A multi-step multi-objective generation expansion planning model-A case study in Mexico

Abstract Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm. Keywords: Energy planning, generation expansion planning, capacity expansion planning, Generation expansion plannin

PI-tuned UPFC damping controllers design for multi-machine power system

This paper presents an adaptive multi-objective algorithm based Unified Power Flow Controller (UPFC) tuned for damping oscillations in two-area multi-machine system formulated as multi- objective optimization problem. The algorithms such as, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II) are proposed for tuning the damping controller with speed deviation and control input as conflicting objectives. The proposed algorithm is implemented in the two area multi-machine system using MATLAB Simulink model, and the simulation results were obtained with respect to the characteristics of damping oscillations and the dynamic stability of power systems. The performance measures such as Integral Time Squared Error (ITSE) and Integral Squared Error (ISE) are considered as the objective functions. The results of the two proposed algorithm has been compared and the outcome shows that the MNSGA-II algorithm performs better compared to the NSGA-II algorithm

A novel MPP-NSGA algorithm and its application in optimization for radiated noises in the aircraft cabin

The paper used the AML method to compute transmission loss of aircraft panels and verifies correctness of the numerical simulation model by experimental test. Finally, this paper used an improved genetic algorithm to conduct a multi-objective optimization for the cabin noise. When the analyzed frequency is less than 250 Hz, transmission loss decreased rapidly with the increased analysis frequency, and decreased from the maximum 63.2 dB to 18.5 dB. Within 250 Hz-4000 Hz, the transmission loss gradually increased with the increased analysis frequency. At 250 Hz, the transmission loss had an obvious valley value. Sound radiation power was then computed based on boundary element method, and panel contribution analysis was conducted to find those panels which had an obvious impact on the cabin noise. Therefore, a multi-objective optimization was conducted on these panels and reinforced ribs. In order to further verify effectiveness of the MPP-NSGA method, it was compared with the traditional GA model and NSGA model. Optimization accuracy using MPP-NSGA model is increased, and optimization time is reduced. Through optimization with traditional GA method, the maximum sound power level decreased by 15.4 %, and the total sound power level decreased by 21.9 %. Through optimization with the NSGA method, the maximum sound power level decreased by 21.7 %, and the total sound power level decreased by 29.0 %. Through optimization with the MPP-NSGA method, the maximum sound power level decreased by 46.3 %, and the total sound power level decreased by 36.0 %. Therefore, compared with other two kinds of genetic algorithms, the MPP-NSGA method is obviously superior in noise optimization in the cabin. In the whole analysis frequency band, noise of the optimized cabin panel at each frequency point was smaller than that of the original structure, fully verifying feasibility of the optimization algorithm proposed in the paper. In addition, in the optimized structure, no panel made obvious contributions to the cabin noise, and each panel showed an equivalent contribution level. Transmission loss of the optimized cabin panel was obviously improved. However, the sound insulation valley still appeared at 250 Hz, but it was not so obvious like the original structure. After optimization, the sound insulation valley was 31.6 dB. The sound insulation valley of the original structure was 18.5 dB. Obviously, the sound insulation valley value of the optimized structure was increased by double compared with the original structure. This paper provided a valuable reference for noise reduction in the aircraft cabin

Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions

Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs

A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation

The growing interest in sustainability increases the challenges for decision makers in selecting the sustainable suppliers in which consider economic, environmental and social aspects. Particularly, decision makers are being increasingly motivated to improve their supply chain activities in coping efficiently with the objectives of sustainable development. Where the era of sustainability threatens the current supply chain partners to either cope with the new regulations of sustainability or leave the field for new players. Notwithstanding, most of the recent studies considered economic and green criteria in handling sustainable supplier selection and order allocation (SSS/OA) problems overlooking the social criteria which represents the third pillar of sustainability. This work aims at putting forward a hybrid Multi Criteria Decision-Making (MCDM)-Fuzzy Multi-Objective Optimization (FMOO) approach for a sustainable supplier selection and order allocation problem by considering economic, environmental and social criteria. Thus, an integrated Fuzzy AHP-Fuzzy TOPSIS is proposed to assess and rank suppliers according to three sets of criteria (i.e. conventional, green and social). A Multi-Objective Optimization Model (MOOM) is developed for choosing suppliers and allocating the optimal order quantities. To cope with the multiple uncertainties in the input data, the MOOM is reformulated into a Fuzzy Multi-Objective Optimization Model. The ε-constraint and LP-metrics approaches are used to reveal two sets of Pareto solutions based on the developed FMOO model. Finally, TOPSIS is applied to select the final Pareto solution that is closest to the ideal solution and furthest from the nadir solution. The effectiveness and the applicability of the developed hybrid MCDM-FMOO approach is demonstrated through a case study

