DETERMINING LOCATION AND SIZING OF A NEW POWER UNIT WITHIN A CURRENT SYSTEM ARCHITECTURE OF A POWER SYSTEM OR A GRID

A system determines a location and a size of a new power generation or power regulating unit within a current system architecture of a power system including a plurality of power generation units. The system comprises a controller including a processor and a memory, computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to execute a hybrid algorithm as a combination of a data-driven algorithm and a model-based algorithm to determine an optimal location and size of the new power generation or power regulating unit. The data-driven algorithm encodes a location and a size information. The controller to enable the model-based algorithm to optimize performance of a selected location and size of the new power generation or power regulating unit, which is based on a linearized system or a nonlinear system to provide guidance for the data-driven algorithm to incorporate physical rules and verify a new system architecture.

BACKGROUND

Aspects of the present invention generally relate to determining location and sizing of a new power generation or power regulating unit within a current system architecture of a power system or a power grid.

2. Description of the Related Art

Integration and installation of new Distributed Energy Resources (DERs) (like solar farms, wind turbines, energy storage systems, fuel cells, different types of generators, etc.) has become more common and more frequent in recent years. This is largely due to international recognition of the need for renewable energy, greenhouse gas reduction, sustainability targets and so on. Utilities and Independent System Operators (ISOs) are constantly adding more generation units or replacing conventional generators with renewable energy resources. This has a severe impact on the dynamic security of the power system especially if the power system gets close to 100% DER peak generation. Dynamic security of a power system refers to its ability to withstand single failures like a power line or a power plant without power outages. Therefore, utilities and ISOs install additional assets like grid-forming batteries or synchronous condensers to improve the dynamic security of the power system. Throughout this disclosure, we call these assets “grid stabilizers”. Two of the most challenging problems in this process is the allocation and sizing of the new grid stabilizers. The locations and sizes of these new installations are crucial because they can significantly change the dynamics and behavior of the power system, even creating additional challenges for the grid operator to maintain stability and reliability of the system. Moreover, these grid stabilizers are very expensive (high CAPEX) and do not create direct benefits during operation (low OPEX). Hence, utilities and ISOs try to minimize the number and size for grid stabilizers. To minimize CAPEX, the allocation and sizing of both DERs and grid stabilizers have to be optimized together. For simplicity of presentation, the term DER is used for both DERs and grid stabilizers.

Existing systems consider placing the DERs at existing nodes, without considering connecting a new DER at a new node. The optimization objectives are minimizing power loss, cost and load curtailment, enhancing voltage profile, and reliability, improving voltage stability, maximizing profit and reducing purchase. Four types of algorithms that are used: heuristic methods, mathematical programming algorithms, analytical approaches and hybrid algorithm with a combination of the two or several methods aforementioned. None of the existing systems consider dynamic security as an optimization objective to ensure system is stable during N−1 contingencies.

Therefore, there is a need to optimally determine location and sizing of a new power generation or power regulating unit within a current system architecture of a power system or a power grid.

SUMMARY

Briefly described, aspects of the present invention relate to determining location and sizing of a new power generation or power regulating unit within a current system architecture of a power system or a power grid. The primary objective of present invention is to make sure when new DERs are installed, the power system is still able to maintain resilient against N−1 contingencies, therefore minimizing the chances of power outages. The proposed framework provides the grid planning and operation personnel a way to assess and optimize the resiliency of the future power system with different generation mix and different renewable integration percentages. A linear model is generated for this approach because dynamic security optimization relies on the linear model. A simulation-based verification step is performed and this step usually requires a simulation software and a nonlinear simulation model. The present invention addresses the Dynamic Security Optimization to achieve N−1 security but it does also include the allocation and sizing of assets. A system considers dynamic security as an optimization objective to ensure that the system is stable during N−1 contingencies. Through its modular design, a system offers a wide variety of analysis functions for the planning, design and operation of power systems. For example, a linear model is generated for this approach because dynamic security optimization relies on the linear model. A simulation-based verification step is included, which requires a simulation software and a nonlinear simulation model.

