A method, system and apparatus are provided for optimized load shaping for optimizing production and consumption of energy. Information signals indicative of a first load shape signal are obtained corresponding to a total load, a renewable energy load of one or more renewable energy sources and a non-renewable energy load of one or more non-renewable energy sources. The first load shape signal corresponding to renewable energy load is removed from a non-renewable energy load to obtain a resulting load shape signal. The resulting load shape is flattened signal by apportioning the resulting load shape signal across time intervals to obtain a flattened load shape signal. At least a portion of the first component corresponding to the renewable energy load is added to the flattened load shape signal to create an optimized load shape signal. The optimized load shape signal is provided to modulate electric loads of energy-consuming devices.

A novel renewable energy system is disclosed. The renewable energy system combines second life electric vehicle batteries to form an energy storage system. The energy storage system may compensate for large variations in operational energy and power requirements. The energy storage system is enabled through reuse of unopened electric vehicle batteries after their end first life use (e.g., in automotive use). The energy storage system within the renewable storage system may include a plurality of second life electric vehicle batteries, which may be configured to controllably store and provide power for a variety of applications. In some embodiments, the energy storage system may be operably coupled to one or more photovoltaics, fast chargers, or other variable loads that operate on DC currents. In various embodiments, the renewable energy system containing the energy storage system may require only one bidirectional inverter, which may be connected to a grid.

Methods, computer program products, and systems are presented. The methods include, for instance: obtaining historical data of demand response programs and demand response agreements respective to each of the users regarding a subject energy. Training dataset for a DR user pooling model includes attributes of the demand response data collected that are relevant to responsivities of the demand response programs. The DR user pooling model is trained by the training dataset by machine learning. A DR user pool is identified amongst users of the demand response program by the DR user pooling model. Users in the DR user pool respond to demands as a group and the DR user pool is adjusted to improve responsivities of the demand response programs.

Provided are a reward generation method for reducing a peak load of power and an action control apparatus for performing the method. The reward generation method generates a reward according to a continuous energy storage system (ESS) action to reduce a peak load of a building by applying power consumption data monitored in the building to an artificial intelligence (AI)-based reinforcement learning scheme.

Examples described herein relate to controlling power available to processes and hardware devices to control a monetary cost of utilized electricity and/or amount of energy utilized from non-renewable energy sources. The system can modify operating configurations of processes and/or hardware based on the available power. The system can control total power drawn to control a monetary cost of power and/or avoid drawing power from non-renewable sources (e.g., fossil fuel sources or grid including gas or coal-based energy sources).

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

A charge control system includes: a charging apparatus including a first processor to cause the charging apparatus to store electric power to be supplied to at least one preset facility; a facility server provided in the facility; and a charge control device including a second processor to acquire facility schedule information regarding a future schedule of the facility from the facility server, calculate an amount of electric power to be supplied to the facility based on a schedule of opening and closure of the facility included in the facility schedule information, and perform charging control for the charging apparatus based on the calculated amount of electric power.

A power controller may monitor a power required by each of a plurality of power loads coupled to a power distribution bus. The power loads can include a plurality of devices for oil and gas exploration or production. The devices can include a plurality of drilling rigs or equipment associated with one or more of the drilling rigs. The power controller may monitor the power supplied by each of a plurality of power sources coupled to the power distribution bus. The power sources can include power from any two or more of the following: an electric utility, an electric grid, a natural gas turbine, a battery, a solar power generator, a wind generator, and a geothermal generator. The power controller may adjust the power supplied to a first one of the plurality of power loads based at least in part on a first one of the plurality of power loads.

A matching unit includes an inquiry unit that receives a suppliable electric power amount of renewable energy predicted for each period from a power generation unit, and inquires of each consumption unit, whether or not to consume the suppliable electric power amount, a feasibility confirmation unit that receives electric power of each time desired to be consumed within a range of the suppliable electric power amount from the consumption unit as proposal electric power consumption, and causes the power generation unit to confirm whether or not the proposal electric power consumption can be supplied, and an output unit that receives and sets a period in which the proposal electric power consumption can be supplied from the power generation unit as a matching establishment period, and notifies the power generation unit and the consumption unit of the proposal electric power consumption in the establishment period.

There is provided a technique of managing an electrical power grid. The technique comprises, by a computer: processing timestamped data informative of weather conditions and of individual grid power consumption by a plurality of consumers to identify dual consumers connected to alternative power sources with power generating dependable on the weather conditions; for the dual consumers, forecasting alternative power production by respective connected alternative power sources; and using the provided forecast to enable management action(s) with regard to power production in the electrical power grid (e.g. issuing command(s) related to charging/discharging one or more batteries connected to the grid, controlling thermostat set-point change in a set of points connected to the grid, etc.). Forecasting alternative power production can be provided using a trained Forecasting Machine Learning Model trained to forecast the alternative power production in accordance with a forecast of the one or more weather conditions in the geographical area.