Abstract: 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.

Abstract: 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.

Abstract: Electrical system controllers, computer-readable storage media, and related methods for generating random variables for stochastic control of electrical systems. An electrical power system controller includes a processor. The processor is configured to generate an uncertainty metric based on observed input data, the uncertainty metric configured to indicate an uncertainty of an input variable. The processor is configured to generate a random variable based on the uncertainty metric to forecast the input data during a future period of time. The processor is also configured to generate a cost function corresponding to a cost of operating the electrical power system during the future period of time. The cost function is a function of the one or more random variables. The processor is further configured to control operation of the electrical power system during the future period of time based on an optimization of the cost function.

Abstract: The present disclosure is directed to machine learning of electrical power system behavior, and related systems, apparatuses, and methods. A controller of an electrical power system includes a data storage device configured to store model data indicating a model load power consumed by loads of the electrical power system. The controller also includes a processor configured to determine current data including current load power consumed by the loads, modify the model data by aggregating the model data with the current data, and determine a set of control values for a set of control variables to effectuate a change to operation of the electrical power system based, at least in part, on the model data.

Abstract: Electrical system controllers, computer-readable storage media, and related methods for stochastic control of electrical systems. An electrical system controller includes one or more data storage devices and one or more processors. The one or more data storage devices are configured to store data corresponding to one or more random variables associated with operation of an electrical system. The one or more processors are configured to determine a set of control values for a set of control variables to effectuate a change to the electrical system toward meeting a controller objective for economical optimization of the electrical system. The set of control values are determined by the one or more processors utilizing an optimization algorithm to identify the set of control values as a function of the one or more random variables. The one or more processors are also configured to control the electrical system based on the control values.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is directed to machine learning of electrical power system behavior, and related systems, apparatuses, and methods. A controller of an electrical power system includes a data storage device configured to store model data indicating a model load power consumed by loads of the electrical power system. The controller also includes a processor configured to determine current data including current load power consumed by the loads, modify the model data by aggregating the model data with the current data, and determine a set of control values for a set of control variables to effectuate a change to operation of the electrical power system based, at least in part, on the model data.

Abstract: 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.

Abstract: The present disclosure is directed to systems and methods for controlling an electrical system using simulation-based setpoints. Some embodiments include control methods that enable recalculation of the optimal setpoints during demand windows. Some embodiments include a multi-mode controller to control an electrical system in a charge mode and a demand mode. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure is directed to systems and methods for controlling an electrical system using simulation-based setpoints. Some embodiments include control methods that enable recalculation of the optimal setpoints during demand windows. Some embodiments include a multi-mode controller to control an electrical system in a charge mode and a demand mode. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

Abstract: 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.

Abstract: The present disclosure is directed to machine learning of electrical power system behavior, and related systems, apparatuses, and methods. A controller of an electrical power system includes a data storage device configured to store model data indicating a model load power consumed by loads of the electrical power system. The controller also includes a processor configured to determine current data including current load power consumed by the loads, modify the model data by aggregating the model data with the current data, and determine a set of control values for a set of control variables to effectuate a change to operation of the electrical power system based, at least in part, on the model data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Prediction of electrical power system behavior, and related systems, apparatuses, and methods are disclosed. A controller includes a data storage device configured to store model data for time points of a time period of operation. The controller also includes a processor configured to determine current data for time points of a current time period of operation. The current time period corresponds to an early portion of the time period of the model data. The controller is also configured to fit the model data to the current data to produce predicted data, a future portion of the predicted data corresponding to time points occurring after the early portion of the time period of the model data. The controller is further configured to determine values for a set of control variables to effectuate a change to operation of the electrical power system based on the future portion of the predicted data.

Abstract: A photovoltaic energy conversion system, apparatus, and method for controlling DC sub-arrays of a photovoltaic array are disclosed. The method may include coupling each of N homerun branches from N sub-arrays to an inverter via N switches and monitoring current through each of the N homerun branches. A forward current through each of the N homerun branches is compared with a forward current threshold, and any backfeed current through any of the N homerun branches is compared with a backfeed current threshold. One or more of the N switches are opened in response to either the forward current exceeding the forward current threshold or the backfeed current exceeding a backfeed current threshold.