Smart Grid Exam 2 Material
Web development languages and technologies
- HTML - CSS - PHP - AJAX
Apache Hadoop framework modules
- Hadoop Common - contains libraries and utilities needed by other Hadoop modules; - Hadoop Distributed File System (HDFS) - a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster; - Hadoop YARN - a platform responsible for managing computing resources in clusters and using them for scheduling users' applications; and - Hadoop MapReduce - an implementation of the MapReduce programming model for large-scale data processing.
PV System Characteristics
- IMAGE 12 - The I‐V curve helps to define the operating point of a PV array for a given solar irradiance S [w/m2] and temperature T [°C] - Maximum power is delivered from the PV‐Array only if the operating point is at the knee of the I‐V Curve. (can be achieved through an MPPT algorithm)
Real-Time Operations Synchrophasor Applications
- Wide‐area situational awareness and intelligence - Frequency stability monitoring and trending - Power oscillation monitoring - Alarming and setting system operating limits, event detection and avoidance - State estimation - Dynamic line ratings and congestion management - Outage restoration - Operations planning - Resource integration - Voltage monitoring and trending
Coordinated Control Strategy (wind power)
- With an energy storage device, the limit on wind power generation imposed by pitch control during a wind gust can be relaxed to some extent and an optimal (maximum) utilization of the wind energy can be realized. - Charging/discharging operations by the SmartParks will depend on the state of charge of the vehicle batteries. - Therefore, continuous monitoring of the aggregated state of charge of the parking lots (SmartParks) is necessary. - Continuous monitoring of demand for wind power (commitment) is also needed. - System is modeled in IMAGE 9
data analytics challenge
- operations - energy trading - RT demand response - asset management
Time Sequence of Big Data Value
1. Build out foundation to handle Data challenges "Big Data Enabled" 2. Reap Benefits of low cost, high performance environment 3. New and improved analytics 4. Collaborative analytics
Big Data
An all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. The challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations.
Smart Park Profit Maximization Equations
IMAGE 10
PV System Architectures
IMAGE 13
"Big Data Initiative"
In March 2012, The White House announced a national ___________ that consisted of six Federal departments and agencies committing more than $200 million to big data research projects.
Recent EPRI study concludes the 3 biggest areas for Big Data are
Visualization, Situational Awareness, and Predictive Forecasting
DA is based on _____
What yields the greatest utility (or maximizes the occurrence of favorable outcomes) no matter how narrow the margin of improvement
Building up from a PV cell
cell -> module (cells in series) -> string (modules in series) -> array (strings in parallel)
Latency
very important to building a data analytic architecture
Cloud Computing
• Cloud computing dates to the 1950s. • The goal of cloud computing is to allow users to take benefit from all of these technologies, without the need for deep knowledge about or expertise with each one of them. • The cloud aims to cut costs, and help the users focus on their core business instead of being impeded by IT obstacles. • The main enabling technology for cloud computing is virtualization. • Virtualization software separates a physical computing device into one or more "virtual" devices, each of which can be easily used and managed to perform computing tasks. • More secure • Focused cybersecurity • Data privacy • Flexible management for utilities • Utilities can deploy data analytics applications for rapidly growing data volumes in a secure and scalable manner. - services: data analytics, performance management, stability analysis, simulation/modeling
August 14, 2003 Blackout
- > 60 GW of load loss; - > 50 million people affected; - Import of ~2GW caused reactive power to be consumed; - Eastlake 5 unit tripped; - Stuart-Atlanta 345 kV line tripped; - MISO was in the dark; - A possible load loss (up to 2.5 GW) - Inadequate situational awareness.
