ECE 6160 Smart Grid Test 2

Data Visualization chart types

- Correlation • scatter plots, combine two line charts with a bar chart - Distribution • box plots, histograms, - Geographic data • Maps

Adaptive Critic designs are used for:

- MIMO system - Nonlinear system - Model uncertainties - Random disturbance - Learns over time - Adaptive - Robustness (Hamilton-Jacobi-Bellman equation, basic equation of stochastic optimal control)

Typical Optimization Problems

1. Demand response management to maximize incentives 2. Energy management systems 3. Optimal power flow 4. Forecasting models 5. Scheduling of generators for dispatch 6. Optimum design of control systems: PSS, excitation systems 7. Big Data - Smart Meters and PMUs

Classification of Optimization Problems

1. Existence of Constraints: Constrained and Unconstrained optimization 2. Nature of the Design Variables: Static and Dynamic optimization. 3. Physical Structure of the Problem: Optimal Control and Nonoptimal control. 4. Nature of the Equations Involved: Linear, Nonlinear, Geometric and Quadratic. 5. Permissible Values of the Design Variables: Integer and Real valued. 6. Deterministic Nature of the Variables: Probabilistic (Stochastic) or NonProbabilistic. 7. Separability of the Functions: Separable and Non-separable objective and constraint functions. 8. Objective Functions: Single and Multi-objective functions.

Optimization

1. Optimization is the act of finding the best result under given circumstances. 2. Optimization can be defined as the process of finding the conditions that give the maximum or minimum value of a function. 3. There is no single method available for solving all optimization problems efficiently. Hence, a number of optimization methods have been developed for solving different types of optimization problems. 4. The optimum seeking methods are also known as mathematical programming techniques and are generally studied as a part of operations research.

Neural Networks

A neural network can be defined as a massively parallel distributed processor made up of simple processing units, which has the natural propensity for storing experiential knowledge and making it available for use. The neural network resembles the brain in two aspects • Knowledge is acquired by the network from its environment through a learning process. • Interneuron connection strengths, known as synaptic weights, are used to store acquired knowledge

Intelligence

Ability to comprehend, to understand and profit from experience, to interpret intelligence, having the capacity for thought and reason

Cellular Computational Networks

Cellular computational networks (CCNs) generally consists computational units connected to each other in an ordered distributed manner. CCNs are suited to model complex systems with temporal and spatial dynamics.

Data Analytics Process

Collection Organization Analysis Presentation(easy to understand)

Applications of Data analytics

Customer Satisfaction Reliability Operational Efficiency Safety

Decision Support Tools

Decision support tools are used for computation of multiobjectives and risk assessment in smart grid planning and operations: • Game theory, • Decision support systems, and • Analytic hierarchy process. Challenge: The diversity in type and quality of information about a decision problem calls for methods and techniques that can assist in information processing.

Three analytics subsets

Descriptive: gauge current performance Predictive: Whats about to go wrong Prescriptive: point to problem prevention

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:

Data Analytics ETL

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.

drivers for SG data analytics

Economic drivers, carbon reduction, regulatory compliance, and an increase in the drive to provide residential, commercial, and industrial customer selfmanagement of energy costs and consumption are creating the perfect storm for grid modernization and smart electrification.

3 domains for analytics

Enterprise: real time predictive, situational awareness, business inteligence Grid operations: Asset management, crisis management, DMS, Outage Consumer: behavioral, tiered pricing, building energy management,DG/EV

Big Data Benefits

Improve reliability and resiliency of grid Optimize the asset management and operations costs share data for improved decision making Integrate legacy systems for improved data flow improved data analytics and enterprise intelligence

Swarm Ring Topology

In this model, the particles do not take into account all of the other particles but only their neighbors. This corresponds to a limited perception of the outside world. This is actually more accurate than assuming total perception.

August 14, 2003 Blackout

Inadequate situational awareness

Situational Intelligence

Integrate historical and real-time data to implement near-future situational awareness

Bellman's Equation

J(t)=sum[ gamma*U(t+k)]

Adaptive Critic Designs

New optimization technique combining concepts of reinforcement learning and approximate dynamic programming. The adaptive critic method determines optimal control laws for a system by successively adapting two networks, namely an Action network (which dispenses control signals) and a Critic network (which learns the desired performance index for some function associated with the performance index).

Smart Grid Data Analytics - Reliability

Predictive: maintenance and modernization Descriptive: leverage real time data for smart control and situational awareness Prescriptive: identify gaps in existing assets (mind the gap)

Resilient Grid

Real-time Grid data Predictive analytics prescriptive analytics operational response planning models

Utility Data sets

Revenue data load data theft data prepay data rate data demand data consumer data outage data distribution data a lot of others

Swarm Intelligence

Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) agents interacting locally with their environment cause coherent functional global patterns to emerge

barriers of PEV's

Technical • Cost - Large, heavy and costly batteries; power electronics • Battery Size/Performance - energy density, storage capacity; deep/shallow charge/discharge cycles; space • Durability - life of a car? • Safety - trauma Infrastructure - Charging/Fueling, Discharging • National grid Overloaded Lack of new investments •Impediment to rapid deployment of plug-in vehicles on the road • Grid Impacts • Reserve margin and load forecasting - dynamic, driving style • Stability - the rapid load fluctuations

Curse of dimensionality "Bellman"

The computational requirements are proportional to the number of the discretization points, which typically grows exponentially with dimensionalities of xk and ak

Objective Function

The criterion with respect to which the designs is optimized expressed as a function of the design variables

Cloud Computing

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.

