In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.

This study investigates outage statistics in the Swedish power system. More specifically, this paper delves into the critical analysis and enhancement of data quality, focusing on inconsistencies and missing values, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work not only illuminate the current challenges within outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation. 

This study investigates outage statistics in the Swedish power system. More specifically, this paper highlights the critical importance of addressing data quality issues such as inconsistencies and missing values, including unknown outage causes and unidentified faulty equipment. Existing research often overlooks the depth of these data quality challenges, leaving significant gaps in the reliability and utility of outage statistics. This paper reveals noticeable deficiencies in the current data and proposes a structured format for improving outage reporting through a database with three relations: outage summary, outage breakdown, and customer breakdown. To tackle these issues, a detailed qualitative analysis of the data is conducted, complemented by the exploration and testing of various machine learning algorithms. These algorithms are employed to predict unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and enabling more precise analytical capabilities. Specifically, methods such as decision trees and random forests are utilized to address the data gaps. The findings and proposals within this work not only illuminate the current challenges in outage data management but also pave the way for more robust, data-driven decision-making in outage management and policy formation.

Reliability indices like the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) serve as the Key Performance Indicators (KPIs) for Distribution System Operators (DSOs). They effectively measure the frequency and impact of outages on end users. Given the criticality of the electrical grid to many functions of the modern world, minimizing these values has been and continues to be a priority for DSOs. SAIDI and SAIFI can, however, be influenced by many factors including but not necessarily limited to the network topology, the type of installed components and the quantity of customers connected to the grid. In this work we thus attempt to predict the reliability indices of a DSO based on financial, customer and grid composition statistics reported to regulatory bodies by DSOs in Sweden between 2010 and 2021. By decomposing which features are the strongest predictors for SAIDI and SAIFI, DSOs can see how changes in their customer base and grid composition impact their reliability KPIs. In addition these indices can potentially be used to indicate which parts of the grid are most vulnerable to outages and thus prioritize mitigations at those locations.

This paper addresses the challenges of integrating large shares of renewable energy sources into the power system, focusing on managing operational reserves in multi-area systems and their long-term adequacy. Unlike previous studies, this paper investigates the long-term impact of procurement and activation of operational reserve in adjacent areas, considering energy scheduling and interconnection line constraints. Three procurement schemes for multi-area energy and reserve exchanges are proposed and analyzed through Sequential Monte Carlo Simulation. These schemes vary in their approach to interconnection line capacity constraints and the simultaneous or phased procurement of energy and synchronized reserve. The mathematical operationalization of these schemes is achieved through simple linear programming models, facilitating the quantification of marginal prices for both products. The impact of these schemes on operational reserve adequacy, marginal prices, and interconnection line utilization is demonstrated using configurations of the IEEE RTS 96 system. This analysis incorporates long-term uncertainty and diverse operational conditions and provides valuable insights into the interplay between energy and reserve procurement strategies in multi-area systems.

The primary objective of this thesis is to enhance the security of electricity supply by providing a holistic perspective and introducing a comprehensive framework for assessing power system health. This novel approach aims for a thorough evaluation of the system’s overall performance and well-being, using the physical dimensions of the security of supply as the foundation for a power system health index. 

After establishing the theoretical framework, relevant and available data is collected in order to analyze and understand the system’s performance. By analyzing outage statistics in Sweden, the research identifies specific trends and performance metrics that can be further investigated and segmented according to various criteria. The insights gained from this research can, in turn, be used to inform proactive maintenance strategies and capacity planning, ultimately mitigating the risks of outages and ensuring a more reliable electricity supply. 

Outage statistics are furthermore analyzed from the aspect of data quality, focusing on inconsistencies and missing values in the outage reports, i.e. unknown outage causes and unidentified faulty equipment. By carefully examining the data, noticeable gaps and deficiencies are revealed. Thus, a format for improving outage reporting using a database with 3 relations (outage summary, outage breakdown and customer breakdown) is proposed. In addition to a qualitative analysis of the data, various machine learning algorithms are explored and tested for their capability to predict the unknown values within the dataset, thereby offering a twofold solution: enhancing the accuracy of outage data and facilitating deeper, more accurate analytical capabilities. The findings and proposals within this work highlight the current challenges within outage data management and also lay the groundwork for a more comprehensive, data-driven approach in outage management and policy development. 

Det primära syftet med denna avhandling är att förbättra elförsörjningstryggheten genom att tillhandahålla ett holistiskt perspektiv och införa ett heltäckande ram- verk för att bedöma kraftsystemets hälsa. Detta nya tillvägagångssätt syftar till en grundlig utvärdering av systemets övergripande prestanda och välbefinnande, med hjälp av de fysiska dimensionerna av försörjningstryggheten som grunden för ett hälsoindex för kraftsystemet. 

