Probabilistic hosting capacity and risk analysis for distribution networks

Hereby I present a PhD thesis by publications. Altogether, the thesis includes: a) two journal papers, b) three IEEE conference papers. The journals include IEEE Transactions on Industrial Informatics while the second has been submitted. The conference list includes World Renewable Energy Congress (WREC), Asian conference on energy, power and transportation electrification (ACEPT) and IEEE Conference on Probabilistic Methods Applied in Power Systems (PMAPS). The PMAPS conference is the only event that exclusively discusses probability and statistic methods applied to power system analysis. The thesis presents several novel methods. The first novelty is the development of a new probabilistic model for estimating the solar radiation incident to residential roofs which is compatible with the Australian meteorological conditions. The second is the development of new probabilistic approach called “probabilistic hosting capacity” to estimate the hosting capacity of distribution networks. The third one is the utilization of sparse grid numerical approximation techniques in handling the uncertainty computations. The last contribution is the new assessment method for quantifying the risk of connecting a large number of correlated distributed generators (DGs) into the distribution networks. In glance, these contributions are highlighted in the following paragraphs. The development of the probabilistic method to estimate the solar irradiation is aimed to represent the uncertainty of produced power from residential solar panels. By utilizing the relation between clearness index and diffuse fraction, a probability density function (PDF) of produced power is derived from the total radiance quantity incident of a tilted area to the horizontal plane. Given the characteristics of the day time and the place, the uncertainty associated with power production by solar panels can be probabilistically estimated from the total solar irradiation of a tilted area. Two mathematical models are proposed: the first utilizes the HDKR (Hay, Davies, Klucher, Reindl) mathematical representation for total irradiance, while the second one involves the use of Hay-Davies mathematical representation. Without losing the scope of the work, only the first model is compared with real data obtain from a site in Adelaide. The second model is used for conducting the power flow calculations due to the low computational time is required to deliver results. The interest in the development of probabilistic hosting capacity comes as DGs in the distribution networks rely mainly on the renewable energy. Probabilistic hosting capacity is aimed to deliver a probabilistic estimate of the maximum amount of DGs that can be connected into the existing distribution network without jeopardizing the utility’s system operation and/or customers’ connected appliances. The approach is built up after defining the main uncertainties, resulted from the stochastic behaviours of the small-scale of wind turbines and solar panels as well as domestic loads. The impacts of these uncertainties on the operation of a distribution network are assessed by establishing a set of operational performance indices and the use of the probability of occurrence notion. Three types of hazardous impacts are defined (tolerable, critical and serious). The approach is time-dependent and includes network bi-directionality feature which complies with the fundamentals of automation approaches for active distribution networks. The third contribution is the use of sparse grid numerical techniques (SGTs) as an efficient tool to handle the uncertainty computation which is multi-dimensional problem. It replaces the use of classical numerical techniques based on tensor product grids which suffers from the curse of dimensionality. Additionally, the SGT in comparison with Monte Carlo Technique (MCT) is able to achieve improved efficiency in computation with acceptable accuracy. The last contribution is the development of a new risk analysis approach to quantify the effect of increasing levels of DG penetration on distribution networks. The proposed novel analysis utilises the following techniques and concepts: the Nataf transformation to represent spatial correlation of the DGs connected in the same distribution network; the consideration of likelihood (relative frequency of event occurrence) as well as severity (accumulative depth of event occurrence) of the performance indices in assessing the operation of distribution networks with the increase of DG connections. The Nataf transformation was used to ensure the rank correlation modelling among the non-Gaussian uncertainty representations in which the inter-dependences are modelled. The risk components, likelihood and severity, are visualized along with the increase of correlated DG connections. The purpose of this analysis is to provide an estimate of degree of risk in assessing the operational performance of a distribution network as whole, instead of the traditional methods that assess the network by parts, such as assessing individually a line or bus. The effectiveness of developed methods in this thesis is demonstrated by performing tests on two actual distribution networks: small and large. The small network consists of 11 buses with one substation transformer; while the existing large distribution network, situated in South Australia, consists of 59 (11/0.4 kV) feeder-transformers serving commercial, residential and industrial loads. The large network is segmented into different zones according to their likelihood of having DGs. The results are visualized, analysed and discussed for each proposed methods or approaches. All system modelling and algorithms are performed using MATLAB software and implemented on the distribution networks modelled in the industry accepted software OpenDSS, introduced by Electrical Power Research Institute (EPRI).Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

The Outsourcing Unit Working Research Paper Series Paper 14/1 – Cloud Services: The Great Equalizer for

Acknowledgements: We thank and acknowledge our research sponsor, Accenture. In particular, we are grateful to Miguel Gabriel Custodio, IT Strategy Australia/Cloud Strategy- APAC, for his support. We also thank the International Association of Outsourcing Professionals for their support in administering a survey, and Ken Saloway and Frank Casale for connecting us with SME cloud adopters

Systems Analysis For Urban Water Infrastructure Expansion With Global Change Impact Under Uncertainties

