Abstract: Systems and methods described herein can involve reducing a feeder system model by node cell segmentation on feeder system according to system topology information and system operation characteristics to generate a node cell segmented distribution grid; constructing observational data from systemwide status information aggregated from nodes identified from the node cell segmented distribution grid to meet a reinforcement learning (RL) policy network input requirement; training an RL policy framework to generate control actions for controllable components of the system; and executing the RL policy framework to generate control actions for controllable nodes in the node cell segmented distribution grid.

Abstract: A method for visualization of an end device. The method may include setting an object display limit on a display map, the display map displaying a plurality of objects, and wherein each of the plurality of objects is assigned a priority status; receiving data from the end device; classifying the data and assigning priority status to an object with the data, wherein the object is added to the plurality of objects; and displaying the plurality of objects based on priority status and the object display limit.

Abstract: Example implementations described herein provide systems and methods for visualization of power distribution feeder networks. The systems and methods involve setting a limit of objects of the power distribution network to be displayed on a map, the objects comprising at least nodes and links between the nodes, the links comprising at least conductive lines; when the total number of the objects exceeds a limit, selecting one or more subsets of the conductive lines based on one or more conditions related to the conductive lines; and generating a first visualization displaying the map comprising a subset of the objects including the one or more subsets of the conductive lines, wherein a number of displayed objects is less than or equal to the limit.

Abstract: Example implementations described herein involve a generic dynamic time-series hosting capacity analysis framework, which take into account distributed energy resources (DERs) dynamics and system dynamics. The example implementations described herein can further improve the computational efficiency and accuracy of hosting capacity analysis process. Example implementations can involve systems and methods that receive data input comprising system profiles and topology information of a distribution system having a plurality of DER nodes in an interconnect; execute feeder topology analysis on the topology information to generate output analysis; execute scenario management on the system profiles to generate simulation scenario sets; and load and execute a simulation flow from the simulation scenario sets and the output analysis.

Abstract: Example implementations described herein are directed to a visualization system and interface for identifying the pros and cons of new solutions of transmission grid management for each stakeholder. Systems and method described herein provide visualizations of pros and cons among stakeholders with respect to countermeasure operations for avoiding overloads to avoid overburdening or stakeholder bias.

Abstract: Example implementations described herein provide systems and methods for visualization of power distribution feeder networks. The systems and methods involve setting a limit of objects of the power distribution network to be displayed on a map, the objects comprising at least nodes and links between the nodes, the links comprising at least conductive lines; when the total number of the objects exceeds a limit, selecting one or more subsets of the conductive lines based on one or more conditions related to the conductive lines; and generating a first visualization displaying the map comprising a subset of the objects including the one or more subsets of the conductive lines, wherein a number of displayed objects is less than or equal to the limit.

Abstract: A system including a peer-to-peer network and cloud data services for DER data acquisition and aggregation is provided. The system may perform a method for managing a plurality of DERs at an edge computing device (e.g., a super edge node or master edge node). The method may include receiving, from a cloud node, a data collection protocol. The method may also include transmitting the data collection protocol to each of a plurality of private-device edge nodes (e.g., a privately-owned mobile devices) in a peer-to-peer network and receiving, based on the data collection protocol, data relating to the plurality of DERs from the plurality of private-device edge nodes through the peer-to-peer network. The method may further include transmitting the received data to a cloud-based node (e.g., a cloud system). The system may provide a cost-effective infrastructure for data acquisition and system control.

Abstract: Example implementations described herein are directed to managing the operation of a plurality of electric vehicles (EVs) for transportation service, power system operating reserve service and operation planning, which is utilized to determine whether to use each EV for transportation travel or power system operating reserve supply for a given time period and location. Such management allows for the increase of the total operation value by transportation travel and power system operation reserve supply.

Abstract: Example implementations described herein involve a multi-layer hybrid model power generation prediction method and computing system. The example implementations described herein are configured to predict power generation that can be utilized for short-term planning by utilities to optimize operation planning to ensure economic and stable operation of the electricity.

Abstract: Example implementations described herein involve a hybrid spatial-temporal event probability prediction method and system configured to predict spatial-temporal event probability that can be utilized for short term planning. For systems such as utility systems that manage multiple locations over time, the example implementations utilize both a spatial model and a temporal model to create a hybrid spatial-temporal model that accounts for data taken over time as well as data across the locations to more accurately forecast the probability of an event occurrence over a period of time.

