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GridControl is an umbrella name for a DOE/ARPAe-funded effort that unites GridCloud, a new platform jointly created by Cornell University and Washington State University for high assurance cloud computing, with a power grid simulation and state estimator.  The resulting platform represents a powerful new solution for smart grid monitoring & control.

Project Overview

The Cornell University and Washington State University GridControl research yielded a platform that we refer to as the the GridCloud system.  GridCloud functions much like an operating system for hosting smart grid applications on cloud systems.  Our initial focus has been on Amazon's AWS (both the public AWS and Amazon's government version), which we augment with additional tools to support 24x7 mission-critical application availability.  GridCloud is composed of a set of standalone components that include the CloudMake management tool, the GridCloud Collaboration Tool, the IronStack SDN network controller, and the Freeze-Frame File System.  These come together in the GridCloud platform.  Our preliminary work has ported a grid simulation system and a real-time linear state estimator onto the platform, which we then evaluated by transmitting simulated data into it much as real PMUs would and then reconstructing the corresponding grid state.  We achieved a latency of 100 to 125ms even with injected component failures, scheduling delays and Internet network delays.  GridCloud is an open source platform, available under 3-BSD free licensing.

Elements of the GridCloud Solution

The transformational energy management and renewable power deployment capabilities needed to create the next generation of the power grid share a common weakness: the most exciting concepts require a highly scalable and secure computing infrastructure that does not currently exist.  Today the best match is with the “cloud computing” model, but existing cloud computing platforms lack the level of reliability and security needed to operate a nationally critical infrastructure.

One option is for each application developer working on the smart grid to set out to solve such problems on their own.  Such an approach cannot scale: the underlying technical issues are daunting and our own research on such systems, over a 25 year period, convinces us that without a reasonably standard approach, few developers would arrive at robust, self-managed solutions.  Yet if only experts can create smart-grid software, then very few smart-grid applications could be deployed.

We created GridCloud to address this issue.  In the system, Cornell University and Washington State University have brought together state of the art solutions, yielding a platform that embodies powerful, comprehensive responses to the key requirements.  Using GridCloud, developers can slash the time and difficulty required to prototype and demonstrate new smart-grid monitoring and control paradigms, and achieve high confidence in the resiliency and security of their solutions.

GridCloud diagram
GridCloud diagram

We view GridCloud not as a turn-key platform for PMU-based power grid state estimation, but rather as a flexible and extensible tool -- a building block that simplifies the otherwise challenging problems that dominate in cloud setting.  Thus for us, state estimation is an important capability but also is offered as a demonstration: the first of many applications hosted on the platform.  GridCloud integrates and standardizes tasks that any system of this kind must address, such as reliable data collection with good realtime properties and security guarantees.  The resulting system looks as familiar as possible: as much as we were able to, we adopted familiar, standard APIs.  As a result, GridCloud offers an easily customized platform that can be extended by application development teams seeking to deploy new capabilities that leverage the framework.

With funding from the ARPAe GridControl project in the GENI program, we also undertook a comprehensive experimental platform validation.  Although academic researchers cannot obtain direct access to the real time network models and power grid state actually used in the bulk power grid, we created a high fidelity simulation, setting up our system in a realistic configuration that uses a network model drawn from public IEEE standard test cases based on the Western Interconnect, and then constructs simulated phasor measurement unit (PMU) data for this model under both steady state and contingency scenarios.  We replay this data in realtime from clock-synchronized "data source" nodes situated in the Internet, and replay the PMU data streams in duplicate or triplicate to overcome network delays.  To explore scale we over-instrument the WEC model, simulating a situation in which each bus in the 198-bus WEC scenario has as many as 30 PMUs deployed on it.  The resulting 18,000-feed configuration allows us to demonstrate the costs, delays and reliability of GridCloud, and to characterize its behavior under the same conditions that would be seen if it were being used to monitor, or control, the entire US Northeast bulk power grid.   We can even simulate PMU failures or inconsistencies between the EMS report of network status and PMU data, which permits us to techniques for localizing the malfunctioning element.

GridCloud is is made up of:

All of these components are available from our download site as open-source technologies, under a standard 3-clause free BSD licensing regime.

