Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions

Most of water distribution systems (WDS) need rehabilitation due to aging infrastructure leading to decreasing capacity, increasing leakage and consequently low performance of the WDS. However an appropriate strategy including location and time of pipeline rehabilitation in a WDS with respect to a limited budget is the main challenge which has been addressed frequently by researchers and practitioners. On the other hand, selection of appropriate rehabilitation technique and material types is another main issue which has yet to address properly. The latter can affect the environmental impacts of a rehabilitation strategy meeting the challenges of global warming mitigation and consequent climate change. This paper presents a multi-objective optimization model for rehabilitation strategy in WDS addressing the abovementioned criteria mainly focused on greenhouse gas (GHG) emissions either directly from fossil fuel and electricity or indirectly from embodied energy of materials. Thus, the objective functions are to minimise: (1) the total cost of rehabilitation including capital and operational costs; (2) the leakage amount; (3) GHG emissions. The Pareto optimal front containing optimal solutions is determined using Non-dominated Sorting Genetic Algorithm NSGA-II. Decision variables in this optimisation problem are classified into a number of groups as: (1) percentage proportion of each rehabilitation technique each year; (2) material types of new pipeline for rehabilitation each year. Rehabilitation techniques used here includes replacement, rehabilitation and lining, cleaning, pipe duplication. The developed model is demonstrated through its application to a Mahalat WDS located in central part of Iran. The rehabilitation strategy is analysed for a 40 year planning horizon. A number of conventional techniques for selecting pipes for rehabilitation are analysed in this study. The results show that the optimal rehabilitation strategy considering GHG emissions is able to successfully save the total expenses, efficiently decrease the leakage amount from the WDS whilst meeting environmental criteria.

Optimum contribution selection using differential evolution

Um programa baseado na técnica de evolução diferencial foi desenvolvido para a definição da contribuição genética ótima na seleção de candidatos a reprodução. A função- objetivo a ser otimizada foi composta pelo mérito genético esperado da futura progênie e pela coascendência média dos animais em reprodução. Conjuntos de dados reais e simulados de populações com gerações sobrepostas foram usados para validar e testar o desempenho do programa desenvolvido. O programa se mostrou computacionalmente eficiente e viável para ser aplicado na prática e as consequências esperadas de sua aplicação, em comparação a procedimentos empíricos de controle da endogamia e/ou com a seleção baseada apenas no valor genético esperado, seriam a melhora da resposta genética futura e limitação mais efetiva da taxa de endogamia.

Dilema da diversidade-acurácia: um estudo empírico no contexto de multiclassificadores

Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, it s necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Spatial electric load forecasting using an evolutionary heuristic

A method for spatial electric load forecasting using elements from evolutionary algorithms is presented. The method uses concepts from knowledge extraction algorithms and linguistic rules' representation to characterize the preferences for land use into a spatial database. The future land use preferences in undeveloped zones in the electrical utility service area are determined using an evolutionary heuristic, which considers a stochastic behavior by crossing over similar rules. The method considers development of new zones and also redevelopment of existing ones. The results are presented in future preference maps. The tests in a real system from a midsized city show a high rate of success when results are compared with information gathered from the utility planning department. The most important features of this method are the need for few data and the simplicity of the algorithm, allowing for future scalability.

Comparison of Reactive Power Support in Distribution Networks Provided by Capacitor Banks and Distributed Generators

This paper presents a comparison of reactive power support in distribution networks provided by switched Capacitor Banks (CBs) and Distributed Generators (DGs). Regarding switched CBs, a Tabu Search metaheuristic algorithm is developed to determine their optimal operation with the objective of reducing the power losses in the lines on the system, while meeting network constraints. on the other hand, the optimal operation of DGs is analyzed through an evolutionary Multi-Objective (MO) programming approach. The objectives of such approach are the minimization of power losses and operation cost of the DGs. The comparison of the reactive power support provided by switched CBs and DGs is carried out using a modified IEEE 34 bus distribution test system.

Evaluation of a multiobjective performance index for distribution systems with distributed generation

Here a multiobjective performance index for distribution systems with distributed generation based on a steady-state analysis of the network is proposed. This index quantifies the distributed generation impact on total losses, voltage profile and short circuit currents, and will be used as objective function in an evolutionary algorithm aimed at searching the best points for connecting distributed generators. Moreover, a loss allocation technique, based on the Zbus method, is applied on the original configuration of the network to obtain a good quality initial population. An IEEE medium voltage distribution network is analysed and results are presented and discussed.