PHP - Hypertext Preprocessor
- A server‐side scripting language. - Server must respond dynamically if it is required to provide different responses depending on the situation -> Specific user requests -> Database contents - Connect to mysql database <?php $servername = "localhost"; $username = "username"; $password = "password"; // Create connection $conn = new mysqli($servername, $username, $password); // Check connection if ($conn->connect_error) { die("Connection failed: " . $conn->connect_error); } echo "Connected successfully"; ?>
JavaScript
- Adds interactivity to your web page. -> Interactive graphs, maps, animated graphics, responses to button clicks, etc.. - Allows you to create variables, write logics, handle events. var myImage = document.querySelector('img'); myImage.onclick = function() { var mySrc = myImage.getAttribute('src'); f(mySrc === 'images/firefox-icon.png') { myImage.setAttribute ('src','images/firefox2.png'); } else { myImage.setAttribute ('src','images/firefox-icon.png'); } } - To include the .js file in your html file <script src="scripts/main.js"></script
Online (Near real-time) Applications: Disturbance Detection and Alarming Studies
- Analyses indicate that the rate of change of the phase angle difference between transmission substations, for example, is an important indicator of growing power-system stress. Increasing phase angle or large phase angle difference is used as a basis for transmission operator alarms. - One application for synchrophasor-based situational awareness and trending tools is to have them show the trend in phase angles compared to phase angle limits in order to warn operators when the stress is increasing. Such a tool offers intelligence to the power system operator. When phase angles exceed critical limits, operators can perform corrective actions.
Planning and Off-line Applications
- Baselining power system performance - Event analysis - Static system model calibration and validation - Power plant model validation - Dynamic System model calibration and validation - Load characterization - Special Protection schemes and islanding - Primary frequency (governing) response)
Server side & Client side (HTTP process)
- Browser sends HTTP requests to the server - If the requested resource is static, web server retrieves the requested file and responses back. - If the requested resource is dynamic (e.g. requires data from a database), request is forwarded to server side code. (e.g. PHP) - Reads required information from the database. - Responses back to browser.
List of some optimization techniques
- Decision Systems - Static - Adaptive Dynamic Programming (ADP) - Swarm and Evolutionary Computation - Soft Computing/ Computational Intelligence
HTML - Hypertext Markup Language
- Defines the structure and the content of a webpage. - HTML elements are the building blocks of HTML pages. - An element has opening tag, closing tag, content and attributes. <!DOCTYPE html> <html> <body> <h1>My First Heading</h1> <p>My first paragraph.</p> <a href = "https :// www.w3schools .com "> This is a link</a> <img src="w3schools.jpg"> </body> </html>
The three analytic subsets used within the utility industry
- Descriptive analytics that gauge current performance - Predictive analytics that tell utilities that's about to go wrong - Prescriptive analytics that point to problem prevention
Computing Platforms
- Desk top computing - Embedded processors - Power PCs - Graphic Processing Units - High Processing Computing - RTPIS Lab • CPU Clusters • GPU clusters • CPU-GPU clusters
SmartPark
- Each inverter can draw ±25 MW of active power (SAE J1772 - AC level 2/3 charging allows for 19kW/ 60 -150 kW, vehicle transactions to ±25 kW and ±25 kVAR, 70A). - Considering each vehicle can draw ±25 kW of peak power, each SmartPark of rating ±25 MW represents ~1000 vehicles aggregated together. - The inverter generates a 2.08 kV three-phase line-to-line rms voltage which is then passed through a 2.08kV/22kV step-up transformer and connected to a SmartPark bus - P_(SP1-6) ≥ (P_w - P_wmin) - (P_(L1-6max) - P_(L1-6)), for > 0 - P_(SP6-4) ≥ (P_wmax - P_w) - (P_(L6-4max) - P_(L6-4)), for > 0 - P_(SP) = Max [P_(SP1-6), P_(SP6-4)] - the duration of operation of the Smart Park as a shock absorber: IMAGE 8
PV Module Equivalent Circuit
- IMAGE 11 - By solving above equation with the availability of the PV‐module parameters at three‐points (short‐circuit, maximum power, open‐circuit) available in the vendor data sheet; the I‐V and P‐V nonlinear characteristic curves of the specific PV‐array is obtained.
