Intelligent sensor and controller framework for the power grid
Disclosed below are representative embodiments of methods, apparatus, and systems for monitoring and using data in an electric power grid. For example, one disclosed embodiment comprises a sensor for measuring an electrical characteristic of a power line, electrical generator, or electrical device; a network interface; a processor; and one or more computer-readable storage media storing computer-executable instructions. In this embodiment, the computer-executable instructions include instructions for implementing an authorization and authentication module for validating a software agent received at the network interface; instructions for implementing one or more agent execution environments for executing agent code that is included with the software agent and that causes data from the sensor to be collected; and instructions for implementing an agent packaging and instantiation module for storing the collected data in a data container of the software agent and for transmitting the software agent, along with the stored data, to a next destination.
Intelligent sensor and controller framework for the power grid
Disclosed below are representative embodiments of methods, apparatus, and systems for monitoring and using data in an electric power grid. For example, one disclosed embodiment comprises a sensor for measuring an electrical characteristic of a power line, electrical generator, or electrical device; a network interface; a processor; and one or more computer-readable storage media storing computer-executable instructions. In this embodiment, the computer-executable instructions include instructions for implementing an authorization and authentication module for validating a software agent received at the network interface; instructions for implementing one or more agent execution environments for executing agent code that is included with the software agent and that causes data from the sensor to be collected; and instructions for implementing an agent packaging and instantiation module for storing the collected data in a data container of the software agent and for transmitting the software agent, along with the stored data, to a next destination.
METHOD FOR ENHANCING THE RESOLVING POWER OF ION MOBILITY SEPARATIONS OVER A LIMITED MOBILITY RANGE
A method for raising the resolving power, specificity, and peak capacity of conventional ion mobility spectrometry is disclosed. Ions are separated in a dynamic electric field comprising an oscillatory field wave and opposing static field, or at least two counter propagating waves with different parameters (amplitude, profile, frequency, or speed). As the functional dependencies of mean drift velocity on the ion mobility in a wave and static field or in unequal waves differ, only single species is equilibrated while others drift in either direction and are mobility-separated. An ion mobility spectrum over a limited range is then acquired by measuring ion drift times through a fixed distance inside the gas-filled enclosure. The resolving power in the vicinity of equilibrium mobility substantially exceeds that for known traveling-wave or drift-tube IMS separations, with spectra over wider ranges obtainable by stitching multiple segments. The approach also enables low-cutoff, high-cutoff, and bandpass ion mobility filters.
Dynamic Contingency Analysis Tool (DCAT) for evaluating power grid cascading outage potential due to extreme events - Open Source
The Dynamic Contingency Analysis Tool (DCAT) is an open-platform and publicly available methodology to help develop applications that aim to improve the capabilities of power system planning engineers to assess the impact and likelihood of extreme contingencies and potential cascading events across their systems and interconnections. Outputs from the DCAT will help find mitigation solutions to reduce the risk of cascading outages in technically sound and effective ways. The current prototype DCAT implementation has been developed as a Python code that accesses the simulation functions of the Siemens PSS (Trademark) E planning tool (PSS/E). It has the following features: It uses a hybrid dynamic and steady-state approach to simulating the cascading outage sequences that includes fast dynamic and slower steady-state events. It integrates dynamic models with protection scheme models for generation, transmission, and load. It models special protection systems (SPSs)/remedial action schemes (RASs) and automatic and manual corrective actions. Overall, the DCAT attempts to bridge multiple gaps in cascading-outage analysis in a single, unique prototype tool capable of automatically simulating and analyzing cascading sequences in real systems using multiprocessor computers. This study has been conducted in close collaboration with grid operators, Siemens Power Technologies International (PTI) and the Electric Power Research Institute (EPRI). While the DCAT has been implemented using PSS/E in Phase I of the study, other commercial software packages with similar capabilities can be used within the DCAT framework.
METHODS AND APPARATUS OF ANALYZING ELECTRICAL POWER GRID DATA
This software is a framework for performing scalable data analysis over large-scale power grid data sets. The framework consists of a statistical analysis package, such as R, running in a robust parallel environment, such as over a Hadoop cluster. This analysis package is used to define rules that identify subsets of data of interest, for example bad data or data indicating events of interest. These rules can be combined in arbitrary ways, for example multiple rules may be required to remove all erroneous data from the original data set. These rules can also be translated to a more efficient encoding, such as a Java program. When events of interest are identified, they are classified within known event types, and the collection of event metadata and underlying data references are stored in a relational database. These higher level metadata descriptions of the events can then be used to quickly respond to queries from either users or other applications, or this information can be displayed in a visual format. This framework provides a unique ability to perform analysis over complete large-scale power grid data sets, such as the PMU or FFT data being generated by smartgrid deployments, as opposed to most traditional analysis techniques that operate over a subset of the data. This enables a more complete data analysis. We have used this framework to identify novel rules that identify erroneous data in PMU data sets. We have also developed rules for identifying events of interest such as generator trips and islanding events.
RECONFIGURATION OF POWER GRIDS DURING ABNORMAL CONDITIONS USING RECLOSERS AND DISTRIBUTED ENERGY RESOURCES
Electric distribution systems around the world are witnessing an increasing number of utility-owned and customer-owned intelligent systems like reclosers, microgrids, distributed automation, solar photovoltaic generation, behind-the-meter energy storage, and electric vehicles being deployed. While these deployments provide potential data and control points, the existing centralized control architectures do not have the flexibility or the scalability to integrate the increasing number or variety of devices. An important element of managing distribution systems is its ability to reconfigure the network to maintain resiliency of critical end-use loads during extreme events. The optimization of network reconfiguration has been studied previously in the literature, however, these works have not included the additional flexibility available through the increasing adoption of distribute energy assets and the additional control capabilities available through distributed automation devices like recloser switches as well as the dynamics involved in using these devices. The invention formulates the reconfiguration problem as an optimization algorithm and handles complex constraints that include the legacy power flow, generating capacity, power demand, and network stability constraints. The optimization also incorporates the availability of flexible distributed energy resources and microgrid assets for increased system resiliency and accounts for the dynamic switching constraints introduced through the adoption of recloser switches in the network that allow for superior control and communications. Additionally, the optimization not only calculates the status of recloser switches but also the optimal sequence of switching operations for reconfiguration which is beneficial in a transactive market capable of leveraging customer-owned assets.
Economic Dispatch Software for Combine Cooling, Heating and Power Systems
A multi-purpose open-source control algorithms that will ensure real-time optimal operation, increase electric grid reliability, and lead to the goal of clean, efficient, reliable and affordable next generation building-integrated combine cooling, heating and power system.The CHP system could include conventional heating, ventilation, air conditioning (HVAC) systems, distributed generation (DG), local storage (both thermal and electric) and local solar photovoltaic (PV) systems. .