A charging/discharging control system of an energy storage apparatus includes a weather information collection module, a load information collection module, a load grouping module which classifies a plurality of loads into at least one load group based on a load correlation according to a predetermined condition, and an energy storage apparatus charging/discharging control module which determines a battery charging or discharging level per time slot of each of a plurality of energy storage apparatuses by using at least one of an estimated load amount of each load per time slot, a battery charging amount of the energy storage apparatus per time slot via solar generation, and a battery charging amount per time slot via system power, wherein a particular load corresponding to a lowest rate for a unit load is determined by using battery charging/discharging information and power rate information per time slot.

Provided is an electric power supply and demand control apparatus (10) including a remaining battery level acquisition unit (11) and a setting unit (12), in order to solve the aforementioned problems. The remaining battery level acquisition unit (11) acquires remaining battery level information indicating a remaining battery level of a storage battery of a consumer. The setting unit (12) sets an expectation value of a reduction in power purchase from a system performed by the consumer, on the basis of the remaining battery level information acquired by the remaining battery level acquisition unit (11). For example, a high expectation value is set for a consumer whose storage battery has a large remaining battery level. According to the present invention, it is possible to create an appropriate plan of a request for a reduction in a power purchase quantity, on the basis of the expectation value of the reduction in power purchase for each consumer determined on the basis of the remaining battery level of a storage battery of the consumer.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

An illustrative network management system obtains sensor data from one or more sensors at a network facility at which network equipment of a communication network is deployed. The sensor data includes image data representing imagery of the network facility. The system determines a utilization of a utility at the network facility based on the sensor data and performs, based on the utilization, a management operation for the communication network. Corresponding methods and systems are also described.

A system includes an entropy device configured to generate and distribute input entropy data and an intelligent electronic device (IED) of an electric power distribution system. The IED is configured to perform operations that include receiving the input entropy data distributed by the entropy device, generating a set of keys using the input entropy data, and establishing a Media Access Control Security (MACsec) communication link using the set of keys.

An energy supply system, which is a system constituting a regional power system in a target region, includes a power transmission system including a first power generation facility and a second power generation facility, a power transmission and distribution system that supplies power to each consumer, a management system, and an unmanned flying object. The unmanned flying object has a transport function of transporting a cargo and a power supply function of supplying power to an outside. When the amount of power supplied by the power transmission system is less than the amount of power required by the power transmission and distribution system, the management system performs a power adjustment process of supplying power from the unmanned flying object to the power transmission and distribution system by using the power supply function of the unmanned flying object.

Systems and methods for performing actions in response to charging events, such as charging events associated with a specific electric vehicle and/or a specific charging station, are described. In some embodiments, the systems and methods may receive a request from an electric vehicle to identify a charging station from which to charge a battery of the electric vehicle, provide information associated with the electric vehicle to one or more charging stations proximate to the electric vehicle, receive from the one or more charging stations information identifying parameters associated with potential charging events provided by the one or more charging stations, and provide the information identifying the parameters associated with potential charging events provided by the one or more charging stations to the electric vehicle.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A demand and supply planning method generates a supply-demand plan including information that specifies operation of an energy generation unit for each unit time in a microgrid having energy generation units being connected to an external energy supply system and generating energy while consuming fuel, an energy storage unit, and a load unit; by calculating an optimization problem related to an objective function including terms related to energy transfer from the external energy supply system in the microgrid and terms related to fuel consumption in the microgrid by using a constraint condition related to a relationship between information related to an operating duration time in each of the one or more energy generation units at a start time for generating the supply-demand plan and a minimum operation time constraint of the energy generation unit; and outputting the supply-demand plan based on a calculation result of the optimization problem.

The present disclosure provides systems and methods for an operation of an electric power plant comprising a renewable energy resource and an energy storage device. The method may comprise determining, at a first time, a forecast of predicted energy production by the electric power plant over a time period subsequent to the first time based on a forecast for the time period; detecting a current state of charge of the energy storage device; calculating a range of automatic generation controls the electric power plant is capable of satisfying for the time period based on the forecast of predicted energy production and the detected current state of charge of the energy storage device; and signaling, from the electric power plant to a central utility controlling a power grid, the range of automatic generation controls the electric power plant is capable of satisfying for the time period.