Situational Intelligence
- Integrate historical and real-time data to implement near-future situational awareness Intelligence (near-future) = function(history, current status, some predictions) - Predict security and stability limits - Contingency analysis - RT operating conditions - Oscillation monitoring - Dynamic models - Forecast load - Predict/forecast generation - Advanced RT and predictive visualizations - Integrate all applications - Topology updates and geographical influence (PI and GIS - Google earth tools)
Plug-in Electric Vehicles (PEVs)
- Integration of large number of smart (controllable) power electronics devices to the grid - Bidirectional power flows: Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) - Grid services: regulation, spinning reserve, load leveling, storage for renewable sources, demand-response - Power transactions: varying price; large power swings are inevitable - Intelligent scheduling for the charging and discharging of the vehicles
Intelligent Scheduling of EV Storage Capacity for Customer and Utility Profit Maximization
- Maximize profit for vehicles in a SmartPark by scheduling grid transactions based on price curves. - Find a suitable good solution much quicker than simply trying every possible combination. - Use an algorithm that is scalable as the number of vehicles, time steps, and constraints used are increased.
Situational Awareness (SA)
- More information (a lot of data) does not necessarily matter in critical operations; rather, what is important is to prioritize the understanding of what matters at the respective instances. - Sense-making is critical and is a process by which individuals attach a meaning to an experience. - It is also critical that an understanding be gained from a shared view because the electric power grid is interconnected, and its dynamics are spatially and temporally connected.
Multi-criteria decision-making
- Multi-criteria decision-making (MCDM) plays a critical role in many real-life problems. - It is not an exaggeration to argue that almost any local or federal government, industry, or business activity involves, in one way or the other, the evaluation of a set of alternatives in terms of a set of decision criteria. - Very often these criteria are conflicting with each other. Even more often the pertinent data are very expensive to collect.
Type C Wind Turbine
- Rotor power output at slip frequency is fed back through a dc link voltage source converter to the stator terminals. - This regulates the reactive power drawn by the DFIG from the utility which allows a larger variation in wind speed than for the IG. Again a gearbox coupling is used. - Typically these wind turbines are also equipped with pitch control of the blades in order to limit the extracted power at high wind speeds. - The particular advantage of the DFIG is that the power electronic converter (PEC) has a rating of only about one third of the nominal power of the turbine. - Approximately 50% of installed wind generators world wide are of this type. - IMAGE 5
PEVs on the Road
- Significant technical barriers must be also overcome before PEVs are available: cost of batteries and power electronics, battery size and performance, durability, safety, infrastructure - national grid overload: lack of new investments, impediment to rapid deployment of plug in vehicles on the road - grid impacts: reserve margin and load forecasting, stability
Online (Near real-time) Applications: Islanding and Restoration
- System frequency is an indicator of power system integrity ("health"). Bus frequencies such as at substations are reliable indicators of power system islands and system separation points. - Frequency information is also very important during black-start conditions (when the power system has to be completely restarted back up from zero generation and load) and in system restoration following power system break-ups; operators can use synchrophasor data to bring equipment and load back into service without risking power instability or without experiencing unsuccessful reclosing attempts that prolong outages.
PV System Components
- System pathway: variable DC voltage and current -> max power point tracking -> regulated AC current injected - Nowadays, PV-inverters utilized in power grids are "smart" in many ways, meaning that: ----> They can monitor the PV-array and operate at Maximum Power Point namely MPPT ----> Sense the grid condition i.e. grid voltage and current phasors with the help of installed PMUs on each PV inverter and synchronize to it with the help of PLL ----> Disconnect their affiliated DER from the grid in case voltage dips or frequency changes occur that is noncompliant to IEEE/IEC standard.
