Network planning is an essential phase in successfully operating state-of-the-art telecommunication systems. It helps carriers increase revenues by deploying the right technologies in a cost effective manner. More importantly, through the network planning phase, carriers determine the capital needed to build the network as well as the competitive pricing for the offered services. Through this phase, radio tower locations are selected from a pool of candidate locations so as to maximize the net revenue acquired from servicing a number of subscribers. In the Universal Mobile Telecommunication System (UMTS) which is based on the Wideband Code Division Multiple Access scheme (W-CDMA), the coverage area of each tower, called a cell, is not only affected by the signal's attenuation but is also affected by the assignment of the users to the towers. As the number of users in the system increases, interference levels increase and cell sizes decrease. This complicates the network planning problem since the capacity and coverage problems cannot be solved separately. To identify the optimal base station locations, traffic intensity and potential locations are determined in advance, then locations of base stations are chosen so as to satisfy minimum geographical coverage and minimum quality of service levels imposed by licensing agencies. This is implemented through two types of power control mechanisms. The power based power control mechanism, which is often discussed in literature, controls the power of the transmitted signal so that the power at the receiver exceeds a given threshold. On the other hand, the signal-to-interference ratio (SIR) based power control mechanism controls the power of the transmitted signal so that the ratio of the power of the received signal over the power of the interfering signals exceeds a given threshold. Solving the SIR based UMTS/W-CDMA network planning problem helps network providers in designing efficient and cost effective network infrastructure. In contrast to the power based UMTS/W-CDMA network planning problem, the solution of the SIR based model results in higher profits. In SIR based models, the power of the transmitted signals is decreased which lowers the interference and therefore increases the capacity of the overall network. Even though the SIR based power control mechanism is more efficient than the power based power control mechanism, it has a more complex implementation which has gained less attention in the network planning literature. In this thesis, a non-linear mixed integer problem that models the SIR based power control system is presented. The non-linear constraints are reformulated using linear expressions and the problem is exactly solved using a Benders decomposition approach. To overcome the computational difficulties faced by Benders decomposition, two novel extensions are presented. The first extension uses the analytic center cutting plane method for the Benders master problem, in an attempt to reduce the number of times the integer Benders master problem is solved. Additionally, we describe a heuristic that uses the analytic center properties to find feasible solutions for mixed integer problems. The second extension introduces a combinatorial Benders decomposition algorithm. This algorithm may be used for solving mixed integer problems with binary variables. In contrast to the classical Benders decomposition algorithm where the master problem is a mixed integer problem and the subproblem is a linear problem, this algorithm decomposes the problem into a mixed integer master problem and a mixed integer subproblem. The subproblem is then decomposed using classical Benders decomposition, leading to a nested Benders algorithm. Valid cuts are generated at the classical Benders subproblem and are added to the combinatorial Benders master problem to enhance the performance of the algorithm. It was found that valid cuts generated using the analytic center cutting plane method reduce the number of times the integer Benders master problem is solved and therefore reduce the computational time. It was also found that the combinatorial Benders reduces the complexity of the integer master problem by reducing the number of integer variables in it. The valid cuts generated within the nested Benders algorithm proved to be beneficial in reducing the number of times the combinatorial Benders master problem is solved and in reducing the computational time that the overall algorithm takes. Over 110 instances of the UMTS/W-CDMA network planning problem ranging from 20 demand points and 10 base stations to 140 demand points and 30 base stations are solved to optimality.

Although China has a huge cyclist population, the cycling condition in large cities is undesirable. The problem is mainly caused by the mixed-flow of cyclists and motorist on the road. Separation of cyclists and motorist is the key to solve the problem. Based on the research of the successful examples in Europe and a PEBOSCA inventory analysis of the cycling traffic in Nanjing, a set of suggestion is proposed on how to plan the bicycle network. A bicycle network separated from motor traffic and the planning on a cycling district division is introduced. The proposal also involves the suggestion on combination between cycling and public transport, and the experience route of historical culture and the natural beauty of the landscape.

