The present disclosure provides systems and methods for managing electrical energy generated by a renewable microgrid. One or more processors of a system may store priorities for one or more consumer loads; forecast an amount of available electrical energy for a time period; allocate a first electrical energy amount to a first consumer load to a first energy limit for the time period; determine more electrical energy is forecast to be available from the RES or the ESS for the time period; identify a second consumer load of the one or more consumer loads; allocate a second electrical energy amount to the second consumer load for the time period; and direct electrical energy from the RES or the ESS to the first and second consumer loads according to the allocations.

Embodiments of the present disclosure relate to load optimization. At least one asset of a set of assets associated with a building may be identified. Dynamic asset classification for the at least one asset may be generated for an optimization period by applying input data associated with the at least one asset to an asset classification machine learning model. Deviation data may be generated for the at least one asset in response to a non-critical classification for the at least one asset by applying a dataset comprising current operating load data for the at least one asset and current environmental condition data to an optimization machine learning model configured to compare the current operating load data for the at least one asset to operating load range for the at least one asset. Optimization data may be generated for the at least one asset based on the deviation data.

Method for controlling at least one power source (1), the at least one power source (1) supplying electrical power to at least one power consumer (3) via an electrical power supply grid (2), wherein the following steps are carried out: measuring and/or determining electrical quantities, preferably a frequency and a power disturbance, of the power supply grid (2) during a time period where the electric power supply grid (2) is excited and/or perturbed, such that there is a frequency deviation from a nominal frequency of the power supply grid, deriving at least one parameter (H,D,K.sub.P,K.sub.I,K.sub.D) of a dynamic grid response model from measured values and/or determined values of the measurement and/or determination of the electrical quantities of the power supply grid (2), and adapting a control algorithm defining a control response of the at least one power source (1) depending on the at least one parameter (H,D,K.sub.P,K.sub.I,K.sub.D) of the dynamic grid response model.

An example operation includes one or more of predicting a time when demand for energy at a location is greater than a first threshold, wherein the energy is received from an energy provider, determining that an amount of energy stored in a battery of an electric vehicle at the location is above a second threshold, wherein the second threshold indicates a minimum state-of-charge of the battery of the electric vehicle, modifying, based on the predicted time and the amount of energy stored in the battery of the electric vehicle, energy consumption by at least one device at the location to reduce energy consumption at the location, and transferring energy from the battery of the electric vehicle to the location while the at least one device at the location is modified to reduce energy consumption.

Systems and methods for controlling a multiple generating resource site. A controller receives from a check meter measured characteristics of power flowing through a power interconnect point. The controller receives measured power characteristics for each generating resource exchanging power through the power interconnect point. Based on the characteristics of power flowing through the power interconnect point and measured power characteristics for each generating resource, the controller determines: loss values for each transmission link system connecting each power generating resource to the check meter, and an apportionment of losses among those transmission link systems. Based on control data defining characteristics of power to be exchanged through the power interconnect point, power adjustments for the generating resources are determined based on the apportionment of losses. One or more of the generating resources are commanded to produce power based on the power adjustments.

The present disclosure relates to a method for determining a status of a power grid, wherein the power grid comprises a plurality of branches each connected between two buses. The method comprises obtaining current measurements of at least one of the plurality of branches, obtaining a grid model, the model comprising the plurality of branches and buses, estimating, using the grid model and the current measurements, a voltage value of at least one of the plurality of buses, obtaining, from a controller, at least one parameter of at least one of remaining branches of the plurality of branches, determining, based on the at least one parameter and current measurements of the at least one of the plurality of branches, currents of the remaining branches of the plurality of branches and determining or correcting the status of the remaining branches of the plurality of branches based on the estimated currents.

A method includes controlling an inverter to operate at an initial frequency, where the inverter is coupled to a load and a source. The method also includes sensing a load power, generating a droop set point frequency in response to a change in the load power, applying a first proportional gain constant (K.sub.p1) and a first integral gain constant (K.sub.i1) to a rate of change of the droop set point frequency to generate an adaptive ramp rate, and controlling a frequency of the inverter based on the initial frequency and the adaptive ramp rate.

Disclosed is a coordinated peak shaving optimization method for a plurality of power supplies based on a fluctuation characteristic of a renewable energy source, including the following steps: s1: determining a weekly generated electricity quantity of hydropower based on an available capacity and a storage capacity of an electricity quantity; s2: predicting a renewable energy power generation curve and a load curve of a system weekly; s3: determining a start point of peak shaving of the hydropower based on an external transmission curve, the renewable energy generation curve, and the load curve of the system and a generating capacity of the hydropower; s4: determining a weekly peak shaving demand of the system; and s5: establishing an optimization model with a maximum peak shaving demand. The present disclosure proposes a reasonable arrangement for peak shaving, so as to resolve an accommodation problem caused by large-scale access of a renewable energy source.

An energy storage converter, a control method and device for the energy storage converter, and a computer readable storage medium are provided. The control method includes: obtaining an operation state of a power grid; under the condition that the power grid operates in a normal state, controlling the energy storage converter to operate in a grid-connected mode; and under the condition that the power grid operates in an abnormal state, controlling the energy storage converter to operate in an island mode. In the grid-connected mode, the energy storage converter uses active power and reactive power decoupling control. In the island mode, the energy storage converter uses virtual magnetic flux control.

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a set of edge devices is configured to communicate with at least one energy generation facility, energy storage facility, and/or energy consumption system and automatically execute a set of preconfigured policies that govern energy generation, energy storage, or energy consumption of the respective energy generation facilities, energy storage facilities, or energy consumption systems. In some embodiments, the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generation entities in an energy grid.