In accordance with one illustrative embodiment of the present invention, a system is configured to determine location and sizing of a new power generation or power regulating unit within a current system architecture of a power system including a plurality of power generation units. The system comprises a controller including a processor and a memory, computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to execute a hybrid algorithm as a combination of a data-driven algorithm and a model-based algorithm to determine an optimal location and size of the new power generation or power regulating unit. The data-driven algorithm encodes a location and a size information. The controller to enable the model-based algorithm to optimize performance of a selected location and size of the new power generation or power regulating unit, which is based on a linearized system or a nonlinear system to provide guidance for the data-driven algorithm to incorporate physical rules. The controller to verify a new system architecture with the new power generation or power regulating unit installation and optimized control parameters to ensure the power system is stable and reliable so that the system can endure a single point failure.

In accordance with another illustrative embodiment of the present invention, a method is provided for determining location and sizing of a new power generation unit within a current system architecture of a power system comprising a plurality of power generation units. The method comprises providing a controller including a processor and a memory, and providing computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to iteratively loop between a Reinforcement Learning (RL)-based asset allocation and sizing algorithm which is a data-driven approach and a Dynamic Security Optimization (DSO) algorithm which is a model-based approach to search for an optimal solution via a hybrid approach as a combination of the data-driven approach and the model-based approach such that results of the optimal solution are then verified by a power system nonlinear simulator. The RL-based asset allocation and sizing algorithm encodes a location and size information in a graph-based representation. The RL-based asset allocation and sizing algorithm also incorporates expert knowledge not only for initial selections but allows a workflow that guides the RL-based asset allocation and sizing algorithm to desired locations and size when they are hard to encode. The controller to enable the DSO algorithm which is based on a linearized system to provide guidance for the RL-based asset allocation and sizing algorithm to follow physical rules. The controller to verify a new system architecture with the new power generation unit installation and optimized control parameters to ensure the power system is stable and reliable during all types of N−1 contingencies so that the power system can endure a single point failure.

In accordance with one illustrative embodiment of the present invention, a system is configured to determine location and sizing of a new power generation unit within a current system architecture of a power system comprising a plurality of power generation units. The system comprises a controller including a processor and a memory, and computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to iteratively loop between a Reinforcement Learning (RL)-based asset allocation and sizing algorithm which is a data-driven approach and a Dynamic Security Optimization (DSO) algorithm which is a model-based approach to search for an optimal solution via a hybrid approach as a combination of the data-driven approach and the model-based approach such that results of the optimal solution are then verified by a power system nonlinear simulator. The RL-based asset allocation and sizing algorithm encodes a location and size information in a graph-based representation. The RL-based asset allocation and sizing algorithm also incorporates expert knowledge not only for initial selections but allows a workflow that guides the RL-based asset allocation and sizing algorithm to desired locations and size when they are hard to encode. The controller to enable the DSO algorithm which is based on a linearized system to provide guidance for the RL-based asset allocation and sizing algorithm to follow physical rules. The controller to verify a new system architecture with the new power generation unit installation and optimized control parameters to ensure the power system is stable and reliable during all types of N−1 contingencies so that the power system can endure a single point failure.

DETAILED DESCRIPTION

To facilitate an understanding of embodiments, principles, and features of the present invention, they are explained hereinafter with reference to implementation in illustrative embodiments. In particular, they are described in the context of a system that is configured to determine location and sizing of a new power generation or power regulating unit within a current system architecture of the system including a plurality of power generation units. Embodiments of the present invention, however, are not limited to use in the described devices or methods.

These and other embodiments of a system for asset allocation and sizing for dynamic secure power systems according to the present disclosure are described below with reference toFIGS.1-7herein. Like reference numerals used in the drawings identify similar or identical elements throughout the several views. The drawings are not necessarily drawn to scale.

Consistent with one embodiment of the present invention,FIG.1represents a block diagram of a system102for asset allocation and sizing for dynamic secure power systems in accordance with an exemplary embodiment of the present invention. The system102comprises a computing environment103and a programming software and simulation platform104. The programming software and simulation platform104comprises MATLAB105(1) and SIMULINK105(2). MATLAB105(1) and SIMULINK105(2) are one example of the programming software and simulation platform104.

A dynamic secure power system can maintain stability after N−1 contingency. In other words, a power system that is able to withstand at all times an unexpected failure or outage of a single system component, has an acceptable reliability level. The system102considers dynamic security as an optimization objective to ensure the system102is stable during N−1 contingencies. An N−1 contingency is a sequence of events consisting of a loss of a single generator or a transmission component in a grid. An N−1 contingency analysis is performed to assure secure operation of a grid while controlling the active power flow. The primary objective of the present invention is to make sure when new Distributed Energy Resources (DERs) are installed, a power system is still able to maintain resilient against N−1 contingencies, therefore minimizing the chances of power outages. The proposed framework provides a grid planning and operation personnel a way to assess and optimize the resiliency of a future power system with different generation mix and different renewable integration percentages.

