2,741 research outputs found
State-of-the-Art Economic Load Dispatch of Power Systems Using Particle Swarm Optimization
Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one
of the most promising optimization techniques in solving highly constrained
non-linear and non-convex optimization problems in different areas of
electrical engineering. Economic operation of the power system is one of the
most important areas of electrical engineering where PSO has been used
efficiently in solving various issues of practical systems. In this paper, a
comprehensive survey of research works in solving various aspects of economic
load dispatch (ELD) problems of power system engineering using different types
of PSO algorithms is presented. Five important areas of ELD problems have been
identified, and the papers published in the general area of ELD using PSO have
been classified into these five sections. These five areas are (i) single
objective economic load dispatch, (ii) dynamic economic load dispatch, (iii)
economic load dispatch with non-conventional sources, (iv) multi-objective
environmental/economic dispatch, and (v) economic load dispatch of microgrids.
At the end of each category, a table is provided which describes the main
features of the papers in brief. The promising future works are given at the
conclusion of the review
Social cognitive optimization with tent map for combined heat and power economic dispatch
Combined heat and power economic dispatch (CHPED) problem is a sophisticated
constrained nonlinear optimization problem in a heat and power production
system for assigning heat and power production to minimize the production
costs. To address this challenging problem, a novel social cognitive
optimization algorithm with tent map (TSCO) is presented for solving the CHPED
problem. To handle the equality constraints in heat and power balance
constraints, adaptive constraints relaxing rule is adopted in constraint
processing. The novelty of our work lies in the introduction of a new powerful
TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of
the presented algorithm is validated by conducting 2 typical CHPED cases. The
numerical results show that the proposed approach has better convergence speed
and solution quality than all other existing state-of-the-art algorithms.Comment: Accepted by International Transactions on Electrical Energy System
A Logic-Based Mixed-Integer Nonlinear Programming Model to Solve Non-Convex and Non-Smooth Economic Dispatch Problems: An Accuracy Analysis
This paper presents a solver-friendly logic-based mixed-integer nonlinear
programming model (LB-MINLP) to solve economic dispatch (ED) problems
considering disjoint operating zones and valve-point effects. A simultaneous
consideration of transmission losses and logical constraints in ED problems
causes difficulties either in the linearization procedure, or in handling via
heuristic-based approaches, and this may result in outcome violation. The
non-smooth terms can make the situation even worse. On the other hand,
non-convex nonlinear models with logical constraints are not solvable using the
existing nonlinear commercial solvers. In order to explain and remedy these
shortcomings, we proposed a novel recasting strategy to overcome the hurdle of
solving such complicated problems with the aid of the existing nonlinear
solvers. The proposed model can facilitate the pre-solving and probing
techniques of the commercial solvers by recasting the logical constraints into
the mixed-integer terms of the objective function. It consequently results in a
higher accuracy of the model and better computational efficiency. The acquired
results demonstrated that the LB-MINLP model, compared to the existing
(heuristic-based and solver-based) models in the literature, can easily handle
the non-smooth and nonlinear terms and achieve an optimal solution much faster
and without any outcome violation
Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem
Optimization problems in design engineering are complex by nature, often
because of the involvement of critical objective functions accompanied by a
number of rigid constraints associated with the products involved. One such
problem is Economic Load Dispatch (ED) problem which focuses on the
optimization of the fuel cost while satisfying some system constraints.
Classical optimization algorithms are not sufficient and also inefficient for
the ED problem involving highly nonlinear, and non-convex functions both in the
objective and in the constraints. This led to the development of metaheuristic
optimization approaches which can solve the ED problem almost efficiently. This
paper presents a novel robust plant intelligence based Adaptive Plant
Propagation Algorithm (APPA) which is used to solve the classical ED problem.
