A power analyser for analysing an electrical supply to a load, comprising: a processor; an analogue signal module interfaced with the processor, wherein the analogue signal module is configured to: make analogue signal measurements of the electrical supply, and provide raw analogue data corresponding to said measurements to the processor; and wherein the processor is configured to: access a model suitable for, or associated with, the electrical supply and/or the load, and generate a modified electrical supply estimate in accordance with the model and the raw analogue data.

Managing control targets, in particular, load balancing processes, when controlling the supply, conversion, storage, feed, distribution, and/or use of energy in an energy network if the increasing, volatile, dynamic, decentralized, local feed and/or storage of electric energy leads to loads at different network connection levels of the energy network to control the energy supply so efficiently and sustainably that load balancing processes resulting from occurring load deviations for example are supported locally and reduced regionally and/or across network connection levels. Provided are hierarchical and network-oriented energy clusters formed within network connection levels of the energy network, and in the process, the expenditure of energy and the local exchange of energy within energy clusters are localized across clusters and/or across network connection levels via a voluntary and incentivized participation in an energy cluster service which manages the control targets in the energy network.

A target power calculation apparatus, a target power calculation method, and a target power calculation program for deriving target power that is to be set when performing a demand control process are provided. A target power calculation apparatus includes a learning unit configured to learn whether a target power set in a first period is adequate, according to control information obtained upon performing a demand control process in the first period based on the set target power, an inference unit configured to infer whether a target power set in a second period is adequate, according to control information obtained upon performing a demand control process in the second period based on a result of learning by the learning unit, and a correction unit configured to correct the target power set in the second period based on a predetermined correction amount upon the inference unit inferring that there is inadequacy.

Methods, systems, and computer readable media for thermal load management of a collection of power consumers managed by an electric power aggregator. In some examples, a system includes a scheduling subsystem, implemented on one or more processors, configured for determining a plurality of cooling or heating control schedules for the collection of power consumers by forecasting one or more wholesale electricity price peaks. The system includes a control system implemented on one or more processors. The control system is configured for carrying out the cooling or heating control schedules at the individual consumer-level. The control system is configured for guaranteeing, for each power consumer of the collection of power consumers, a comfort constraint specified by a bilevel thermostat for the power consumer, wherein the comfort constraint comprises a lower level bound temperature and an upper bound temperature that the power consumer is willing to tolerate.

A method of controlling battery energy storage comprises a renewable active power source (P.sub.R), a battery energy storage system (BESS), at least one static load (P.sub.Load) and at least one high dynamic load P.sub.H.LOAD. Active power demand of the system in the point of common coupling is at quasi-constant level. The method further includes gathering constant active power limit from the external grid (P.sub.GRID) and historical data of active power profiles of: predicting the following active power profiles for day+1, calculating required active power of the battery energy storage system, setting daily peak of state of charge and minimum state of charge of the battery energy storage system, and verifying whether daily peak of state of charge and minimum state of charge ensure that instantaneous values of state of charge through the entire day of the battery energy storage system is within range of 20% to 80%.

A system controlling equipment to serve energy loads of a building includes a cost function generator configured to obtain a cost function of decision variables representing an amount of resources consumed or produced by the equipment, an optimizer configured to perform a first optimization of the cost function subject to a first set of constraints defining first values of constraint variables to generate a first result defining first values of decision variables, a constraint modifier configured determine a cost gradient, recommend changes to constraint variables, and modify constraint variables to modified values in response to changes. The optimizer is configured to perform an optimization of the cost function subject to a second set of constraints to generate a second optimization result defining second values of the decision variables. The system includes a controller that operates equipment to consume or produce resources defined by second values of the decision variables.

The present application relates to the control and management of an energy system in coordination with a fluctuating energy market and, in one form provides a method for managing supply of energy to a number of energy consuming devices connected to a power distribution system of an energy supply system. The method may include the steps of: (1) monitoring one or more parameters affecting energy usage, (2) acquiring a first set of values based on the parameters, (3) calculating a set of predicted future values based on the first set of values, (4) identifying a context related to the first set of values, and (5) presenting control actions to increase efficiency of energy supply in response to the context.

An energy control system (ECS) for controlling an energy storage system (ESS's) that includes energy storage devices(s) or an energy storage combination (ESDC's) including ≥1 of the energy storage devices and ≥1 of the energy storage combinations. A power conversion system is coupled to an output of the ESDC, and a transformer is coupled to an output of the power conversion system. The ECS includes an ECS server and an ESS adapter configured for providing an interface between ECS server and the ESS. The ECS server is configured for reading status data from the ESS and submitting schedules including selected charging and discharging times to the ESS, monitoring or displaying a variance between an expected performance of the ESS based on the schedules and an actual ESS performance, and responsive to the variance being determined to be above a predetermined threshold, sending an update of the schedules to the ESS.

The present disclosure discloses methods, systems, and non-transitory computer-readable medium for off-grid load stabilization. For instance, the method may include: receiving generator data from at least one generator and energy storage data from at least one energy store; determining, based on the generator data and/or the energy storage data, a moving average of a system load of the at least one generator; and determining, based on the generator data and/or the energy storage data, an instantaneous load value of the at least one generator. The method may further include: determining a first electronic storage dispatch (ESD) based on the moving average of the system load and the instantaneous load value of the at least one generator; and providing the first ESD to the at least one energy store.

A process includes calculating a first predicted-power-consumption obtained by predicting a first power-consumption of a first job in a first period based on information before the first period, when an error between the first power-consumption and the first predicted-power-consumption is less than a threshold-value, at time of scheduling to allocate second jobs to calculation-nodes so that a total estimated-power-consumption in a second period after the first period of each of the second jobs including the first job allocated to the calculation-nodes is equal to or less than a predetermined first power, determining a first estimated-power-consumption of the first job in the second period, as a second predicted-power-consumption obtained by predicting a power-consumption when the first job is executed in the second period, and when the error is equal to or larger than the threshold-value, determining the first estimated-power-consumption at the time of the scheduling as a predetermined second power.