Notice that the primary GridCloud applications do not include any power systems applications.  This reflects our view that a platform is like an operating system, on which applications can be launched.  However, as part of our project, we ported two important applications into the system:

GridCloud thus incorporates a number of substantial technologies, presenting them as a single solution that incorporates the elements required to make sure the platform is easily useable in realistic deployment scenarios. 

GridControl, our DOE funding source, enabled us to create GridCloud.  The GridControl project also defined a series of experiments which enabled us to fully qualifiy performance, scalability and other considerations.


Details: IronStack

IronStack Summary
                                                                      Summary of IronStack.

IronStack is an optimal component of GridCloud, aimed at the communication network that connects the PMU and PDC devices to GridCloud's cloud-hosted data center.  Some utilities have in-house solutions, and would not need IronStack, but for those faced with creating such a system or desiring to upgrade a balky and idiosyncratic one, IronStack could be a good option.  Developer Z. Teo has focused on creating an open source preliminary version for GridCloud, but is planning to spin off a commercialized version soon, with 24x7 support and deployment help.

IronStack focuses on the ways that power systems sensor technologies connect to services running on clusters or in cloud-styled data centers, and connects those data centers back to actuators that might control devices in the power grid.   The data center could be a GridCloud instance, but nothing about the solution is specific to power grid uses. IronStack could thus be employed stand-alone or in combination with other kinds of systems. 

The core goals of the IronStack layer are to offer a high-assurance replication and coordination technology for communication-network management and digital data routing.  This is needed because many smart grid applications center on capturing data from sensors deployed over a wide area under challenging conditions, then computing control actions centrally, and then relaying the actions to the actuators that will carry them out.  Power grid operators are not networking specialists, hence owning and operating a dedicated network that must run even during punishing storms, and do so largely unattended, is a major cost. 

IronStack seeks to provide a seamless end-user experience in which the administration of the network is primarily through drag-and-drop actions carried out on system schematics that support simple physical intuition: "We are moving this server from that room to this other location; please adjust the network firewall rules appropriately", or "Make a 3-redundant connection from this PMU to that data collector in GridCloud."  The system automatically uses redundant network routes to avoid disruption if an Internet delay occurs, can encrypt data for security, and will assist untrained network technicians in identifying damaged hardware and repairing it, without them needing to get an IT degree to understand the system.

Details: CloudMake


A key task in operating a power grid analysis platform is ensure that needed applications will be up and running 24x7, even when nodes come and go, or crash, loads change, demands on the system evolve, new applications are launched or terminated, etc. In GridCloud we use a system called CloudMake for this purpose. CloudMake uses a syntax much like the Linux MakeFile syntax, but whereas the standard Linux Make system is normally used to build binaries from source files, CloudMake also is able to sense node and program state.  We do this by creating XML formatted files that describe state.  When those change, CloudMake will run the associated dependent rules to initiate any needed reconfigurations. As new applications are brought into GridCloud, CloudMake only needs to be instructed to manage them.  It will handle the rest, such as auto-restart for system repair after failures, load balancing, mapping of the computation to the cloud computing nodes, and more. A built-in SMT constraint solver offers a simple way to tap into optimization software that will lay out components in a way that minimizes costs and, after failure, ensures the quickest possible self-repair.  Despite its extensive functionality, CloudMake is very easy to use.

Details: Freeze Frame File System

The Freeze-Frame File System (FFFS) offers secure, strongly consistent real-time mirrored data sharing. The name evokes the image of a film strip, and this intuition is appropriate because FFFS offers a novel real-time snapshot capability.  FFFS allows applications to explore the evolution of the power grid network state at any desired temporal resolution: in contrast to standard parallel computing tools that take a data set from some single instant in time, and apply large numbers of CPUs to carry out a computation on that single data set.  Indeed, with FFFS we can also parallelize by spawning a set of tasks that access the power grid state at various points in time, permitting analysis of trends that evolved over a period of time, and we can leverage the massive on-demand parallelism of the cloud.  The FFFS system also handles data replication, maintains a remote backup, and is designed to detect any tampering, so that the past state can be used as a trustworthy record of precisely how the power grid state evolved over time.

Details: GridCloud Collaboration System

GridCloud diagram

The GridCloud Collaboration Tool is a tool for creating a kind of sharable virtual iPad. The basic configuration displays the current power network and can show the status of any line at a click. Various "apps" can then be dragged onto the network and this triggers actions, like a transient stability analysis or listing "similar network states seen in the past".   The resulting application can then be shared with operators in the same ISO, but also with those in a neighboring ISO or distribution network.  Over time, we see the collaboration tool as a powerful and extensible framework. When data is shared, the tool guarantees real-time consistency as needed. 

