Deregulated Electricity Structures and Smart Grids

Baseem Khan, Om Mahela, Sanjeevikumar Padmanaban, Hassan Haes Alhelou, Baseem Khan, Om Prakash Mahela, Sanjeevikumar Padmanaban, Hassan Haes Alhelou

• Publisher: CRC Press
• Year: 2022
• ISBN:
  9781000567274
• Edition: 1
• Topic: Technologie et ingénierie
• Subtopic: Ingénierie de l'électricité et des télécommunications

The goals of restructuring of the power sector are competition and operating efficiency in the power industry that result in reliable, economical, and quality power supply to consumers. This comprehensive reference text provides an in-depth insight into these topics.

Deregulated Electricity Structures and Smart Grids discusses issues including renewable energy integration, reliability assessment, stability analysis, reactive power compensation in smart grids, and harmonic mitigation, in the context of the deregulated smart electricity market. It covers important concepts including AC and DC grid modelling, harmonics mitigation and reactive power compensation in the deregulated smart grid, and extraction of energy from renewable energy sources under the deregulated electricity market with the smart grid.

The text will be useful for graduate students and professionals in the fields of electrical engineering, electronics and communication engineering, renewable energy, and clean technologies.

1 Optimal Decision Making under Uncertainty Using Heuristic Approach in Restructured Power System

DOI: 10.1201/9781003278030-1

Arvind Kumar Jain

Contents

1. 1.1 Introduction
2. 1.2 Background: Restructured Power System
   1. 1.2.1 Competitive Electricity Markets: An Overview
3. 1.3 Technical and Policy Issues in the Electricity Markets
4. 1.4 Strategic Bidding
5. 1.5 Risk of Generators
6. 1.6 Congestion Management
   1. 1.6.1 LMP
   2. 1.6.2 Zonal Price
   3. 1.6.3 Market Split
   4. 1.6.4 Counter Flow Re-dispatch
7. 1.7 An Overview of the Few EM Models
   1. 1.7.1 Nord Pool Electricity Market
   2. 1.7.2 PJM Electricity Market
   3. 1.7.3 Indian Electricity Market
8. 1.8 ABC Algorithm
9. 1.9 Stochastic Programming Problem for Optimal Decision Making
   1. 1.9.1 BLOP
   2. 1.9.2 Uncertainty Modelling through Monte Carlo Method
10. 1.10 Solution Algorithm
11. 1.11 Case Study
12. 1.12 Future Research Directions
13. 1.13 Conclusion
14. References

1.1 Introduction

Over the past two decades, several countries around the world have restructured their power sectors. The basic objective behind restructuring of the power industry has been to introduce competition for improving efficiency. However, due to several barriers caused by huge capital cost, transmission congestion and losses, the competitive power markets are imperfect due to oligopoly nature. This flawed nature of the electricity markets has forced the participants to adopt a new way of understanding their participation in the market. In the competitive environment, the suppliers’ revenue depends on their ability to sell the energy, and buyers’ saving depends on their active participation in the Electricity Market (EM). To facilitate competitive trading of energy, most of the day-ahead electricity markets require their participants to submit their bids. The System Operator (SO) or Power Exchange (PX) clears the market based on their bids [1, 2, 3, 4 and 5].

In the last two decades, some research has been carried out to obtain profitable strategies of a power supplier. While developing the bidding strategies, a supplier estimates the market clearing price (MCP) and rivals’ bidding strategies and utilizes the game theory approach. Due to technical and regulatory constraints, various bidding models, forecasted demand and rivals’ bid strategies make bidding strategy problem a stochastic optimization problem. Application of heuristic methods to the strategic bidding problem has been reported in the literature. Because heuristic techniques are less affected by the size and non-linearity of the problem and can converge to the optimal solution, where most of the analytical methods fail to converge. However, application of Artificial Bee Colony (ABC) algorithm, which has several advantages over similar population-based heuristic methods, has not been reported for developing the optimal bidding strategy in electricity markets. Separate energy and ancillary services markets have been developed in various parts of the world. In most of the countries, the procurement of ancillary services is contracted to the SO. However, in some countries, the SO purchases some of the ancillary services through market mechanism. Generally, energy market is a forward market, and ancillary services market is close to real-time market. During the actual time of delivery, imbalance between supply and demand is experienced due to frequent change in load. This imbalance may disturb the system frequency. Therefore, matching of supply and demand is continuously required. Energy-balancing services are traded in both the day-ahead and the real-time balancing market.