Abstract: The multi-time scale battery energy/storage system (BES) management framework proposed in the present disclosure can be used for day-ahead capacity scheduling and real-time BES operation control in the latest performance-based frequency regulation market. This multi-time scale BES management framework includes three control layers: long-term battery performance planning layer, day-ahead regulation capacity scheduling layer, real-time battery operation control layer. These three layers will coordinate with each other, and operate at different time scales.

Abstract: Example implementations described herein involve a multi-layer hybrid model power generation prediction method and computing system. The example implementations described herein are configured to predict power generation that can be utilized for short-term planning by utilities to optimize operation planning to ensure economic and stable operation of the electricity.

Abstract: The multi-time scale battery energy/storage system (BES) management framework proposed in the present disclosure can be used for day-ahead capacity scheduling and real-time BES operation control in the latest performance-based frequency regulation market. This multi-time scale BES management framework includes three control layers: long-term battery performance planning layer, day-ahead regulation capacity scheduling layer, real-time battery operation control layer. These three layers will coordinate with each other, and operate at different time scales.

Abstract: Example implementations described herein involve a hybrid spatial-temporal event probability prediction method and system configured to predict spatial-temporal event probability that can be utilized for short term planning. For systems such as utility systems that manage multiple locations over time, the example implementations utilize both a spatial model and a temporal model to create a hybrid spatial-temporal model that accounts for data taken over time as well as data across the locations to more accurately forecast the probability of an event occurrence over a period of time.

Abstract: Example implementations described herein are directed to managing the operation of a plurality of electric vehicles (EVs) for transportation service, power system operating reserve service and operation planning, which is utilized to determine whether to use each EV for transportation travel or power system operating reserve supply for a given time period and location. Such management allows for the increase of the total operation value by transportation travel and power system operation reserve supply.

Abstract: A modular multilevel converter for hybrid energy storage includes three phases connectable in series to a battery, and in parallel to one another. Each phase includes at least two arms of sub-modules and buffer inductors, and each of the sub-modules comprises a half-bridge and an ultracapacitor. A two layer controller, including a coordination layer and a converter layer, is configured to independently control battery output power and ultracapacitor output power, and to distribute a power load between the battery and the ultracapacitor to optimize the performance of a hybrid energy storage system.

Abstract: A method of managing power between the multiple components of a hybrid electrical energy storage system (HESS) that includes providing at least two power storage elements, and at least one renewable power source. The method further includes managing the power flow among the at least two power storage elements with a fuzzy logic controller. The fuzzy logic controller uses a hardware processor that is configured to increase or decrease current to each of the at least two power storage elements using a fuzzy rule base that is dependent upon at least one of a state of charge for each of the at least two power storage elements, and a requested power demand of the hybrid electrical storage system.

Abstract: Systems and methods are disclosed to control a power system with an energy generator and a hybrid energy storage system. The system includes two or more energy storage system, each with different energy storage capacity and energy discharge capacity. The system includes developing data for one or more control variables refined from expert knowledge, trials and tests; providing the control variables to a fuzzy logic controller with a rule base and membership functions; and controlling the energy generator and the hybrid energy storage system using the fuzzy logic controller.

Abstract: Aspects of the present disclosure describe a multi-layer chiller operation management framework and associated methods for managing heating, ventilation, and air conditioning (HVAC) multi-chiller unit operation in real time serving varying system loads. According to the present disclosure, the framework includes two layers—a first layer providing 24-hour chiller operation planning thereby optimizing chiller operation using forecasted load profiles to minimize energy consumption. To this is applied a mixed-integer linear programming (MILP) based optimization. A second layer adjusts chiller operation status in real-time based on actual system load demand. Load forecasting uncertainty is cured in a hierarchical manner based on the level of load uncertainty. Two approaches are employed namely rule-based load sharing adjustment and MILP-based rolling optimization.

Abstract: Systems and methods are disclosed for storing photovoltaic (PV) generation by applying reinforcement learning (RL)-based control to battery storages for PV ramp rate control; and exchanging energy dynamically to limit a ramp rate of the PV power output and maintaining a battery state of charge level at a predefined level to minimize required battery size and extend the battery life cycles.