NETWORK ELEMENT CONSOLIDATION FOR

RAPID DISCRETE NETWORK SIMULATIONS

STATEMENT OF RELATED CASES

[0001] The present application claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 61/527,207 filed on August 25, 2011, U.S. Provisional Patent Application Serial No. 61/527,211 filed on August 25, 2011 and U.S. Provisional Patent Application Serial No. 61/527,212 filed on August 25, 2011, which are all three incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] The present invention relates to Smart Grid (SG) communication networks. More in particular it relates to the simulation of Smart Grid communication and evaluation of different SG communication options based on different constraints based on the simulation.

[0003] The Smart Grid (SG) communication network is expected to be robust enough to handle various applications with unique requirements in terms of frequency of packets, packet size, delay, etc. Large scale simulations of SG communications, if feasible, would be valuable to identify the robustness issues. In accordance with various aspect of the present invention a toolkit has been developed to evaluate a mix of Smart Grid applications under specific network, topology and geographical constraints using a discrete event simulator. In one embodiment of the present invention the discrete event simulator is the OPNET Modeler®. The OPNET Modeler® and related products are developed and marketed by OPNET Technologies, Inc. of Bethesda, MD. The methods provided herein apply in general to any discrete event simulator that models a Smart Grid communications network.

[0004] Current simulation models of SG communications that include large numbers of users require very long simulation times and do not consider all relevant constraints.

[0005] Accordingly, novel and improved methods and systems for large scale simulations of

SG communications which capture environmental characteristics such us terrain profile and

population density and that mimic realistic results are required.

SUMMARY OF THE INVENTION

[0006] Aspects of the present invention provide systems and methods to model a

communication network that supports an electrical Smart Grid. Operating behavior of the

Smart Grid generates communication traffic in the communication network. The

communication network operates under at least one communication protocol. Based on

operating conditions of the Smart Grid the model can generate a network performance

scoring index. The scoring index, which is based on low level statistics such as

communication delay and message reception rate, gives an indication of how successful the

studied communication protocol was for supporting the studied application.

[0007] In accordance with another aspect of the present invention, a system is provided to

model a communication system in an electrical utility in a geography containing a first

plurality of electricity devices, each electricity device transmitting data over a communication

channel during a transmission time to a first node. The method includes the steps of

determining from the first plurality of electricity devices a second plurality of electricity

devices that are characterized as each having similar transmission characteristics and a

similar communication channel to the first node; grouping of the second plurality of

electricity devices into a plurality of bins, including a first bin, each bin capturing different

electricity devices; and simulating by a processor of data traffic generated by the electricity

devices captured by the first bin. The requirements of similarity are described herein.

[0008] The method can also include applying simulated data traffic generated by the

electricity devices captured by the first bin to create simulated data traffic generated by the electricity devices captured by the remaining bins in the second plurality of electricity devices.

[0009] In accordance with one aspect of the invention, the transmission times of the electrical devices in the second plurality are distributed uniformly.

[0010] In accordance with another aspect of the invention, the transmission channel is a wireless channel and the first node is a wireless transmission tower.

[0011] In accordance with another aspect of the invention, the transmission performance of the electricity devices in the second plurality of electricity devices relative to the first node is determined by the processor based on a topological map of a location of the second plurality of electrical devices.

[0012] In accordance with another aspect of the invention, the method is applied in a discrete event simulator. The discrete event simulator can be applied to analyze a configuration of a communication network in support of an electrical energy grid.

[0013] In accordance with another aspect of the invention, the processor simulates data traffic in the electrical utility in the geography covering an operational period of at least 8 hours.

[0014] In accordance with another aspect of the invention, the simulated data traffic includes data related to Smart Meters and at least one other Smart Grid application in the group of electric utility Smart Grid applications consisting of Delivery Management and Optimization, Demand Management and Optimization and Asset Management and Optimization.