The application of the proposed method to the 3-generator and 6-generator
systems shows the efficiency and robustness of the proposed algorithm. A
comparative study with another state-of-the-art algorithm (APSO) demonstrates
the quality of the solution achieved by the proposed method along with the
convergence characteristics of the proposed approach.Comment: 11 pages, 2 figure
A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Economic Load Dispatch (ELD) is one of the essential components in power
system control and operation. Although conventional ELD formulation can be
solved using mathematical programming techniques, modern power system
introduces new models of the power units which are non-convex,
non-differentiable, and sometimes non-continuous. In order to solve such
non-convex ELD problems, in this paper we propose a new approach based on the
Social Spider Algorithm (SSA). The classical SSA is modified and enhanced to
adapt to the unique characteristics of ELD problems, e.g., valve-point effects,
multi-fuel operations, prohibited operating zones, and line losses. To
demonstrate the superiority of our proposed approach, five widely-adopted test
systems are employed and the simulation results are compared with the
state-of-the-art algorithms. In addition, the parameter sensitivity is
illustrated by a series of simulations. The simulation results show that SSA
can solve ELD problems effectively and efficiently
The Economic Dispatch for Integrated Wind Power Systems Using Particle Swarm Optimization
The economic dispatch of wind power units is quite different from that in
conventional thermal units, since the adopted model should take into
consideration the intermittency nature of wind speed as well. Therefore, this
paper uses a model that takes into account the aforementioned consideration in
addition to whether the utility owns wind turbines or not. The economic
dispatch is solved by using one of the modern optimization algorithms: the
particle swarm optimization algorithm. A 6-bus system is used and it includes
wind-powered generators besides to thermal generators. The thorough analysis of
the results is also provided.Comment: This paper is a partial work of M.S.Thesis in Electrical and Computer
Engineering at Southern Illinois University Carbondal
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives
Particle Swarm Optimization (PSO) is a metaheuristic global optimization
paradigm that has gained prominence in the last two decades due to its ease of
application in unsupervised, complex multidimensional problems which cannot be
solved using traditional deterministic algorithms. The canonical particle swarm
optimizer is based on the flocking behavior and social co-operation of birds
and fish schools and draws heavily from the evolutionary behavior of these
organisms. This paper serves to provide a thorough survey of the PSO algorithm
with special emphasis on the development, deployment and improvements of its
most basic as well as some of the state-of-the-art implementations. Concepts
and directions on choosing the inertia weight, constriction factor, cognition
and social weights and perspectives on convergence, parallelization, elitism,
niching and discrete optimization as well as neighborhood topologies are
outlined. Hybridization attempts with other evolutionary and swarm paradigms in
selected applications are covered and an up-to-date review is put forward for
the interested reader.Comment: 34 pages, 7 table
Selection of Most Effective Control Variables for Solving Optimal Power Flow Using Sensitivity Analysis in Particle Swarm Algorithm
Solving the optimal power flow problem is one of the main objectives in
electrical power systems analysis and design. The modern optimization
algorithms such as the evolutionary algorithms are also adopted to solve this
problem, especially when the intermittency nature of generation resources are
included, as in wind and solar energy resources, where the models are
stochastic and non-linear. This paper uses the particle swarm optimization
algorithm for solving the optimal power flow for IEEE-30 bus system. In
addition to selection of the most effective control variables based on
sensitivity analysis to alleviate the violations and return the system back to
its normal state. This adopted strategy would decrease the optimal power flow
calculation burden by particle swarm optimization algorithm, especially with
large systems.Comment: This article is a partial work of the author's M.Sc thesis at
department of Electrical and Computer Engineering Southern Illinois
University Carbondale, US
Improvement of PSO algorithm by memory based gradient search - application in inventory management
Advanced inventory management in complex supply chains requires effective and
robust nonlinear optimization due to the stochastic nature of supply and demand
variations. Application of estimated gradients can boost up the convergence of
Particle Swarm Optimization (PSO) algorithm but classical gradient calculation
cannot be applied to stochastic and uncertain systems. In these situations
Monte-Carlo (MC) simulation can be applied to determine the gradient. We
developed a memory based algorithm where instead of generating and evaluating
new simulated samples the stored and shared former function evaluations of the
particles are sampled to estimate the gradients by local weighted least squares
regression. The performance of the resulted regional gradient-based PSO is
verified by several benchmark problems and in a complex application example
where optimal reorder points of a supply chain are determined.Comment: book chapter, 20 pages, 7 figures, 2 table
Optimal Power Flow with Disjoint Prohibited Zones: New Formulation and Solutions
The constraints induced by prohibited zones (PZs) were traditionally
formulated as multiple disjoint regions. It was difficult to solve the optimal
power flow (OPF) problems subject to the disjoint constraints. This paper
proposes a new formulation for the OPF problem with PZs. The proposed
formulation significantly expedites the algorithm implementation. The
effectiveness of the new approach is verified by different methods including
traditional optimization methods, PSO and particle swarm optimization with
adaptive parameter control which is conducted on the IEEE 30-bus test system.Comment: Accepted in 2019 IEEE TPE
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