Abstract: A computer system for real-time coordinated operation of power distribution systems and electric vehicles identifies a set of integrated hybrid resources (IHRs), wherein each IHR within the set of IHRs comprises one or more of: energy storage (ES) systems, solar generating units, electric vehicles (EVs), and/or inflexible loads. The computer system executes, at an IHR selected from the set of IHRs, a deep deterministic policy gradient (DDPG) algorithm, the DDPG algorithm utilizing a critic deep neural network and an actor deep neural network. The critic deep neural network estimates a Q-value of an action for a given state, and the actor deep neural network estimates a best action for the given state. Based upon an output of the DDPG algorithm, the computer system generates a charging schedule for the ES systems and the EVs within the IHR.

Abstract: A computer system for real-time coordinated operation of power distribution systems and electric vehicles accesses vehicle location information for one or more vehicles. Additionally, the system accesses charging station locations for one or more charging stations within a power distribution system. The computer system also accesses power distribution information describing voltage and/or current flow limits for the power distribution system. Further, the computer system routes a vehicle selected from the one or more vehicles to a first charging station location for charging, wherein the routing of the vehicle accounts for a current battery charge level of the vehicle and the power distribution information.

Abstract: An operating configuration for a power system during a particular time period may be derived from a net load forecast for the power system during the particular time period. The operating configuration may be based on inter-temporal and/or continuous-time characteristics of the net load forecast. A power system manager may schedule power generation and/or energy storage units to satisfy the net load forecast at minimal cost. The power generation and/or energy storage units may be scheduled in accordance with inter-temporal and/or continuous-time characteristics of the net load. The schedule may comply with generation trajectory and/or ramping constraints of the power generating units, power trajectory and/or ramping constraints of the energy storage units, and so on.

Abstract: An operating configuration for a power system during a particular time period may be derived from a net load forecast for the power system during the particular time period. The operating configuration may be based on characteristics of power generation units (PGUs) available within the power system (e.g., power generators). The characteristics of a PGU may include a generation trajectory for the PGU that defines, inter alia, power generated by the PGU as the PGU ramps up power production within the power system. The generation trajectory of PGUs may be evaluated in view of the net load forecast to ensure that adequate power resources are available within the power system and avoid ramping scarcity conditions.

Abstract: An optimal operating configuration for a power system may be derived from a continuous-time model of generation and ramping constraints of power generation units available within the power system. The optimal operating configuration may be leveraged to calculate a valuation metric for power generated by the power generating units. The valuation metric may include costs incurred by the power generating units due to ramping events. In addition, incremental generation and ramping cost metrics may be used to identify power generation units to use in responding to short-term load variations. Power generating units may be selected based, at least in part, on the incremental ramping costs of the power generating units.

Abstract: An operating configuration for a power system during a particular time period may be derived from a net load forecast for the power system during the particular time period. The operating configuration may be based on inter-temporal and/or continuous-time characteristics of the net load forecast. A power system manager may schedule power generation and/or energy storage units to satisfy the net load forecast at minimal cost. The power generation and/or energy storage units may be scheduled in accordance with inter-temporal and/or continuous-time characteristics of the net load. The schedule may comply with generation trajectory and/or ramping constraints of the power generating units, power trajectory and/or ramping constraints of the energy storage units, and so on.

Abstract: An optimal operating configuration for a power system may be derived from a continuous-time model of generation and ramping constraints of power generation units available within the power system. The optimal operating configuration may be leveraged to calculate a valuation metric for power generated by the power generating units. The valuation metric may include costs incurred by the power generating units due to ramping events. In addition, incremental generation and ramping cost metrics may be used to identify power generation units to use in responding to short-term load variations. Power generating units may be selected based, at least in part, on the incremental ramping costs of the power generating units.

Abstract: An operating configuration for a power system during a particular time period may be derived from a net load forecast for the power system during the particular time period. The operating configuration may be based on characteristics of power generation units (PGUs) available within the power system (e.g., power generators). The characteristics of a PGU may include a generation trajectory for the PGU that defines, inter alia, power generated by the PGU as the PGU ramps up power production within the power system. The generation trajectory of PGUs may be evaluated in view of the net load forecast to ensure that adequate power resources are available within the power system and avoid ramping scarcity conditions.