Type D Wind Turbine
- The SG shown below can have a wound rotor or be excited by permanent magnets. - It is typically a multi-pole low speed machine without a gearbox. - The SG output voltage magnitude and frequency varies continuously, but is coupled to the point of common coupling (PCC) through a dc link voltage source converter. - Power extracted at high wind speeds is limited by controlling the pitch of the blades. - In the direct drive SG system, no gearbox is needed, but this advantage must be paid for by the disadvantage of a larger PEC and a more complicated heavier and thus expensive generator. - IMAGE 6
World PV (photovoltaic) Trends
- The global solar industry is on the verge of a booming era - PV installation exceeded 177GW in 2014 with Germany, China, the U.S., Italy, and Japan leading - Projection in 2050 (by IEA): 4600GW providing 6300TWh energy per year, which will be 16% of global electricity production - The U.S. installed 7.5GW of cumulative utility-scale, commercial, and residential solar Photovoltaic only in 2015, 17% more than that in 2014. Reaching over 29GW of total installed PV capacity in Q1 2016 - The world's largest PV solar park in 2014, Topaz Power plant was commissioned in California with an installed capacity of 550MW - The average price per kWh of utility-scale PV projects has dropped almost 50% from 2010 to 2013 - Cost reduction is mostly achieved by lowering PV module costs - Top solar users: Target, Walmart, Apple, Costco, IKEA, Macy's
PSO Tuned Contrtollers
- The level of sophistication of the Inverters is partly dependent on its control structure performance. ---> The more optimal the PI controllers are, the better PEI performance will be, especially during disturbances such as cloud covers, in which require rapid ramping and power balancing capabilities. ---> As a result, Particle Swarm Optimization (PSO) has been used as an efficient and practical optimization method in order to optimally tune Inverters controller parameters.
Electric Vehicle Market Forecasts
- The rapidly changing market for electric vehicles (EVs), which includes hybrid, plug-in hybrid, and battery electric vehicles (HEVs, PHEVs, and BEVs), is a small but important part of the global automotive industry. - Governments worldwide are keen to see increasing penetrations of EVs due to the environmental, economic, and energy security benefits they provide. Consequently, governments have both pushed automakers to develop EVs and incentivized citizens to buy them. - Growth in HEV, PHEV, and BEV platforms will be contingent upon expanding EV availability outside of the small hatchback segment into larger vehicle formats such as SUVs and trucks - 35,000 public charging outlets across the US
analytics process flow
1. data processing 2. analytics 3. correlations and rules 3. visualization engine 4. visualization
Smart Grid Data Analytics - Vendors
Analytics and Applications (Real Time Intelligence): - ACLARA - IBM - DataRaker - Siemens - eMeter - Itron - SAS Data Management and Movement (Platforms) - Cisco - EMC^2 - ORACLE - SAS - splunk - cloudera Data Infrastructure and Storage (Public/Private Clouds) - Dell - ORACLE - hp - Microsoft - amazon - intel - cisco (big data infrastructure ) + (universal access) + (data and info management) + (analytics) = (smart grid)
Apache Hadoop
- is an open-source software framework used for distributed storage and processing of datasets of big data using the MapReduce programming model. - It consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework. - The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. - Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality, where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking
Electric Utility Challenges
- optimizing workforce - cyber security - Control Room IT architecture & services - business model change - system dynamic - system scalability - storm restoration - sustainability - environment - communication - big data
Types of Solar Concentrators (Concentrated Solar Power (CSP))
- parabolic trough - tower with dual axis tracking reflectors - dish
smart grid data analytics - reliability
- predictive analytics are utilized for the maintenance and modernization of an aging infrastructure and for improved visibility across automated systems - descriptive analytics leverage real time data for smart control to enable situational awareness in operations - prescriptive analytics identify gaps in existing assets and establish sound asset management practices and programs (mind the gap)
Resiliency Aspects
- real time grid data - predictive analytics - prescriptive analytics - operational response - planning models
Smart Grid Utilities Data Sources
- revenue data - load data - theft data - prepay data - rate data - demand data - consumer data - outage data - distribution data - AMI network data - service level data - peak demand readings - home area networks data - electric vehicles data - voltage data - power quality data - etc..
