A power generation amount prediction apparatus includes a first detector, acquisition unit, database, and controller. The first detector detects a measured value of the power generation amount of a photovoltaic power generation device. The acquisition unit acquires a predicted value of the power generation amount by the photovoltaic power generation device at specific times. The database stores the measured value and the predicted value for each of the specific times for a plurality of days. The controller calculates a corrected predicted value for each of the specific times based on a maximum measured value, a maximum predicted value, and a predicted value newly received by the acquisition unit. The maximum measured value and the maximum predicted value are respectively the maximum value, for each of the specific times, of the measured value and of the predicted value for a predetermined number of days.

There discloses a ubiquitous energy network for optimum utilization of energy, which includes nodes connected by an interconnected network architecture of virtual pipelines transferring a ubiquitous energy flow, with the ubiquitous energy flow being transferred among the nodes bi-directionally. The node includes a system energy efficiency controller, and at least one of other nodes, an energy generation device, an energy storage device, an energy utilization device, and an energy regeneration device connected to the controller. The controller controls the input and output of the ubiquitous energy flow of the at least one of the other nodes, the energy generation device, the energy storage device, the energy utilization device, the energy regeneration device. Furthermore, the node, an access terminal, a virtual tag, and the virtual pipeline of the ubiquitous energy network, and a server and method for providing energy transaction and service by the ubiquitous energy network are disclosed.

An electrical equipment is supplied with electric power from one of an electric power supply system and a secondary battery. The electrical equipment includes a switch circuit and a module. The switching circuit switches a power supply source for supplying electric power to the electrical equipment between the electric power supply system and the secondary battery. The module includes a processor to execute a program to perform a controlling process including a process of controlling the switch circuit to selectively connect the electrical equipment to one of the electric power supply system and the secondary battery according to a control signal supplied from an external device.

An adaptive control device and adaptive control method, and a control device of an injection molding machine, which allow optimal adaptive control to be performed automatically and easily, while preventing a degradation of responsiveness. The adaptive control device is configured to perform feedback control in such a manner that an operation value is output based on a command value and a feedback value which is a sum of a controlled value output from a controlled target and a compensation value output from a parallel feed-forward compensator; wherein the parallel feed-forward compensator includes: an identification section which sequentially estimates a frequency response characteristic of the controlled target and an adjustment section which adjusts the compensation value based on the estimated frequency response characteristic.

Systems, methods, and messages of the present invention provides IP-based messages associated with the grid elements, wherein each IP-based message includes an internet protocol (IP) packet that is generated autonomously and/or automatically by the grid elements, intelligent messaging hardware associated with the grid elements, at least one coordinator, and/or a server associated with the electric power grid and its operation, energy settlement, and/or financial settlement for electricity provided or consumed, transmitted, and/or curtailed or reduced. The IP packet preferably includes a content including raw data and/or transformed data, a priority associated with the IP-based message, a security associated with the IP packet, and/or a transport route for communicating the IP-based message via the network.

A microgrid includes a plurality of distributed energy resources such as controllable distributed electric generators and electrical energy storage devices. A method of controlling operation of the microgrid includes periodically updating a distributed energy resource schedule for the microgrid that includes on/off status of the controllable distributed electric generators and charging/discharging status and rate of the electrical energy storage devices and which satisfies a first control objective for a defined time window, based at least in part on a renewable energy generation and load forecast for the microgrid. The method further includes periodically determining power set points for the controllable distributed energy resources which satisfy a second control objective for a present time interval within the defined time window, the second control objective being a function of at least the distributed energy resource schedule for the microgrid.

An electricity demand prediction system includes electricity usage data collection means, production schedule storage means, model creation means, and short-term electricity demand prediction means. The electricity usage data collection means collects electricity usage data of each device and causes electricity usage data storage means to store the collected electricity usage data. The model creation means creates an electricity amount calculation model of each prescribed product type for each device on the basis of the electricity usage data stored in the electricity usage data storage means and a past production schedule stored in the production schedule storage means. The short-term electricity demand prediction means computes future electricity demand for each device on the basis of the electricity amount calculation model created by the model creation means and a future production schedule stored in the production schedule storage means.

The present disclosure provides a dispatching method and a dispatching device for an integrated transmission and distribution network. The integrated transmission and distribution network include a transmission network and at least one distribution network. The method includes: establishing a dispatch model of the integrated transmission and distribution network; solving the dispatch model to obtain dynamic dispatch parameters for the integrated transmission and distribution network, in which the dynamic dispatch parameters comprise a boundary transferred power from the transmission network to each of the at least one distribution network, and power outputs of all generators in the transmission network and each of the at least one distribution network; and dispatching the integrated transmission and distribution network based on the boundary transferred power and the power outputs of all the generators in the transmission network and each of the at least one distribution network.

There is provided a power management device including a load current control unit configured to set an upper limit on a load current supplied from a connected feeding device and to control the load current on the basis of the upper limit, and a determination unit configured to, when the load current control unit has reset the upper limit to a higher value, determine if the upper limit has exceeded a current capacity of the feeding device on the basis of a voltage drop level of an input voltage. The load current control unit may reset the upper limit in increments or decrements of a predetermined value, and the load current control unit may, when the determination unit has determined that the upper limit had exceeded the current capacity of the feeding device, control the load current by resetting the upper limit to a value not exceeding the current capacity.

Energy and demand costs reduction method through management of an entity load using an energy storage system; simulating the entity load including using predictive analytics; or receiving user inputs of an event schedule impacting entity load; performing optimization at start of billing cycle including establishing a demand reduction target achievable with an energy storage system device implemented; setting a periodic schedule for energy storage system discharge and allocating a portion of energy storage capacity to a reserve to be used in event of volatility in entity load; determining any unused capacity of the energy storage system at an end of a period, and responsive to the determining, redistributing any unused capacity to a remaining period of the schedule; and resetting the demand reduction target for each time-of-use period to what was achieved previously for the same billing period.