[0015] A system corresponding to the method is also contemplated. Thus a system to model a communication system which transmits data related to a first plurality of electricity devices in an electric utility in a geography including a device that is a first communication node. The system includes a memory to store data including instructions; a processor to execute instructions to perform the steps of: entering a topographical model of the geography; determining from the first plurality of electricity devices a second plurality of electricity

devices that are characterized as each having similar transmission characteristics and a similar communication channel to the first node; grouping of the second plurality of electricity devices into a plurality of bins, including a first bin, each bin capturing different electricity devices; and generating simulated data traffic generated by the electricity devices captured by the first bin.

[0016] The system can perform the methods as described above and in the following description of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG. 1 illustrates attributes of the modeled network in accordance with an aspect of the present invention;

[0018] FIGS. 2 and 3 illustrate a performance of a simulation model as a function of the number of simulated users;

[0019] FIG. 4 illustrates Meter clustering in accordance with an aspect of the present invention;

[0020] FIG. 5 illustrates node consolidation in accordance with an aspect of the present invention;

[0021] FIG. 6 illustrates network traffic in accordance with an aspect of the present invention;

[0022] FIGS. 7-10 illustrate network performance in accordance with one or more aspects of the present invention;

[0023] FIG. 11 illustrates a topography related to a network in accordance with an aspect of the present invention;

[0024] FIGS. 12-15 illustrate a performance of the network in accordance with an aspect of the present invention;

[0025] FIG. 16 illustrates a property of communication rules applied in accordance with an aspect of the present invention;

[0026] FIG. 17 is a table which illustrates communication network properties;

[0027] FIGS. 18-36 illustrate packet and message transfer provided in accordance with one or more aspects of the present invention;

[0028] FIG. 37 illustrates network traffic data representation in accordance with an aspect of the present invention;

[0029] FIGS. 38-39 illustrate various aspects of customized models as provided in accordance with one or more aspects of the present invention;

[0030] FIG. 40 illustrates a geography with assets in a electric grid in accordance with an aspect of the present invention;

[0031] FIG. 41 illustrates an interactive display provided in accordance with an aspect of the present invention;

[0032] FIGS. 42-46 illustrate display screens generated by a system in accordance with various aspects of the present invention;

[0033] FIG. 47 illustrates a network performance scoring in accordance with one or more aspects of the present invention;

[0034] FIG. 48 illustrates a co-simulation platform in accordance with an aspect of the present invention;

[0035] FIG. 49 illustrates a processor based system enabled to perform steps of methods provided in accordance with various aspects of the present invention; and

[0036] FIGS. 50-53 illustrate steps in accordance with one or more aspects of the present invention.

DETAILED DESCRIPTION

[0037] New Smart Grid applications will be supported via the deployment of robust information and communication infrastructures, which will enable the exchange of large quantities of data and control commands between the Smart Field Devices (home electricity meter or pole-top devices such as reclosers, capacitor banks, switches, sectionalizers, etc.) and the utility's Control Center. Currently, utilities from all over the world are facing transformations in their infrastructures and are assessing which available technology will satisfy their requirements. The long-term success of their strategic objectives, such as improved efficiency, integration of renewable energies or increased consumer engagement will directly rely on the communication infrastructure that they deploy today.

[0038] Good decisions are not straightforward in the vast landscape of available communication technologies (e.g. RF-Mesh, cellular, WiMax, PLC, point to point RP, private Wi-Fi). The choice of an optimal technology depends on a large numbers of factors, such as types of applications deployed, existing technology infrastructure, and the geographical characteristics of the deployment region.

[0039] In order to address the aforementioned challenges and assist utility companies to individually migrate and modernize their communication infrastructures, the Smart Grid Communications Assessment Tool (SG-CAT) has been developed, which is capable of simulating a mix of Smart Grid applications under various geographical topologies and topographies, user orientations, and applications configurations. Throughout herein the above tool will be identified with the acronym SG-CAT.

[0040] SG-CAT has been designed to exploit a discrete event network simulator, which allows it to reproduce realistic scenarios and simulate complex Smart Grid applications using a broad range of wireless protocols and technologies, such as LTE, WiMax, RP-Mesh or Private Tower systems.