A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design

Stakeholders are being increasingly encouraged to improve their supply chain risk management in order to cope efficiently and successfully with disruption risks due to unexpected events. Notwithstanding, supply chain managers lagged behind this target overlooking green development in considering environmental impact which has become a main criterion in supply chain management. Where the era of greenness threatens current supply chain partners with the need to either cope with the new green regulations or leave the field for new players. Thus, an approach to design supply chains that are simultaneously resilient, and green is needed. This study satisfies this need by developing a green and resilient (G-resilient, here after) fuzzy multi-objective programming model (GR-FMOPM) to present a G-resilient supply chain network design in determining the optimal number of facilities that should be established. The objectives are minimization of total cost and environmental impact and maximization of Value of resilience pillars where Redundancy, Agility, Leanness and Flexibility (V-RALF) can be seen four of main pillars required for supply chain resilience. Fuzzy AHP is used for determining the importance weight for each pillar followed by a usage of a Fuzzy technique for assigning the importance weight for each potential facility with respect to RALF. The importance weights obtained by Fuzzy AHP and the Fuzzy technique are then integrated in the third objective (maximization of V-RALF) to maximize the value of resilience pillars. Based on the fuzzy multi-objective model, the ε-constraint method is used to reveal Pareto optimal solutions and TOPSIS was then used to select the final Pareto solution. A case study is used to validate the applicability of the developed GR-FMOPM in obtaining a G-resilient supply chain network design and a trade-off among economic, green and resilience objectives. Finally, a sensitivity analysis is performed on the importance weight for facilities Pareto solutions with respect to the importance weight of RALF. Research findings proved that the developed GR-FMOPM could be used as a tool in evaluating and ranking related facilities with respect to their resilience performance. It can also be used to obtain a G-resilient supply chain network design in terms of facilities that should be established towards a trade-off among the three aforementioned objectives

A multi-agent optimisation model for solving supply network configuration problems

Supply chain literature highlights the increasing importance of effective supply network configuration decisions that take into account such realities as market turbulence and demand volatility, as well as ever-expanding global production networks. These realities have been extensively discussed in the supply network literature under the structural (i.e., physical characteristics), spatial (i.e., geographical positions), and temporal (i.e., changing supply network conditions) dimensions. Supply network configuration decisions that account for these contingencies are expected to meet the evolving needs of consumers while delivering better outcomes for all parties involved and enhancing supply network performance against the key metrics of efficiency, speed and responsiveness. However, making supply network configuration decisions in the situations described above is an ongoing challenge. Taking a systems perspective, supply networks are typically viewed as socio-technical systems where SN entities (e.g., suppliers, manufacturers) are autonomous individuals with distinct goals, practices and policies, physically inter-connected transferring goods (e.g., raw materials, finished products), as well as socially connected with formal and informal interactions and information sharing. Since the structure and behaviour of such social and technical sub-systems of a supply network, as well as the interactions between those subsystems, determine the overall behaviour of the supply network, both systems should be considered in analysing the overall system

Modelo de decisão integrado para a priorização multiestágio de projetos de distribuição considerando a qualidade da energia elétrica

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2013.O presente trabalho aborda o problema de priorização dos projetos de melhoria e expansão do sistema de distribuição de energia elétrica, cujo foco é a maximização do valor do portfólio corporativo. Esse problema combinatório multiobjetivo é estruturado na forma de um modelo de decisão, formulado por meio de programação matemática binária, cuja solução envolve uma técnica de otimização bioinspirada, combinada a um conjunto de métodos de análise multicritério que buscam subsidiar o processo decisório da empresa. A primeira etapa faz uso do algoritmo genético multiobjetivo NSGA-II para obter um conjunto de portfólios Pareto-Ótimos, onde os projetos são selecionados e alocados em um horizonte de planejamento multiestágio, de acordo com os objetivos e restrições do problema. Os objetivos estão associados aos critérios de valor dos portfólios, os quais consideram os impactos financeiros potenciais dos projetos, o número de consumidores atendidos, as condições operacionais das instalações elétricas e a qualidade da energia elétrica no sistema de distribuição. As restrições envolvem a disponibilidade orçamentária e as relações de condicionamento e excludência entre os projetos. Na segunda etapa, os métodos de análise multicritério SMART e TOPSIS são utilizados para determinar os ranques das atratividades dos portfólios, incorporando o perfil das preferências dos decisores por meio dos pesos dos critérios, os quais são obtidos pelos métodos ROC e AHP. Os estudos de caso demonstram o comportamento da prioridade dos projetos nos portfólios não somente quando a qualidade da energia e o desempenho operacional são incluídos na análise, mas também em função da variação dos pesos dos critérios de planejamento. A metodologia proposta permite auxiliar na prospecção de investimentos estratégicos e contribuir para um melhor planejamento do sistema de distribuição Abstract : This work tackles the problem of project selection for the improvement of power distribution systems, which is part of the utilities planning task. The developed model is composed by a multi-objective optimization module and a multi-criteria decision support module. The first finds the Pareto-optimal portfolios, by using the multi-objective genetic algorithm NSGA-II to select and allocate the projects in a multistage planning horizon, according to the problem objectives and constraints. The latter, based on both TOPSIS and SMART multicriteria techniques, searches for the most appropriate project portfolio for the utility, considering the decision maker profile embedded into the model by the ROC and AHP weights. The optimization and decision making models take into account aspects of power quality, operational performance, number of consumers, and potential financial impacts of the projects. The presented case studies show the choice of priority projects not only when power quality and operational performance are included in the analysis, but also when the weights of planning criteria are changed. The proposed method helps in raising strategic investments and allows for better distribution system planning