Intelligence-based approach to Smart Grid network communications

Transcription

1 Intelligence-based approach to Smart Grid network communications Modeling and simulation tool ensures a cost-effective, successful strategy Executive summary Smart Grid objectives are driving utilities to increase automation and optimization in their electric power system operations. Each new initiative is expanding the technology mix and putting greater demands on the network communications infrastructure. The dynamic nature of the communications technology market poses a challenge to utilities that are trying to understand how to choose and deploy the right solutions for their particular environment, with sufficient bandwidth for the foreseeable future. To make the investment worthwhile, the communications strategy for a Smart Grid network must be efficient, cost effective to deploy, and able to adapt to growth and evolving technology. However, an optimal strategy will not be realized without understanding the utility s underlying geographic, topological and technological constraints. The purpose of this white paper is to discuss the considerations that enable a high-performance, cost-effective Smart Grid communications strategy and roadmap, the costs and consequences of poor planning, and how the right tools speed decision making and enable timely, efficient and successful infrastructure deployments. The cases illustrated in this paper are based on real-world utility analysis output of the Smart Grid Communications Assessment Tool (SG-CAT), a communications modeling and simulation software tool developed by Siemens. The need to replace guesswork with evidence-based analysis When utilities are asked about Smart Grid pain points, they commonly identify the communications infrastructure as a prime concern. The organizations possess tremendous experience in power systems but their in-house communications expertise tends to be less robust. Lacking other alternatives, utilities often rely on the advice of technology vendors or consultants when developing their communications strategies. Technology vendors offer sophisticated systems but not holistic, strategic direction. Consultants offer Smart Grid expertise, but their communications guidance is generalized in broad categories rather than based on realistic scenarios and expectations. Moreover, the only way to validate strategy recommendations has been to perform timeconsuming and costly pilot deployments. If a technology is ill-configured or unsuited to the application, or the scale is too limited to reflect the broader environment, the strategy s flaws might be discovered too late. The consequences of an inadequate or poorlydefined deployment are great. Each Smart Grid application affects the bottom line in its own right, whether it involves voltage conservation, automated meter reading, fault current telemetry, or a combination of these or other applications. But when an application is improperly defined and the communications infrastructure does not perform as expected, it introduces unnecessary costs to the utility. White paper usa.siemens.com/smartgrid

2 For example, if latency slides or packets are delivered too late, reworking the configuration and adding or replacing assets will present an unwelcome time and cost burden. Neither the utility nor its customers want to absorb these unnecessary expenses. To avoid these risks, the actual behavior of the communications technology must be predicted in advance of its deployment. How a technology will behave cannot be adequately determined using generalized scenarios or average and standard deviation calculations. Rather, realistic expectations can only be established by either piloting the actual infrastructure or modeling and simulating the precise environment, including: Terrain of the service area Topology of the asset deployment Cross-cell interference Applications and their requirements. When these and other factors are not considered, costly mistakes can be made that may require infrastructure upgrades, asset additions, or worse, the deployment of a completely new infrastructure. The examples below show how discounting these considerations will directly impact performance. Terrain profiles affect coverage Each utility s terrain is unique and presents special challenges. Variances in a utility s service area terrain have a direct effect on communication channels and performance. Geographic features, including elevation changes and land use types, impact the propagation channel between a transmitter and receiver. A flat, unobstructed terrain supports higher reception rates, while the presence of hills, buildings and trees can block access, causing assets to fall outside of the coverage area. Even within a coverage area, assets are greatly affected by geography; some may have clear channels while others are problematic. Where coverage is lacking, the utility must decide whether to add additional base stations or reposition existing base stations by moving them to a higher location or using a higher pole antenna. Figure 1: When a simulation does not consider terrain, performance can be significantly overstated. Asset topology affects spectrum utilization The topological layout of a utility s communications assets, including the number and density of devices, affect the utilization of the available spectrum. Reclosers, cap banks, meters and faulted circuit indicators are among the assets mapped. Like terrain, topological features vary between utilities and often within a utility s service area. Assets within relative proximity must share a limited amount of spectrum resources. To achieve desired performance characteristics, utilities must decide how to effectively and efficiently configure the asset topology. Simulating multiple configuration scenarios, using the same communications technology, yields diverse results. Figure 2 illustrates six combinations of asset quantity and density, and no two results are alike. Specifically, it compares low, medium and high asset quantities in relation to low and high asset density, and the resultant performance characteristics. In simulations that do not take terrain into account, performance is likely to be unrealistically high. In Figure 1, we can see the simulation resulting when terrain is and isn t taken into account in the form of a cumulative distribution function (CDF) of the percentage of assets in the service area versus the minimum reception rate, which is the percentage of transmitted packets that are successfully received. When excluding terrain, the expectation is that approximately 50% of the assets have 100% of their packets received while 100% of the devices have at least 50% of the packets received. However, taking terrain into account, just 10% of the assets have 100% of their packets received while 100% of the devices have at least 50% of the packets received. This is the more realistic expectation. Results will vary with different terrain profiles. Figure 2: The same technology can behave very differently given varying asset topologies. 2