[0041] Smart Grid application library for a discrete event simulator

[0042] The ultimate goal of any Smart Grid deployment is to reach a level of robustness, reliability and security that allows the full implementation of a plethora of Smart Grid applications with different requirements and characteristics. In order to accomplish this target, detailed knowledge about these applications and their associated traffic models is essential. The following Table I summarized the characteristics of a number of Smart Grid applications to date.

[0043] TABLE 1

A different characterization of Smart Grid applications is in three groups of electric utility Smart Grid applications: (1) Delivery Management and Optimization, (2) Demand Management and Optimization and (3) Asset Management and Optimization. These applications in one embodiment of the present invention include: Advanced Meter Reading, Remote connect/disconnect, Outage detection/last gasp, Feeder automation (FLISR), Feeder automation (NOP/load balancing), Volt/VAR optimization, FCI Telemetry, Voltage regulator bank control, Transformer monitoring, Substation RTU connectivity, Demand response - baselining, Demand response - load control.

[0044] The performance of these applications in real environments will vary based on the communication technologies used to deploy them, the geographic elements (terrain and land use types) of the location and the topological nature (number of devices and location) of the network. Which technologies would be able to optimally handle all the communication requirements? What is the necessary throughput? Can all these applications be implemented in the existing utility's networks? General application requirements such low latency and high bandwidth are not enough to answer such questions. The actual application definitions need to be considered such as the packet size, the packet generation timing, synchronization among devices, etc. Without considering these aspects, packet collisions or network bottlenecks can't be understood,

[0045] In order to answer such questions insight is needed from large scale simulations that will allow reproducing realistic deployments.

[0046] With this goal in mind, an entire library of Smart Grid applications has been developed in accordance with one or more aspects of the present invention and the corresponding traffic models using discrete event simulators such as the modeling tools available in OPNET®. These tools enable a detailed definition of the different tasks included in each application and all the phases for each task, as can be seen in FIG. 1.

[0047] The following applications are used:

[0048] A. Advanced Metering Infrastructure (AMI)

[0049] AMI is the first block, on top of which utilities can develop an entire library of Smart Grid applications. It allows direct connection between the Utility Servers and the user home Meters. It includes capabilities such us remote measurement readings, remote management as for instance described in [1] G. Deconinck, "An evaluation of two-way communication means for advanced metering in Flanders (Belgium)," in Instrumentation and Measurement Technology Conference Proceedings, 2008. IMTC 2008. IEEE. May 2008. pp.900-905 and remote reporting as for instance described in [2] D. Hart, "Using AMI to realize the Smart Grid." in Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, July 2008, pp.1-2. The infrequent uplink transmission and the short length of the packets have been reflected in the definition of the

AMI tasks and phases as applied.

[0050] B. Automated Demand Response (ADR)

[0051] While the main beneficiaries of AMI are the utilities, through remote measurement readings, in the case of ADR both the utility companies and the customers will be able to take profit from its implementation. The utilities will be able to avoid peak consumption (and its associated high costs due to peak generation of energy) and the customers can save money by shifting their demands over time according to the real-time pricing schedules broadcasted by the Utility as described for instance in [3] S. Valero. M. Ortiz. C. Senabre, C. Alvarez, F. Franco, and A. Gabaldon, "Methods for customer and demand response policies selection in new markets," Generation, Transmission Distribution, IET, vol. I, no. 1. pp. 104 -110, January 2007. In ADR, the packet transmission is more frequent and the response should be quick enough for granting the proper service operation. Included in the OPNET library provided in accordance with various aspects of the present invention are methods for PULL and PUSH the information with different frequencies and packet sizes.

