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Application of PSO algorithm to choose the best scenario for energy demand forecast of residential and commercial sectors in Iran

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2016年第4期10卷 576-593页

作者： Nazari, Hesam Kazemi, Aliyeh Univ Tehran Fac Management Dept Ind Management Tehran Iran

Purpose - This paper aims to select the best scenario for energy demand forecast of residential and commercial sectors in Iran by using particle swarm optimization algorithm. Design/methodology/approach - In this study, using variables affecting energy demand of residential and commercial sectors in Iran, the future status of energy demand in these sectors is predicted. Using the particle swarm optimization algorithm, both linear and exponential forms of energy demand equations were studied under 72 different scenarios with various variables. The data from 1968 to 2011 were applied for model development and the appropriate scenario choice. Findings - An exponential model with inputs including total value added minus that of the oil sector, value of made buildings, total number of households and consumer energy price index is the most suitable model. Finally, energy demand of residential and commercial sectors is estimated up to the year 2032. Results show that the energy demand of the sectors will achieve a level of about 1,718 million barrels of oil equivalent per year by 2032. Originality/value - To the best of our knowledge in this study a suitable model is selected for energy demand forecast of residential and commercial sectors by evaluation of various models with different variables as inputs.

关键词： Residential and commercial Forecasting Energy sector evolutionary programming Particle swarm optimization (PSO)

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evolutionary combinatorial chemistry: application of genetic algorithms

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DRUG DISCOVERY TODAY 1998年第8期3卷 379-385页

作者： Weber, L Morphochem AG D-82152 Martinsried Germany

evolutionary chemistry combines the evaluation of molecular properties and synthesis of novel compounds in a feedback loop to arrive at molecules with the desired properties. Inspired by natural evolutionary processes, combinatorial chemistry in combination with mathematical optimization methods and biological testing provides new approaches to drug discovery. Genetic algorithms have been applied with success in the design and automated synthesis of combinatorial compound libraries.

关键词： genetic algorithm combinatorial chemistry diversity evolutionary chemistry similarity evolutionary programming

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Security constrained economic load dispatch using improved particle swarm optimization suitable for utility system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2008年第10期30卷 609-613页

作者： Baskar, G. Mohan, M. R. Anna Univ Dept Elect & Elect Engn Madras 6000025 Tamil Nadu India

The objective of this paper is to evolve simple and effective methods for the economic load dispatch (ELD) problem with security constraints in thermal units, which are capable of obtaining economic scheduling for utility system. In the proposed improved particle swarm optimization (IPSO) method, a new velocity strategy equation is formulated suitable for a large scale system and the features of constriction factor approach (CFA) are also incorporated into the proposed approach. The CFA generates higher quality solutions than the conventional PSO approach. The proposed approach takes security constraints such as line flow constraints and bus voltage limits into account. In this paper, two different systems IEEE-14 bus and 66-bus Indian utility system have been considered for investigations and the results clearly show that the proposed IPSO method is very competent in solving ELD problem in comparison with other existing methods. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Security constraints Economic load dispatch evolutionary programming Improved particle swarm optimization

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Multi-fuel energy source dispatch considering bi-objectives using ant lion algorithm

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2018年第1期12卷 2-27页

作者： Pandiyan, Balachandar Ganesan, Sivarajan Jayakumar, Nadanasabapathy Subramanian, Srikrishna Annamalai Univ Chidambaram India

Purpose The ever-stringent environmental regulations force power producers to produce electricity at the cheapest price and with minimum pollutant emission levels. The electrical power generation from fossil fuel releases several contaminants into the air, and this becomes excrescent if the generating unit is fed by multiple fuel sources (MFSs). Inclusion of this issue in operational tasks is a welcome perspective. This paper aims to develop a multi-objective model comprising total fuel cost and pollutant emission. Design/methodology/approach The cost-effective and environmentally responsive power system operations in the presence of MFSs can be recognised as a multi-objective constrained optimisation problem with conflicting operational objectives. The complexity of the problem requires a suitable optimisation tool. Ant lion algorithm (ALA), the most recent nature-inspired algorithm, was used as the main optimisation tool because of its salient characteristics. The fuzzy decision-making mechanism has been integrated to determine the best compromised solution in the multi-objective framework. Findings This paper is the first to propose a more precise and practical operational model for studying a multi-fuel power dispatch scenario considering valve-point effects and CO2 emission. The modern meta-heuristic algorithm ALA is applied for the first time to address the economic operation of thermal power systems with multiple fuel options. Practical implications Power companies aim to make profit by abiding by the norms of the regulatory board. To achieve economic benefits, the power system must be analysed using an accurate operational model. The proposed model integrates total fuel cost, valve-point loadings and CO2 emission, which are prevailing power system operational objectives. The economic advantages of the operational model can be observed through economic deviation indices, and the performed analysis validates that the developed model corresponds to the actual p