Network planning is an essential phase in successfully operating state-of-the-art telecommunication systems. It helps carriers increase revenues by deploying the right technologies in a cost effective manner. More importantly, through the network planning phase, carriers determine the capital needed to build the network as well as the competitive pricing for the offered services. Through this phase, radio tower locations are selected from a pool of candidate locations so as to maximize the net revenue acquired from servicing a number of subscribers. In the Universal Mobile Telecommunication System (UMTS) which is based on the Wideband Code Division Multiple Access scheme (W-CDMA), the coverage area of each tower, called a cell, is not only affected by the signal's attenuation but is also affected by the assignment of the users to the towers. As the number of users in the system increases, interference levels increase and cell sizes decrease. This complicates the network planning problem since the capacity and coverage problems cannot be solved separately. To identify the optimal base station locations, traffic intensity and potential locations are determined in advance, then locations of base stations are chosen so as to satisfy minimum geographical coverage and minimum quality of service levels imposed by licensing agencies. This is implemented through two types of power control mechanisms. The power based power control mechanism, which is often discussed in literature, controls the power of the transmitted signal so that the power at the receiver exceeds a given threshold. On the other hand, the signal-to-interference ratio (SIR) based power control mechanism controls the power of the transmitted signal so that the ratio of the power of the received signal over the power of the interfering signals exceeds a given threshold. Solving the SIR based UMTS/W-CDMA network planning problem helps network providers in designing efficient and cost effective network infrastructure. In contrast to the power based UMTS/W-CDMA network planning problem, the solution of the SIR based model results in higher profits. In SIR based models, the power of the transmitted signals is decreased which lowers the interference and therefore increases the capacity of the overall network. Even though the SIR based power control mechanism is more efficient than the power based power control mechanism, it has a more complex implementation which has gained less attention in the network planning literature. In this thesis, a non-linear mixed integer problem that models the SIR based power control system is presented. The non-linear constraints are reformulated using linear expressions and the problem is exactly solved using a Benders decomposition approach. To overcome the computational difficulties faced by Benders decomposition, two novel extensions are presented. The first extension uses the analytic center cutting plane method for the Benders master problem, in an attempt to reduce the number of times the integer Benders master problem is solved. Additionally, we describe a heuristic that uses the analytic center properties to find feasible solutions for mixed integer problems. The second extension introduces a combinatorial Benders decomposition algorithm. This algorithm may be used for solving mixed integer problems with binary variables. In contrast to the classical Benders decomposition algorithm where the master problem is a mixed integer problem and the subproblem is a linear problem, this algorithm decomposes the problem into a mixed integer master problem and a mixed integer subproblem. The subproblem is then decomposed using classical Benders decomposition, leading to a nested Benders algorithm. Valid cuts are generated at the classical Benders subproblem and are added to the combinatorial Benders master problem to enhance the performance of the algorithm. It was found that valid cuts generated using the analytic center cutting plane method reduce the number of times the integer Benders master problem is solved and therefore reduce the computational time. It was also found that the combinatorial Benders reduces the complexity of the integer master problem by reducing the number of integer variables in it. The valid cuts generated within the nested Benders algorithm proved to be beneficial in reducing the number of times the combinatorial Benders master problem is solved and in reducing the computational time that the overall algorithm takes. Over 110 instances of the UMTS/W-CDMA network planning problem ranging from 20 demand points and 10 base stations to 140 demand points and 30 base stations are solved to optimality.

Hårdare krav ställs på leveranssäkerheten i de svenska elnätet. Kraven har ställts som följd av att flertal stormar och oväder har orsakat väldigt långa avbrott historiskt sett i Sverige. Det är nu olagligt att ha ett avbrott längre än 24 timmar. För att nå upp till kraven som ställs på leveranssäkerhet behöver nätföretag investera i sina befintliga nät. Generellt sett sker investering i att byta ut luftledning mot jordkabel. Fortum äger en 12 kV-linje i ett område lokaliserat nordöst om Charlottenberg som är drabbad av avbrott och har hög genomsnittlig ålder. Ett mer tillförlitligt elnät måste upprättas. I den här rapporten ges ett nätförslag som skulle leda till en förbättrad leveranssäkerhet och ökad tillförlitlighet. / Swedish legislation regarding network reliability has changed after the historic storm named Gudrun. It is now a violation to have interruptions in the distribution of electricity lasting longer than 24 hours. To reach needed reliability in the network, companies that distribute electricity need to invest in their existing grids. The general investment performed is exchanging overhead-lines to underground cables. Fortum is the owner of a 12 kV rural power-line, located northeast of Charlottenberg, Sweden. This power-line has interruptions and an overall high age. A more reliable network has to be planned. In this report, an alternative network is proposed, that would lead to improved reliability in the network.

