Sawan Sen

Bio: Sawan Sen is an academic researcher from Kalyani Government Engineering College. The author has contributed to research in topics: Particle swarm optimization & Electric power system. The author has an hindex of 5, co-authored 18 publications receiving 83 citations. Previous affiliations of Sawan Sen include Techno India.

Power Network Reconfiguration For Congestion Management And Loss Minimization Using Genetic Algorithm

Abstract: Network reconfiguration is referred as operational schemes to alter the network topology by closing and opening the sectionalizing and tie switches of power distribution system and thus it allows to control the power flow from substation to power consumers with additional benefits such as load balancing, real power loss reducing, optimizing the load sharing between parallel circuits by directing power flow along contractual paths. Restructuring of the power network, however, may increase the tendency of overloading and thus congestion in certain areas. This congestion may lead to violation of system voltage or transmission capacity limits and threaten the power network security and reliability. On this view point, this paper presents a Genetic Algorithm (GA) based new reconfiguration algorithm of the network which will able to identify the most congested area of power network and fabricate the least loss condition after alleviating overload and overvoltage as well as ensuring efficient network operation. To establish the efficacy of the proposed algorithm, case studies have been carried out on modified IEEE-14 bus and IEEE-30 bus power network and the results are found to be encouraging.

Generator Contribution Based Congestion Management using Multiobjective Genetic Algorithm

Abstract: Congestion management is one of the key functions of system operator in the restructured power industry during unexpected contingency. This paper proposes a method for generator contribution based congestion management using multiobjective genetic algorithm. In the algorithm, both real and reactive losses have been optimised using optimal power flow model and the contributions of the generators with those optimised losses are calculated. On second level, the congested lines are identified by the proposed overloading index (OI) during contingency and those lines are relieved with the new contribution of generators, which is the outcome of the developed algorithm. The planned method depicts the information related to congestion management to minimize the investment cost, without installing any external devices and to maximise the consumer welfare by avoiding any load curtailment without affecting the voltage profile of the system as well as the optimised total system loss. IEEE 30 bus system is used to demonstrate the effectiveness of the method.

Electricity Pricing: Regulated, Deregulated and Smart Grid Systems

Abstract: List of Figures List of Tables Preface About the Authors List of Principal Symbols List of Abbreviations Prologue Motivation of the Book Contributions of the Book Organization of the Book Background and Literature Survey Introduction Power Network Performance Evaluation Importance of Voltage Stability on Performance Evaluation Significance of Compensation Techniques Optimization Methods with System Performance and Cost Emphasis Enrichment of Cost-Governed System Performance in Smart Grid Arena Concluding Remarks on Existing Efforts Annotating Outline Analysis of Voltage Stability of Longitudinal Power Supply System Using an Artificial Neural Network Introduction Theoretical Development of Voltage Stability and Voltage Collapse Theoretical Background of Voltage Instability and Its Causes Few Relevant Analytical Methods and Indices for Voltage Stability Assessment Theory of ANN Attributes of ANNs Analysis of Voltage Stability of Multi-Bus Power Network Classical Analysis of Voltage Stability Application of ANN on Voltage Stability Analysis Summary Annotating Outline Improvement of System Performances Using FACTS and HVDC Introduction Development of FACTS Controllers Modeling of Shunt Compensating Device Modeling of Series Compensating Device Prologue of High-Voltage Direct Current (HVDC) System Modeling of DC Link Improvement of System Performance Using FACTS and HVDC Improvement of Voltage Profile of Weak Bus Using SVC Application of ANN for the Improvement Voltage Profile Using SVC Application of TCSC and HVDC for Upgrading of Cost-Constrained System Performance Summary Annotating Outline Multi-Objective Optimization Algorithms for Deregulated Power Market Introduction Deregulated Power Market Structure Soft Computing Methodologies for Power Network Optimizations Overview of Genetic Algorithm Overview of Particle Swarm Optimization Overview of Differential Evolution Algorithms for Utility Optimization with Cost and Operational Constraints Genetic Algorithm-Based Cost-Constrained Transmission Line Loss Optimization GA-Based Generation Cost-Constrained Redispatching Schedules of GENCOs Congestion Management Methodologies Generator Contribution-Based Congestion Management Using Multi-Objective GA DE- and PSO-Based Cost-Governed Multi-Objective Solutions in Contingent State Mitigation of Line Congestion and Cost Optimization Using Multi-Objective PSO Swarm Intelligence-Based Cost Optimization for Contingency Surveillance Summary Annotating Outline Application of Stochastic Optimization Techniques in the Smart Grid Introduction Smart Grid and Its Objectives Concept of the Smart Grid Elementary Objectives of the Smart Grid and Demand Response Demand Response-Based Architecture of the Smart Grid Effect of DR on the Smart Grid Scenario Cost Component of the Smart Grid Smart Grid: Cost-Benefit Analysis Swarm Intelligence-Based Utility and Cost Optimization Cost Objective and Operating Constraints of the Work Theory of Cost-Regulated Curtailment Index (CI) Cost Realization Methodology Implementation with Swarm Intelligence Implementation of the Cost-Effective Methodology with DR Connectivity Summary Annotating Outline Epilogue Summary and Conclusions Future Scope References Appendix A: Description of Test Systems Appendix B: Development of System Performance Indices Index

Alleviation of line congestion using Multiobjective Particle Swarm Optimization

Abstract: This paper presents a methodology based on Particle Swarm Optimization technique for rescheduling of generation patterns to manage congestion in contingent power networks. In deregulated systems, line congestion attract additional penalties which add on to the overall operational cost to be incurred by the Independent System Operators (ISO), apart from causing limit violations, and stability problems. Thus, limiting the congestion level of lines and restricting power flows within the safe limits is important from stability as well as economy point of view. The algorithm proposed in the present paper uses a Standard Sensitivity Index to identify the congested zone(s) in a large power network and then adopt corrective actions for limiting line congestion at the cost of a nominal rescheduling cost without any load curtailment and installation of FACTS devices. It has been demonstrated that the proposed method can reduce congestion even below the minimum level obtained from the conventional cost optimization results. It has been depicted that the methodology on application can provide better operating conditions in respect of improvement of bus voltage profile. The efficiency of the proposed methodology has been tested on a IEEE 30 bus benchmark system and the results look promising.

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Abstract: Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.

A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm

Abstract: Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has been established in 1995 and became a very mature and most popular domain in SI. Multi-Objective PSO (MOPSO) established in 1999, has become an emerging field for solving MOOs with a large number of extensive literature, software, variants, codes and applications. This paper reviews all the applications of MOPSO in miscellaneous areas followed by the study on MOPSO variants in our next publication. An introduction to the key concepts in MOO is followed by the main body of review containing survey of existing work, organized by application area along with their multiple objectives, variants and further categorized variants.

Can We Afford Integrity by Proof-of-Work? Scenarios Inspired by the Bitcoin Currency

Abstract: Proof-of-Work (PoW), a well-known principle to ration resource access in client-server relations, is about to experience a renaissance as a mechanism to protect the integrity of a global state in distributed transaction systems under decentralized control. Most prominently, the Bitcoin cryptographic currency protocol leverages PoW to 1) prevent double spending and 2) establish scarcity, two essential properties of any electronic currency. This paper asks the important question whether this approach is generally viable. Citing actual data, it provides a first cut of an answer by estimating the resource requirements, in terms of operating cost and ecological footprint, of a suitably dimensioned PoW infrastructure and comparing them to three attack scenarios. The analysis is inspired by Bitcoin, but generalizes to potential successors, which fix Bitcoin’s technical and economic teething troubles discussed in the literature.