[0052] C. Feeder Automation (FA)

[0053] Automated supervision and control of substations allows overall coordination in case of emergencies and optimizations of operating costs. The communication system is a vital part of the wide area power system relaying and coordination. Relays isolate local failures in generation, transmission and distribution so that they do not spread to other parts of the grid. Distribution feeder automation refers to substation equipment for the detection, location and isolation of faults and a means to restore power to undamaged sections of lines. This functionality is referred to as Fault Location, Isolation and Supply Restoration (FLISR). A typical feeder system includes a circuit breaker and at least three of the following types of switching devices along the line: reclosers, disconnect switches, sectionalizers, airbreak switches and fuses.

[0054] Distribution networks need to evolve and transform the static and conventional grids into dynamic and reliable smart grids. Optimizing the operation and maintenance and improving the overall coordination in case of emergencies are challenges that the new grid will face. In order to accomplish these goals, a robust and trustworthy communication system is needed. However, nowadays, the level of communication and automation along the feeder and the distribution substations is really basic and will not be able to handle the future smart grid applications in security and isolation. In one embodiment a system distributes logic amongst relays to implement a decentralized control system. In an emergency, the fault location, isolation and supply restoration should be done as fast as possible. Thus the communication technology should be fast enough for handling fast transmissions with extremely low delay, as for instance described in [4] A. Smit. "Distribution Feeder Automation using IEC61850 GOOSE Messaging over WIMAX Wireless Communications." Several technologies (GPRS, PLC, WIMAX, etc) have been proposed but there is no silver bullet that covers all applications. Therefore, simulations of different scenarios and technologies need to be performed.

[0055] D. Electric Vehicle Charging (EV)

[0056] Electric Vehicles will cause a significant growth in the energy demand in the upcoming years as described in [5] "United States Department of Energy, Energy Information Administration, 2011 Energy Outlook." This and other studies show that Electric Vehicles will have a penetration of 5% of the market by 2020 and they will continue to grow even faster over the following years. This means that millions of vehicles need to be integrated into the power supply infrastructure. A large fleet of EVs needs to be managed in an intelligent way in order to optimize and control the charging of their batteries without

generating uncontrollable load peaks. During the charging process, car chargers will need to communicate with the utility servers in a fast, secure and cost-efficient way. A realistic simulation of the different proposed standards (SIP, IEC61850, etc) is necessary for a realistic evaluation of the communication requirements. EV application presents different real time characteristics depending on the scenario: roadside chargers need to finish the process as soon as possible, while in-house chargers can schedule the charging for a longer period of time.

[0057] E. Others

[0058] As the grid gets more and more intelligent, the number of possible applications will increase. New applications, such us Mobile Workflow Management, Renewable Sources Monitoring, etc, will require more demanding channels with higher throughput and lower latency, specially for emergency situations and video surveillance applications. The herein provided library will allow the testing of such applications in realistic environments for evaluating the future performance of the Smart Grid communication networks.

[0059] Challenges of implementing Smart-Grid applications for discrete event simulation

[0060] In order to model and evaluate the different communication capabilities of a given Smart Grid deployment and guide Smart Grid application implementations, the Smart Grid Communications Assessment Tool (SG-CAT) has been developed.

[0061] OPNET® was chosen as a basis for the herein provided communication simulation tool due to its powerful and high-fidelity simulation and modeling capabilities. OPNET® allows to creating a complete library of applications, to evaluate their behavior with different communication technologies, and perform simulations with realistic terrain. Although the OPNET Modeler® is powerful, modeling an entire network with thousands of wireless nodes, e.g. LTE or ZigBee, is still a challenging task because of computational limitations with scale-up. For wireless nodes, OPNET Modeler® creates message passing pipelines

between each pair of nodes that represents the wireless medium from every user's perspective. As a result, the number of pipelines increases quadratically with the number of users, as shown in FIG. 2. Therefore, real scale simulations are challenging in discrete events simulators including OPNET for large scale simulations, especially wherein a simulation involves thousands and up to hundreds of thousands of devices. It is to be understood that such a challenge exists for any discrete event simulator that has to simulate very large numbers of devices in a utility over a significant period of time. The methods and approaches as provided herein in accordance with one or more aspects of the present invention are intended to be applicable and applied to discrete event simulators.