关键词： CO2 emission Optimisation evolutionary programming Fossil fuel Thermal power Cost minimisation

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On the Convergence of Adaptive Stochastic Search Methods for Constrained and Multi-objective Black-Box Optimization

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2016年第3期170卷 932-959页

作者： Regis, Rommel G. St Josephs Univ Dept Math Philadelphia PA 19131 USA

Stochastic search methods for global optimization and multi-objective optimization are widely used in practice, especially on problems with black-box objective and constraint functions. Although there are many theoretical results on the convergence of stochastic search methods, relatively few deal with black-box constraints and multiple black-box objectives and previous convergence analyses require feasible iterates. Moreover, some of the convergence conditions are difficult to verify for practical stochastic algorithms, and some of the theoretical results only apply to specific algorithms. First, this article presents some technical conditions that guarantee the convergence of a general class of adaptive stochastic algorithms for constrained black-box global optimization that do not require iterates to be always feasible and applies them to practical algorithms, including an evolutionary algorithm. The conditions are only required for a subsequence of the iterations and provide a recipe for making any algorithm converge to the global minimum in a probabilistic sense. Second, it uses the results for constrained optimization to derive convergence results for stochastic search methods for constrained multi-objective optimization.

关键词： Constrained optimization Multi-objective optimization Random search Convergence evolutionary programming

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Using genetical and cultural search to design unorganised machines

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evolutionary INTELLIGENCE 2012年第1期5卷 23-33页

作者： Bull, Larry Univ West England Dept Comp Sci Bristol BS16 1QY Avon England

In 1948 Turing presented a general representation scheme by which to achieve artificial intelligence-his unorganised machines. Significantly, these were a form of discrete dynamical system and yet dynamical representations remain almost unexplored within evolutionary computation. Further, at the same time as also suggesting that natural evolution may provide inspiration for search mechanisms to design machines, he noted that mechanisms inspired by the social aspects of learning may prove useful. This paper presents results from an investigation into using Turing's dynamical representation designed by evolutionary programming and a new imitation-based, i. e., cultural, approach. Moreover, the original synchronous and an asynchronous form of unorganised machines are considered.

关键词： A-type unorganised machines Discrete dynamical systems evolutionary programming Imitation

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A hierarchical evolutionary algorithm for constructing and training wavelet networks

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NEURAL COMPUTING & APPLICATIONS 2002年第4期10卷 357-366页

作者： He, YY Chu, FL Zhong, BL Tsing Hua Univ Dept Precis Instruments Beijing 10084 Peoples R China

The wavelet network has been introduced as a special feed-forward neural network supported by the wavelet theory, and has become a popular tool in the approximation and forecast fields. In this paper, an evolutionary algorithm is proposed for constructing and training the wavelet network for approximation and forecast. This evolutionary algorithm utilises the hierarchical chromosome to encode the structure and parameters of the wavelet network, and combines a genetic algorithm and evolutionary programming to construct and train the network simultaneously through evolution. The numerical examples are presented to show the efficiency and potential of the proposed algorithm with respect to,function approximation, sunspot time series forecast and condition forecast for a hydroturbine machine, respectively. The study also indicates that the proposed method has the potential to solve a wide range of neural network construction and training problems in a systematic and robust way.

