A power management system including a management apparatus configured to assign divided computation processing constituting at least a part of predetermined computation processing to a distributed computing device placed in a facility, wherein the management apparatus includes a controller configured to perform assignment processing configured to assign the divided computation processing to the distributed computing device based on at least one of a prediction value of an output power of a distributed power supply placed in the facility, a prediction value of power consumption of the facility, and a prediction value of a surplus power of the facility.

The present invention relates to an aggregation method for dispatching the wind and solar power plants. The primary technical solutions include: introducing the power output complementarity indexes to characterize the average effect of the degree of power output complementarity between different power stations, using cohesive hierarchical clustering to identify the optimal cluster division under different division quantities, and introducing the economic efficiency theory to determine the optimal cluster quantity, which avoids the randomness and irrationality that may result from relying on the subjective determination of the number of clusters. According to the analysis of dozens of real-world wind and solar power cluster engineering in the Yunnan Power Grid, the results show that the invention can effectively reduce the number of directly dispatched power stations, and the uncertainty of wind and solar power output can be more accurately described in a cluster manner, presenting better reliability, concentration, and practicality.

A method for describing power output of a cluster of wind and solar power stations considering time-varying characteristics. The error function is employed to characterize the degree of difference in power output within periods, and split-level clustering is used to determine the optimal period division under different period division quantities. The economic efficiency theory is introduced to determine the ideal number of periods, avoiding the randomness and unreasonableness that may result from relying on the subjective determination of the number of clusters. This method can reasonably divide the wind and solar power output period, fully reflecting the time-varying law of wind and solar power generation. The results also can accurately reflect the distribution characteristics of the power output of the power station group at each time period, and the power output each time period shows better reliability, concentration, and practicality.

Ultra-high accuracy elevation and pressure telemetry devices are used to develop an autonomous, self-calibrating hydraulic piping network computer simulation model. Virtual pressure reducing valve (PRV) model elements force a local downstream calibration of the model using the pressure telemetry data, overcoming the potential ill conditioned state when simulating wide ranging, real world operational conditions. This technique also creates a smaller solution set for calibration optimization algorithms such as machine learning. Additional benefits of this technique include the ability to ignore complex facilities such as pump stations, water storage tanks, and control valves enabling a more rapid development of the real-time water piping network computer simulation model.

The present disclosure provides techniques for estimating network states using asynchronous measurements by leveraging network inertia. For example, a device configured in accordance with the techniques of the present disclosure may receive electrical parameter values corresponding to at least one first location within a power network and determine, based on the electrical parameter values and a previous estimated state of the power network, an estimated value of unknown electrical parameters that correspond to a second location within the power network. The device may further cause at least one device within the power network to modify operation based on the estimated value of the unknown electrical parameters. The leveraging of network inertia may obviate the need for probabilistic models or pseudo-measurements.

The present disclosure provides systems and methods for flexible renewable energy power generation. The present disclosure also provides systems and methods for firming power generation from multiple renewable energy sources.

The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm. The invention can provide load dispatch scheme suitable for current microgrid and reduce operating cost of residential microgrid.

A rapidly deployable modular microgrid including a plurality of renewable and other energy generation technologies, energy storage technologies, energy distribution networks, and intelligent control systems capable of managing the flow of electrical energy between one or more locations of energy generation, storage, and consumption are disclosed. The aforementioned microgrid may be delivered and rapidly deployed to provide primary or secondary electricity for a variety of purposes; including but not limited to household electrification, commercial or industrial productivity, grid resiliency, water pumping, telecommunication systems, medical facilities, and disaster relief efforts.

A power management system includes a photovoltaic power generation device installed in a predetermined area and connected to a power grid disposed in the predetermined area, an acquisition device configured to acquire a wind direction at a reference point at which the photovoltaic power generation device is installed in the predetermined area, and an arithmetic device configured to calculate a predicted value of a solar radiation amount at the reference point at a prediction target time and calculate generated power of the photovoltaic power generation device by using the predicted value.

In one embodiment, the method includes obtaining, by a first processing device, energy demand data representative of the energy consumption of respective tasks of a processing pipeline, obtaining, by the first processing device, battery availability data representative of the available energy of the batteries of other respective processing devices, for respective tasks of the processing pipeline, selecting, by the first processing device, one of the processing devices for executing the task, as a function of the energy demand data and the battery availability data, and controlling, by the first processing device, the execution of the respective tasks on the selected processing devices.