Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Paints supply chain optimisation

Imperial Users onl

Task scheduling for application integration: A strategy for large volumes of data

Enterprise Application Integration is the research field, which provides methodologies, techniques and tools for modelling and implementing integration processes. An integration process performs the orchestration of a set of applications to keep them synchronised or to allow the creation of new features. It can be represented by a workflow composed of tasks and communication channels. Integration platforms are tools for the design and execution of integration processes in which, the runtime system is the component responsible for execution time of the tasks and the allocation of computational resources that perform them. The processing of a large volume of data, corresponding to execution of millions of tasks, can cause situations of overload, characterised by the accumulation of tasks in internal queues awaiting computational resources in the runtime systems, resulting in unacceptable response time for the external applications and users. Our research hypothesis is that the runtime systems of the integration platforms use simplistic heuristics for scheduling tasks, which does not allow them to maintain acceptable levels of performance when there are overload situations. In this research work, we developed (i) a representation for integration processes, (ii) a characterisation for your task schedules, (iii) a heuristic to deal with situations of overload, (iv) a mathematical model for a performance metric of the execution of integration processes and (v) a simulation tool for task scheduling heuristics. Our research results indicate that, in situations of overload, our heuristic promotes a balanced workload distribution and an increase in the performance of the execution of the integration processes.Integração de Aplicações Empresariais é o campo de pesquisa, que fornece metodologias, técnicas e ferramentas para modelar e implementar processos de integração. Um processo de integração executa a orquestração de um conjunto de aplicações para mantê-las sincronizadas ou para permitir a criação de novas funcionalidades. Ele pode ser representado por um fluxo de trabalho composto por tarefas e canais de comunicação. Plataformas de integração são ferramentas para projetar e executar processos de integração, nas quais o motor de execução é o componente responsável pelo tempo de execução das tarefas e pela alocação de recursos computacionais que as executam. O processamento de um grande volume de dados, correspondendo a execução de milhões de tarefas, pode causar situações de sobrecarga, caracterizadas pelo acúmulo de tarefas em filas internas que aguardam recursos computacionais nos motores de execução, resultando em tempos de resposta inaceitáveis para aplicações e usuários externos. Nossa hipótese de pesquisa é que os motores de execução das plataformas de integração usam heurísticas simplistas para agendar tarefas, o que não lhes permitem manter níveis aceitáveis de desempenho em situações de sobrecarga. Neste trabalho de pesquisa, desenvolvemos (i) uma representação para processos de integração, (ii) uma caracterização para seus agendamentos de tarefas, (iii) uma heurística para lidar com situações de sobrecarga, (iv) um modelo matemático para uma métrica de desempenho da execução de processos de integração e (v) uma ferramenta de simulação para heurísticas de agendamento de tarefas. Nossos resultados de pesquisa indicam que, em situações de sobrecarga, nossa heurística promove uma distribuição equilibrada da carga de trabalho e um aumento no desempenho da execução dos processos de integração

Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model

Traditionally, supply chain planning problems consider variables with uncertainty associated with uncontrolled factors. These factors have been normally modelled by complex methodologies where the seeking solution process often presents high scale of difficulty. This work presents the fuzzy set theory as a tool to model uncertainty in supply chain planning problems and proposes the particle swarm optimisation (PSO) metaheuristics technique combined with a backward calculation as a solution method. The aim of this combination is to present a simple effective method to model uncertainty, while good quality solutions are obtained with metaheuristics due to its capacity to find them with satisfactory computational performance in complex problems, in a relatively short time period.This research is partly supported by the Spanish Ministry of Economy and Competitiveness projects 'Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity' (PLANGES-FHP) (Ref. DPI2011-23597) and 'Operations design and Management of Global Supply Chains' (GLOBOP) (Ref. DPI2012-38061-C02-01); by the project funded by the Polytechnic University of Valencia entitled 'Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics' (PAID-06-12); and by the Ministry of Science, Technology and Telecommunications, government of Costa Rica (MICITT), through the incentive program of the National Council for Scientific and Technological Research (CONICIT) (contract No FI-132-2011).Grillo Espinoza, H.; Peidro Payá, D.; Alemany Díaz, MDM.; Mula, J. (2015). Application of particle swarm optimisation with backward calculation to solve a fuzzy multi-objective supply chain master planning model. International Journal of Bio-Inspired Computation. 7(3):157-169. https://doi.org/10.1504/IJBIC.2015.069557S1571697

Residential Demand Side Management model, optimization and future perspective: A review

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints

Partner selection in green supply chains using PSO – a practical approach

Partner selection is crucial to green supply chain management as the focal firm is responsible for the environmental performance of the whole supply chain. The construction of appropriate selection criteria is an essential, but often neglected pre-requisite in the partner selection process. This paper proposes a three-stage model that combines Dempster-Shafer belief acceptability theory and particle swarm optimization technique for the first time in this application. This enables optimization of both effectiveness, in its consideration of the inter-dependence of a broad range of quantitative and qualitative selection criteria, and efficiency in its use of scarce resources during the criteria construction process to be achieved simultaneously. This also enables both operational and strategic attributes can be selected at different levels of hierarchy criteria in different decision-making environments. The practical efficacy of the model is demonstrated by an application in Company ABC, a large Chinese electronic equipment and instrument manufacturer

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)