The system102is configured to determine location and sizing of a new power generation or power regulating unit such as a new Distributed Energy Resource (DER)107within a current system architecture110(1) of a power system106including a plurality of power generation units such as a plurality of Distributed Energy Resources (DERs)107(1-n) in accordance with an exemplary embodiment of the present invention. Examples of the plurality of Distributed Energy Resources (DERs)107(1-n) include solar farms, wind turbines, energy storage systems, fuel cells, and different types of generators, etc.

The computing environment103comprises a controller115including a processor117(1) and a memory117(2). The system102further comprises computer-readable logic code120stored in the memory117(2) which, when executed by the processor117(1), causes the controller115to execute a hybrid algorithm125as a combination of a data-driven algorithm127(1) and a model-based algorithm127(2) to determine an optimal location130(1) and an optimal size130(2) of the new DER107. The data-driven algorithm127(1) encodes location and size information135. The computer-readable logic code120comprises programmable and executable software instructions.

The computer-readable logic code120when executed by the processor117(1) further causes the controller115to enable the model-based algorithm127(2) to optimize performance of a selected location and size137of the new DER107, which is based on a linearized system or a nonlinear system to provide guidance for the data-driven algorithm127(1) to incorporate a plurality of physical rules140. Examples of the physical rules140include power flow constraints, protection device requirements, operational limit, transient behavior, and any other characteristics of the system102that are not captured in a linearized model. The computer-readable logic code120when executed by the processor117(1) further causes the controller115to verify a new system architecture110(2) with the new DER107installation and optimized control parameters145to ensure the power system106is stable and reliable so that the power system106can endure a single point failure.

The computer-readable logic code120comprises a power system nonlinear simulator150to build up a nonlinear simulation of the power system106. The model-based algorithm127(2) is configured to derive a corresponding linearized system.

The computer-readable logic code120stored in the memory117(2) which, when executed by the processor117(1), causes the controller115to iteratively loop between a Reinforcement Learning (RL)-based asset allocation and sizing algorithm which is a data-driven approach and a Dynamic Security Optimization (DSO) algorithm which is a model-based approach to search for an optimal solution via a hybrid approach as a combination of the data-driven approach and the model-based approach such that results of the optimal solution are then verified by the power system nonlinear simulator150.

The RL-based asset allocation and sizing algorithm encodes a location and size information in a graph-based representation155. The RL-based asset allocation and sizing algorithm also incorporates the physical rules140not only for initial selections but allows a workflow that guides the RL-based asset allocation and sizing algorithm to desired locations and size when they are hard to encode. The controller115to enable the DSO algorithm which is based on a linearized system to provide guidance for the RL-based asset allocation and sizing algorithm to follow the physical rules140. The controller115to verify the new system architecture110(2) with the new DER107and the optimized control parameters145to ensure the power system106is stable and reliable during all types of N−1 contingencies so that the power system106can endure a single point failure.

In operation, the processor117(1) to execute a model-based algorithm engine without new assets including the new DER107. The model-based algorithm engine to send updated models and results of the model-based algorithm to a data-driven algorithm engine. The processor117(1) to execute the data-driven algorithm engine with a graph-based representation to determine optimal asset locations and sizing incorporating the expert knowledge. The data-driven algorithm engine sends an optimized asset location and sizing to the model-based algorithm engine. The processor117(1) executes the model-based algorithm engine with new asset locations and sizing. The processor117(1) to send the optimized control parameters145to the power system nonlinear simulator150for simulation and verification. The processor117(1) to simulate a nonlinear system and send results with system status information back to the model-based algorithm engine.

A combination of two techniques is provided to solve power system assets allocation and sizing problem in that a reinforcement learning and graph-based optimization framework to determine candidate location and sizing and a dynamic security optimization of power system to guarantee system is resilient against N−1 contingencies. In other words, the system102integrates the tasks of stability study and controller tuning (which usually happens during commissioning) into the system designing phase. The system102considers resiliency and dynamic security conditions when designing DER locations and sizes.