3 Interference compromises performance Cross-cell interference is caused when assets in adjacent cells compete for spectrum resources. The existence and consequence of interference may be overlooked if a large-scale deployment looks at each base station in isolation. Breaking the project into multiple, smaller units, focuses the analysis on cells formed by each base station rather than other potential interference sources, including adjacent cells on the same channel. Most service areas require more than one base station for full coverage, and adjacent cells may have to share the same limited spectrum resources. With strategic asset placement, utilities can optimize performance in relation to the service area s unique characteristics. For instance, two base stations may intentionally be located in close proximity if the terrain isolates them from interference. Utilities with well-defined applications and properly selected technology benefit from better performance and reliability, and greater capacity for other applications. Conversely, utilities that choose a generic application definition rather than designing and tailoring the application to the functional needs of the utility will experience inferior results. Figure 4 compares the performance of a communications technology for a single application defined three different ways. The generic application definition exhibits a high generation rate. The ideal application definition performs significantly better, but reliability is at risk because there is no allowance for lost packets. Because systems cannot sustain ideal packet rates, the proper definition is tailored to the utility s needs while still providing a buffer by sending more packets than necessary, ensuring enhanced performance and reliability. The CDF in Figure 3 reflects the difference in performance when analyzing individual cells in a service area as compared to simulating the entire service area collectively. The degree of impact is highly variable and depends on the spectrum resources used by the adjacent cells, their proximity and their service area properties. Simulating and understanding the impact of cross-cell interference on performance in this manner allows utilities to make better asset investment and placement decisions. Figure 4: Properly defined applications exhibit better performance and free up capacity for other applications. Figure 3: Performance is impacted by cross-cell interference. The degree of impact depends greatly on the characteristics of the adjacent cells. Properly defined applications ensure performance and reliability The nature and requirements of a utility s Smart Grid application must be considered when choosing a communications technology. Some technologies don t support all protocols, like multicasting, and others may not be capable of handling an application s requisite capacity and latency demands. Using default application definitions wastes resources that could otherwise be used by new or upgraded applications. Meeting immediate application requirements An application s output requirements must be understood to ensure a successful deployment, including power levels, frequencies, modulations and other communications specifications. For example, latency between output and input will vary, sometimes significantly, over time. Some applications will be able to tolerate higher packet losses or latencies, whereas others will have firm thresholds. Making decisions based on averages is not sufficient; utilities need to identify the various output requirements and observe those with the greatest impact on performance. The example in Figure 5 demonstrates an average latency of 48.6 milliseconds (ms) for a given communications technology, and an event that caused latency to rise above 250 ms. The application studied requires latency below 150 ms, which means the utility must either change or reconfigure its technology or redefine the application. Without observing the raw statistics in this manner, the deficiency might not have been detected. 3