Renewable energy generation is becoming a major part of energy supply, often in the form of distributed generation (DG) connected to distribution networks. While growth has been rapid, there is awareness that limitations on spare capacity within distribution (and transmission) networks is holding back development. Developments are being shelved until new network reinforcements can be built, which may make some projects non-viable. Reinforcements are costly and often underutilised, typically only loaded to their limits for a few occasions during the year. In order to accommodate new DG without the high costs or delays, active network management (ANM) is being promoted in which generation and other network assets are controlled within the limits of the existing network. There is a great deal of complexity and uncertainty associated with developing ANM and devising coherent plans to accommodate new DG is challenging for Distribution Network Operators (DNOs). As such, there is a need for robust network planning tools that can explicitly handle ANM and which can be trusted and implemented easily. This thesis describes the need for and the development of a new distribution expansion planning framework that provides DNOs with a better understanding of the impacts created by renewable DG and the value of ANM. This revolves around a heuristic planning framework which schedules necessary upgrades in power lines and transformers associated with changes in demand as well as those driven by the connection of DG. Within this framework a form of decentralised, adaptive control of DG output has been introduced to allow estimation of the impact of managing voltage and power flow constraints on the timing and need for network upgrades. The framework is initially deployed using simple scenarios but a further advance is the explicit use of time series to provide substantially improved estimates of the levels of curtailment implied by ANM. In addition, a simplified approach to incorporating demand side management has been deployed to facilitate understanding of the scope and role this may play in facilitating DG connections.

Recent technological developments are bringing about substantial changes that are converting traditional distribution networks into "smart" distribution networks. In particular, it is possible to observe seamless integration of Information and Communication Technologies (ICTs), including the widespread installation of automatic equipment, smart meters, etc. The increased automation facilitates active network management, interaction between market actors and demand side participation. If we also consider the increasing penetration of distributed generation, renewables and various emerging technologies such as storage and dynamic rating, it can be argued that the capacity of distribution networks should not only depend on conventional asset. In this context, taking into account uncertain load growth and ageing infrastructure, which trigger network investments, the above-mentioned advancements could alter and be used to improve the network design philosophy adopted so far. Hitherto, in fact, networks have been planned according to deterministic and conservative standards, being typically underutilised, in order for capacity to be available during emergencies. This practice could be replaced by a corrective philosophy, where existing infrastructure could be fully unlocked for normal conditions and distributed energy resources could be used for post fault capacity services. Nonetheless, to thoroughly evaluate the contribution of the resources and also to properly model emergency conditions, a probabilistic analysis should be carried out, which captures the stochasticity of some technologies, the randomness of faults and, thus, the risk profile of smart distribution networks. The research work in this thesis proposes a variety of post fault capacity services to increase distribution network utilisation but also to provide reliability support during emergency conditions. In particular, a demand response (DR) scheme is proposed where DR customers are optimally disconnected during contingencies from the operator depending on their cost of interruption. Additionally, time-limited thermal ratings have been used to increase network utilisation and support higher loading levels. Besides that, a collaborative operation of wind farms and electrical energy storage is proposed and evaluated, and their capacity contribution is calculated through the effective load carrying capability. Furthermore, the microgrid concept is examined, where multi-generation technologies collaborate to provide capacity services to internal customers but also to the remaining network. Finally, a distributed software infrastructure is examined which could be effectively used to support services in smart grids. The underlying framework for the reliability analysis is based on Sequential Monte Carlo Simulations, capturing inter-temporal constraints of the resources (payback effects, dynamic rating, DR profile, storage remaining available capacity) and the stochasticity of electrical and ICT equipment. The comprehensive distribution network reliability analysis includes network reconfiguration, restoration process, and ac power flow calculations, supporting a full risk analysis and building the risk profile for the arising smart distribution networks. Real case studies from ongoing project in England North West demonstrate the concepts and tools developed and provide noteworthy conclusions to network planners, including to inform design of DR contracts.

