources (DER) to achieve common objectives is provided. The present technology organizes and/or aggregates DERs by routing a (DER) program request for resources to DER contributors capable of responding to and performing the request using a routing system. The system accesses a plurality of DER profiles, each profile associated with a DER contributor capable of contributing a resource to the request, and calculates an initial value for each DER profile based on request attributes and scoring metrics associated with the profile. The system then calculates a fitness metric for each DER profile based on the initial value using a neural network having weights based on the plurality of performance indicators and selects the DER profile and contributors to whom to route the request.

Disclosed are a demand management control method and a demand management control apparatus for inducing dynamic participation of a consumer in an energy consumption load management program. The demand management control method refers to a promotion method for expanding an active participation of a consumer through a dynamic order of charging power of a charging load device with respect to a load having a consumption characteristic in an energy accumulation form in a distribution network management area in which an energy charging supply device is installed, and distributes and manages a demand within an energy supply goal of the consumer through a participation promotion. Also, the demand management control method provides changed cost information to the consumer using an incentive through a demand management contribution weight according to an energy demand and supply status and encourages or promotes the dynamic participation of the consumer.

An energy storage system for a building includes a battery asset configured to store electricity and discharge the stored electricity for use in satisfying a building electric load. The system includes a planning tool configured to identify one or more selected functionalities of the energy storage system and generate a cost function defining a cost of operating the energy storage system over an optimization period. The cost function includes a term for each of the selected functionalities. The planning tool is configured to generate optimization constraints based on the selected functionalities, attributes of the battery asset, and the electric energy load to be satisfied. The planning tool is configured to optimize the cost function to determine optimal power setpoints for the battery asset at each of a plurality of time steps of the optimization period.

A prediction calculator uses a prediction model to determine a predicted value of an amount of power to be generated by a power generation apparatus and a predicted value of an amount of power to be consumed by an appliance. The predicted values are associated with accuracy information. An operation planner makes an operation plan using the predicted values determined by the prediction calculator, a user schedule included in user setting information, and an electricity rate table or weather forecast information. When the accuracy of the predicted value is lower than a predetermined reference, the operation planner makes the operation plan only using other information without using the predicted value.

An energy control and storage system includes an energy monitor, a power controller, an energy storage device, and a computing unit. The energy monitor monitors power provided between an electric distribution system and a load. The power controller exchanges power with the energy monitor and receives power from a power generation system. The energy storage device stores energy received through the power controller. The computing unit receives a load shape from outside the energy control and storage system. The computing unit controls power exchanged between the energy control and storage system and the electric distribution system based on power indicated by the load shape that changes in response to varying conditions affecting the electric distribution system.

A system state estimation device according to the present invention includes: a classification unit that classifies first meter data into a plurality of groups, the first meter data being meter data metered in a first time zone by at least some of a plurality of smart meters; a statistic calculation unit that calculates variation of the first meter data for each of the groups; used data determination unit that determines, for each of the groups, whether to use meter data in a second time zone or a substitute value in estimation computation for estimating a system state in the second time zone on the basis of the variation; and an aggregation unit and a power flow calculation unit that perform the estimation computation on the basis of a result of determination performed by the used data determination unit.