4 Modeling and simulations take the guesswork out of decision making The Smart Grid space is so vast that a combination of knowledge, tools and data is required to form educated, cost-effective infrastructure decisions. Utilities need an intelligent, unbiased means to assess their communications options in detail, relative to their specific and varying service area properties, terrain, topology, applications and other key considerations. They need a comprehensive method to select, deploy and configure a communications infrastructure that will perform as expected now and over time. The fastest, most accurate and cost-effective analysis method is modeling and simulation. It enables the evaluation and comparison of multiple deployment scenarios to determine which strategy works best given the utility s distinctive considerations. Figure 5: The average latency achieved by this technology for this application is 48.6 ms. However, at certain events, latency actually increases to above 250 ms. Managing long-term costs While it is imperative to analyze each Smart Grid application individually, the most efficient, cost-effective, lasting technology solutions are achieved when the utility also understands how the applications perform collectively and configures them with future needs in mind. A communications solution that performs well today for a given application may not necessarily support changes over time. For example, if a new application exceeds the capacity of existing technology, expensive upgrades or new infrastructure build-outs may be required. Assessing the entire application roadmap upfront will prevent these cost challenges in the future. Figure 6 illustrates two different applications that perform well when deployed individually, but when combined, the system begins to reach its limits. With proper application definition, no single application would consume the majority of the resources and the system would be more adaptable to future requirements. Understanding that each situation is unique and that there is no silver bullet solution, Siemens created its own proprietary modeling and simulation environment from which to analyze and contrast Smart Grid applications and communications technologies, and determine a utility s optimal strategy. The Siemens Smart Grid Communications Assessment Tool (SG-CAT) is a pre-deployment planning tool that aids utilities that are seeking to refine, upgrade or expand their existing infrastructure as well as those pursuing new deployments. The SG-CAT is used by Siemens professionals with cross-domain expertise in power systems and communications, and it is supported by Siemens extensive technical data. An SG-CAT study simulates the entire utility environment and shows the impact of decisions before they are made. It allows actual application, technology and deployment scenarios to be assessed in advance of investment decisions. The tool helps utilities to choose which communications technology to pilot while avoiding those that are not viable. It allows the technology to be evaluated on a larger scale, for instance an entire service area with varying terrain and asset density, rather than just the pilot location. It also provides a sanity check; it is the only way to determine with technical justification whether a technology vendor s recommendations will work without doing a pilot. Likewise, the SG-CAT is used to evaluate and improve existing deployments. For example: A poorly defined application with excessive redundancy was consuming more resources than necessary, and reconfiguring the network was more cost effective than adding new base stations. A problematic recloser was identified, and it was more costeffective to relocate it than to try to tailor it to work. A utility didn t realize that a certain technology would cover its entire territory, and eliminating the monthly charges for an existing technology resulted in substantial cost savings. Siemens remains vendor-neutral and technology-neutral throughout the process. Neither its consultants nor its tools assess or recommend specific vendors. The focus is strictly on the communications strategy and which technology best suits a given application. Figure 6: Two different applications deployed individually perform well, but in combination, they do not. The ultimate benefit of the SG-CAT is its ability to generate application and deployment cost savings and ensure the most efficient, cost-effective and forward-looking implementation. 4

5 Figure 7: Proper modeling and analysis ensures an intelligent, lasting investment. The Siemens SG-CAT analysis process: How does it work? The SG-CAT consulting engagement requires minimal utility resources. It commences with a kickoff meeting and data transfer, and then the Siemens analyst proceeds independently with the analysis until the findings are presented to the utility interactively. Application definitions and communications specifications are introduced at the kickoff. The analyst leverages utility-provided GIS and GPS data on the topology, which is automatically imported into SG-CAT. The terrain and elevation is captured using U.S. Geological Survey data, and interference is determined by modeling communications technology such as RF Mesh, WiMAX, WiFi and cellular. The final deliverable is a management study and strategic roadmap with technology choices and supporting evidence. The findings are concrete and customized for the utility and based on actual service area models and simulated networks. The SG-CAT report provides justification for investment decisions to the project sponsors, regulators and the Board of Directors, if that is how the utility operates. It is a business case, a propagation study and a performance assessment of electrical applications all in one document, with very specific recommendations that account for customer-specific requirements for latency, bandwidth, signal-tonoise ratio, number of hop constraints, packet loss rates, interference and cost. Oftentimes, the final report will recommend a combination of technologies. For instance, it might make the most sense, both economically and in terms of performance, for a utility to absorb monthly cellular costs for certain portions of its service area while using WiMAX in other key areas of the distribution network. Conclusion The Smart Grid is a complex ecosystem with no silver bullet for the communications infrastructure. Each utility s situation is unique, and even within a utility s service territory, the requirements may vary. Unless the terrain, topology, interference and precise needs of the application are factored in the decision-making process, utilities may be subject to unexpected and costly mistakes. Simulating and analyzing a utility s specific environment and its application and technology choices is the only way to determine with any precision vital conclusions, such as how many base stations are required and where they are needed; the minimum types and heights of antennas required; proper configuration or reconfiguration requirements; which assets should be relocated and where; which application requirements have the greatest impact on performance; and which technology works best for each scenario. SG-CAT allows these and other considerations to be assessed both individually and collectively to establish the most cost-effective, highperforming Smart Grid communications strategy with the flexibility to adapt to future needs. Author biography Amar H Patel, Principal Consultant, Siemens Smart Grid Division Amar Patel is the lead consultant for Communications Technology Consulting for Siemens Smart Grid Division in North America and was the head architecture of the Smart Grid Communications Assessment Tool (SG-CAT). Since joining Siemens in 2006, he has been involved in extensive wireless communications R&D in the fields of automation, sensor networks, intelligent buildings and Smart Grid. Patel attended Rutgers University as a fellow and earned his B.S.E and M.S. degrees with highest honors in Electrical and Computer Engineering with a specialization in electromagnetic theory and communications engineering. 5

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