A hybrid (data-driven+model-based) approach provides more realistic and feasible optimization results compared to pure data-driven approaches. On the one hand, reinforcement learning approach finds the optimal solution without simplifying assumptions, combining prior knowledge and both simulation and real data. On the other hand, dynamic security optimization and nonlinear simulation verification are important model-based steps to set the physical constraints for an optimization problem and provide a sanity check.

Referring toFIG.2, it illustrates an overview of a proposed methodology205in accordance with an exemplary embodiment of the present invention. The proposed methodology205comprises a reinforcement learning (RL)-based asset allocation and sizing algorithm207as further shown inFIG.3, a dynamic security optimization (DSO) algorithm210as further shown inFIGS.4A-4Cand a power system nonlinear simulator212as further shown inFIG.5.

A, B and C represent the three main components of the proposed methodology205. The core innovation is the interconnection between these three components A, B, and C. They provide necessary data for each other and also constraint each other to make sure the final solution is optimal.

The reinforcement learning (RL)-based asset allocation and sizing algorithm207utilizes RL techniques over graphs and solves the location and sizing of the new DER107for the following challenges:

Possible locations and sizes: The underlying optimization problem have discrete (location) and continuous (size) variables.

Type of generation for the new DER107and its characteristics: The assets have different characteristics (grid-forming, grid-following, fast response, slow response, etc.) which makes the optimization problem difficult.

System information (model, topology, etc.): The model has dynamics and certain topology structure. The reinforcement learning (RL)-based asset allocation and sizing algorithm207incorporates this dynamics and topology structure.

Historical operation data (e.g., Phasor Measurement Unit (PMU)/Remote Terminal Unit (RTU) data, event logs, operator notes, etc.): The reinforcement learning (RL)-based asset allocation and sizing algorithm207uses prior knowledge to learn faster and real measurement data to balance the synthetic simulation data.

Financial and environmental impact: The reinforcement learning (RL)-based asset allocation and sizing algorithm207not only considers the financial aspect but also takes into the account of environmental impact.

N−1 contingencies analysis: Asset allocation and sizing by the reinforcement learning (RL)-based asset allocation and sizing algorithm207includes stability analysis so that the power system106can endure a single point failure.

The reinforcement learning (RL)-based asset allocation and sizing algorithm207enables the system102to perform financial and environmental decision-making for robust asset allocation and sizing with discrete/continuous choices over internal dynamics model and topological structures utilizing previous knowledge and data. It utilizes reinforcement learning techniques focusing on a mixed-type optimization with graph structure and specialized on power system with DER.

The dynamic security optimization (DSO) algorithm210is provided for optimization of power system control parameters: in this step, the system102solves dynamic security optimization problem to make sure the power system106with new DERs107can withstands almost all N−1 contingencies (loss of any powerline, transformer, or generator) without major load shedding or a complete blackout of the system.

The power system nonlinear simulator212: in this step, the system102verifies the new system architecture110(2) with the new DER107installations and optimized control parameters to make sure the power system106is stable and reliable during all types of N−1 contingencies.

Turning now toFIG.3, it illustrates a reinforcement learning (RL)-based asset allocation and sizing algorithm305in accordance with an exemplary embodiment of the present invention. The reinforcement learning (RL)-based asset allocation and sizing is based on: possible locations, possible sizes, type of generation units and characteristics, historical operation data, grid topology, optimization objectives, system model (linear/nonlinear) and/or other factors: financial, environmental, etc.

FIG.4A-4Cillustrates a dynamic security optimization (DSO) algorithm405in accordance with an exemplary embodiment of the present invention. Dynamic Security Optimization of power system control parameters determines if the chosen allocation and sizing is dynamic secure.

In this step inFIG.4A, the system102solves a dynamic security optimization problem to make sure the power system106with new DERs can withstand almost all N−1 contingencies (loss of any powerline, transformer, or generator) without major load shedding or a complete blackout of the system106. In these two graphs, a left graph407shows that frequency signals of the DERs oscillation are a lot during operation. A right graph410shows a frequency domain response that has very significant resonant peaks.

InFIG.4B, a left graph415shows that the resonant peaks of the frequency domain response have been greatly suppressed, compared to the graph from previousFIG.4A, therefore improving system stability. A right graph420shows that frequency signals of the DERs oscillation much less during operation, i.e., oscillations have been damped, compared to the graph from previousFIG.4A. These two graphs415,420show the effectiveness of the DSO algorithm405.

