The present application discloses a fault prediction method, apparatus, and a storage medium, and relates to fields of cloud computing and fault processing. An embodiment includes acquiring a fault alarm request, wherein the fault alarm request is obtained by at least two fault triggering parameters of a fault generated by cloud operation, a hidden danger generated by cloud operation, and a change generated by terminal operation at a user level; analyzing the fault alarm request, to obtain at least two fault triggering parameters of the fault, the hidden danger, and the change; establishing an association between the at least two fault triggering parameters of the fault, the hidden danger, and the change, to obtain an association result; and predicting a fault causing the fault alarm request according to the association result, to obtain a fault prediction result.

An electrical energy storage unit and control system, and applications thereof. In an embodiment, the electrical energy storage unit may include a battery system controller and battery packs having a battery pack operating system. Each battery pack may have battery cells, a battery pack controller that monitors the cells, and a battery pack operating system that includes a suite of modules including among other modules, a module that tracks battery lifetime usage, a module that ensures the battery cells are used in accordance with warranty requirements, and a balancing module. The balancing module may control a battery pack cell balancer that adjusts the amount of energy stored in the cells. In an embodiment, the cells may be lithium ion battery cells.

Various embodiments of the teachings herein include a device for controlling energy flows between participants in an energy network which are connected to one another via lines, the device comprising a processor configured to calculate the energy flows in advance for a period of time using an optimization process and to control the energy flows in the period of time on the basis of the result of the calculation. The processor is further configured to include losses that occur in the energy flows in the lines in the calculation using the optimization process.

Methods for implementing power delivery transactions between a buyer and a seller of electrical energy supplied to an electrical grid by an integrated renewable energy source (RES) and energy storage system (ESS) of a RES-ESS facility are provided. Estimated total potential output of the RES is compared to a point of grid interconnect (POGI) limit to identify potential RES overgeneration, and the buyer is charged if potential RES overgeneration is less than potential overgeneration during one or more retrospective time windows. The method provides a basis for the RES-ESS facility owner to be paid for an estimated amount of energy that did not get stored as a result of a grid operator not fully discharging an ESS prior to the start of a new day.

Techniques are disclosed for implementing a scalable hierarchical energy distribution grid utilizing homogeneous control logic are disclosed that provide distributed, autonomous control of a multitude of sites in an energy system using abstraction and aggregation techniques. A hierarchical energy distribution grid utilizing homogeneous control logic is provided that includes multiple control modules arranged in a hierarchy. Each control module can implement a same energy optimization scheme logic to directly control site energy resources and possibly energy resources of sites associated with control modules existing below it in the hierarchy. Each control module can act autonomously through use a similar set of input values to the common optimization scheme logic.

A method for energy flow optimization in a multi-energy system based on spatiotemporal network flows is disclosed. The multi-energy system includes M energy storage devices, photovoltaic power generators and N micro gas turbines (MGTs). The method includes: obtaining an objective function of the network flows in the multi-energy system; determining constraints of the network flows, where the constraints include a power balance constraint, an energy storage period constraint, an energy storage capacity constraint, an energy storage charging and discharging constraint, a generator power constraint, and an energy storage charge quantity constraint; establishing a network flow model with the objective function and constraints; solving the network flow model with a shortest-path and max-flow algorithm, to obtain an optimal power flow (OPF) in the multi-energy system; operating the multi-energy system by adjusting parameters of the energy storage devices and the MGTs based on the OPF.

A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.

A controller issues an instruction of charge and discharge control to charge a power storage device up to a first target SOC and to discharge the power storage device up to a second target SOC. The controller lowers the first target SOC and the second target SOC with an increase in predicted temperature of the power storage device in a next charge and discharge cycle. The controller lowers the first target SOC and the second target SOC such that the second target SOC is lower than the first target SOC.

An industrial asset management system includes a data acquisition system configured to receive asset data associated with at least one industrial asset and to modify the data acquisition system to enable the continued receipt of asset data associated with the at least one industrial asset in response to a detection of an internal change at the data acquisition system by the data acquisition system and a data processing system communicatively coupled to the data acquisition system and configured to process the asset data received from the data acquisition system and to modify the data processing system for the continued processing of the asset data in response to a detection by the data processing system of an internal change at the data processing system or the data acquisition system.

A system tool that provides dispatchers in power grid control centers with a capability to manage changes. Included is a user interface and a plurality of scheduler engines. Each scheduler engine is configured to look ahead at different time frames to forecast system conditions and alter generation patterns within the different time frames. A comprehensive operating plan holds schedules generated by the plurality of scheduler engines. A relational database is coupled to the comprehensive operating plan. Input data is initially received from the relational database for each scheduling engine, and thereafter the relational database receives data from the scheduling engines relative to forecast system conditions.