[0062] FIG. 3 shows the effect the number of users has on simulation execution time. The Opnet Modeler® is installed on a Dell Precision T7500 workstation that is equipped with Windows 7 (64-bit), 12 Intel Xeon X5650 @ 2.67 GHz (dual-core) processors, and 24 GB of RAM. The Opnet DES Kernel was configured as optimized, sequential, and 64-bit addressing space. (It should be noted that a parallel setup was attempted, but it showed no increase in execution time.) The simulation setup was for a Zigbee deployment for a simulation length of 2 hrs. Note that simulation time increases from just half a second for 50 nodes to over 65 hours for 3200 nodes, which suggests 0(nA4) time complexity.

[0063] In accordance with the discussion above, the number of pipelines and the execution time are correlated.

[0064] Another challenge is due to the interest in studying RP Mesh technology in the Smart Grid. The OPNET library contains the Zigbee RF Mesh model. However, unlike many of the other models available in the library, the Zigbee application process does not make use of OPNET's standard application model object. Thus, it is currently impossible to use the Smart Grid application library for Zigbee studies, since the library has been developed within this framework. Presently, the Zigbee application process only allows nodes to behave as single traffic generators.

[0065] Although results will not be provided, it should be mentioned that a similar LTE deployment takes orders of magnitude longer to simulate. Additionally, the built-in efficiency modes (i.e. physical layer disabled) cannot be used to speed up simulation time, since one interest is in observing physical layer statistics and the effect of terrain and topology configurations.

[0066] It is also expected that a large scale power line communication or PLC network will be significantly faster. However, at present, PLCs are not a viable option as MV. LV transformers are known to kill PLC signals as described in [6] S. Galli, A. Scaglione and Z. Wang, "For the grid and through the grid: The role of power line communications in the smart grid," CoR , vol. abs/1010.1973, 2010. Until research allows communications through them, wireless alternatives seem to be the best option for Smart Grid communication. However, non-wireless communication technologies, including PLC networks are specifically included herein as an aspect of the present invention.

[0067] Approaches to address simulation challenges

[0068] In order to accomplish realistic wireless simulations for a Smart Grid network with a large number of nodes, the challenges discussed above must be addressed sufficiently and effectively. The first step in the process of solving these challenges is to clearly identify an objective of the simulations in order to redefine them to a perspective of interest. This principle will be used below.

[0069] Scale-up challenges

[0070] The first task in determining how to address the issue of large scale simulation is identifying the minimum number of Meters that is needed to be studied. That is, ideally it would be desirable to simulate the entire service area of a utility (potentially having more than 100K Meters). However, considering the network in a hierarchical fashion, it is realized that it is made up of multiple small sub areas, namely individual cells containing a single takeout tower. The individual cells are interconnected via a backhaul to the central location and from a cell perspective behave independently, which indicates that it may be sufficient to study the cells sequentially.

[0071] It is not uncommon to find single cells that enclose about 1000-5000 Meters, which provides a huge scale-up advantage over the initial problem of 100K Meters with multiple cells. However, even this scale-up factor may not be sufficient enough as FIG. 3 suggests that networks of 5000 nodes could potentially still take on the order of weeks to simulate. As a result, it is apparent that even further node reduction is required.

[0072] If one wants to study overall throughput constraints in a communication network and reduce the number of simulation nodes, the most direct strategy is to group the nodes into "aggregated-nodes", where each of the resultant nodes will transmit the sum of the individual nodes' traffic. This strategy can be optimal when one wants to study the capacity at the access point or in the backhaul area. Unfortunately, one would lose individual behavior and statistics, which are critical elements in evaluating Smart Grid applications.

[0073] Since one interest is in observing individual level statistics, such as end-to-end delay, scale up techniques must preserve a certain level of individualism. Although it seems like a tough task, certain assumptions and constraints imposed by the Smart Grid network help to simplify the problem.

