A method for optimising the consumption of an installation includes, carried out before a specified period, implementing a disaggregation method, so as to predict, for each appliance, an expected individual consumption profile, predicting an expected renewable production profile by the renewable energy source, defining first optimised individual consumption profiles for the appliances, making it possible to maximise a use of renewable electrical energy, and the second step of controlling the appliances during the specified period, by using the first optimised individual consumption profiles.

Systems and methods are provided for allocating and providing power to electric loads based on load classification information. A power system, such as a local electrical utility, may receive load demand requests from customers or loads wanting to consume power, and the system may allocate power to the loads based on load classifications of each of the loads. The system may then communicate the power allocations to the loads, and the loads may then consume power based on the power allocated to each of the loads. These improvements may be used to, for example, reduce the likelihood or frequency of demand response events in which power demand exceeds supply since the system may limit the total amount of power that is allocated at any given time.

An energy storage system is provided as a demand response resource which is associated with a virtual power plant (VPP) system. The energy storage system can include a power generation device for producing electric power, a battery for storing power, an energy management apparatus for monitoring a power generation state and a power consumption state and to establish an operation schedule for the battery, and a power converter for controlling charging/discharging operation of the battery according to the established operation schedule.

The present disclosure includes devices, systems, and methods for managing vehicle functions according to power demand by vehicle subsystems. Example methods include receiving available power data indicating available vehicle battery power, the vehicle having vehicle subsystems. Methods include receiving demand data indicating a battery power demand by the vehicle subsystems that perform vehicle functions. Methods include receiving modification data associated with modifying the vehicle functions. Methods include determining, at a first time, based on the demand data and the available power data, that the power demand exceeds the available power. Methods include selecting, in response to that determining, target vehicle functions among the vehicle functions. Methods include selecting, based at least in part on the modification data, modifications for the target vehicle functions. Methods include initiating the modifications to the target vehicle functions.

A grid management platform is disclosed for predicting and mitigating peak electricity demand events using probabilistic modeling and cost-optimized control. The system includes a likelihood model that evaluates the probability of each remaining day within an evaluation interval being the peak load day. This is achieved through a Monte Carlo simulation engine that generates multiple stochastic realizations of future load trajectories based on historical data, forecast data, and forecast error profiles derived from back testing. The system computes likelihood scores and classifies days using optimized bin thresholds that minimize operational cost, considering demand charges, available distributed energy resources, and forecast uncertainty. Based on the predicted likelihoods, the platform generates actionable insights and transmits control signals to grid-connected assets such as batteries, HVAC systems, and electric vehicle chargers to shift or reduce load during predicted peak periods.

Systems and methods are provided for managing power usage in mining environments. The amount of power needed to operate a set of electrical machines that are to run simultaneously is determined. Power usage by the electrical machines during a time period can be predicted based on how much power is expected to be needed by each machine. If the predicted power usage exceeds a threshold power usage, power to one or more electrical machines in the mining environment is restricted so that actual power usage in the mining environment does not exceed the threshold power usage during the specified time period. The electrical machines which will have power restricted thereto can be selected based at least in part on relative priorities for the electrical machines in the set. Power can be restricted to machines as a whole (an effective shutdown), or to specific operations of the machines.

A power system supervisory control apparatus, a power system supervisory control system, and a power system supervisory control method for reducing social cost and improving resilience of a power system are provided. The power system supervisory control apparatus including multiple renewable energy power supplies includes: a system influence degree evaluation section that evaluates a system influence degree when a renewable energy fluctuation or an assumed fault has an influence on the power system by using, as computation conditions, system data for obtaining a state of the power system, renewable energy fluctuation data indicative of a fluctuation of a power generation output, and assumed fault data of an assumed fault in the power system, and calculates system influence degree evaluation result data; a computation condition selection index calculation section that calculates a selection index for the computation conditions; and a condition selection section that selects the computation conditions.