Smarts in the Smart Grid
- state of the art electricity delivery network - energy services broker - network enabled services and solutions (IoT & SDN) - computing scale - economies of scale - cloud
Classes of Utility Data
- telemetry: continuous flow measurement of grid equipment parameters and other grid variables - oscillographic: data made up of voltage and current waveform samples that can create a graphical record - consumption data: most often smart meter data, but any node that measures usage data may be included - asynchronous event messages: grid devices with embedded processors generating messages under a variety of conditions, both as responses and commands - metadata: any data that us used to describe other data
models in analytics
- the "heart and lungs" of advanced analytics - it is a science and art to develop a model
smart grid data analytics - privacy
- understand your company's compliance and culture - align and train management and staff on security practices - know your data, where it is, and what must be protected - ensure third parties comply with your privacy policies - understand your threats and controls - test and update controls regularly - be prepared to respond to incidents
Decision Support Tools
- used for computation of multi objectives and risk assessment in smart grid panning and operations: -> game theory -> decision support systems -> analytic hierarchy process
Server
Browser sends a HTTP request to the server, ______ retrieves the request
How to design your visualization
Consider important facts: - Properties of Data - Volume, Variety, Velocity, Veracity, Variability - Data presentation format - graphs, maps, bar charts, pie charts - Structure, Proper Colors, Fonts, etc..
Decision Support Tools (Methods)
Decision Matrix: - considers performance value, of the alternative and the weight of the criterion. - get the geometric mean (nth root of sum(n_i)) and the priority is the normalized GM
Human in the Loop
Designing intuitive systems that users can operate with a minimum of cognitive friction is the goal of user-interface designers who realize the stakes are high
The Data Analytics Infrastructure
ETL is the gold standard when handling of data needs to be consistent, repeatable, and tagged with a verifiable chain of custody. • E - Extract - Reading the data from a data source that could be in a variety of formats, including relational or raw data. • T - Transform - Converting the extracted data from its current form into the form of the target database. • L - Load - Writing the data into the target data warehouse. However, for big data, the ETL infrastructure is expensive and doesn't scale as readily as new technologies—such as Hadoop. • Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers; and the "Swiss Army knife of the 21st century"—that support the ability to process, manage, and give users the ability to directly consume data without moving it around. • Hadoop - Scalable, cost-effective, flexible and fault-tolerant.
Utilities' 3 primary domains for Analytics
Enterprise Analytics - Moving from traditional, historical analytics to real time predictive analytics - complete situational awareness - business intelligence (BI) - trading with "live look" at the grid simulation/ visualization Grid operations analytics - asset management analytics - crisis management analytics - DMS analytics - outage management analytics/fault detection and correction - weather/location data - mobile workforce management - energy theft Consumer Analytics - behavioral analytics - tiered pricing - trading, selling megawatts (DR) - building energy management - power analytics (load flow) - social media data integration - DG/EV/microgrid analytics
smart grid data analytics - efficiency
- utilizing predictive customer analytics for successful rollout of demand/supply programs - predicting demand and supply, which reduces outages by geographic area with high efficiency - hold down energy costs and offer enhanced services through smart integrated control systems - boost consumer engagement through new communications initiatives related to smart grid programs - leverage data to improve relationship with customers through direct marketing programs and new tailored services
PV system cahracteristics
1. PV Module 2. DC-DC Converter 3. Single Phase Inverter 4. Filter and grid interface 5. connection to a voltage source (2. and 3. are where a controller for MPPT, PWM, reactive motor control, voltage control happen; there should be an interface to home area network or smart meter here)
two Big Ideas that are of particular interest to the CISE research community.
Harnessing the Data Revolution for 21st-Century Science and Engineering (HDR); and the Future of Work at the Human-Technology Frontier (FW-HTF)
Modeling output of a PV panel (equation)
IMAGE 3
Wind Systems Equation
IMAGE 4
Active and Reactive Power Control Equations
IMAGE 7
Rule Base for Coordination Control
If the difference between available wind power and the demand is negative big and the overall state of charge is medium then the pitch control reference is very high and the SmartPark power command is positive (discharging) big.