Particle Swarm Optimization

The system initially has a population of random solutions called particles Each particle has random velocity and memory that keeps track of previous best position and corresponding fitness The previous best value of the particle position is called the 'pbest' It has another value called 'gbest', which is the best value of all the 'pbest' positions in the swarm Basic concept of PSO lies in accelerating each particle towards its pbest and the gbest locations at each time step In local PSO, the 'gbest' is changed to 'lbest' where 'lbest' is the best value of all the particles in local neighborhood.

Mind-the-gap (big data problem)

There are not enough people who have significant expertise of big data management and analytics

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.

Dynamic Programming

This approach also results in a greater robustness to noise than calculus of variation methods. DP provides a computational technique to apply the principle of optimality to sequences of decisions which define an optimal control policy or trajectory.

Swarm Star topology

This results in the entire swarm particles trying to converge on one spot.

Smart Grid Data Analytics - Privacy

Understand company's compliance and culture train staff on security practices know your data, where it is, what must be protected ensure third parties comply with privacy policies understand threats and controls test and update regularly be prepared

Fuzzy Systems

Use approximate reasoning, elements belong to sets to a certain degree of certainty. Modeling of common sense

SG data analytics

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.

Smart Grid Data Analytics - Efficiency

Utilize predictive customer analytics for demand/supply programs Predicting demand and supply which reduces outages Hold down energy costs boost customer engagement though energy saving programs

SG utility Priorities

Visualization Situational Awareness Predictive Forecasting

Big Data (6 V's)

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. Accuracy of analysis depends on the veracity of the source data. Variability - This is a factor which can be a problem for those who analyse 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 6. Visualization...

Wind Systems dependencies

wind speed, direction, wind turbine type, generator type, power converter

ACDs (Types of primary reinforcement)

• 1) Explicit targets for system outputs are provided at every step. • 2) Explicit differentiable cost as a function of system variables is provided at every step. • 3) A graded cost is provided at each step but explicit relationship with system states is not given. • 4) Ungraded reinforcement is provided when appropriate, e.g. binary outcome at the end of a game.

Utility data sources

• AMI - Advanced Metering Infrastructure • MDMS - Meter Data Management Systems • OMS - Outage Management Systems • DMS - Distribution Management Systems • EAS - Enterprise Asset Management Systems

Artificial Immune systems

• Artificial Immune Systems (AIS) are biologically inspired models for immunization of engineering systems. • The pioneering task of AIS is to detect and eliminate non‐self materials, called antigens such as virus or cancer cells. • The AIS also plays a great role to maintain its own system against dynamically changing environment. • The immune systems thus aim at providing a new methodology suitable for dynamic problems dealing with unknown/hostile environments

Situational Awareness

• 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.

Cloud Computing pros

• 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.

Principles of Swarm Intelligence

• Proximity principle: the population should be able to carry out simple space and time computations. • Quality principle: the population should be able to respond to quality factors in the environment. • Diversity principle: the population should not commit its activities along excessively narrow channels. • Stability principle: the population should not change its mode of behavior every time the environment changes. • Adaptability principle: the population must be able to change behavior mode when it's worth the computational price.

Computational Systems Thinking Machine (CSTM)

• Sense-making • Decision-making • Adaptation In the center of all these ia Real-Time Wealth of Knowledge

Decision Analysis Stages

• Stage 1: • Evaluate the expected monetary value (EMV) from the profit and loss data, and their 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.

Types of Wind turbine systems

• Type A: Constant Speed Wind Turbine (WT) • 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.

Lack of It-OT integration results in

• Uninformed or poor decision-making • Lack of compliance • Poor communications • Inefficient field operations • Inability to effectively report to external stakeholders.

Analytic Hierarchy Process (AHP)

• a multi-criteria decision-making approach. • 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 importance of the decision criteria, and the relative performance measures of the alternatives in terms of each individual decision criterion. AHP has applications in: • Planning • Resource allocation • Conflict resolution.

Computational Intelligence

• capable of taking large raw numerical sensory data directly, • processing them by exploiting the representational parallelism and pipelining the problem, • generating reliable and timely responses, & • withstanding high fault tolerance.

Causes of severe blackouts

aging infrastructure high power demand natural events

Big Data Challenges

analysis, capture, curation, search, sharing, storage, transfer, visualization, privacy violations.

Control Variables

define the system and govern the evolution of the system from one stage to the next

state variables

describe the behavior of the system in any stage

HILF

high impact low frequency

Optimal Control Problem

mathematical programming problem involving a number of stages, where each stage evolves from the preceding stage in a prescribed manner

Hadoop

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.

Operational technology

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.

Information Technology

staff typically manages the transactional side of the enterprise: billing, accounting, asset management, human resources, and customer records.