Lost in optimisation of water distribution systems? A literature review of system design

This is the final version of the article. Available from MDPI via the DOI in this record.Optimisation of water distribution system design is a well-established research field, which has been extremely productive since the end of the 1980s. Its primary focus is to minimise the cost of a proposed pipe network infrastructure. This paper reviews in a systematic manner articles published over the past three decades, which are relevant to the design of new water distribution systems, and the strengthening, expansion and rehabilitation of existing water distribution systems, inclusive of design timing, parameter uncertainty, water quality, and operational considerations. It identifies trends and limits in the field, and provides future research directions. Exclusively, this review paper also contains comprehensive information from over one hundred and twenty publications in a tabular form, including optimisation model formulations, solution methodologies used, and other important details

Planning of power distribution systems with high penetration of renewable energy sources using stochastic optimization

Driven by techno-economic and environmental factors, there is a global drive to integrate more distributed energy resources in power systems, particularly at the distribution level. These typically include smart-grid enabling technologies, such as distributed generation (DG), energy storage systems and demand-side management. Especially, the scale of DG sources (mainly renewables) integrated in many distribution networks is steadily increasing. This trend is more likely to continue in the years to come due to the advent of emerging solutions, which are expected to alleviate existing technical limitations and facilitate smooth integration of DGs. The favorable agreements of countries to limit greenhouse gas (GHG) emissions and mitigate climate change are also expected to accelerate the integration of renewable energy sources (RESs). However, the intermittent and volatile nature of most of these RESs (particularly, wind and solar) makes their integration in distribution networks a more challenging task. This is because such resources introduce significant operational variability and uncertainty to the system. Hence, the development of novel methodologies and innovative computational tools is crucial to realize an optimal and cost-efficient integration of such DGs, minimizing also their side effects. Novel methodologies and innovative computational tools are developed in this thesis that take into account the operational variability and uncertainty associated with the RES power generation, along with the integration of smart-grid enabling technologies. The developed methodologies and computational tools are tested in real-life power systems, as well as in standard test systems, demonstrating their computational proficiency when compared with the current state-of-the-art. Due to the inherent uncertainty and variability of RESs, stochastic programming is used in this thesis. Moreover, to ensure convergence and to use efficient off-the-shelf solvers, the problems addressed in this thesis are formulated using a mixed integer linear programming (MILP) approach.Atualmente há um esforço global para integrar mais recursos energéticos distribuídos nas redes elétricas, impulsionado por fatores técnico-económicos e ambientais, particularmente ao nível da rede de distribuição. Estes recursos incluem tipicamente tecnologias facilitadoras das redes elétricas inteligentes, tais como geração distribuída, sistemas de armazenamento de energia, e gestão ativa da procura. A integração de fontes de geração distribuída (energias renováveis, principalmente) está a aumentar progressivamente em muitas redes de distribuição, e é provável que esta tendência continue nos próximos anos devido ao avanço de soluções emergentes, esperando-se assim que as limitações técnicas existentes sejam ultrapassadas e que facilitem a integração progressiva das fontes de geração distribuída. Espera-se também que os acordos feitos pelos países para limitar as emissões de gases de efeito de estufa e para mitigar as alterações climáticas acelerem a integração de fontes de energia renováveis. No entanto, a natureza intermitente e volátil da maioria das fontes de energia renováveis (em particular, eólica e solar) faz com que a sua integração nas redes de distribuição seja uma tarefa complexa. Isto porque tais recursos introduzem variabilidade operacional e incerteza no sistema. Assim, é essencial o desenvolvimento de novas metodologias e ferramentas computacionais inovadoras para beneficiar uma integração óptima da geração distribuída renovável e minimizar os possíveis efeitos colaterais. Nesta tese são desenvolvidas novas metodologias e ferramentas computacionais inovadoras que consideram a variabilidade operacional e a incerteza associadas à geração a partir de fontes de energia renováveis, juntamente com a integração de tecnologias facilitadoras das redes elétricas inteligentes. As metodologias e ferramentas computacionais desenvolvidas são testadas em casos de estudo reais, bem como em casos de estudo clássicos, demonstrando a sua proficiência computacional comparativamente ao atual estado-da-arte. Devido à inerente incerteza e variabilidade das fontes de energia renováveis, nesta tese utiliza-se programação estocástica. Ainda, para assegurar a convergência para soluções ótimas, o problema é formulado utilizando programação linear inteira-mista

Upgrading Plan for Conventional Distribution Networks Considering Virtual Microgrid Systems

It is widely agreed that the integration of distributed generators (DGs) to power systems is an inevitable trend, which can help to solve many issues in conventional power systems, such as environmental pollution and load demand increasing. According to the study of European Liaison on Electricity grid Committed Towards long-term Research Activities (ELECTRA), in the future, the control center of power systems might transfer from transmission networks to distribution networks since most of DGs will be integrated to distribution networks. However, the infrastructure of conventional distribution networks (CDNs) has not enough capabilities to face challenges from DG integration. Therefore, it is necessary to make a long-term planning to construct smart distribution networks (SDNs). Although many planning strategies are already proposed for constructing SDNs, most of them are passive methods which are based on traditional control and operating mechanisms. In this thesis, an active planning framework for upgrading CDNs to SDNs is introduced by considering both current infrastructure of CDNs and future requirements of SDNs. Since conventional centralised control methods have limited capabilities to deal with huge amount of information and manage flexible structure of SDNs, virtual microgrids (VMs) are designed as basic units to realise decentralised control in this framework. Based on the idea of cyber-physical-socioeconomic system (CPSS), the structure and interaction of cyber system layer, physical system layer as well as socioeconomic system layer are considered in this framework to improve the performance of electrical networks. Since physical system layer is the most fundamental and important part in the active planning framework, and it affects the function of the other two layers, a two-phase strategy to construct the physical system layer is proposed. In the two-phase strategy, phase 1 is to partition CDNs and determine VM boundaries, and phase 2 is to determine DG allocation based on the partitioning results obtained in phase 1. In phase 1, a partitioning method considering structural characteristics of electrical networks rather than operating states is proposed. Considering specific characteristics of electrical networks, electrical coupling strength (ECS) is defined to describe electrical connection among buses. Based on the modularity in complex network theories, electrical modularity is defined to judge the performance of partitioning results. The effectiveness of this method is tested in three popular distribution networks. The partitioning method can detect VM boundaries and partitioning results are in accord with structural characteristics of distribution networks. Based on the partitioning results obtained in phase 1, phase 2 is to optimise DG allocation in electrical networks. A bi-level optimisation method is proposed, including an outer optimisation and an inner optimisation. The outer optimisation focus on long-term planning goals to realise autonomy of VMs while the inner optimisation focus on improving the ability of active energy management. Both genetic algorithm and probabilistic optimal power flow are applied to determine the type, size, location and number of DGs. The feasibility of this method is verified by applying it to PG&E 69-bus distribution network. The operation of SDNs with VMs is a very important topic since the integration of DGs will lead to bidirectional power flow and fault current variation in networks. Considering the similarity between microgrids and VMs, a hybrid control and protection scheme for microgrids is introduced, and its effectiveness is tested through Power Systems Computer Aided Design (PSCAD) simulation. Although more research is needed because SDNs are more complicated than microgrids, the hybrid scheme has great potential to be applied to VMs

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems