Optimal Utilization of Smart Grid Ressources to Offer Social Welfare.Theory, Concept and Implementation

Table of Contents

1 Introduction

1.1 Motivation behind the Present Research Work Conducted

1.1.1 Social Welfare Optimisation-National Scenario

1.1.2 Social welfare Optimisation –International Scenario

1.2 Thesis Objectives

1.3 Literature Review

1.3.1 Operational Standard Management Strategies Emphasizing Transmission Line Congestion

1.3.2 Optimisation of Operational Standard Management Cost

1.3.3 Price Sensitive Modelling of Power Networks

1.3.4 Small Signal Stability Analysis of Social Welfare Optimization Techniques

1.4 Tools and Methodologies used

1.4.1 Optimal Power Flow (OPF)

1.4.2 Base Case Analysis

1.4.3 Congestion Management with Contingency Filtering

1.4.3.1 Loading Margins and their Impacts

1.4.3.2 Congestion Management Cost

1.4.4 Social Welfare Optimisation

1.4.4.1 Price Sensitive Modelling of Smart Power Networks

1.4.5 Small Signal Analysis of Social Welfare Optimisation Technique

1.4.5.1 Eigen Value Analysis

1.4.5.2 Time Domain Simulation

1.4.6 Hardware and Software

1.5 Thesis Organisation

2 Power System Optimisation With Operational Constraints

2.1 Power System Optimisation

2.1.1 Classical Optimisation Technique

2.1.2 Particle Swarm Optimisation Technique

2.1.3 Differential Evolution based Optimisation Technique

2.1.4 Application of Stochastic Optimization Algorithms on System Model

2.2 Identification of Limit Violation

2.2.1 Theory of Line Loading Index

2.2.2 Contingency Filtering employing the Index

2.3 Congestion Management based Optimal Power Flow

2.3.1 Traditional and Developed Objective Function

2.3.2 Operational Constraints

2.3.3 Technical Limits

2.3.4 Description of the Methodology

2.4 Illustrative Case Study

2.4.1 Base Case

2.4.2 Solving the Operational Standard Constraint OPF

2.4.3 Remarks on Penalty Factor

2.4.4 Improvement in Voltage Profile

2.4.5 Improvement in Transmission Loss Profile

2.4.6 The Operational Standard Management Cost

2.4.7 The Generation Shift

2.4.8 Comparison of Operational Cost

2.4.9 Saving in Operational Cost

2.4.10 Simulation time

2.5 Summary and Conclusions

2.6 Publications on the Present Work

3 Optimisation of Operational Standard Management Cost

3.1 Operational Standard and their Management

3.1.1 System Model

3.1.2 Operational Standard Violation Analysis

3.1.2.1 Congestion Relief Cost

3.1.2.2 Operational Cost

3.2 Generation Rescheduling

3.2.1 The Operational Standard Cost Management Problem

3.2.1.1 Objective Function

3.2.1.2 Variation of Congestion Relief Cost

3.2.1.3 Operational Constraints and Technical Limits

3.2.2 Description of the Developed Methodology

3.3 Illustrative Case Study

3.3.1 Base Case

3.3.2 Contingency Selection

3.3.3 The Congestion Sensitivity Index Computation

3.3.4 Contribution Schedule of the Generators

3.3.5 Reduction in Generation Cost w. r. to Loss Optimisation Technique

3.3.6 Relieving Line Congestion by Imposing Penalty with the Developed Algorithm

3.3.7 Improvement in Voltage Profile

3.3.8 Saving in Congestion Management Charge

3.4 Economic Integration of Hybrid Wind Thermal Networks with Congestion Management Cost Optimisation Technique

3.4.1 Wind Source Modelling

3.4.2 Selection of Buses to apply Loading Stress

3.4.3 Flow Chart of the Methodology

3.4.4 Base Case

3.4.5 Reactive Loading Stress

3.4.6 Experimentation on Contingent State of System

3.4.7 Active Power Loading Stress

3.4.8 Simulation time

3.5 Summary and Conclusions

3.6 Publications on the Present Work

4 Optimisation of Social Welfare in Smart Grid Scenario

4.1 Smart Grid

4.1.1 Features of Smart Grid

4.1.1.1 Reliability

4.1.1.2 Flexibility in Network Topology

4.1.1.3 Efficiency

4.1.1.3.1 Load Adjustment/Load Balancing

4.1.1.3.2 Peak Curtailment

4.1.1.4 Sustainability

4.1.1.5 Market-Enabling

4.1.1.6 Demand Response Support

4.1.2 Market Structure in Smart Grid

4.1.3 Price Sensitive Modelling of Generation and Demand

4.1.4 State Space Modelling of Power System

4.1.5 Traditional and State Space based Model

4.2 Social Welfare

4.2.1 Social Welfare Optimisation Problem

4.2.1.1 Objective Function

4.2.1.2 A Novel Load Curtailment Strategy

4.2.1.3 Operational Constraints

4.2.1.4 The Price Equilibrium Problem

4.2.2 Description of the Developed Methodology

4.3 Illustrative Case Study

4.3.1 Base Case

4.3.2 Selection of Stressed Loading Conditions

4.3.3 Comparison of Traditional Optimisation with Developed Optimisation Technique

4.3.4 Performance of Developed Methodology with Intermittent Energy Sources

4.3.5 Conservative Load Curtailment Attribute of the Developed Methodology

4.3.6 Simulation time

4.5 Summary and Conclusions

4.6 Publications on the Present Work

5 Small Signal Stability Analysis of Social Welfare Optimisation Strategy

5.1 Small Signal Stability

5.1.1 System Model

5.1.2 Small Signal Stability Assessment

5.1.2.1 Linearization

5.1.2.2 Stability Criteria

5.1.2.3 Bifurcation Analysis

5.1.2.4 Development of Bifurcation Index

5.2 Price Sensitive Re-dispatching

5.2.1 SSSC-OPF Problem Description

5.2.1.1 Objective Function

5.2.1.2 Operational Constraints

5.2.1.3 Technical Limits

5.2.2 Description of the Developed Methodology

5.3 Illustrative Case Study

5.3.1 Base Case

5.3.2 Selection of Stressed Operational Conditions

5.3.3 Improvement of Stability Margins

5.3.4 Performance with Intermittency of Generation

5.3.5 Simulation time

5.4 Summary and Conclusions

5.5 Publications on the Present Work

6 Summary, Conclusions, Contributions and Future Research

6.1 Thesis Summary

6.2 Conclusions

6.3 Major Contributions of the Present Thesis