FIG.4Cis a mathematical representation of the DSO algorithm405on a high level. Details of the DSO algorithm405are well known.

As seen inFIG.5, it illustrates a power system nonlinear simulator505in accordance with an exemplary embodiment of the present invention. This is an example of a power system nonlinear simulation component. The simulation can be performed on any power system simulation platform (including software/hardware) that has the capabilities to run simulation with detailed power system models for an entire transmission or distribution system. Such platform include: Simulink, OPAL-RT, RTDS, or any other lab simulation environment that is set up to do so.)

For example, Simulink is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems. MATLAB® and Simulink® can be used together to combine textual and graphical programming to design a system in a simulation environment. One can directly use the thousands of algorithms that are already in MATLAB. Or simply add MATLAB code into a Simulink block or Stateflow® chart. One can use MATLAB to create input data sets to drive simulation. Also run thousands of simulations in parallel. Then analyze and visualize the data in MATLAB.

As shown inFIG.6, it illustrates a process workflow600of asset allocation and sizing for dynamic secure power systems in accordance with an exemplary embodiment of the present invention.

Step601: Build up nonlinear simulation of the system102.

Step602: Derive a corresponding linearized system.

Step603: Execute a Dynamic Security Optimization (DSO) algorithm without new assets.

Step604: Send updated models, results of the DSO algorithm to a “Reinforcement Learning-based asset allocation and sizing” engine.

Step605: Execute a RL algorithm with a graph-based representation to determine optimal asset locations and sizing incorporating expert knowledge.

Step606: Send optimized asset location and sizing to the DSO algorithm.

Step607: Execute the DSO algorithm with new asset locations and sizing

Step608: Send optimized control parameters to the “Power System Nonlinear Simulator”150for simulation and verification

Step609: Simulate the nonlinear system and send results and system status information back to the DSO algorithm.

Steps601-603are initialization steps that will only run once during the entire process. Steps604-609form a loop. This approach iteratives between “RL-based asset allocation and sizing” and “Dynamic Security Optimization” to search for an optimal solution. The RL algorithm encodes the location and size information in a graph-based representation. It also incorporates expert knowledge not only for initial selections but allows an operator to guide the RL algorithm to desired locations and size when they are hard to encode. DSO is based on a linearized system so it is a model-based approach, which provides guidance for the RL algorithm to follow the physical rules140. Steps608and609are verification steps based on nonlinear simulation, this is to guarantee the performance of the system102and to make sure the solution is feasible and practical.

Steps605and606are executed by a reinforcement learning (RL)-based asset allocation and sizing algorithm engine. Steps602-604and607are executed by a Dynamic Security Optimization (DSO) algorithm engine. Steps601,608and609are executed by a Power System Nonlinear Simulator engine.

InFIG.7, it illustrates a logic code705including a Reinforcement Learning (RL)-based asset allocation and sizing algorithm engine710and a Dynamic Security Optimization (DSO) algorithm engine715that handle a new DER720installation request to determine a location and a size of the new DER720in accordance with an exemplary embodiment of the present invention.

A dynamic secure power system702comprises existing DERs720(1-9). The new DER720is to be located and sized by the logic code705. A located and sized new DER720(10) is shown inFIG.4.

FIG.7shows an example of when the system102is used. In a transmission or distribution power system, when a new DER installation request comes in, the system102will determine the best size and location of the DER to make sure all requirements are satisfied. In this particular case, a grid operator enters the request of New DER #1 installation in the system702with all the requirements. Those requirements could include possible installation sites, stability margins, N−1 contingency types, cost limits, environmental concerns, etc. Then the system102executes a hybrid algorithm and propose to install new DER #1 at the northeast corner of the system702with proper size.

With regard toFIG.8, it illustrates a schematic view of a flow chart of a method800of determining location and sizing of a new power generation unit within a current system architecture of a power system comprising a plurality of power generation units in accordance with an exemplary embodiment of the present invention. Reference is made to the elements and features described inFIGS.1-7. It should be appreciated that some steps are not required to be performed in any particular order, and that some steps are optional.

The method800performed by the system102comprises a step805of providing a controller including a processor and a memory. The method800further comprises a step810of providing computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to iteratively loop between a Reinforcement Learning (RL)-based asset allocation and sizing algorithm which is a data-driven approach and a Dynamic Security Optimization (DSO) algorithm which is a model-based approach to search for an optimal solution via a hybrid approach as a combination of the data-driven approach and the model-based approach such that results of the optimal solution are then verified by a power system nonlinear simulator.

