Abstract: A source-network-load-storage coordination dispatching method in a background of a coupling of renewable energy sources, including: taking an expectation of a minimum grid operating cost in a dispatching cycle as an objective function; generating an approximate value function of an output of a set for generating electricity from renewable energy sources and a user load, and constructing a source-network-load-storage coordination dispatching model with combination of the objective function; obtaining forecast data of the output of a set for generating electricity from renewable energy sources and the user load, and inputting the forecast data into the dispatching model for solving; performing iterative updating on the approximate value function, importing the approximate value function after the iterative updating into the dispatching model for iterative solving, and terminating an iterative process until a solving result satisfies a preset convergence condition; and using a solving result of a last iteration

Abstract: Disclosed are a photovoltaic energy storage offline coordination system and method based on demand management. The system includes a photovoltaic generator set, an inverter, a dispatch and control device for charge-discharge, a load monitor, a first and second energy storage device, and a remote console. The photovoltaic generator set is electrically connected to the dispatch and control device for charge-discharge via the inverter; the first energy storage device is connected to a load-side power supply bus via a first dispatch and control unit; the second energy storage device is connected to the load-side power supply bus via a third dispatch and control unit; a second dispatch and control unit is connected to the load-side power supply bus; the load monitor is provided at the load-side power supply bus, and configured to monitor a load of the load-side.

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for improving query processing of a database. An example method can include: storing table data for a table in a plurality of micro-partitions, each micro-partition comprising a portion of the table data for the table; for each micro-partition of the plurality of micro-partitions, storing metadata for the micro-partition in at least one of a plurality of expression properties; and selecting, by a processing device, a subset of the plurality of expression properties to be grouped into a grouping expression property based at least partially on the metadata of the subset of the plurality of the expression properties. The grouping expression property may include cumulative metadata associated with the metadata of the subset of the plurality of expression properties.

Abstract: Embodiments of the present disclosure describe systems, methods, and computer program products for improving query processing of a database. An example method can include: storing table data for a table in a plurality of micro-partitions, each micro-partition comprising a portion of the table data for the table; for each micro-partition of the plurality of micro-partitions, storing metadata for the micro-partition in at least one of a plurality of expression properties; and selecting, by a processing device, a subset of the plurality of expression properties to be grouped into a grouping expression property based at least partially on the metadata of the subset of the plurality of the expression properties. The grouping expression property may include cumulative metadata associated with the metadata of the subset of the plurality of expression properties.

Abstract: A frequency conversion power transmission system includes: a new energy power generation base, a first isolation device, a second isolation device, an alternating current-alternating current (AC-AC) frequency conversion device and a power transmission cable; the new energy power generation base is configured to supply electrical energy to an AC power grid, and operate at a constant voltage and a constant or variable frequency according to environmental conditions including weather, an environment or a distance; the first isolation device is connected to the new energy power generation base; the second isolation device is connected to the AC power grid; an input terminal of the AC-AC frequency conversion device is connected to the first isolation device, an output terminal of the AC-AC frequency conversion device is connected to the second isolation device, and the power transmission cable is configured to connect the new energy power generation base and the first isolation device.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology stores a first data structure and a second data structure in storage as a first file and a second file, first data structure including the first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores third metadata for a table, the third metadata comprising information about data stored in a micro-partition of the table.

Abstract: A method for intelligently adjusting a power flow based on a Q-learning algorithm includes: converting a variable, an action, and a goal in a power grid to a state, an action, and a reward in the algorithm, respectively; selecting an action from an action space, giving an immediate reward based on a result of power flow calculation, and correcting a next state; forwardly observing a next exploration action based on a strategy in the Q-learning algorithm; updating a Q value in a corresponding position in a Q-value table based on the obtained reward; if a final state is not reached, going back to step 2; otherwise, increasing the number of iterations by 1; if the number of iterations does not reach predetermined value K, that is, Episode<K, going back to step 2; otherwise, that is, Episode=K, outputting the Q-value table; and outputting an optimal unit combination.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology stores a first data structure and a second data structure in storage as a first file and a second file, first data structure including the first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores third metadata for a table, the third metadata comprising: cumulative table metadata comprising global information about a plurality of micro-partitions of the table, the cumulative table metadata being stored in a metadata micro-partition associated with the table.

Abstract: The subject technology generates a first data structure including first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores the first data structure and the second data structure in storage as a first file and a second file. The subject technology stores third metadata for a table, the third metadata comprising: cumulative table metadata comprising global information about a plurality of micro-partitions of the table, the cumulative table metadata being stored in a metadata micro-partition associated with the table.

