A control system of a wind power installation and/or a wind farm is provided. The control system has a plurality of operating modes and has an interface. The interface is configured for providing maximum adjustability of the wind power installation and/or the wind farm in critical power grid situations. The interface is configured to receive a signal of a power grid operator, as a result of which all of the plurality of operating modes are released and made available to the power grid operator.

The present disclosure is directed to systems and methods for controlling an electrical system using setpoints. Some embodiments include control methods that monitor an adjusted net power associated with the electrical system and adjust the setpoint based on a comparison of the adjusted demand to the setpoint. If the adjusted demand has not exceeded the demand setpoint, the setpoint is reduced. If the adjusted demand has exceeded the demand setpoint, the setpoint is increased.

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

The invention includes systems and methods for power cogeneration. In certain embodiments the cogeneration systems include one or more units that are modularized; in some of these embodiments, the modules contain components that are integrated and ready for use with a control system that optimizes a result for the cogeneration plant. In some cases, the cogeneration system is part of a network of cogeneration systems.

The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

A method for automatically managing a flow of electrical energy produced by a first group of prosumers and consumed by the first group of prosumers and a second group of consumers, wherein each entity determines, at the beginning of a predetermined period, a production forecast and/or a consumption forecast of electrical energy, the method including determining, for each group, an order for consulting the entities of the group, consulting all the prosumers according to the order determined previously, each prosumer calculating a residual production forecast taking account of its own production forecasts, a residual production forecast transmitted by a preceding entity and a portion of a consumption forecast that can be supplied by the residual production forecast, and transmitting this residual production forecast to the prosumer according to the determined order, when the production forecasts are in surplus, transmitting the residual production forecasts to the group of consumers.

A method for determining a sequence of operation of at least one set of generator units of an electrical network includes assigning a rank to each generator unit; from a projected load curve, determining a minimum number of generator units; for each generator unit having a rank less than or equal to the minimum number of generator units, allocating in the sequence of operation the on state to the generator unit for each time interval; for each generator unit having a rank greater than the minimum number of generator units, allocating, by increasing rank, for each time interval of the sequence of operation the on state or the off state to the generator unit, and for each generator unit to which is assigned the on state at a given time interval of the sequence of operation, allocating an operating power, by optimisation of a cost function over a period.

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

A method tests the configuration of an aggregated DERs system using distributed asset managers in a decentralized hardware-in-the-loop (“HIL”) scheme. The managers contain the model of the asset they are meant to control. The method programs an asset manager with a model of a DERs asset. A plurality of asset managers are connected to a central controller. The plurality of asset managers are also connected to a simplified hardware-in-the-loop platform. The simplified HIL platform is configured to solve a network model, a load model, a non-controllable asset model, and a grid model. The method tests the DERs system control structure by using: (a) the simplified HIL platform to solve the network model, the load model, the non-controllable asset model, and the grid model, and (b) the asset manager to solve the model of the DERs asset, without any simulation between the central controller and the distributed asset managers.

A system and method for regulating charge/discharge of a battery to stabilize a regional power grid includes a regulation control module that monitors a frequency regulation signal from the regional power grid and market conditions for obtaining power from the regional power grid. A machine learning module predicts from the frequency regulation signal and market conditions a future beneficial period when the battery may be exposed to the regional power grid to charge/discharge power in accordance with the frequency regulation signal to stabilize the regional power grid through participation in the regulation of the regional power grid. Another machine learning module calculates a regulation control signal that tracks the frequency regulation signal during the future beneficial period and outputs the regulation control signal to at least one battery control module that manages charging/discharging of the battery to selectively withdraw/apply power from/to the regional power grid during the future beneficial period.