A method for predicting the operation state of a power distribution network based on scene analysis is provided, comprising the following steps of step 10) obtaining the network structure and historical operation information of a power distribution system; step 20) extracting representative scene sequence fragments of output of the DGs according to historical output sequences of the DGs; step 30) obtaining a multi-scene prediction result of a future single-time section T.sub.0 through matching the historical similar scenes; step 40) establishing a future multi-time section operation scene tree; and step 50) deeply traversing all scenes in the future multi-time section operation scene tree, performing power distribution network load flow analysis for each scene, calculating the line current out-of-limit risk and the busbar voltage out-of-limit risk of the power distribution network, and obtaining a future operation state variation tendency of the power distribution network with the DGs.

A method includes obtaining an optimized setpoint schedule for a time period, identifying a pre-cooling or pre-heating segment of the time period of the optimized setpoint schedule, adjusting the optimized setpoint schedule based on a characteristic of the pre-cooling or pre-heating segment to obtain an adjusted setpoint schedule, and operating the building equipment in accordance with the adjusted setpoint schedule.

Methods, systems, apparatus, and non-transitory computer readable media are described for using a vehicle as a payment device. Various aspects may include receiving a selection of a stored financial card or financial account at a vehicle head unit. The selected financial card or financial account may be transmitted to a point-of-sale (POS) terminal for making a payment by transmitting a tokenized card number to the POS terminal. The tokenized card number may be transmitted over a very short-range communication link to ensure that the transmission is secure. For example, electronic circuitry may be attached to the exterior of the vehicle, where the electronic circuitry may be within a threshold distance (e.g. one inch, three inches, six inches, one foot, three feet, etc.) of the POS terminal. The tokenized card number may be transmitted from the vehicle head unit to the electronic circuitry and then to the POS terminal.

Methods, systems, apparatus, and non-transitory computer readable media are described for using a vehicle as a payment device. Various aspects may include receiving a selection of a stored financial card or financial account at a vehicle head unit. The selected financial card or financial account may be transmitted to a point-of-sale (POS) terminal for making a payment by transmitting a tokenized card number to the POS terminal. The tokenized card number may be transmitted over a very short-range communication link to ensure that the transmission is secure. For example, electronic circuitry may be attached to the exterior of the vehicle, where the electronic circuitry may be within a threshold distance (e.g. one inch, three inches, six inches, one foot, three feet, etc.) of the POS terminal. The tokenized card number may be transmitted from the vehicle head unit to the electronic circuitry and then to the POS terminal.

Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

A power management system obtains first data regarding several groups of electrical devices, including a budget and a respective priority metric associated with each of the groups. The system allots a respective amount of electrical power for use by each of the groups based on the first data. Further, the system obtains second data regarding the groups, including a respective amount of electrical power consumed by each of the groups. The system determines that a first group has consumed a first amount of electrical power that is greater than or equal to a second amount of electrical power that had been allotted for use by the first group. In response, the system re-allots at least a portion of a third amount of electrical power that had been allotted for use by a second group for use by the first group instead.

A power management system obtains first data regarding several groups of electrical devices, including a budget and a respective priority metric associated with each of the groups. The system allots a respective amount of electrical power for use by each of the groups based on the first data. Further, the system obtains second data regarding the groups, including a respective amount of electrical power consumed by each of the groups. The system determines that a first group has consumed a first amount of electrical power that is greater than or equal to a second amount of electrical power that had been allotted for use by the first group. In response, the system re-allots at least a portion of a third amount of electrical power that had been allotted for use by a second group for use by the first group instead.

A battery charging system includes a battery removably mounted on an electric power device using electric power, a charging device configured to charge the battery using renewable power which is electric power generated from renewable energy, and a server configured to communicate with the charging device. The charging device is configured to control charging of the battery accommodated in an accommodation unit on the basis of reception information received from the server. The server is configured to compare receivable power, which is the renewable power capable of being received by the charging device, with a threshold value and configured to transmit transmission information for causing the charging device to control the charging of the battery to the charging device on the basis of a result of comparing the receivable power with the threshold value.

The present application provides a method and apparatus for evaluating the health state of a distribution transformer, and a medium and a program. In an embodiment, the method for evaluating the health state of a distribution transformer includes: extracting features from electric power parameters of a plurality of distribution transformers; clustering the plurality of distribution transformers into M subsets based on the extracted features, M being a natural number greater than 1 and less than a number of the distribution transformers; and respectively executing processing for each of the M subsets. In an embodiment, the processing includes determining a cluster center of the subset as a reference transformer for the subset; calculating the similarity between each of the distribution transformers in the subset and the reference transformer therein; and sorting the distribution transformers in the subset according to the calculated similarity.