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A swarm intelligence approach to harness maximum techno-commercial benefits from smart power grids

A swarm intelligence approach to harness maximum techno-commercial benefits from smart power grids

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Modern electricity power market has evolved from conventional vertically integrated structure to the present deregulated form and eventually to the development of the smart power grids, over the past few decades. Different market players like generation, transmission and distribution companies with their individual and collective goals and constraints are now participating in real time, prompting the need for optimal allocation and utilization of the smart grid infrastructure and resources. The objective of the optimization thus received a paradigm shift from the traditional generation cost optimization, to optimal utilization of the available resources to deliver maximum benefit to all the power market participants and the so-called social welfare. Maximization of social welfare is a highly nonlinear optimization problem and generally requires application of an efficient stochastic optimization method with in-built ability of avoiding local optima. The swarm intelligence-based optimization algorithms, developed and presented in this book chapter, offer substantial improvement in the quality of solution to the problem over the conventional solution methods. The chapter presents real-time simulation and experiments on benchmark power system networks with the developed optimization algorithms. The results are found to be quite encouraging.

Chapter Contents:

  • Abstract
  • 21.1 Evolution of vertically integrated power system to deregulated structure and smart power grids
  • 21.1.1 Paradigm shift in power system optimization methods
  • 21.1.1.1 Optimization of VIU with congestion management constraint
  • 21.1.1.2 Application of particle swarm optimization in maintaining operational standards in modern power system
  • 21.2 Resources and components of deregulated power systems and smart power grids
  • 21.2.1 Reliability
  • 21.2.2 Flexibility in network topology
  • 21.2.3 Efficiency
  • 21.2.4 Load adjustment/load balancing
  • 21.2.5 Peak curtailment
  • 21.2.6 Sustainability
  • 21.2.7 Market-enabling
  • 21.2.8 Demand response support
  • 21.3 Market structure in smart grid
  • 21.4 Modeling and computer simulation of smart grid
  • 21.4.1 The state variables in smart grid infrastructure
  • 21.5 The social welfare optimization problem
  • 21.5.1 Social welfare optimization — Indian scenario
  • 21.5.2 Social welfare optimization — international scenario
  • 21.6 Application of stochastic optimization algorithms in power system optimization problems
  • 21.6.1 Classical optimization technique
  • 21.6.2 Particle swarm optimization technique
  • 21.6.3 Application of stochastic optimization algorithms on system model
  • 21.7 Development of swarm intelligence based social welfare optimization algorithm
  • 21.7.1 Development of the objective function
  • 21.7.2 A novel load curtailment strategy
  • 21.7.3 Operational constraints
  • 21.7.4 The price equilibrium problem
  • 21.7.5 Description of the methodology
  • 21.7.6 Illustrative case studies and comparison with traditional optimization algorithms
  • 21.7.7 Base case
  • 21.7.7.1 Comparison of the developed methodology and the traditional cost optimization
  • 21.7.8 Performance evaluation of the developed algorithm with intermittent renewable energy sources
  • 21.8 Summary and conclusions
  • References

Inspec keywords: power markets; particle swarm optimisation; stochastic processes; smart power grids; optimisation

Other keywords: different market players; transmission; smart grid infrastructure; power market participants; smart power grids; distribution companies; social welfare; constraints; maximum benefit; traditional generation cost optimization; benchmark power system networks; harness maximum techno-commercial benefits; modern electricity power market; deregulated form; conventional solution methods; optimal utilization; collective goals; swarm intelligence-based optimization algorithms; swarm intelligence approach; developed optimization algorithms; highly nonlinear optimization problem; conventional vertically integrated structure; efficient stochastic optimization method; optimal allocation

Subjects: Optimisation techniques; Optimisation techniques; Power system management, operation and economics

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