Evolutionary modeling of larval dispersal in blowflies using non-uniform cellular automata

When the food supply flnishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the Initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatiotemporal behavior. © 2006 IEEE.

Evolutionary heuristic to determine future land use

In the spatial electric load forecasting, the future land use determination is one of the most important tasks, and one of the most difficult, because of the stochastic nature of the city growth. This paper proposes a fast and efficient algorithm to find out the future land use for the vacant land in the utility service area, using ideas from knowledge extraction and evolutionary algorithms. The methodology was implemented into a full simulation software for spatial electric load forecasting, showing a high rate of success when the results are compared to information gathered from specialists. The importance of this methodology lies in the reduced set of data needed to perform the task and the simplicity for implementation, which is a great plus for most of the electric utilities without specialized tools for this planning activity. © 2008 IEEE.

An efficient codification to solve distribution network reconfiguration for loss reduction problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Short-term planning of electric power distribution networks using multiobjective genetic algorithim

The high active and reactive power level demanded by the distribution systems, the growth of consuming centers, and the long lines of the distribution systems result in voltage variations in the busses compromising the quality of energy supplied. To ensure the energy quality supplied in the distribution system short-term planning, some devices and actions are used to implement an effective control of voltage, reactive power, and power factor of the network. Among these devices and actions are the voltage regulators (VRs) and capacitor banks (CBs), as well as exchanging the conductors sizes of distribution lines. This paper presents a methodology based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II) for optimized allocation of VRs, CBs, and exchange of conductors in radial distribution systems. The Multiobjective Genetic Algorithm (MGA) is aided by an inference process developed using fuzzy logic, which applies specialized knowledge to achieve the reduction of the search space for the allocation of CBs and VRs.

Power flow optimization for grid connected inverter using evolutionary algorithm and additional control loop

In this work it is proposed to validate an evolutionary tuning algorithm in plants composed by a grid connected inverter. The optimization aims the tuning of the slopes of P-Ω and Q-V curves so that the system is stable, damped and minimum settling time. Simulation and experimental results are presented to prove the feasibility of the proposed approach. However, experimental results demonstrate a compromising effect of grid frequency oscillations in the active power transferring. In addition, it was proposed an additional loop to compensate this effect ensuring a constant active power flow. © 2011 IEEE.

Distributed generators as providers of reactive power support - A market approach

Traditionally, ancillary services are supplied by large conventional generators. However, with the huge penetration of distributed generators (DGs) as a result of the growing interest in satisfying energy requirements, and considering the benefits that they can bring along to the electrical system and to the environment, it appears reasonable to assume that ancillary services could also be provided by DGs in an economical and efficient way. In this paper, a settlement procedure for a reactive power market for DGs in distribution systems is proposed. Attention is directed to wind turbines connected to the network through synchronous generators with permanent magnets and doubly-fed induction generators. The generation uncertainty of this kind of DG is reduced by running a multi-objective optimization algorithm in multiple probabilistic scenarios through the Monte Carlo method and by representing the active power generated by the DGs through Markov models. The objectives to be minimized are the payments of the distribution system operator to the DGs for reactive power, the curtailment of transactions committed in an active power market previously settled, the losses in the lines of the network, and a voltage profile index. The proposed methodology was tested using a modified IEEE 37-bus distribution test system. © 1969-2012 IEEE.

Multiobjective short-term planning of electric power distribution systems using NSGA-II

This paper presents a mixed integer nonlinear programming multiobjective model for short-term planning of distribution networks that considers in an integrated manner the following planning activities: allocation of capacitor banks; voltage regulators; the cable replacement of branches and feeders. The objective functions considered in the proposed model are: to minimize operational and investment costs and minimize the voltage deviations in the the network buses, subject to a set of technical and operational constraints. A multiobjective genetic algorithm based on a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is proposed to solve this model. The proposed mathematical model and solution methodology is validated testing a medium voltage distribution system with 135 buses. © 2013 Brazilian Society for Automatics - SBA.

Algoritmo genético especializado na resolução de problemas com variáveis contínuas e altamente restritos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)