Methods, systems, and computer readable mediums for protecting and controlling a microgrid with a dynamic boundary are disclosed. One method includes detecting a fault in a microgrid that includes a dynamic point-of-common-coupling (PCC), in response to determining that the microgrid is operating in a grid-connected mode, isolating the fault by tripping a microgrid side smart switch and a grid side smart switch that are located immediately adjacent to the fault, initiating the reclosing of the grid side smart switch, and initiating the reclosing for the microgrid side smart switch via resynchronization if the grid side smart switch is successfully reclosed, and in response to determining that the microgrid is operating in an islanded mode, isolating the fault by tripping a microgrid side smart switch that is located immediately adjacent to the fault, and initiating the reclosing of the microgrid side smart switch.

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

A system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high. The invention uses two different types of pricing data: forecasted price data and real-time price data. The forecasted price data is used to set a threshold. When the real-time price data of electricity exceeds this threshold, the energy storage device is set to discharge and send power to the grid. Otherwise the energy storage device is set to charge. The threshold is set periodically, typically in 30 minute to several hour intervals to capture the latest data.

System, method, and interface for visualized resource allocation and algorithms for the reallocation of resources to achieve a goal. The system analyses an initial state of resource allocation, a cost function for undesirable resources, and a set of potential incremental improvements, each with an associated cost, and determines a step-wise path of applying the incremental improvements to achieve an ultimate resource-allocation goal in an economically feasible way. Simultaneously, a user interface depicts the state of the allocation at the beginning, at the end, and along the path, allowing an intuitive understanding of how the goal will be achieved.

Systems and methods for operating a hybrid power system are disclosed. A controller may perform operations, including: obtaining load data for the hybrid power system; obtaining power availability data and energy cost data for each power asset in each power asset group of a plurality of power asset groups; and determining active power commands for each power asset by performing at least one optimization, such that the determined active power commands optimize a total operating cost, wherein: the at least one optimization is based on at least one cost function that accounts for asset degradation, asset maintenance cost, asset operation efficiency cost, and the energy cost data; and the at least one optimization is constrained by a plurality of constraints based on the load data, the power availability data, and characteristics of the power assets; and operating each power asset based on the determined active power commands.

Disclosed is an optimal power flow acquiring method for regional distribution network of small hydropower groups based on deep learning, which specifically includes the following steps: generating required data sets by adopting continuous power flow and power flow equation calculation methods; the data set is randomly divided into training data (80 percent) and test data (20 percent); training the built convolutional neural network model with training data to learn the mapping relationship between load and generator output power; inputting test data, and directly obtaining P.sub.G and Q.sub.G from the trained convolutional neural network; and solving residual variables V.sub.i and θ.sub.i with traditional power flow solver. The application can accelerate the solving speed of the optimal power flow problem with higher prediction accuracy.

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.

Controlling a current injected to a power grid from a renewable power plant, in response to a voltage event in the power grid At least a current at a point of common coupling between the renewable power plant and the power grid is determined and provided to a power plant controller (PPC). The power PPC derives individual current setpoint corrections for at least some wind turbines, based on the determined current, and dispatches each derived current setpoint correction to wind turbine controllers of the corresponding wind turbines. The wind turbine controllers control a current output of the respective wind turbines, based on measurements of current and/or voltage at a point of connection between the wind turbine and an internal grid of the renewable power plant, and by taking into account the dispatched current setpoint correction .

A method described delivers power to a first load and a second load from networked power plants. The method may include receiving a power delivery profile for the first load, receiving a power delivery profile for a second load, determining a power output capability of a first renewable energy power plant (REPP), and determining a power output capability of a second REPP. The method may also include setting a power output for the first REPP and a power output for the second REPP based on the power delivery profile for the first and second loads and the power output capabilities of the first and second REPPs. The method may also include allocating a combined power output of the first and second REPPs to the first and second loads and delivering the allocated combined power output to the first and second loads.

A method for stabilizing a DC voltage in a DC grid that includes a DC bus connected to a higher-order grid and to which an energy generating system and at least one load are connected. A variable electric grid output is exchanged between the DC bus and the higher-order grid in order to keep the DC voltage in the DC bus at a nominal voltage. The energy generating system includes a PV generator connected to the DC bus via a DC-to-DC converter and which exchanges an electric generator output with the DC bus. In a normal operating mode, the generator output is set to a normal operating output by the DC-to-DC converter on the basis of an MPP output of the PV generator. In a grid support mode, the generator output is set to a grid support output on the basis of the DC voltage in the DC bus in order to counteract a power imbalance between the electric power supplied in total to the DC bus and the power drawn in total from the DC bus.