Smart Grid Exponential Data Growth
Image 1
High Level Flow of Data
Image 2
Eluvaitivu, Sri Lanka
Name of the island Dr. Kumar went to and helped make 100% renewable energy (but also with diesel generators?) - wind and solar PV - energy storage - about 60 families of fishermen
Data Visualation
Presenting information clearly and efficiently using visual objects
Two stages of DA (decision analysis) support
Stage 1: - Evaluate the expected monetary value (EMV) from the profit and loss data, and the associated probabilities. - Draw the decision flow tree which should yield a best decision based on the highest EMV and/or the lowest expected loss. Stage 2: - Consider the possibilities of sampling and accurate information and re-evaluate the new EMV. - Draw a new decision flow diagram. - This should yield a best decision based on the highest EMV and/or the lowest expected loss.
Web Server
Stores website component files and provides control to access the hosted files
Smart Grid - Utility Priorities
Visualization (Presentation): - accurate distribution grid connectivity information - accurate meters, DG, and energy storage information - cyber security and MERC security management Situational Awareness (Decision Making) - Demand Response and Distributed Generation contributions - Grid Management - Reliability and Resiliency - Grid Efficiency - optimal capital and O&M costs Predictive Forecasting (Prescriptive Decision Options) - wide-area situational awareness - system protection & restoration - grid security
The Big Data 5 or 6 Vs
Volume - The quantity of data that is generated is very important in this context. It is the size of the data which determines the value and potential of the data under consideration and whether it can actually be considered as Big Data or not. Variety - The next aspect of Big Data is its variety. This means that the category to which Big Data belongs to is also a very essential fact that needs to be known by the data analysts. Velocity - The term 'velocity' in the context refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development. Veracity - The quality of the data being captured can vary greatly. The accuracy of the analysis depends on the veracity of the source data. Variability - This is a factor which can be a problem for those who analyze the data. This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively. (Visualization)
Hypertext Transfer Protocol (HTTP)
Web browser communicates with web server using __________
Web Browser
Web site is accessible through a web browser
PV characteristics
Works best under 40 V, at either 3 or 6 amps
Smart Grid data analytics applications
customer satisfaction (targeted interactions) - segmentation-driven marketing offers - proactive alerting - personalized communication Reliability (more effective monitoring and proactive maintenance) - asset management - transformer load management Operational efficiency (better planning and execution) - employee utilization - revenue assurance - optimized field work Safety (understanding and mitigating hidden risks) - reducing public safety hazards - vegetation management - field work management
Smart Grid Data Analtyics
• AMI - Advanced Metering Infrastructure • MDMS - Meter Data Management Systems • OMS - Outage Management Systems • DMS - Distribution Management Systems • EAS - Enterprise Asset Management Systems
Online (Near real-time) Applications: Wide-Area Monitoring and Visualization (WAMV) Systems
- These systems collect phasor data across an area as wide as an entire interconnection, which could be 100s of miles in size, in real time and display it for operators to understand grid conditions. - Digital displays provide alerts to indicate possible levels of stress in the grid such as areas of low voltage, frequency oscillations, or rapidly changing phase angles between two locations (such as substations) on the grid. - Many WAMV applications have diagnostic capabilities that can identify grid stress (measured by the changing phase angles of synchrophasors at different substation locations, termed phase angle separation), grid robustness in terms of system events (oscillations, damping and trends), instability (frequency and voltage instability), or reliability margin (which describes how close the system is to the edge of its stability boundary). - These systems provide context-appropriate graphics and visualizations, basic data archiving, the ability to drill-down into specific locations or conditions on the grid (e.g., voltage or a frequency oscillation), and playback capabilities.
types of wind turbine systems
- Type A: constant speed wind turbine - Type B:variable speed WT - Type C: variable speed WT with partial-scale frequency converter - Type D: variable speed WT with full-scale frequency converter - Variable speed turbines such as the DFIG and the SG with their power electronic converters allow the turbine to run at optimal tip speed ratio and thus extract maximum power from the wind as the wind speed varies. - This outweighs the small amount of losses in the power electronic converter (PEC). - The PEC also allows control of both active and reactive power. This control is important to utilities connected to large wind farms.
