GRIDLAB-D, NEXT GENERATION SIMULATION SOFTWARE

The coupling of engineered systems and markets impacts broader and broader areas of the electric power industry. Energy trading products cover shorter time periods and demand response programs are moving more and more toward real-time pricing. Market-based trading activity impacts ever more directly the physical operation of the system and the boundaries of these coupled systems extend beyond the traditional boundaries of utility-centric energy system operations. To address the gaps in our simulation capabilities, the US Department of Energy is developing GridLAB-D at Pacific Northwest National Laboratory in collaboration with industry and academia. This is the first of a new generation of power distribution system simulation software.

GridLAB-D combines end-use and power distribution automation models with other software tools, resulting in a powerful tool for power system analysis capable of running on high-performance computers. It is a flexible simulation environment that can further be integrated with a variety of third-party data management and analysis tools.

At its core, GridLAB-D has an advanced algorithm that can determine the simultaneous state of millions of independent devices, each of which is described by multiple differential equations solved only locally for both state and time. The advantages of this algorithm over traditional finite difference-based simulators are: 1) it is much more accurate; 2) it can handle widely disparate time scales, ranging from sub-seconds to many years; and 3) it is easy to integrate with new modules and third-party systems. The advantage over traditional differential-based solvers is that it is not necessary to integrate all the device's behaviors into a single set of equations that must be solved.

At its simplest GridLAB-D can examine in detail the interplay of every part of a distribution system with every other. GridLAB-D does not require the use of reduced-order models, so the danger of erroneous assumptions is averted. GridLAB-D is more likely to find problems with programs and business strategies than any other tool available. It is therefore an essential tool for industry and government planners.

GridLAB-D Current Status

The GridLAB-D system currently implements modules to perform the following functions:

Power flow and controls, including distributed generation and storage
End-use appliance technologies, equipment and controls
Data collection on every property of every object in the system
Boundary condition management including weather and electrical boundaries.

Power Flow

The power flow component of GridLAB-D is separated into a distribution module and a transmission module. While the distribution systems are the primary focus of GridLAB-D, the transmission module is included so that the interactions between two or more distribution systems can be simulated.

Transmission System. The transmission system is included to allow for the interconnection of multiple distribution feeders. If a transmission module was not included each distribution system could only be solved independently of other systems. While distribution systems can be solved independently, as is common in current commercial software packages, GridLAB-D will have the ability to generate a power flow solution for multiple distributions systems interconnected via a transmission or sub-transmission network. Traditionally the ability to examine interactions at this level has been limited by computational power. To address this limitation, GridLAB-D is being developed for execution on multiple processor systems. In the current version of GridLAB-D the AC power flow solution method used for the transmission system is the Gauss-Seidel (GS) method, chosen for its inherent ability to solve for poor initial conditions, and to operate well in multiprocessor environments.

Distribution System. In order to accurately represent the distribution system the individual feeders are expressed in terms of conductor types, conductor placement on poles, underground conductor orientation, phasing, and grounding. GridLAB-D does not simplify the distribution system component models.

The distribution module of GridLAB-D utilizes the traditional forward and backward sweep method for solving the unbalanced 3-phase AC power flow problem. This method was selected in lieu of newer methods such as current injection methods for the same reasons that the GS method was selected for the transmission module; converging in the fewest number of iterations is not the primary goal. Just as with the transmission module the distribution modules will only start with a "flat start" at initialization and all subsequent solutions will be derived from the previous time step.

Metering is supported for both single/split phase and three phase customers. Support for reclosers, islanding, distributed generation models, and overbuilt lines are anticipated in coming versions.

The following power distribution system components are implemented and available for use:

Overhead and underground lines
Transformers
Voltage regulators
Fuses
Switches
Shunt capacitor banks

Commercial and Residential End Use

Commercial and residential end-uses are implemented using the Equivalent Thermal Parameters model. These are differential models solved for both time as a function of state and state as a function of time. Currently implemented residential end-uses are:

Electric water heaters
Refrigerators
Dishwashers
Cloth washers and dryers
Ranges and microwaves
Electric plugs and lights
Internal gains
House loads (including air conditioning, heat pumps, and solar loads)

Commercial loads are simulated using an aggregate multi-zone Energy Technology Perspectives (ETP) model that will be enhanced with more detailed end-use behavior in coming versions.