We envision many uses for the tool because power systems operators are cautious about what they share, limiting the shared system state to data that should be continuously available to their collaborators and in a consistent real-time state.  However, if a contigency arises there may be a sudden need to share data that is not normally a part of this body of normally shared state.  In such cases, GridCloud's collaboration tool can be used to dynamically pull normally proprietary data into its collaborative framework for a brief period of time.  The resulting virtual iPad can be shared selectively.  Later when the problem is resolved, closing the application has the effect of permantly withdrawing access to the sensitive data.


Details: Vsync Cloud Computing Toolkit

One of the most difficult challenges faced by software developers who wish to create highly secure, highly available applications is that existing development environments are poorly matched to such goals.  Indeed, many who use the cloud become aware of its CAP philosophy, in which consistency is sacrificed to achieve higher performance and better availability during network failures (partitioning).  Yet inconsistency is dangerous when managing the power grid: it could lead to errors in which equipment damage might ensue, or costly outages.

The Vsync system is the product of a 25-year DARPA research funded effort to create the ultimate software platform for assisting in creation of fault-tolerant and secure distributed applications with strong consistency.  Vsync takes the widely popular state machine replication methodology in which every replica of an application starts in a consistent initial state (obtained by loading a checkpoint that was created by some other replica that itself was in a consistent state when it made that checkpoint), then applying the same events in the same order.  A majority-progress rule is used to avoid split-brain problems in the event of a network partitioning event.

Vsync isn't a magic wand: it takes the form of an open-source software library, coded in the Microsoft C# language but useful from any program that can run on a Microsoft platform, including C# but also languages such as C++/CLI, IronPython, IronRuby, standard C, F#, J#, etc (.NET supports more than 40 languages and in principles, any could leverage Vsync).  Through the Mono cross-compiler, Vsync can also be used on Linux platforms.  In our development work on GridCloud, we made heavy use of Vsync, which endows CloudMake with its fault-tolerance and consistency guarantees, helps FFFS carry out consistent data replication at the blinding speeds feasible with modern RDMA networking hardware, and is integrated into the collaboration tool as its preferred data transport solution when a virtual iPad is being shared and receives data updates in realtime as the network state evolves or other data is captured and rendered onto the shared display.

But many GridCloud users would be completely unaware of Vsync, because users of technologies like these three aren't exposed directly to any of the Vsync APIs.  Thus a developer who creates an HPC application for the smart grid and then wishes to port it to GridCloud could certainly employ Vsync to extend their solution with replication features, but could equally well ignore Vsync completely and simply load their application into our cloud environment and then program the needed control policies into CloudMake, which would then administer the solution for them.  In such cases Vsync orchestrates the 24x7 management and availability needed in the cloud-hosted solution, yet the coding style used to create it remains completely standard: that HPC application can be created "at home" using any tools the developer is familiar with.

Vsync (coupled with the previous Isis2 downloads) has been downloaded more than 5000 times from, our web distribution site.  Video instructional materials and extensive documentation can be found on that site.

Vsync Library
Vsync Library: our (hidden) secret sauce

The Washington State University Grid Simulator and Linear State Estimator

WSU's additional role has been to port a number of their power grid tools to run on GridCloud. These include GridStat, used internally by the WSU software solutions to share grid state among computational applications, GridSim to simulate PMU data from a given network model, and the OpenPDC framework for PMU registry information. More information about WSU's GridStat framework can be found here.

GridCloud Communication Design
GridCloud communication design, with experimental and instrumentation aspects at the left and right sides, and the GridCloud platform itself in the center box.

Details: GridSim

GridSim and GridStat diagram
GridSim architecture

GridSim is a real-time end-to-end power grid simulation package designed to explore a wide range of scenarios at the resolution of a 30 sample per second data rate (which can be adjusted to other values if needed). The role of this application is to simulate power grid operation, control and communications at grid-wide scale (e.g. the western interconnection), as well as provide utilities the ability to explore new equipment deployments. Example deployments would include the ability to simulate large-scale phasor measurement unit (PMU) installation, as well as the ability to simulate the possible uses for power applications able to utilize the vast quantities of data generated in such a situation. With the objective of providing tools to simulate real world equipment usage, and the ability to be used in conjunction with readily available utility industry equipment, the GridSim simulation package uses the industry standard C37.118 data format for all streaming measurement data.