Abstract: The subject technology stores database data in a table, the table comprising a plurality of micro-partitions each having rows and columns. The subject technology stores metadata for the table in a data structure, the metadata comprising a first level of the data structure comprising a column expression property comprising information about data stored in a column of a micro-partition of the table, a micro-partition expression property comprising information about data stored in the micro-partition of the table, and a second level of the data structure comprising a grouping expression property comprising information about data stored in the micro-partition and one or more additional micro-partitions of the table.

Abstract: The present invention relates to a method for identifying parameters of a 10 kV static load model based on similar daily load curves. In the present invention, an optimization model for identifying full-period parameters of the static load model is proposed based on a large number of daily load curves with response characteristic, a structure and constraints of the static load model, and two theoretical basic assumptions about loads. Full-period (including 96 moments) static voltage model parameters of 10 kV loads are given through optimization solution. A rule that active power and reactive power of the loads at each moment change with voltage is obtained. In addition, a change rule of load constituents is obtained. The method delivers good applicability, satisfies actual demands, and is suitable for large-scale static model analyses for 10 kV loads.

Abstract: Systems, methods, and devices for multiple level metadata organization in database systems. A system includes a plurality of shared storage devices collectively storing database data across a plurality of tables that each comprise one or more micro-partitions constituting immutable storage devices. The system includes a resource manager configured to manage the storage of database data stored in a table across one or more of the plurality of shared storage devices. The resource manager is configured to manage the storage of metadata for the table. The metadata includes a column expression property with information about data stored in a column, a micro-partition expression property with information about data stored in a micro-partition, a grouping expression property with information about data stored in two or more micro-partitions, and cumulative table metadata with global information about all micro-partitions of the table.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology stores a first data structure and a second data structure in storage as a first file and a second file, first data structure including the first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores third metadata for a table, the third metadata comprising information about data stored in a micro-partition of the table.

Abstract: The subject technology generates a first data structure including first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores the first data structure and the second data structure in storage as a first file and a second file. The subject technology stores third metadata for a table, the third metadata comprising: cumulative table metadata comprising global information about a plurality of micro-partitions of the table, the cumulative table metadata being stored in a metadata micro-partition associated with the table.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology stores a first data structure and a second data structure in storage as a first file and a second file, first data structure including the first metadata and a second data structure including second metadata, the first metadata corresponding to a set of micro-partitions, the second metadata for a grouping of the first metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores third metadata for a table, the third metadata comprising: cumulative table metadata comprising global information about a plurality of micro-partitions of the table, the cumulative table metadata being stored in a metadata micro-partition associated with the table.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology generates second metadata for a grouping of the first metadata. The subject technology generates a first data structure including the first metadata and a second data structure including the second metadata, the second data structure including information associating the second metadata to the first metadata. The subject technology stores the first data structure and the second data structure in persistent storage as a first file and a second file. The subject technology receives a query on a table. Further, the subject technology analyzes the query against cumulative table metadata to determine whether data stored in the table matches the query.

Abstract: The subject technology stores database data in a table, the table comprising a plurality of micro-partitions each having rows and columns. The subject technology stores metadata for the table in a data structure, the metadata comprising a first level of the data structure comprising a column expression property comprising information about data stored in a column of a micro-partition of the table, a micro-partition expression property comprising information about data stored in the micro-partition of the table, and a second level of the data structure comprising a grouping expression property comprising information about data stored in the micro-partition and one or more additional micro-partitions of the table.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology generates second metadata for a grouping of the first metadata. The subject technology generates a first data structure including the first metadata and a second data structure including the second metadata, the second data structure including information associating the second metadata to the first metadata. Further, the subject technology stores the first data structure and the second data structure in persistent storage as a first file and a second file.

Abstract: Systems, methods, and devices for multiple level metadata organization in database systems. A system includes a plurality of shared storage devices collectively storing database data across a plurality of tables that each comprise one or more micro-partitions including immutable storage devices. The system includes a resource manager configured to manage the storage of database data stored in a table across one or more of the plurality of shared storage devices. The resource manager is configured to manage the storage of metadata for the table. The metadata includes a column expression property with information about data stored in a column, a micro-partition expression property with information about data stored in a micro-partition, a grouping expression property with information about data stored in two or more micro-partitions, and cumulative table metadata with global information about all micro-partitions of the table.

Abstract: The subject technology receives first metadata corresponding to a set of micro-partitions. The subject technology generates second metadata for a grouping of the first metadata. The subject technology generates a first data structure including the first metadata and a second data structure including the second metadata, the second data structure including information associating the second metadata to the first metadata. Further, the subject technology stores the first data structure and the second data structure in persistent storage as a first file and a second file.