The RL-based asset allocation and sizing algorithm encodes a location and size information in a graph-based representation. The RL-based asset allocation and sizing algorithm also incorporates expert knowledge not only for initial selections but allows a workflow that guides the RL-based asset allocation and sizing algorithm to desired locations and size when they are hard to encode.

The controller to enable the DSO algorithm which is based on a linearized system to provide guidance for the RL-based asset allocation and sizing algorithm to follow physical rules. The controller to verify a new system architecture with the new power generation unit installation and optimized control parameters to ensure the power system is stable and reliable during all types of N−1 contingencies so that the power system can endure a single point failure.

While “a RL-based asset allocation and sizing algorithm” and “a DSO algorithm” are described here a range of one or more other algorithms, or other forms of algorithms are also contemplated by the present invention. For example, other types of data-driven or model-based algorithms may be implemented based on one or more features presented above without deviating from the spirit of the present invention.

The techniques described herein can be particularly useful for power generation or power regulating units. While particular embodiments are described in terms of the power generation or power regulating units, the techniques described herein are not limited to power generation or power regulating units but can also be used with other systems.

With respect toFIG.9, it shows an example of a computing environment within which embodiments of the disclosure may be implemented. For example, this computing environment900may be configured to execute the system102discussed above with reference toFIG.1or to execute portions of the method800described above with respect toFIG.8. Computers and computing environments, such as computer system910and computing environment900, are known to those of skill in the art and thus are described briefly here.

As shown inFIG.9, the computer system910may include a communication mechanism such as a bus921or other communication mechanism for communicating information within the computer system910. The computer system910further includes one or more processors920coupled with the bus921for processing the information. The processors920may include one or more central processing units (CPUs), graphical processing units (GPUs), or any other processor known in the art.

The computer system910also includes a system memory930coupled to the bus921for storing information and instructions to be executed by processors920. The system memory930may include computer readable storage media in the form of volatile and/or nonvolatile memory, such as read only memory (ROM)931and/or random access memory (RAM)932. The system memory RAM932may include other dynamic storage device(s) (e.g., dynamic RAM, static RAM, and synchronous DRAM). The system memory ROM931may include other static storage device(s) (e.g., programmable ROM, erasable PROM, and electrically erasable PROM). In addition, the system memory930may be used for storing temporary variables or other intermediate information during the execution of instructions by the processors920. A basic input/output system (BIOS)933containing the basic routines that helps to transfer information between elements within computer system910, such as during start-up, may be stored in ROM931. RAM932may contain data and/or program modules that are immediately accessible to and/or presently being operated on by the processors920. System memory930may additionally include, for example, operating system934, application programs935, other program modules936and program data937.

The computer system910also includes a disk controller940coupled to the bus921to control one or more storage devices for storing information and instructions, such as a hard disk941and a removable media drive942(e.g., floppy disk drive, compact disc drive, tape drive, and/or solid state drive). The storage devices may be added to the computer system910using an appropriate device interface (e.g., a small computer system interface (SCSI), integrated device electronics (IDE), Universal Serial Bus (USB), or FireWire).

The computer system910may also include a display controller965coupled to the bus921to control a display966, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user. The computer system includes an input interface960and one or more input devices, such as a keyboard962and a pointing device961, for interacting with a computer user and providing information to the processor920. The pointing device961, for example, may be a mouse, a trackball, or a pointing stick for communicating direction information and command selections to the processor920and for controlling cursor movement on the display966. The display966may provide a touch screen interface which allows input to supplement or replace the communication of direction information and command selections by the pointing device1361.