[0074] Firstly, one can expect the network within a cell to have a somewhat balanced nature when the Meter density is high, since a typical suburban layout promotes a repetitive pattern. As the number of users increase, the probability that the resultant network will be scattered reduces. This observation is used to an advantage when creating the present consolidation rule in accordance with an aspect of the present invention, which is detailed further below. [0075] Secondly, one benefits from the realization that for the non-real time Smart Grid applications, nodes are not required to transmit simultaneously (and will not, due to technological constraints). As a result, a pseudo-scheduler that organizes the transmission times to minimize channel access clashes is applicable. In real-time applications, such as Feeder Automation, the number of nodes is expected to be low and such applications are not as susceptible to scale-up issues as others.

[0076] The issue of large scale simulation for viewing individual level statistics is solved as an aspect of the present invention by introducing a method of consolidation that uses the principle of statistically independent time bins. This method can be explained as follows: assuming there are n users in the cell and all must transmit within t seconds, one can visually represent the transmission behavior of the network as a single bin of length t, capacity of n and average inter-arrival time of t/n (as shown in FIG. 5, graph 501). Similarly, a network of n/2 users and a transmit interval of t/2 seconds would have the same average inter-arrival time t/n (as shown in FIG. 5 graph 502b). Notice that if there is a second t/2 bin next to the first one, a scenario is generated (as shown in FIG. 5, graph 503) that is strikingly similar to the first one with n users. Hence, hypothetically, scenarios as illustrated in 501 and 503 might be equivalent in terms of the network behavior observed and the statistics gathered. It might be sufficient to execute a smaller (thus quicker) simulation to get the same results as the original larger scenario. However, for this hypothesis to be true, the following conditions must be met:

[0077] 1) The user transmission times must be distributed uniformly over time as otherwise the number of transmissions per bin (i.e. inter-arrival times throughout the entire simulation) would not be consistent. As the amount and the frequency of traffic generated in Smart Grid applications are rather small compared to other traditional applications, the network is expected to function near optimal with minimum number of clashes, if large transmit times

(t) are assumed. Hence, this behavior can be approximated with a network scheduler that assigns arbitrary transmission times to each user with a uniform distribution.

[0078] 2) The users that are split into different bins must be of a similar nature in order to exhibit an independent nature of each bin. If this criterion is not met, then each bin would behave differently and must be simulated separately. As typical Smart Grid deployments include several Meters located in close geographic proximity, the transmission characteristics of such Meters will be of a similar nature. In accordance with an aspect of the present invention a similarity includes a similarity in channel characteristics between the Meter and a tower. This may be expressed in a transmission loss or similar characteristics. In one embodiment of the present invention two Meter related channels are deemed to be similar if their path loss over a pre-defined frequency range does not differ more than 3dB. In one embodiment of the present invention two Meter related channels are deemed to be similar if their path loss over a pre-defined frequency range does not differ more than ldB. In one embodiment of the present invention two Meter related channels are deemed to be similar if their path loss over a pre-defined frequency range does not differ more than .5 dB.

[0079] In one embodiment of the present invention one criterion for two Meters being similar is its distance to a tower. For instance, two Meters meet at least one criterion for similarity if their distance to a tower is the same within a 20 % margin; two Meters meet at least one criterion for similarity if their distance to a tower is the same within a 10% margin; and two Meters meet at least one criterion for similarity if their distance to a tower is the same within a 5 % margin.

[0080] In one embodiment of the present invention one criterion for two Meters being similar is its presence in a common neighborhood, for instance as defined by an area. For instance, two Meters meet at least one criterion for similarity if they both are located within an area of 25,000 m2. For instance, two Meters meet at least one criterion for similarity if they both are located within an area of 10,000 m2. For instance, two Meters meet at least one criterion for similarity if they both are located within an area of 1,000 m2.

[0081] When at least both of the criteria of transmission path and uniform distribution are met, one is able to create the so-called ghost bins as illustrated in FIG. 5, graph 503. Hence, this enables to justify the equivalence of scenarios illustrated in 501 and 503 and validates that simulating scenario illustrated in 502 with n/2 users produces similar network behaviors a would be observed if scenario illustrated in 501 with n users was simulated.

