The present disclosure provides systems and methods for managing electrical energy generated by a renewable microgrid. One or more processors of a system may store priorities for one or more consumer loads; forecast an amount of available electrical energy for a time period; allocate a first electrical energy amount to a first consumer load to a first energy limit for the time period; determine more electrical energy is forecast to be available from the RES or the ESS for the time period; identify a second consumer load of the one or more consumer loads; allocate a second electrical energy amount to the second consumer load for the time period; and direct electrical energy from the RES or the ESS to the first and second consumer loads according to the allocations.

Management methods and systems for energy and charging requests of an electric vehicle charging field are provided. First charging data corresponding to at least one first charging operation is received by a server from each of electric vehicle charging stations in a charging field via a network during a first predetermined period, wherein the charging data includes at least a charging start time, a charging period, and an output power. According to the first charging data corresponding to the at least one first charging operation received from each of the electric vehicle charging stations during the first predetermined period, the server generates an energy prediction data of the charging field in a second predetermined period, wherein the energy prediction data includes at least an energy consumption estimation of the charging field at a specific time point.

An electricity demand prediction system includes electricity usage data collection means, production schedule storage means, model creation means, and short-term electricity demand prediction means. The electricity usage data collection means collects electricity usage data of each device and causes electricity usage data storage means to store the collected electricity usage data. The model creation means creates an electricity amount calculation model of each prescribed product type for each device on the basis of the electricity usage data stored in the electricity usage data storage means and a past production schedule stored in the production schedule storage means. The short-term electricity demand prediction means computes future electricity demand for each device on the basis of the electricity amount calculation model created by the model creation means and a future production schedule stored in the production schedule storage means.

An operation plan creating system, by simulation based on a supply and demand scenario, extracts for each time point during operation for one day, a scenario that raises the effect of correction of the operation at the time point. The operation plan creating system classifies action-requiring scenarios by action similarity. The operation plan creating system determines an action-requiring scenario group for which actions are similar, and a correction time. The operation plan creating system judges whether an early action scenario group can be identified by data available at an early action time on the current day. If identifiable, the operation plan creating system 103 creates an identification condition for the early action scenario group and an optimal operation plan for the scenarios satisfying the identification condition.

A for ensuring grid stability by using a distributed ledger, the method comprising the steps: measuring metrics from a plurality of distributed energy resource, DER, wherein said metrics are related to grid stability; storing (a) said metrics/measurements in the distributed ledger; transferring said metrics from the distributed ledger into a smart contract (2); computing any deviations between said metrics and predetermined values stored in advance in the smart contract (2) by executing the smart contract; transferring and storing results from the computing step in the distributed ledger; and transferring said results from the distributed ledger to the respective DER of the plurality of DERs, wherein said DER receives positive or negative values which regulates the amount of contribution to the grid.

A method for computer-implemented maximization of annual energy production of several wind turbines of a wind park is provided. The method considers the impact of individual turbine manufacturing tolerances on the turbine performance, thereby avoiding under-utilization of those wind turbines. The method includes: receiving, by an interface, one or more actual manufacturing tolerances of characteristic values for each of the number of wind turbines; determining, by a processing unit, for each of the number of wind turbines a power versus wind speed map which is calculated from a given turbine model with the one or more actual manufacturing tolerances of the respective wind turbines as input parameters; determine, based on the power versus wind speed map of each of the number of wind turbines a respective performance measure; and assign a selected siting position for each wind turbine in the wind park according to its determined performance measure.

Included are an energy management apparatus that manages at least one or more management target that consumes energy; and a user terminal that transmits and receives information to and from the energy management apparatus via a network. The energy management apparatus includes an energy management controller that prepares a question regarding a behavior of a user who uses the at least one or more management target, and transmits it to the user terminal. The user terminal includes a user terminal controller that transmits a question answer regarding the received question to the energy management controller when receiving the question transmitted from the energy management controller. The energy management controller predicts an energy consumption amount of the at least one or more management target based on the received question answer when receiving the question answer transmitted from the user terminal controller.

Regarding an independent system, a prediction value of charged/discharged power of a storage battery is calculated, based on a prediction value of generated power of a renewable energy power generator, a prediction value of demanded power of a control device, and a prediction value of demanded power of a load on an assumption that a power supply limit is applied to the load. Whether or not charge or discharge of the storage battery with charged/discharged power matching the prediction value of the charged/discharged power of the storage battery is possible is determined. The power supply limit is tightened when it is determined that the charge or discharge of the storage battery is not possible. A limit data indicating a detail of the power supply limit is output when it is determined that the charge or discharge of the storage battery is possible.

A demand response method, a demand response system, and a recording medium are provided. The response control method includes: determining priorities of devices to be controlled to participate in a demand response; generating respective participation profiles for the devices based on the determined priorities; and changing the priorities of the devices included in the participation profiles, based on whether participation in the demand response according to the generated participation profiles succeeds. Therefore, the user easily participates in the demand response, and thus energy can be saved.

A technological solution for controlling generation or distribution of electric power in a power generation and distribution network. The solution includes modeling and forecasting electricity demand in the power generation and distribution network, which includes a plurality of nodes each having cooling appliances, heating appliances, or both cooling and heating appliances. The solution includes, among other things, building a per-capita parametric model of an electric demand curve for a geographic region, modeling residual intraday variations in electric demand, determining an average residual variation in demand, iteratively optimizing the residual intraday variations in demand and the average residual variation in demand, and determining electricity demand per-capita for the geographic region.