The computer system910may perform a portion or all of the processing steps of embodiments of the invention in response to the processors920executing one or more sequences of one or more instructions contained in a memory, such as the system memory930. Such instructions may be read into the system memory930from another computer readable medium, such as a hard disk941or a removable media drive942. The hard disk941may contain one or more datastores and data files used by embodiments of the present invention. Datastore contents and data files may be encrypted to improve security. The processors920may also be employed in a multi-processing arrangement to execute the one or more sequences of instructions contained in system memory930. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

As stated above, the computer system910may include at least one computer readable medium or memory for holding instructions programmed according to embodiments of the invention and for containing data structures, tables, records, or other data described herein. The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processor920for execution. A computer readable medium may take many forms including, but not limited to, non-volatile media, volatile media, and transmission media. Non-limiting examples of non-volatile media include optical disks, solid state drives, magnetic disks, and magneto-optical disks, such as hard disk941or removable media drive942. Non-limiting examples of volatile media include dynamic memory, such as system memory930. Non-limiting examples of transmission media include coaxial cables, copper wire, and fiber optics, including the wires that make up the bus921. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

The computing environment900may further include the computer system910operating in a networked environment using logical connections to one or more remote computers, such as remote computer980. Remote computer980may be a personal computer (laptop or desktop), a mobile device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to computer system910. When used in a networking environment, computer system910may include modem972for establishing communications over a network971, such as the Internet. Modem972may be connected to bus921via user network interface970, or via another appropriate mechanism.

In some embodiments, the computer system910may be utilized in conjunction with a parallel processing platform comprising a plurality of processing units. This platform may allow parallel execution of one or more of the tasks associated with optimal design generation, as described above. For the example, in some embodiments, execution of multiple product lifecycle simulations may be performed in parallel, thereby allowing reduced overall processing times for optimal design selection.

The embodiments of the present disclosure may be implemented with any combination of hardware and software. In addition, the embodiments of the present disclosure may be included in an article of manufacture (e.g., one or more computer program products) having, for example, computer-readable, non-transitory media. The media has embodied therein, for instance, computer readable program code for providing and facilitating the mechanisms of the embodiments of the present disclosure. The article of manufacture can be included as part of a computer system or sold separately.

A graphical user interface (GUI), as used herein, comprises one or more display images, generated by a display processor and enabling user interaction with a processor or other device and associated data acquisition and processing functions. The GUI also includes an executable procedure or executable application. The executable procedure or executable application conditions the display processor to generate signals representing the GUI display images. These signals are supplied to a display device which displays the image for viewing by the user. The processor, under control of an executable procedure or executable application, manipulates the GUI display images in response to signals received from the input devices. In this way, the user may interact with the display image using the input devices, enabling user interaction with the processor or other device.

The functions and process steps herein may be performed automatically or wholly or partially in response to user command. An activity (including a step) performed automatically is performed in response to one or more executable instructions or device operation without user direct initiation of the activity.

The system and processes of the figures are not exclusive. Other systems, processes and menus may be derived in accordance with the principles of the invention to accomplish the same objectives. Although this invention has been described with reference to particular embodiments, it is to be understood that the embodiments and variations shown and described herein are for illustration purposes only. Modifications to the current design may be implemented by those skilled in the art, without departing from the scope of the invention. As described herein, the various systems, subsystems, agents, managers and processes can be implemented using hardware components, software components, and/or combinations thereof.

Although specific embodiments of the disclosure have been described, one of ordinary skill in the art will recognize that numerous other modifications and alternative embodiments are within the scope of the disclosure. For example, any of the functionality and/or processing capabilities described with respect to a particular device or component may be performed by any other device or component. Further, while various illustrative implementations and architectures have been described in accordance with embodiments of the disclosure, one of ordinary skill in the art will appreciate that numerous other modifications to the illustrative implementations and architectures described herein are also within the scope of this disclosure. In addition, it should be appreciated that any operation, element, component, data, or the like described herein as being based on another operation, element, component, data, or the like can be additionally based on one or more other operations, elements, components, data, or the like. Accordingly, the phrase “based on,” or variants thereof, should be interpreted as “based at least in part on.”

Additionally, any examples or illustrations given herein are not to be regarded in any way as restrictions on, limits to, or express definitions of, any term or terms with which they are utilized. Instead, these examples or illustrations are to be regarded as being described with respect to one particular embodiment and as illustrative only. Those of ordinary skill in the art will appreciate that any term or terms with which these examples or illustrations are utilized will encompass other embodiments which may or may not be given therewith or elsewhere in the specification and all such embodiments are intended to be included within the scope of that term or terms.

Respective appearances of the phrases “in one embodiment,” “in an embodiment,” or “in a specific embodiment” or similar terminology in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any particular embodiment may be combined in any suitable manner with one or more other embodiments. It is to be understood that other variations and modifications of the embodiments described and illustrated herein are possible in light of the teachings herein and are to be considered as part of the spirit and scope of the invention.