In this thesis a rural network in Norrbotten, Sweden, has been to designed and planned with the purpose of improving distribution reliability by exchanging existing overhead lines with underground cables. General network design considerations are discussed as well as the ones for the specific low voltage grid. During the design phase the maximum power of domestic consumers has been estimated using Velander’s method. Currents, voltage drop and impedance of the network have been calculated and fuse operation and selectivity has been considered. The network has been planned, parts lists have been compiled and an economical estimation of the project costs has been made. The project comprises of a local low voltage grid and high voltage cables and overhead lines. It has been concluded that with the suggested dimensioning and material, the network design criteria are met.

Coincidence factors have been researched since the late 1800s, as they displays the ratio between the maximum coincidencental power usage of a system, and the sum of the maximum individual loads of the system. Accurate estimations of the highest coincidental power usage allow for minimal material usage when constructing substations, transformers, overhead lines, and cables for power transmission. Scania is large bus and truck production industry in Sweden, and has realised that it over-estimate the largest coincidental power usage of production facilities, which leads to unnecessary investment costs and power subscription with the power distribution utility. This study is special, as the area of coincidence factors for industrial purposes are rarely investigated. In combination with modelling of BESS for power supply, this study aims to investigate established methods of calculating coincidence factors for industrial purposes and their relevance, as the results will be compared to actual values from measurements. The results of the study showed that Velander ́s method, used by utilities in Sweden and a few other countries, is not very relevant for estimating highest coincidental power usage, as this requires accurate estimation of yearly energy usage, and two other parameters, k1 & k2 . The normal distribution is better for this purpose but also requires accurate data. This study proposed a method based on the normal distribution, that requires follow-up in order to guarantee that it is accurate in multiple cases. In addition, a BESS was modelled using Matlab, with the initial aim of peak-shaving. Since this did not prove profitable with Scania ́s standards, the modelling simply aimed at being profitable using Net Present Value as economical tool for evaluating profitability. The results displayed a lot of profitable sizes of the BESS where the battery became profitable after five years minimum. / Sammanlagringsfaktorer har studerats sedan slutet på 1800-talet. De visar kvoten mellan den högsta sammanfallande effekten och summan av de högsta individuella effekterna per last i ett system. En mer träffsäker uppskattning av sammanfallande effekt, reducerar materialanvändning vid byggande av ställverk, transformatorer och elkablar som ska användas vid strömöverföring. Scania är en stor produktionsindustri i Sverige som tillverkar bussar och lastbilar. De har insett att de överdimensionerar sina effektbehov hos olika produktionsfabriker, vilket leder till onödigt höga investeringskostnader och höga effektabonnemang gentemot eldistributören. Studien är ovanlig, då sammanlagringsfaktorer inom industrin är väldigt lite forskat kring. Studien undersöker redan etablerade metoder för att beräkna sammanlagringsfaktorer och hur relevanta de är för området. Dessutom studeras batterier för energilagring. Detta görs genom jämförelse av mätningar av strömmar i en av Scanias lokaler. Resultaten av studien visar att Velanders metod är olämplig för användning då den kräver kunskap om årlig energiförbrukning, samt korrekta konstanter k1 & k2. Normaldistribution som är ett statistikt verktyg, gav mer liknande värden av de uppmätta strömmarna i B178, men hade sin svaghet i att metoden också krävde kunskaper om effektanvändning, vilket blir problematiskt när en ny fabrik ska byggas, samt uppskatta effektbehovet för denna. Studien föreslår en modell som baseras på normaldistribution, men som kräver uppföljning för att säkerställa relevans. Utöver detta, används Matlab för att modellera ett batteri, vars primära syfte är att kapa effekttoppar. När detta visade sig vara icke lönsamt med Scanias standarder blev målet att istället modellera ett batteri vars enda mål att vara lönsamt. Där visade det sig finnas flera storlekar på batterier som var lönsamma efter minst fem år.