[0082] With Smart Grid deployment in mind, FIG. 4 shows an example of the consolidation algorithm where groupings of ten nearby Meters have been identified allowing each of these clusters to be represented by a single node. The groupings are identified as the light lines tracing grouped Meter areas. For illustrative purposes areas 401, 402 and 403 are highlighted. Using this strategy allows to simulate only this single node, since the remaining nine nodes will be scheduled to transmit in one of the nine ghost bins subsequently. Hence, a cell that fits the criteria can be simulated quicker, since one is able to represent a single cell which was made-up of, for example, 5,000 nodes with only 500.

[0083] In accordance with an aspect of the present invention, a determination of a similarity of communication channels of electricity devices such as smart meters related to a node such as a wireless tower is performed automatically or interactively by a computer. A topologic or topographic map is applied in accordance with an aspect of the present invention to determine a path loss for a wireless channel over a terrain with certain topological features. Accordingly, a computer can decide from a digitized map that meters located within a predefined area of a tower will have similar transmission channels to the tower. A user can assist in marking the digitized map for areas that would be considered as preferred areas for combining into bins, as shown for instance in FIG. 4. A computer can also group certain areas, based on location, such as street names, pre-defined neighborhoods and the like.

[0084] In accordance with an aspect of the present invention a computer searches one or more topological maps of predefined areas and selects meter locations with a similar loss to a tower as being enabled to be placed in one of a plurality of bins.

[0085] Application Challenges in Mesh Networks

[0086] As mentioned before, currently OPNET Modeler® does not include a wireless mesh technology, e.g. Zigbee, which is compatible with the application, task and profile objects available in the OPNET® palette. As a result, one is precluded from assigning multiple applications to a single node as well as designing new applications that require two way communications .

[0087] For this challenge, again advantage is taken of the non-real time behavior of Smart Grid applications by creating a super-application that aggregates the traffic of all the applications in one node with certain statistical distributions for the size and the inter-arrival time between packets. Knowledge of the application behavior allows to create packet distributions that can be applied to the mesh nodes.

[0088] FIG. 6 illustrates the multi application throughput from a node using EV AMR and DR. The method provided herein in accordance with one or more aspects of the present invention allows to model this entire traffic pattern within a single distribution. Using this distribution mesh nodes are created that emulate the behavior of multi-application nodes.

[0089] Results

[0090] Testing of the consolidation method as described above is described next. For the simulations networks were created based on typical US suburban towns, which obey common Smart Grid deployment assumptions; hence, outlined conditions for the herein provided methods are met. Findings for the consolidation methods from the perspective of reception rate and end-to-end delay are also provided.

[0091] FIG. 7 displays the reception rate (number of packets received divided by the number of packets transmitted) for varying scenario sizes as the average inter-arrival time increases. It is observed that regardless of the number of users, average inter-arrival time has a similar affect on reception rate. Moreover, the level of this similarity tends to diverge as the consolidation factor (i.e. the number of ghost bins used) increases, which intuitively suggests that further consolidation erodes the independence condition for having ghost bins (emulating 5,000 nodes with a single node may be too optimistic!). Hence, for reasonable consolidation factors, the herein provided consolidation method is expected to perform well and create smaller networks (which are much easier to simulate) that closely behave the same as the original network of interest.

[0092] In FIG. 8, the effect of average inter-arrival time on end-to-end delay is further investigated. Particularity for Smart Grid applications end-to-end delay is an important statistic so as to allow a certain level of service. In accordance with the results gathered in FIG. 7 it is observed that regardless of the number of users, average inter-arrival time has a similar affect on end-to-end delay.

[0093] Hence, if reception rate and end-to-end delay are the statistics of interest, then the herein provided methods can be used safely without loss of accuracy of the results.

