A method for managing electrical loads includes monitoring current of the branch circuits to determine an electricity demand of the branch circuits. The method includes comparing an available supply with the electricity demand. Each of the branch circuits are connected to the available supply by a circuit breaker that requires manual reset (e.g., if tripped) connected in series with a controllable relay. The method includes controlling the controllable relays to connect and disconnect the branch circuits from the available supply.

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 computer-implemented method for aggregating electric vehicle loads for demand response events includes receiving a demand response (DR) event request from a utility system indicative of a DR event for an area. The DR event request includes at least one event parameter for participation in the DR event. The method includes determining a first original equipment manufacturer (OEM) DR event load for the area based on the DR event request and charging data received from electric vehicles associated with a first OEM. Upon determining the first original OEM DR event load does not meet the at least one event parameter, the method includes aggregating charging data from electric vehicles associated with a second OEM with the first OEM DR event load to determine an aggregated DR load for the area.

Systems and methods for predicting forward market pricing for renewable energy credit based on human behavioral data are disclosed. An example transaction-enabling system may include a forward market circuit to access a forward energy credit market and a market forecasting circuit to automatically generate a forecast for a forward market price of an energy credit in the forward energy credit market where the forecast is based at least in part on a human behavior information collected from at least one human behavioral data source. The example system may further include wherein the energy credit includes a renewable energy credit associated with a renewable energy system, and a smart contract circuit to perform at least one of selling the renewable energy credit or purchasing the renewable energy credit on the forward energy credit market in response to the forecasted forward market price of the energy credit.

A method of controlling an energy storage system to reduce a peak energy procurement includes obtaining a load forecast for an energy consumption system, and, at each of a plurality of predetermined time intervals during a predetermined time period, observing a charge state of an energy storage component and a load presented by the energy consumption system, determining an energy action for the energy storage component as a function of the load forecast, observed load and observed charge state, and executing the determined energy action. Determining the energy action can include composing and optimizing a sample average approximation of a cost function for the energy storage component and energy consumption system, where the sample average approximation is composed by generating a predetermined number of random load trajectories for the energy consumption system, and forming the sample average approximation as an average of a maximum energy purchase function for each of the random load trajectories as a function of the energy action.

A power distribution management apparatus (10) includes acquiring the amount of demand obtained by forecasting the amount of power to be used by load equipment of a customer, and the amount of power generation obtained by forecasting the amount of power to be generated by power generation equipment of the customer, and setting a plurality of voltage values as a plurality of candidates for a transmission voltage from a substation of a power distribution system, and calculating a voltage at a connection point of the load equipment of the customer and equipment of the power distribution system at each of the plurality of candidates by utilizing a difference between the demand amount and the power generation amount of each customer, and determining the transmission voltage of the substation from the plurality of candidates based on a result of the calculating.

Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance obj ective.

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.

Provided is an operation plan creation device that creates an operation plan for a load device in a microgrid including a renewable energy generation device and the load device. The operation plan includes a plan for performing a start operation or a stop operation of the load device. The operation plan creation device includes a creation unit that creates the operation plan on the basis of information related to a prediction value of power generated in the renewable energy generation device for an entire creation period of the operation plan, information related to power consumption characteristics of the load device, and information related to temporary power consumption necessary for the start operation or the stop operation of the load device included in the operation plan.

A power supply management system according to an embodiment of the present invention includes a fee setting unit configured to set power fee unit prices (first power fee unit price and second power fee unit price for each time slot, and a notification unit configured to notify a consumer of information on the set power fee unit prices. The first power fee unit price is a power unit price for a household electrical load, and the second power fee unit price is a power unit price in a case of using a charge/discharge device to charge a vehicle-mounted storage battery. The fee setting unit is configured to set, by predicting a load factor of a transformer, a discount rate of the second power fee unit price to become higher (second power fee unit price to become lower) as the predicted load factor becomes lower. The second power fee unit price is determined on the basis of the first power fee unit price and the discount rate.