Toppeffekter används av elnätsbolag för att dimensionera elnätet, vilket blir allt viktigare för varje år. Fler och fler invånare och företag ökar sin elkonsumtion och förväntar sig en driftsäker och stabil elförsörjning. Det finns två vanliga metoder att beräkna toppeffekter. Första sättet är Velanders formel som är en enkel metod för att uppskatta toppeffekter. Velanders formel behöver bara årsenergi och vetskap om kundkategori med tillhörande Velanderkonstanter för beräkning av uppskattad toppeffekt. Sedan finns den mer komplexa typkurvemetoden som behöver flera olika parametrar, t.ex. graddagtal, dygnsmedeltemperatur, gränssannolikhet och kundkategori. Detta examensarbete undersöker en enkel metod för att ta fram konstanter till Velanders formel för beräkning av toppeffekter. Detta genomfördes med hjälp av regressionsanalys av historiska elanvändningsdata från Mälarenergi Elnät AB:s (MEE) kunder från 12 olika kundkategorier. Detta på grund av att MEE önskade att utveckla en metod för att ta fram konstanter till Velanders formel baserad på historiska elanvändningsdata. Metoden för att ta fram konstanter till Velanders formel går ut på att med hjälp av MATLAB utföra en regressionsanalys på simulerade kundgrupper skapade från timvis historiska elanvändningsdata. En kurva baserad på Velanders formel tas sedan fram som beskriver den övre gränsen till toppeffekterna för de simulerade kundgrupperna. Från kurvan fås sedan de Velanderkonstanter som söks. Resultaten av den undersökta metoden presenteras i form av grafer och tabeller för tre utvalda kundkategorier. Alla kategorier och deras resultat finns som bilagor till rapporten. Valideringen av resultaten och metoden gjordes med hjälp av korsvalidering och jämförelse mot heterogena simulerade kundgrupper. Känslighetsanalysen visar att den undersökta metoden var känslig för flera faktorer såsom kategorisering av kunder, tidsspann för historiska elanvändningsdata, antal simulerade kundgrupper och kundantal. Med tillräcklig dimensionering av dessa faktorer bedömdes metoden vara användbar. Resultaten visade på att de framtagna Velanderkonstanterna gav en god uppskattning av toppeffekter för de kundkategorier som undersökts. Jämförelse av de uppskattade toppeffekterna och de observerade visade på att det fanns en viss differens mellan dem. Detta var dock förväntat eftersom de uppskattade toppeffekterna ska avspegla den övre toppeffektsgränsen. / This degree project has examined a simple method aiming to obtain coefficients for Velanders formula which purpose is to calculate peak loads. This was done by using regression analysis on historical data on consumption of electricity from 12 different customer categories acquired from Mälarenergi Elnät AB (MEE). The reason being that MEE wanted to examine a method which could obtain coefficients for Velanders formula based on hourly historical electricity consumption data. The method for obtaining Velander coefficients uses MATLAB to do regression analysis on simulated customer groups, created from hourly historical electricity consumption data. The Velander coefficients are then obtained from a regression curve based on Velanders formula. Results from the evaluation of the method is presented with the help of plots and tables for three chosen customer categories. Validation of the method was done by cross-validation and comparison against heterogeneous customer groups. Sensitivity analysis showed the examined method to be sensitive to several factors such as categorization of customers, the timespan of historical electricity consumption data, the number of simulated customer groups that were used and how many customers a category contained. By dimensioning these factors carefully, the method examined was assessed to be viable. The results indicated that the obtained Velander coefficients gave a good estimation of the peak loads for the chosen customer categories. Comparison between the estimated and observed peak loads indicated that there was a certain difference between them. This was to be expected since the estimated peak loads are to reflect the upper peak load limit.

It is time to phase out fossil fuels and invest our efforts in green energy production through a major restructuring of the energy system. At the same time, more people are acquiring electric vehicles (EVs), thus creating a higher demand of electricity, and solar panels, allowing the consumer to also be a micro-producer. In order to systematically perform these changes, it is important to gain a better knowledge of the current customers as well as be able to make more accurate predictions about their future consumption. Vattenfall Eldistribution (VE) is one of several operators of the electric grid and, as of this day, still produces effect forecasts based on static estimations using the Velander formula. This has been a successful method in the past, however, with the current rate of change and the complexity in the consumption behaviour, it has become more difficult to estimate the aggregated load on the grid. It is also unattainable to cover the future demands by only expanding the grid. This creates the need for optimising the current grid, making more dynamic effect forecast and creating a smart grid. Our purpose is to help VE develop typical load profiles (TLPs), a more dynamic way to estimate peak loads, for private customers in the Uppsala region. VE provided us with time series data regarding the customers' consumption, as well as, attribute data describing the fuse size, heating system, contract type, etc., of these customers. A third dataset was also acquired through the Swedish Transport Agency regarding EV owners. These datasets allowed us to implement the three different parts of this project. The first part involved the creation of Attribute based TLPs with the help of the different attributes found in the VE's database. The goal for this part was to investigate the impact of specific attributes on the TLPs. The second part concerned the development of Behaviour based TLPs by implementing clustering algorithms that groups the customers based on behaviour alone. Thereafter, the distribution of attributes in the different groups was examined, in order to evaluate if there is a connection between the attributes and the consumption patterns identified. The third part studied the effect of EVs on the consumption behaviour. For this part, we implemented both attribute and behaviour based TLPs. The results of the Attribute based TLPs part concluded that fuse size has minimal impact on the TLPs whereas heating system entails a larger variation. In the second part of the project, Behaviour based TLPs, TLPs were successfully created with the help of clustering algorithms. However, no clear linkage between the consumption patterns and the attributes could be determined due to an evident overlap in the attributes between the created clusters. The final part of this project, EV owner based TLPs, verified the hypothesis that EV owners most likely charge their vehicles during the evening and night and established a clear visual increase in the consumption pattern in relation to non EV owners. An overall uncertainty that affects the results of all parts of this project is the accuracy of VE's data attributes and in order to confirm the conclusions of this thesis the degree of accuracy of the attributes should be determined.