This chapter includes background of restructured power system along with technical and policy issues, congestion management methods, strategic bidding, generators financial risk and brief introduction about the Pennsylvania-New Jersey-Maryland (PJM), Nord Pool and Indian electricity markets. ABC algorithm-based approach for optimal decision making under uncertainty has been discussed to solve stochastic optimization problem. A case study to show the impact of ABC algorithm on decision making is presented in ensuing sections along with future directions and conclusion.

1.2 Background: Restructured Power System

Deregulation of power sector has forced the suppliers to adopt a new way of understanding their business. In vertically integrated structure, the electric utilities were guaranteed regulated rates of the electric supply with some profit above the production cost. However, in competitive power markets, suppliers’ revenue depends on their ability to sell electricity with maximum possible profit. Further, with the introduction of the competitive EMs, area of optimal bidding strategy has gained attention of the researchers to analyse the techno-economic issues related to the EMs and estimate the risk associated with the strategic behaviour of the participants. The complexity in electricity markets arises due to large number of entities, contractual obligations, separation of energy and ancillary services markets, and different market models [6, 7, 8, 9 and 10].

1.2.1 Competitive Electricity Markets: An Overview

The electricity markets have been established globally to facilitate economical operation through competition. Power system security is the significant feature of the system operation for reliable wheeling of electrical energy. In a competitive environment, ancillary services are utilized for managing secure operation of power system. A general introduction to the EM models and entities is given below.

• EM models
  Market models can be classified, based on the nature of transactions, as following.
• Pool market model
  In this model, the SO or market administrator electronically receives price quantity bids from power suppliers and buyers in the energy exchange. The SO clears the market using merit order dispatch and sends the information to the qualified participants.
• Bilateral market model
  In bilateral market model, suppliers and buyers enter into power purchase agreement directly. Settlement of energy price, quantity and duration depends on the discretion of supplier and buyer. The SO doesn’t interfere in between. Quantities traded and trade prices are at the discretion of these parties and not a matter of the SO.
• Pool + bilateral market model
  In this model, suppliers and buyers submit their bids into the day-ahead pool market as well as sign bilateral contract with each other for a certain period. Hence, this model provides more flexible options for transmission...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgements
• Editors
• Contributors
• Chapter 1 Optimal Decision Making under Uncertainty Using Heuristic Approach in Restructured Power System
• Chapter 2 Harmonic Mitigation Methods in Microgrids
• Chapter 3 Energy Management in Deregulated Power Market with Integration of Microgrid
• Chapter 4 Business Models for Different Future Electricity Market Players
• Chapter 5 Distributed Generation, Storage and Active Network Management
• Chapter 6 Internet of Things (IoT) in Renewable Energy Utilities towards Enhanced Energy Optimization
• Chapter 7 Congestion Management and Market Analysis in Deregulated Power System
• Chapter 8 Grid Synchronization of Photovoltaic System with Harmonics Mitigation Techniques for Power Quality Improvement
• Chapter 9 A Comprehensive Formal Reliability Study of Advanced Metering Infrastructure on Smart Grid
• Chapter 10 An Optimized Approach for Restructuring of Transmission System to Mitigate Renewable Energy Constraints
• Chapter 11 Performance of Multifunctional Grid-tied Photovoltaic Inverters with Active and Harmonic Power Weight (AHPW) Control Technique
• Chapter 12 Valuation of Dynamic VAR Support in Deregulated Power System
• Chapter 13 Smart Grid Cyber Security Threats and Solutions
• Chapter 14 Review of Congestion Management in Deregulated Power System
• Chapter 15 Restructuring of Power System Network to Mitigate Renewable Energy Evacuation Constraints: A Comprehensive Study
• Index