[0094] Further Communication Challenges

[0095] Terrain information is important in large scale studies, as hilly regions offer different challenges compared to flat regions. When considering the signal attenuation of a transmitter as a function of distance, many path loss models exist. Free space pathloss is not valid after some distance as foliage and obstacles begin to play a role. The Suburban pathloss model defined in V. Erceg, "An empirically based path loss model for wireless channels in suburban environments," IEEE JSAC, vol. 17, no. 7 pp. 1205-1222, 1999 and which is incorporated

herein by reference shows that wireless communication in an outdoor environment is affected by the amount of hilliness and tree density in the region.

CLAIMS:

1. A method of modeling a communication system in an electrical utility in a geography containing a first plurality of electricity devices, each electricity device transmitting data over a communication channel during a transmission time to a first node, comprising:

determining from the first plurality of electricity devices a second plurality of electricity devices that are characterized as each having similar transmission characteristics and a similar communication channel to the first node;

grouping of the second plurality of electricity devices into a plurality of bins, including a first bin, each bin capturing different electricity devices; and

simulating by a processor of data traffic generated by the electricity devices captured by the first bin.

2. The method of claim 1, further comprising;

applying simulated data traffic generated by the electricity devices captured by the first bin to create simulated data traffic generated by the electricity devices captured by the remaining bins in the second plurality of electricity devices.

3. The method of claim 1, wherein the transmission times of the electrical devices in the second plurality are distributed uniformly.

4. The method of claim 1, wherein the transmission channel is a wireless channel.

5. The method of claim 1, wherein the first node is a wireless transmission tower.

6. The method of claim 1, wherein a similarity of the transmission performance of the electricity devices in the second plurality of electricity devices relative to the first node is determined by the processor based on a topological map of a location of the second plurality of electrical devices.

7. The method of claim 1, wherein the method is applied in a discrete event simulator.

8. The method of claim 7, wherein the discrete event simulator is applied to analyze a configuration of a communication network in support of an electrical energy grid.

9. The method of claim 1, wherein the processor simulates data traffic in the electrical utility in the geography covering an operational period of at least 8 hours.

10. The method of claim 1, wherein the simulated data traffic includes data related to Smart Meters and at least one other Smart Grid application in the group of electric utility Smart Grid applications consisting of Delivery Management and Optimization, Demand Management and Optimization and Asset Management and Optimization.

11. A system to model a communication system which transmits data related to a first plurality of electricity devices in an electric utility in a geography including a device that is a first communication node, comprising:

a memory to store data including instructions;

a processor to execute instructions to perform the steps:

entering a topographical model of the geography;

determining from the first plurality of electricity devices a second plurality of electricity devices that are characterized as each having similar transmission characteristics and a similar communication channel to the first node;

grouping of the second plurality of electricity devices into a plurality of bins, including a first bin, each bin capturing different electricity devices; and

generating simulated data traffic generated by the electricity devices captured by the first bin.

12. The system of claim 11, further comprising;

the processor applying simulated data traffic generated by the electricity devices captured by the first bin to create simulated data traffic generated by the electricity devices captured by the remaining bins in the second plurality of electricity devices.

13. The system of claim 11, wherein the transmission times of the electrical devices in the second plurality are distributed uniformly.

14. The system of claim 11, wherein the transmission channel is a wireless channel.

15. The system of claim 11, wherein the first node is a wireless transmission tower.

16. The system of claim 11, wherein a similarity of the transmission performance of the electricity devices in the second plurality of electricity devices relative to the first node is determined by the processor based on a topographical map of a location of the second plurality of electrical devices.

17. The system of claim 11, wherein the system is a discrete event simulator.

18. The system of claim 17, wherein the discrete event simulator is applied to rate a configuration of a communication network in support of one or more applications in an electrical energy grid.

19. The system of claim 11, wherein the processor simulates data traffic in the electrical utility in the geography covering an operational period of at least 8 hours.

20. The system claim 11, wherein the simulated data traffic includes data related to Smart Meters and at least one other Smart Grid application in the group of electric utility Smart Grid applications consisting of Delivery Management and Optimization, Demand Management and Optimization and Asset Management and Optimization.