Systems, methods, and controllers for controlling an adjustment process for a target system are provided. A central controller receives time-related electrical energy demand data, sensor data from controllers of devices which selectively apply electrical power to the target system, and commands the controllers to apply power to exceed a target control value up to a maximum control value where the electrical energy demand data remains below a threshold and only apply sufficient energy to reach the target control value, at least within a margin, where the electrical energy demand data remains above the threshold.

A building energy system includes equipment configured to consume, store, or discharge one or more energy resources purchased from a utility supplier. At least one of the energy resources is subject to a demand charge. The system further includes a controller configured to determine an optimal allocation of the energy resources across the equipment over a demand charge period. The controller includes a stochastic optimizer configured to obtain representative loads and rates for the building or campus for each of a plurality of scenarios, generate a first objective function comprising a cost of purchasing the energy resources over a portion of the demand charge period, and perform a first optimization to determine a peak demand target for the optimal allocation of the energy resources. The peak demand target minimizes a risk attribute of the first objective function over the plurality of the scenarios.

A method includes determining control setpoints for equipment based on a time-varying availability of green energy and revenue from an incentive program of an energy provider. The method also includes controlling the equipment using the control setpoints.

An electric energy dispatching method and an electric energy dispatching system are provided. The electric energy dispatching method includes: receiving a contracted power supply and a contract period of an electric energy dispatch contract in an area; controlling energy storage devices in the area to provide a plurality of stored electric energy to a grid according to the contracted power supply during the contract period; and stabilizing power supply of the grid using the plurality of stored electric energy.

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.

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.

An energy management device that interworks with an electric power grid, a power generation device, an Energy Storage System (ESS), and a bidirectional Electric Vehicle (EV) charger includes: at least one processor; and a memory storing at least one instruction executed via the at least one processor. At least one instruction may include: an instruction for collecting basic information including information regarding a power generation state and a power consumption state, and grid electric power cost information; an instruction for establishing, by using the collected basic information, an ESS operation schedule for controlling charging and discharging operations of an ESS battery and an EV operation schedule for controlling charging and discharging operations of an EV battery; and an instruction for controlling the ESS battery and the EV battery to be charged/discharged in accordance with the ESS operation schedule and the EV operation schedule.