AJAX - Asynchronous JavaScript and XML
- Use to communicate with server‐side scripts. - Update the webpage without reloading the page. - Request/Receive data from the server after the page has loaded. - Send data to the server in background.
CSS - Cascading Style Sheets
- Use to style the webpage. - Allows you apply styles selectively to elements in HTML documents. body { background-color: lightblue; } h1 { color: white; text-align: center; } p { font-family: verdana; font-size: 20px; } - To include the css file in your html file <head> <linkhref="style.css"rel="stylesheet"type="text/css"> </head>
wind system
- Wind turbine output power is dependent on the dynamics of wind speed, direction, wind turbine type, generator type, power converter, and their associated controls - Wind turbines are classified based on mechanical power control such as pitch control or stall regulation; fixed speed, variable speed and full power electronic conversion based on speed control. - Modeling of wind turbine is thus a challenge due to complex function approximations between wind speed, wind direction and associated turbine-generator dynamics and controls.
Analytic Hierarchy Process (AHP)
- a multi-criteria decision-making approach. - introduced by Saaty (1977 and 1994) - a decision support tool which can be used to solve complex decision problems. - It uses a multi-level hierarchical structure of objectives, criteria, subcriteria, and alternatives. - The pertinent data are derived by using a set of pairwise comparisons. These comparisons are used to obtain the weights of the importance of the decision criteria, and the relative performance measures of the alternatives in terms of each individual decision criterion - Appropriate for cases involving both qualitative and quantitative analysis - AHP has applications in: planning, resource allocation, and conflict resolution - A nonlinear framework for carrying out both deductive and inductive thinking: -> without the use of the syllogism by considering several factors simultaneously, -> allowing for dependence and feedback, and -> making numerical tradeoffs to arrive at a synthesis or a conclusion.
cybersecurity analytics
- communications: data transport - advanced components: smart switches, storage devices, smart appliances, transformers - automates control systems: monitoring and control systems - sensing and measurement: smart meters and PMUs - decision support: operational applications to manage the electricity system - customer-facing systems: web-based systems that provide account access to customers
Decision Analysis (DA)
- depends on information about the alternatives - The quality of information varies from hard data to subjective interpretations, from certainty about decision outcomes (deterministic information) to uncertain outcomes, represented by probabilities and fuzzy numbers - challenge: The diversity in type and quality of information about a decision problem calls for methods and techniques that can assist in information processing
Complex data processing and analytics environment
- hierarchical to distributed - multiple data classes - latencies
key considerations for establishing big data cybersecurity analytics in the utility
- identify information security issues and evaluate the role of big data analytics - seek to resolve deficiencies in cyber-readiness, including in professional staff, governance, and IT - work away from defensive, reactive posture and work towards proactive systems that considers nonlinear characteristics - consider roles of collection, storage, processing apart from desires analytics and workflows - enable data and information sharing among utilities and cybersecurity entities - create a small pilot opportunity to prove the value of big data analytics to the role of cybersecurity - develop use cases that support business and operational vulnerability and threat detection
How to start building an analytically driven customer operations strategy
- identify ket customer service initiatives - design analytical models for a micro-segmentation level - measure how key initiatives are meeting customer goals - try to understand why customer desires are different than what is provided - tune operations - measure and adjust
Smart Grid - Big Data Benefits
- improve the reliability and resiliency of the electric grid - optimize the asset management and operations costs - share the data/intelligence for improved decision making - integrate legacy systems for improved data flow - improved data analytics and enterprise intelligence
IT‐OT Convergence or Bankruptcy?
• Excessively conservative decision-making and low investment levels by utilities and regulators have created a slow pace of innovation for grid modernization. • A bias toward proven and mature solutions has retarded the implementation of technologies that may ultimately be required for cost-effective operations. • Without advanced systems and analytics controlling the network and subsequent improved decision-making, the elevated costs for managing the network will only go higher. • As energy efficiency and distributed generation grow and consumption decreases, revenue will decline more quickly than delivery costs, resulting in revenue inadequacy. • It is the smart grid infrastructure and the associated use of the data make decisions that will ultimately decrease operational costs related to improved forecasting of demand, better ability for customers to manage their loads, enhanced service delivery and reliability, and an infrastructure that will allow new cost-recovery mechanisms. • This requires new models of data management including the movement away from siloed storage and access amid new cyber security concerns. • It also calls for a renewed focus on analytics to breakdown big data into descriptive, predictive and prescriptive subsets.