Context With the rapidly growing demand for cellular networks’ capacityand coverage, effective planning of Network Infrastructure (NI) has been amajor challenge for the telecom operators. The mobility patterns of different subscriber groups in the networks have been found to be a crucialaspect in the planning of NI. For a telecom operator, it is important to havean estimate of the efficiency (in terms of the Network Capacity - numberof subscribers that the network can handle) of the existing NI. For thispurpose, Lundberg et. al., have developed an optimization based strategycalled as Tetris Strategy (TS), based on the standard subscriber groupingapproach called MOSAIC. The objective of TS is to calculate the upperbound estimate of the efficiency of the NI. Objectives The major objective of this thesis is to compare the efficiencyvalue of the NI when the subscribers are grouped (clustered) based on theirmobility patterns (characterized by a mobile trajectory) with the efficiencyvalue obtained when the subscribers are grouped based on the standardsubscriber grouping approach - MOSAIC. Methods Literature Review (LR) has been conducted to identify the stateof the art similarity/distance measures and algorithms to cluster trajectory data. Among the identified ones, for conducting experiments, LongestCommon Subsequences has been chosen as a similarity/distance measure,and Spectral and Agglomerative clustering algorithms have been chosen.All the experiments have been conducted on the subscriber trajectory dataprovided by the telecom operator, Telenor. The clusters obtained from theexperiments have been plugged into TS, to calculate the upper bound estimate of the efficiency of the NI. Results For the highest radio cell capacity, the network capacity valuesfor Spectral clustering, Agglomerative clustering and MOSAIC groupingsystem are 207234, 148056 and 87584 respectively. For every radio cellcapacity value, the mobility based clusters resulted in a higher network efficiency values than the MOSAIC. However, both spectral and agglomerativealgorithms have generated a very low quality clusters with the silhouettescores of 0.0717 and 0.0543 respectively. Conclusions Based on the analysis of the results, it can be concluded that,mobility based grouping of subscribers in the cellular network provide highernetwork efficiency values compared to the standard subscriber grouping systems such as MOSAIC.

One of EU’s actions against climate change is to meet 20% of our energy needs from renewable resources. Given that the renewable resources are becoming more economical to extract electricity from, this will result in that more and more distributed generation (DG) will be connected to power distribution. The increasing share of DG in the electricity networks implies both increased costs and benefits for distribution system operators (DSOs), customers and DG producers. How the costs and benefits will be allocated among the actors will depend on the established regulation. Distribution networks are traditionally not designed to accommodate generation. Hence, increasing DG penetration is causing profound changes for DSOs in planning, operation and maintenance of distribution networks. Due to the unbundling between DSOs and electricity production, DSOs can not determine either the location or the size of DG. This new power distribution environment brings new challenges for the DSOs and the electric power system regulator. The DSOs are obliged to enable connection of DG meanwhile fulfilling requirements on power quality and adequate reliability. Moreover, regulatory implications can make potential DG less attractive. Therefore regulation should be able to send out incentives for the DSOs to efficiently plan the network to accommodate the increasing levels of DG. To analyze the effects of regulatory polices on network investments, risk analysis methods for integrating the DG considering uncertainties are therefore needed. In this work, regulation impact on network planning methods and network tariff designs in unbundled electricity network is firstly analyzed in order to formulate a realistic long-term network planning model considering DG. Photovoltaic (PV) power and wind power plants are used to demonstrate DG. Secondly, this work develops a deterministic model for low-voltage (LV) networks mainly considering PV connections which is based on the worst-case scenario. Dimension the network using worst-case scenario is the convention in the long-term electricity distribution network planning for the reliability and security reason. This model is then further developed into a probabilistic model in order to consider the uncertainties from DG production and load. Therefore more realistic operation conditions are considered and probabilistic constrains on voltage variation can be applied. Thirdly, this work develops a distribution medium-voltage (MV) network planning model considering wind power plant connections. The model obtains the optimal network expansion and reinforcement plan of the target network considering the uncertainties from DG production and load. The model is flexible to modify the constraints. The technical constraints are respected in any scenario and violated in few scenarios are implemented into the model separately. In LV networks only PV connections are demonstrated and in MV networks only wind power connections are demonstrated. The planning model for LV networks is proposed as a practical guideline for PV connections. It has been shown that it is simple to be implemented and flexible to adjust the planning constraints. The proposed planning model for MV networks takes reinforcement on existing lines, new connection lines to DG, alternatives for conductor sizes and substation upgrade into account, and considers non-linear power flow constraints as an iterative linear optimization process. The planning model applies conservative limits and probabilistic limits for increasing utilization of the network, and the different results are compared in case studies. The model’s efficiency, flexibility and accuracy in long-term distribution network planning problems are shown in the case studies. / <p>QC 20140217</p> / Elforsk Risknanlys II