6.4 Scope of Future Work

Appendix A Base Case Operating Conditions

A.1 Introduction function

A.2 Traditional and Developed Objective

A.3 Operational Constraint

A.4 Operational Limits

Appendix B Modelling of Power System Components

B.1 Transmission Line Model

B.2 Transformer Model

B.3 Load Model

B.4 Generator Model

Appendix C System Data

C.1 IEEE 30 Bus data

C.1.1 IEEE 30 Bus System

C.1.2 Bus data

C.1.3 Line Data

C.1.4 Generator Cost Characteristics

C.1.5 Operational Limits

C.2 Modified IEEE 30 bus data

C.1.1 Bus data

C.1.2 Line Data

C.1.3 Generator Cost Characteristics

C.1.4 Consumer Cost Benefit function

Research Objectives and Core Topics

The primary research objective is to develop advanced optimization models and methodologies, specifically incorporating optimal power flow (OPF) and stochastic optimization algorithms like Particle Swarm Optimization (PSO) and Differential Evolution (DE), to ensure operational excellence and social welfare in traditional and smart grid power networks, particularly under contingency conditions.

• Development of congestion-constrained OPF methods to minimize operational costs and line congestion.
• Integration of demand-side management and price-sensitive modeling within the Smart Grid framework.
• Implementation of small-signal stability analysis and the development of a bifurcation index for system monitoring.
• Optimization of generation rescheduling strategies to enhance system reliability and disruption resilience.
• Economic integration of hybrid wind-thermal systems into the existing grid infrastructure.

Excerpt from the Book

Summary of Chapters

Introduction: Provides a broad overview of power system challenges, focusing on grid security, congestion management, and the motivation for developing optimization models for social welfare.

Power System Optimisation With Operational Constraints: Explores the application of stochastic optimization techniques (PSO and DE) to handle non-linear operational constraints, including limit violation assessment through line loading indices.

Optimisation of Operational Standard Management Cost: Discusses methodology for minimizing management costs in contingent systems, utilizing congestion sensitivity indices and integrating wind-thermal hybrid systems.

Optimisation of Social Welfare in Smart Grid Scenario: Focuses on incorporating demand response and price-sensitive models into OPF to maximize social welfare and ensure reliable load catering in smart grids.

Small Signal Stability Analysis of Social Welfare Optimisation Strategy: Introduces bifurcation indices to assess and ensure small-signal stability, preventing frequency and voltage instabilities in optimized systems.

Summary, Conclusions, Contributions and Future Research: Synthesizes the core findings, contributions, and potential future research directions for power system operation and stability enhancement.

Keywords

Optimal Power Flow (OPF), Social Welfare, Smart Grid, Congestion Management, Particle Swarm Optimization (PSO), Differential Evolution (DE), Small Signal Stability, Hopf Bifurcation, Demand Response, Contingency Filtering, Grid Security, Voltage Profile, Transmission Loss, Line Loading Index, Independent System Operator (ISO)

Frequently Asked Questions

What is the fundamental focus of this research?

The work focuses on developing novel models and optimization techniques to ensure the "operational excellence" and economic efficiency of modern power grids, balancing the interests of market participants to maximize social welfare.

What are the primary thematic areas covered?

The research spans congestion management, operational cost minimization, Smart Grid integration, demand response mechanisms, and small-signal stability analysis.

What is the primary goal of the proposed optimization?

The goal is to maintain secure system operation (within voltage, line flow, and stability limits) while simultaneously minimizing operational costs and maximizing load catering through sophisticated generation rescheduling and demand management.

Which scientific methods are utilized in this work?

The authors primarily employ stochastic optimization algorithms, specifically Particle Swarm Optimization (PSO) and Differential Evolution (DE), along with state-space modeling and Eigenvalue analysis to assess system stability.

What topics are addressed in the main body of the book?

The book progresses from traditional grid optimization constraints to advanced Smart Grid scenarios, concluding with the integration of small-signal stability analysis into the social welfare optimization framework.

What are the key terms that define this work?

Keywords include Optimal Power Flow, Social Welfare, Smart Grid, Congestion Management, Particle Swarm Optimization, Differential Evolution, Small Signal Stability, and Grid Security.

How is the "Small Signal Stability" evaluated?

The authors use Eigenvalue analysis of the Jacobian matrix, developed as a "Bifurcation Index," to identify potential instabilities and incorporate these as constraints into the optimization model.

How does the research approach congestion management?

Instead of merely applying penalties, the research develops sensitivity indices—such as the 'Line Loading Index' and 'Congestion Sensitivity Index'—to intelligently filter worst-case contingencies and guide optimal generation rescheduling.

What is the role of Demand Response in this study?

Demand Response is modeled as an active, price-sensitive component where consumers participate in the market based on their willingness to pay, allowing for more efficient load-shaping and peak shaving.