Expanding GridLAB-D Capabilities

New planned capabilities for GridLAB-D include:

Extended quasi-steady state time-series solutions
Distributed energy resource models, including appliance-based load shedding technology and distributed generator and storage models
Business and operations simulation tools
Retail market modeling tools, including contract selection, to study consumer behaviors such as daily, weekly, and seasonal demand profiles, price response, and contract choice
Energy operations such as distribution automation, load-shedding programs, and emergency operations.
Models of SCADA controls, and metering technologies
External links to Matlab™, MySQL™, Microsoft™ Excel™ and Access™, and text-based tools
External links to SynerGEE's power distribution modeling system
Efficient execution on multi-core and multi-processor systems
Interfaces to industry-standard power systems tools and analysis systems
Graphical user interface for creating input models
Graphical user interface for execution and control of the simulation

The addition of these new capabilities combined with the coupling of GridLAB-D with other tools will offer unparalleled analysis capabilities, including the ability to study phenomenon such as:

Rate cases to determine whether the price differentials are sufficient to encourage customer adoption
The potential and benefit of deploying distributed energy resources, such as Grid-Friendly appliances
Business cases for technologies like automated meter reading, distribution automation, retail markets, or other late-breaking technologies
Interactions between multiple technologies, such as how under-frequency load-shedding remedial action strategies might interact with appliance-based load-relief systems
Distribution utility system behavior ranging from a few seconds to decades, simulating the interaction between physical phenomena, business systems, markets and regional economics, and consumer behaviors.

Simulation results will include many power system statistics, such as reliability metrics (e.g., CAIDI, SAIDI, SAIFI), and business metrics such a profitability, revenue rates of return, and per customer or per line-mile cost metrics.

GridLAB-D Applications

Today's power systems simulation tools don't provide the analysis capabilities needed to study the forces driving change in the energy industry. The combined influence of fast-changing information technology, novel and cost-effective distributed energy resources, multiple and overlapping energy markets and new business strategies result in very high uncertainty about the success of these important innovations. Concerns expressed by utility engineers, regulators, various stakeholders, and consumers can be addressed by GridLAB-D. Some example uses included:

Rate structure analysis. Multiple differentiated energy products based on new rate structure offerings to consumers is very attractive to utilities because it creates the opportunity to reveal demand elasticity and gives utilities the ability to balance supplier market power in the wholesale markets. The challenge is designing rate structures that are both profitable and attractive to consumers. GridLAB-D will provide the ability to model consumer choice behavior in response to multiple rate offerings (including fixed rates, demand rates, time-of-day rates, and real-time rates) to determine whether a suite of rate offerings is likely to succeed.
Distributed resources. The advent of new distributed energy resource DER technologies, such as on-site distributed generation, BCHP and Grid-Friendly™ appliance controls creates a number of technology opportunities and challenges. GridLAB-D will permit utility managers to better evaluate the cost/benefit trade-off between infrastructure expansion investments and distributed resources investments by including the other economic benefits of DER (e.g., increase wholesale purchasing elasticity, improved reliability metrics, ancillary services products to sell in wholesale markets).
Peak load management. Many peak-shaving programs and emergency curtailment programs have failed to deliver the expected benefits. GridLAB-D can be calibrated to observed consumer behavior to understand its interaction with various peak shaving strategies. The impact of consumer satisfaction on the available of peak-shaving resources can be evaluated and a more accurate forecast of the true available resources can be determined. GridLAB-D will even be able to evaluate the consumer rebound effect following one or more curtailment or load-shed events in a single day.
Distribution automation design. GridLAB-D will offer capabilities that support the design and analysis of distribution automation technology, to allow utilities to offer heterogeneous reliability within the same system but managing power closer to the point of use.

Open Source Distribution

GridLAB-D will be released as an open-source system to a limited group of charter developers in early 2008. This release was the basis for selected partners to develop additional proposed modules. A more public release is planned for October 2008, making GridLAB-D widely available to the public under open-source licensing. Industry partners and collaborators will be invited to examine and test the system, and submit new modules to the open-source library. When modules are vetted and approved by the partners, they will be added to the standard release. Unvetted modules will be available as add-ons from the open-source repository, but will not be installed as a part of the standard download available from the repository.

Developers will be permitted to create proprietary modules, however they will be required to distribute required GridLAB-D components free of charge, and provide prominent acknowledgement of the authors and funding used to create those modules. Any changes made to the open-source modules comprising the GridLAB-D system must be resubmitted to the open-source library for distribution.

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