L'objectif de cette thèse est de réunir les deux approches suivantes qui existent actuellement pour étudier une foule: ou bien à temps fixé on s'intéresse à la distribution spatiale des individus, ou bien on suit un seul individu à la fois au cours du temps. On se propose de construire des processus spatio-temporels, qui, comme leur nom l'indique, permettraient de rendre compte du caractère aléatoire des usages d'une foule dans un réseau de télécommunication, à la fois du point de vue spatial (modèles de route) et du point de vue temporel (déplacements sur ces routes, usages qui varient au cours de ces déplacements…). Une fois ces processus construits de manière rigoureuse, on étudie leur comportement d'une manière fine. Nous développons trois modèles différents qui chacun mènent à des formules analytiques fermées, ce qui permet de les utiliser d'une manière très confortable à des fins de dimensionnement. / This thesis consists in joining two approaches that currently exist when one wants to study crowds phenomena: either taking a snapshot by freezing time to study the spatial repartition of the individuals, or following one given individual over time.We build space-time processes that let us model random phenomena in a crowd, being on a spatial level (roads models) or a time level (movings on these roads, space-dependent behaviors…). Once we have built them in a rigorous manner, we study their properties, which let us obtain analytical closed formulas that can be widely used for dimensioning purposes.

Load models play an essential role in many studies, including calculating voltage drops and technical losses in distribution systems, for distributed generator (DG) integration planning, and in tariff analysis and design models. The Herman-Beta transform used in the low voltage network modelling studies in South Africa is based on loads modelled as Beta probability density functions. Recently, the transform was extended to make it useful also for probabilistic load flow modelling in medium voltage (MV) networks with non-unity power factor loads and DGs. The electricity supply industry in South Africa has transformed and saw an increased penetration of Independent Power Producers as a result of the government encouraged the renewable independent power procurement programme (REIPPP). There has also been a steady decrease in the costs of procuring power from renewable energy sources, mainly from photovoltaic (PV) systems. South Africa also saw significant tariff increases in the recent past. These have resulted in both new load patterns and uncertainties in the power systems inputs required for network planning and tariff development. Other factors affecting loads and renewable energy output include weather, location and economic factors. Load models are essential for technical and tariff studies. Long term and short term planning models in both technical and tariff modelling require information about the usage behaviour of customers. Planning cannot be separated from the financial impact and tariffs in general. The literature review indicated that planning has the objective of designing a network for optimal usage, thus minimising the costs and deferring investment where possible. Load patterns have been recognised to represent the usage behaviours of customers better and these behaviours influence the planning parameters. There have been studies by numerous researchers to extract parameters from the load profiles for load flow modelling and simulation purposes. The same challenge exists for South Africa, where there has been progress made on the development of LV models, and the same is not replicated in the MV network space. The derivation of load models primarily involves the classification of loads, identifying and estimating the parameters of loads, and assigning load profiles to different loads for studies. Customer measurements are an essential input in load model development and load estimation. Identification of parameters is one of the areas where research is ongoing since there is no global consensus on which attributes best describe customer load profiles. In this study, a proposition on how the parameters for technical and tariff analysis models should be defined was made. The use of 24-hour load profiles to classify calendar days into typical days was also suggested. The availability of measurements data made it possible to develop load models for MV and conduct a study on actual customer data. The customers' measurements data, made it possible to identify the parameters and develop load models that could be used for technical and tariff analysis and conduct a pilot study to evaluate the load models. This study proposes a load model that can be used to model typical days and to model customer loads. The load models proposed here uses the k-means clustering algorithm as the basis for classification. The load models enable the classification of loads and assignment of load profiles accordingly. The results of this study indicated that load parameter models could be extracted from the customer measurements, for technical and tariff studies in distribution networks. It has also been possible to identify and determine the parameters from the load profiles and proposed a process for developing a load model for technical, economic and tariff analysis. The results also indicate that of the five identified parameters, the most significant parameters that affected the clustering results were the load factor, average power and the normalised peak usage parameter when the results of each of the factors were compared on an individual basis. The study also revealed improvements to the clustering results when all the parameters identified in this study were combined and a PCAbased clustering algorithm was used. Finally, the results indicate that the loads in the different economic activitybased classifications do not necessarily have similar shapes although they belong to the same cluster. The modelling process developed in this study may be implemented by utilities for determining load parameter models for MV feeders when measurements are available. The process may also be used to guide future data collection.