Over the past decades, cost-effectiveness principle or cost-benefit analysis has been employed oftentimes as a typical assessment tool for the expansion of drinking water utility. With changing public awareness of the inherent linkages between climate change, population growth and economic development, the addition of global change impact in the assessment regime has altered the landscape of traditional evaluation matrix. Nowadays, urban drinking water infrastructure requires careful long-term expansion planning to reduce the risk from global change impact with respect to greenhouse gas (GHG) emissions, economic boom and recession, as well as water demand variation associated with population growth and migration. Meanwhile, accurate prediction of municipal water demand is critically important to water utility in a fast growing urban region for the purpose of drinking water system planning, design and water utility asset management. A system analysis under global change impact due to the population dynamics, water resources conservation, and environmental management policies should be carried out to search for sustainable solutions temporally and spatially with different scales under uncertainties. This study is aimed to develop an innovative, interdisciplinary, and insightful modeling framework to deal with global change issues as a whole based on a real-world drinking water infrastructure system expansion program in Manatee County, Florida. Four intertwined components within the drinking water infrastructure system planning were investigated and integrated, which consists of water demand analysis, GHG emission potential, system optimization for infrastructure expansion, and nested minimax-regret (NMMR) decision analysis under uncertainties. In the water demand analysis, a new system dynamics model was developed to reflect the intrinsic relationship between water demand and changing socioeconomic iv environment. This system dynamics model is based on a coupled modeling structure that takes the interactions among economic and social dimensions into account offering a satisfactory platform. In the evaluation of GHG emission potential, a life cycle assessment (LCA) is conducted to estimate the carbon footprint for all expansion alternatives for water supply. The result of this LCA study provides an extra dimension for decision makers to extract more effective adaptation strategies. Both water demand forecasting and GHG emission potential were deemed as the input information for system optimization when all alternatives are taken into account simultaneously. In the system optimization for infrastructure expansion, a multiobjective optimization model was formulated for providing the multitemporal optimal facility expansion strategies. With the aid of a multi-stage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing with respect to multiple competing objectives across a suite of management strategies. In the decision analysis under uncertainty, such a system optimization model was further developed as a unique NMMR programming model due to the uncertainties imposed by the real-world problem. The proposed NMMR algorithm was successfully applied for solving the real-world problem with a limited scale for the purpose of demonstration

Turku Centre for Computer Science – Annual Report 2013

Due to a major reform of organization and responsibilities of TUCS, its role, activities, and even structures have been under reconsideration in 2013. The traditional pillar of collaboration at TUCS, doctoral training, was reorganized due to changes at both universities according to the renewed national system for doctoral education. Computer Science and Engineering and Information Systems Science are now accompanied by Mathematics and Statistics in newly established doctoral programs at both University of Turku and &Aring;bo Akademi University. Moreover, both universities granted sufficient resources to their respective programmes for doctoral training in these fields, so that joint activities at TUCS can continue. The outcome of this reorganization has the potential of proving out to be a success in terms of scientific profile as well as the quality and quantity of scientific and educational results.&nbsp; International activities that have been characteristic to TUCS since its inception continue strong. TUCS&rsquo; participation in European collaboration through EIT ICT Labs Master&rsquo;s and Doctoral School is now more active than ever. The new double degree programs at MSc and PhD level between University of Turku and Fudan University in Shaghai, P.R.China were succesfully set up and are&nbsp; now running for their first year. The joint students will add to the already international athmosphere of the ICT House.&nbsp; The four new thematic reseach programmes set up acccording to the decision by the TUCS Board have now established themselves, and a number of events and other activities saw the light in 2013. The TUCS Distinguished Lecture Series managed to gather a large audience with its several prominent speakers. The development of these and other research centre activities continue, and&nbsp; new practices and structures will be initiated to support the tradition of close academic collaboration.&nbsp; The TUCS&rsquo; slogan Where Academic Tradition Meets the Exciting Future has proven true throughout these changes. Despite of the dark clouds on the national and European economic sky, science and higher education in the field have managed to retain all the key ingredients for success. Indeed, the future of ICT and Mathematics in Turku seems exciting.</p

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc

Advancing Model Diagnostics To Support Hydrologic Prediction And Water Resources Planning Under Uncertainty

Computational models are essential tools for prediction and planning in water resources systems to ensure human water security and environmental health. Water systems models merely approximate the processes by which water moves through natural and built environments; their value depends on assumptions regarding climate, demand, land use, and other uncertain factors that may influence decision making. Numerical techniques to explore the role of these uncertain factors, known as diagnostic methods, can highlight opportunities to improve the accuracy of prediction as well as identify influential uncertainties to inform additional research and policy. This dissertation advances diagnostic methods for water resources models to identify (1) time-varying dominant processes driving modeled hydrologic predictions in flood forecasting, and (2) tradeoffs and vulnerabilities to changing climate and demands in regional urban water supply systems planning for drought. This work proposes diagnostic methods as a key element of a posteriori decision support, in which decision alternatives and vulnerable scenarios are identified following computational modeling and data analysis. Consistent with this theme, this work follows a multi-objective approach in which stakeholders can analyze tradeoffs between conflicting objectives as part of an iterative constructive learning process. For a spatially distributed flood forecasting model, results show that dominant uncertainties vary in space and time, and can inform model-based scientific inference as well as decision making. Similarly, the results of the urban water supply study indicate that sensitivity analysis can suggest costeffective paths to mitigate vulnerability to deeply uncertain future scenarios, for which likelihoods remain unknown or disputed. The multi-objective approach allows stakeholders to explore tradeoffs in their modeled robustness to inform intra-regional policies such as transfer contracts and shared infrastructure investments. Bridging the areas of hydrology and water systems planning is increasingly valuable, as hydrologic modelers begin to incorporate anthropogenic influences on the water cycle, and water systems planners begin to explore uncertainty in hydrologic process representation. In summary, this work develops diagnostic methods to identify time-varying dominant processes in distributed flood forecasting as well as tradeoffs and vulnerabilities under change in regional urban water supply, ultimately seeking to improve model-based planning for extreme floods and droughts in water resources systems