Smart Grid Data Analytics 2
• It's not as simple as picking up some data and churning out statistics. The analysis itself is just a piece of the whole smart grid data analytics puzzle. • Before the daunting techniques such as data fusion, network analysis, cluster analysis, time-series analysis, and machine learning are even contemplated, the underlying data must be collected and organized. • Collection itself is a challenge, given the wide variety of data available across the utility. • Organizing data is where the coherence trial really begins. The process includes cleaning (fixing bad values, smoothing and filling in gaps), joining various data sets, and storing it all in a data warehouse of some type. • Analysis can then begin, but even advanced analysis does not complete the picture. • Once analyzed, the processed data must be presented to users in a functional and low-friction manner so that it improves actions and outcomes. • Even squeaky-clean data and advanced analytical processes amount to nothing if the resulting information cannot be understood by the users, if conclusions can't be drawn, and if no action can be taken.
Smart Grid Data Analytics
• Smart grid data analytics are playing an increasingly critical role in the business and physical operations of delivering electricity and managing consumption. • Economic drivers, carbon reduction, regulatory compliance, and an increase in the drive to provide residential, commercial, and industrial customer self- management of energy costs and consumption are creating the perfect storm for grid modernization and smart electrification. • The current centralized model of power delivery, with its fragile, legacy, and manual componentry, simply cannot accommodate energy and efficiency demands in the way that an intelligent distributed power system can. • With sensors, intelligent devices, advanced equipment, and distributed systems are integrated into the grid, various of data will start flooding the utility enterprise. "Utility big data analytics are the application of techniques within the digital energy ecosystem that are designed to reveal insights that help explain, predict, and expose hidden opportunities to improve operational and business efficiency and to deliver real-world situational awareness."
IT-OT Convergence
• The IT staff typically manages the transactional side of the enterprise: billing, accounting, asset management, human resources, and customer records. • The OT side of the house manages the distribution operations, monitors infrastructure and control center-based systems, and oversees a lot of nonhuman inter- action between systems on the grid. • Grid modernization leads to and requires business changes. • The IT and OT departments must be integrated and work well together. • Lack of IT-OT integration will result in: - Uninformed or poor decision-making - Lack of compliance - Poor communications - Inefficient field operations - Inability to effectively report to external stakeholders
Mind the Gap
• The significant expertise deficit related to big data management, analytics, and data science is one of the major reasons utilities have not been able to effectively use smart grid data. • Data scientists not only need to know how to data wrangle, they must also know how to operate a variety of tools on a variety of platforms fed with vast amounts of varied data. • Energy-savvy data scientists are capable of changing the way the utility views the world and gets business done. Data -> Information -> Knowledge -> Understanding
Electric Grid
• When Hurricane Sandy tore through the United States' Atlantic and Northeast regions, it left as many as 8.5 million people across 21 states without power, in some cases for weeks. • The challenging situation demonstrated the fragility of the electricity grid infrastructure, and the difficult restoration underscored an inescapable fact: The largest machine in the world is crumbling in a graceless display of accelerating decay. • While a smart grid certainly cannot totally prevent outages during a natural disaster, its information infrastructure brings the promise of a new level of service to the customer during major disruptive events and to our daily lives. - Resiliency • Yet, despite incremental improvements, the global electric grid is plagued by a worsening trend of severe blackouts caused by the combined effect of aging infrastructure, high power demands, and natural events. • In the US alone, the power system has experienced a major blackout about every 10 years since the 1960s, and power disruptions have increased steadily both in frequency and duration over the last decade. • HILF Events - High Impact Low Frequency - Smart grid will reduce improve the occurrence of HILF events.
