An embodiment of the invention includes a non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to carry out a method for scheduling at least one activity to achieve a time-varying energy balance of a power system. A mission plan for a mission is received. The mission plan includes at least one activity and at least one route. Each route of the at least one route includes at least one time and at least one location. A plurality of power load identifications is received for at least one time-varying power load for use in the mission plan. A plurality of energy storage device identifications is received for at least one energy storage device. Based on the plurality of power load identifications, a power requirement required to complete the mission plan is determined using the at least one power load. Based at least in part on the mission plan and the plurality of energy storage device identifications, an available power for the mission is determined. The at least one activity along the at least one route is scheduled based on the power requirement and the available energy.

A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

A hybrid machine-learning and simulation-based system provides forecasting for an energy system. The system predicts day-ahead and real-time supply and demand, and prices of energy, and generates inputs to an optimization algorithm performed by an Independent System Operator (ISO) that affects behavior of electricity generators and electricity consumers to improve the economic efficiency of electricity grids, and reduce harmful emissions.

A power generation amount prediction apparatus includes a first detector, acquisition unit, database, and controller. The first detector detects a measured value of the power generation amount of a photovoltaic power generation device. The acquisition unit acquires a predicted value of the power generation amount by the photovoltaic power generation device at specific times. The database stores the measured value and the predicted value for each of the specific times for a plurality of days. The controller calculates a corrected predicted value for each of the specific times based on a maximum measured value, a maximum predicted value, and a predicted value newly received by the acquisition unit. The maximum measured value and the maximum predicted value are respectively the maximum value, for each of the specific times, of the measured value and of the predicted value for a predetermined number of days.

A demand side electric power supply management system is disclosed. The system comprises an islanded power system having a point of coupling to a supply grid. The islanded power system supplies a plurality of electric loads, each of which is associated with a load controller to control the maximum power demanded by that load. A measuring means associated with the point of coupling measures the total power transfer between the grid and the islanded system, and a system controller monitors the measured power transfer relative to a set point and provides a control signal to a plurality of load controllers. Each load controller receives substantially the same control signal and determines the maximum power which the or each load associated with the load controller is allowed to draw from the islanded power system based on information contained in the control signal.

A method for power management includes applying a decentralized control to manage a large-scale community-level energy system; obtaining a global optimal solution satisfying constraints between the agents representing the energy system's devices as a state-based potential game with a multi-agent framework; independently optimizing each agent's output power while considering operational constraints and assuring a pure Nash equilibrium (NE), wherein a state space helps coordinating the agents' behavior in energy system to deal with system-wide constraints including supply demand balance, battery charging power constraint and satisfy system-wide and device-level (local) operational constraints; and controlling distributed generations (DGs) and storage devices using the agent's output.

The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method includes a monitoring step, a prediction step, an error calculation step and an adjustment step including adjusting power supply plan or a power demand of a user when at least one of a first error is greater than a first predetermined threshold or a second error is greater than a second predetermined threshold.

The invention relates to a method of operating at least one distributed energy resource comprising a refrigeration system (1) with a number of cooling entities, wherein a power consumption information is communicated to a smart-grid setup (SG). According to the invention the method comprises the steps of: requesting (S0) a power consumption information from the refrigeration system; transmitting (S1) the power consumption information from the refrigeration system (1), wherein a total amount of power consumption (Pmin, Pmax) of the refrigeration system (1) is provided; wherein: a cooling capacity (dQ/dt_i) of at least one cooling entity is determined wherein an entity operation condition (CE) of the cooling entity (E1, E2) is taken into account (D1); a power consumption (W_i) of at least one cooling entity (E1, E2) is determined from the cooling capacity (dQ/dt_i) wherein a performance estimation (COP) of a refrigeration cycle for the cooling entity (E1, E2) is taken into account (D2); providing (D3) the total amount of power consumption (Pmin, Pmax) as a sum of power consumptions (W_i) of at least the one cooling entity of the number of cooling entities (E1, E2), in particular as a sum of relevant power consumptions of the number of cooling entities (E1, E2); receiving (S2) at the refrigeration system (1) a power reference (Wref) from the smart-grid setup (SG). The method presented enables power control of a centralized refrigeration system in a smart-grid setup where an aggregator provides the power reference. In addition, the method also enables the refrigeration system to improve determining flexibility margins beyond absolute max./min values of nominal and zero.

A method for reducing aggregate power cost that includes determining an end device power consumption profile for each of a plurality of end devices, storing the end device power consumption profiles in a switching device configured to determine a desired power flow timing for each of the plurality of end devices, determining an optimal rate period with the switching device, determining, with the switching device, the desired power flow timing for each of the plurality of end devices such that a power consumption of the plurality of end devices is preferably within the optimal rate period, and controlling, via the control device, the plurality of end devices such that the desired power flow timing is substantially obtained, thereby reducing aggregate cost per kWhr of the plurality of end devices.

A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.