关键词： approximation evolutionary programming forecast genetic algorithm wavelet network

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Optimum management of manual sectionalizers in electric power distribution networks integrating distributed generations using binary exchange market algorithm

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2021年第2期15卷 328-351页

作者： Farhadi, Payam Tafreshi, Seycd Masoud Moghaddas Univ Guilan Fac Engn Dept Elect Engn Rasht Iran

Purpose The use of power sectionalizers in electric power distribution networks as disconnecting devices for optimum network configuration is indispensable. Major reasons to use sectionalizers, here manual sectionalizers, is their lower installation and operating prices compared to other types of disconnecting devices and that most of conventional realistic electric power distribution systems are still using manual sectionalizers due to their ease of procurement. However, in case of failure for these switches, power supply interruptions are unavoidable unless optimum solutions are used for configuration (and possibly reconfiguration) of sectionalizers. Thus, in this research, binary exchange market algorithm (BEMA) as a novel evolutionary metaheuristic is used to meet the maximized customer satisfaction by optimized configuration of sectionalizers within electric power distribution networks in the presence of distributed generations (DGs). To solve the problem, BEMA is used on sectionalizing switch placement problem, which has only two open and close (0/1) states. A novel multi-objective optimization problem has been formulated as a function of two aspects, namely, improved reliability index (for customer benefit) and minimized sectionalizing switch costs (for utility benefits). Simulations are carried out in three different case studies to validate the effectiveness of the BEMA both in theory and practice: Standard IEEE 33-bus test system, practical feeder-8 of MeshkinShahr Town's electric power distribution network in northwest of Iran;and Roy Billinton test system Bus 4 (RBTS-Bus 4). The obtained results are compared with those of the previously validated ant colony optimization (ACO) technique in RBTS-Bus 4. Design/methodology/approach The optimum configuration of sectionalizers in the presence of DGs has been formulated as a multi-objective function consisting of two conflicting objectives. First objective is to improve the power distribution network reliabilit

关键词： Energy sector Multiobjective optimization Energy conversion evolutionary programming Simulation Reliability Electricity Distribution network Exchange market algorithm Optimized configuration Sectionalizers

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Twenty years of artificial life art

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DIGITAL CREATIVITY 2010年第3期21卷 197-204页

作者： Penny, Simon Univ Calif Irvine Irvine CA 92717 USA

This essay begins with discussion of four relatively recent works which are representative of major themes and preoccupations in Artificial Life Art: 'Propagaciones' by Leo Nunez;'Sniff' by Karolina Sobecka and Jim George;'Universal Whistling Machine' by Marc Boehlen;and 'Performative Ecologies' by Ruari Glynn. This essay is an attempt to contextualise these works by providing an overview of the history and forms of Artificial Life Art as it has developed over two decades, along with some background in the ideas of the Artificial Life movement of the late 1980s and 1990s.1.

关键词： Artificial Life Artificial Life Art media art digital art interdisciplinarity cybernetics Emergence interactivity autonomous agent genetic algorithm evolutionary programming

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APPLICATION OF PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMAL REACTIVE POWER PLANNING

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CONTROL AND INTELLIGENT SYSTEMS 2007年第1期35卷 66-72页

作者： Al-Hamouz, Z. Faisal, S. F. Al-Sharif, S. Department of Electrical Engineering King Fahd University of Petroleum and Minerals (KFUPM) Dhahran 31261 Saudi Arabia Department of Electrical and Computer Engineering University of South Alabama Mobile AL 36688-002 307 N. University Blvd. United States

This paper investigates the applicability of the particle swarm optimization (PSO) algorithm to the optimal reactive power planning (ORPP) problem. The paper uses the fuel cost minimization approach to solve the ORPP problem. The problem is decomposed into the real power (P) and the reactive power (Q) optimization subproblems. The P optimization minimizes the operation cost by adjusting P generation, while Q optimization adjusts transformer tap settings. Q generation and VAR source investment minimizes the operation cost and the investment on VARs. The P and Q subproblems are each optimized by the PSO in an iterative manner until the global minimum is obtained. The effectiveness of the proposed PSO is tested on the IEEE 30- bus system and the results are compared with those of evolutionary programming, evolutionary strategy, and linear programming.

关键词： Optimal reactive power planning particle swarm optimization artificial intelligence evolutionary programming evolutionary strategy

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