The GridSim platform is composed of a real-time transient power stability simulator, specially modified to output streaming data. The streaming data is then encoded into C37.118 and sent to several substation processes. At each of these processes, the data is aggregated as would be done in a real power utility substation, as well as being sent to any of the substation level power applications that are running. Both the raw substation data and any power application outputs are then published to GridStat.

The published data is then available to any subscribers of GridStat, including the two applications included in the simulation project, the Hierarchical State Estimator and the Oscillation and Damping Monitor.

Details: Linear State Estimator

The Washington State University hierarchical linear state estimator collects incoming PMU data in the form of PDC flows, fits the data against the network model, and then outputs a continuously updated visualization of power system state.  A further option is to record the evolving state estimates as a series of files for later offline analysis.  Internally, the Linear State Estimator depends on GridStat as the communications bus combining its components, OpenPDC as a registry of PDC and PMU definitions and status data, and the LSE itself, implemented by Anjan Bose's group at WSU.  A widely cited research platform, the WSU LSE solution can be obtained in open source from from the developers, but has also been commercialized by Alstom and is available with comprehensive documentation and support from that company.

Click here to open video in new window.

In this experiment we show GridCloud being configured with 3 replicas, each of which runs the WSU State Estimator with 291 substations, on a total of 144 Amazon EC2 compute instances. Midway through the run, a power line trips, and the state estimator tracks the resulting transients and then shows the new state. Communication network delays, scheduling delays and node failures on Amazon AWS are all tolerated automatically by the platform, which continuous to deliver steady behavior even as such events impact the data stream or the nodes used to collect data and compute the state estimate.

Details: GridStat

GridStat logo
Read more and view recent publications at

GridStat is a communications product developed by WSU and commercialized by GridStat Inc.  This tool is used internally in GridSim and in the Linear State Estimator.  The technology is a publish-subscribe message bus that employs APIs compatible with the widely recognized NASPInet standards.  Further, GridStat is designed to support the major power systems data formats, such as the standard representation of PMU and PDC data, and can automatically carry out rate transformations.

GridStat is not a core component of GridCloud, although we do use it in the Washington State University GridSim and LSE systems.  Further, although GridStat is a digital data communications tool and hence has some overlap with IronStack, we do not think of the technologies as competiting with one-another.  IronStack is a lower-level capability for us: it supports network routing on SDN networks, but the end-user sees a standard Internet environment and uses the standard TCP and IP protocols in their software.  Connectivity is highly robust where authorized and active and no connectivity can occur where unauthorized.  In contrast, although GridStat also has security features, the best way to think of GridStat is by understanding it as a higher-level application-oriented communications bus (message bus) complying with the NASPInet design and standards for power systems data transport betwen power systems applications that employ a publish-subscribe model.  In effect, GridStat is a power systems specialist, with many features specific to power systems data formats and data rates. 

Developed as a research system at Washington State University, GridStat plays an important role in the Washington State University applications that run within the GridCloud demo.  These use GridStat as an internal data delivery overlay network framework that takes incoming data from GridCloud's data collectors, and then delivering rate-based updates of sensor variables with a wide range of QoS+ guarantees (latency, rate, availability) in a publish-subscribe model.

GridStat’s data delivery plane is a flat graph of forwarding engines (FEs), each of which stores state for every subscription whose updates it forwards. FEs forward sensor updates on outgoing links at the highest rate any subscriber downstream requires, using a Delay-EDD queuing discipline. GridStat’s management plane is implemented as a hierarchy that can be mapped onto the natural hierarchy of the power grid. Each node in the hierarchy can contain policies for resource permissions, security permission, aggregation, and adaptations to anomalies. With these, the management plane calculates the paths required for the data delivery (with the given number of disjoint paths) and updates the forwarding tables in the FEs.

As noted, GridStat was commercialized in 2014 through a WSU spin-off called  Information and contacts can be found on that company's welcome page.


1. In the wake of the terrorist group often referred to as ISIS in the media, the entire system has been renamed and the project moved. Please download the system from The version of the system will not be maintained anymore, but will remain live for now. As of December 2015, the two sites were identical; going forward, new features will appear in and will gradually go stale.