Efter att ha upprättat det teoretiska ramverket samlas relevant och tillgäng- lig data in för att analysera och förstå systemets prestanda. Genom att analysera avbrottsstatistik i Sverige identifierar forskningen specifika trender och prestations- mått som kan undersökas ytterligare och segmenteras enligt olika kriterier. Insik- terna från denna forskning kan i sin tur användas för att informera om proaktiva underhållsstrategier och kapacitetsplanering, för att i slutändan minska riskerna för avbrott och säkerställa en mer tillförlitlig elförsörjning. 

Avbrottsstatistiken analyseras vidare ur aspekten datakvalitet, med fokus på inkonsekvenser och saknade värden i avbrottsrapporterna, det vill säga okända av- brottsorsaker och oidentifierad felaktig utrustning. Genom att noggrant granska uppgifterna avslöjas märkbara luckor och brister. Därför föreslås ett format för att förbättra avbrottsrapporteringen med hjälp av en databas med 3 relationer (avbrottsöversikt, avbrottsuppdelning och kunduppdelning). Förutom en kvalitativ analys av data, utforskas och testas olika maskininlärningsalgoritmer med avseen- de på deras förmåga att förutsäga de okända värdena i datamängden, och erbjuder därmed en tvåfaldig lösning: förbättrar avbrottsdatans noggrannhet och underlättar djupare, mer exakta analytiska möjligheter. Resultaten och förslagen inom detta arbete belyser de nuvarande utmaningarna inom hantering av avbrottsdata och lägger också grunden för ett mer omfattande, datadrivet tillvägagångssätt inom avbrottshantering och policyutveckling. 

Fault location methods are crucial for reducing fault restoration time, and thus improving a network's system average interruption duration index (SAIDI) and customer outage cost. Resonant-earthed systems pose problems for traditional fault location methods, leading to poor accuracy and a need for additional complexity. In this context, methods that detect fault direction (fault-passage indicators, FPI) at multiple points in the network may show advantages over a central distance-estimation method using fault locators (FL) of poor accuracy. This paper includes a comparative study of these two major fault location methods, comparing the reliability benefit from a varied number of FPIs or a central method. The optimal placement of the fault locating devices is found by formulating a mixed-integer linear programming (MILP) optimization approach that minimizes both outage and investment costs and assesses SAIDI. This approach has been tested on an example distribution system. However, to justify the universality of the algorithm, the RBTS reliability test system has also been analysed. The comparison of location methods and placement method of FPIs are useful for reliability centred planning of resonant-earthed distribution systems where fault location is to be used. Results show that a small number of FPIs that give accurate identification of direction may give more cost effective increase in reliability than a distance estimate by FL with typical levels of inaccuracy.

Data analysis plays a pivotal role in identifying patterns and relationships within data sets. By examining historical outage statistics in power systems, trends in system performance can be revealed, contributing to a better understanding of its behavior. Furthermore, by understanding the past performance of the power system, utility companies can make better decisions to enhance system reliability and resilience. This study investigates outage statistics in the Swedish power system from 2009 to 2019 and examines in depth the reporting mechanism. The data is clustered and analyzed according to three different criteria: voltage level of the breaking device, cause of the failure, and faulty equipment. Although the presented overview highlights key trends in system performance, the analysis has uncovered issues related to data quality and availability, such as missing values and inconsistencies that require further attention.

Distribution utilities aim to operate and plan their network in a secure and economical way. The prime focus of this work is to assist utilities by developing a new integrated approach which considers the impacts of system reliability in distribution system planning (DSP). This approach merges different problems together and solves them in a two-stage process, as follows: 1. cable routing and optimal location and number of switching devices (circuit breakers and reclosers); 2. optimal location and number of tie switches. Moreover, the possibility of installing different cable options, with different prices and capacities, is included. The optimization algorithm is designed using mixed-integer programming (MIP). The developed algorithm analytically evaluates relationships between different components in the system and dynamically updates reliability indices, failure rate and restoration time, of every node in the system. This approach has been tested on two distribution systems. Despite the complexity and the exhaustiveness of the problem, MIP converges and provides the optimal solution for every studied scenario. The results show that an integrated approach enables utilities to obtain more comprehensive solutions. Moreover, by understanding the impact of parameter variation enables utilities to categorize their priorities in the decision making process and optimally invest in distribution network with respect to reliability.

Designing an onshore wind farm is a complex planning process that requires various stages to be completed. The prime focus of this work is to assist planners and experts in finding the optimal cable layout of the onshore wind farm. The optimization algorithm is designed using mixed integer linear programming (MILP). The MILP algorithm takes into account system reliability, power transfer capacities and power quality issue. The novelty in this optimization algorithm is to simultaneously minimize cable installation cost and the cost of lost energy production and therefore maximize the reliability of the system. Additionally, the algorithm supports the optimal selection among different cable options, with different features, prices and capacities. By calculating voltage increase at the point of connection (POC), power quality issue is considered as well. The designed algorithm provides optimal results for four different wind farm layouts. Every layout is tested for three different case scenarios, where different number and type of cables are considered. The results show that more cable options contribute in lowering the total costs. Moreover, cables with higher capacity can help in improving the power quality issue.