Dynamic modeling of Thyristor Controlled Series Capacitor in PSCAD and RTDS environments

Fast development of power electronics have enabled wide utilization of Flexible AC Transmission System (FACTS) devices for increasing electricity transmission capacity and stability of power systems. Among others, Thyristor Controlled Series Capacitor (TCSC) has been found to have great potential as a device capable of damping small signal oscillations in the power system. In addition, also noticeable voltage support and transient stability enhancement of the power system can be achieved using TCSC. In addition to the great possibilities of FACTS technology increase of these nonlinear and complex devices emphasizes the importance of extensive power system analysis to verify the overall stability of the power system. Consequently, in this paper research concerning operation of TCSC as a part of power system is presented. Main targets of the research are to develop new modeling techniques for TCSC and to study the effect of control system structure and surrounding network on operational characteristics of TCSC.reviewe

Short-term load forecasting at electric vehicle charging sites using a multivariate multi-step long short-term memory

This study assesses the performance of a multivariate multi-step charging load prediction approach based on the long short-term memory (LSTM) and commercial charging data. The major contribution of this study is to provide a comparison of load prediction between various types of charging sites. Real charging data from shopping centres, residential, public, and workplace charging sites are gathered. Altogether, the data consists of 50,504 charging events measured at 37 different charging sites in Finland between January 2019 and January 2020. A forecast of the aggregated charging load is performed in 15-min resolution for each type of charging site. The second contribution of the work is the extended short-term forecast horizon. A multi-step prediction of either four (i.e., one hour) or 96 (i.e., 24 h) time steps is carried out, enabling a comparison of both horizons. The findings reveal that all charging sites exhibit distinct charging characteristics, which affects the forecasting accuracy and suggests a differentiated analysis of the different charging categories. Furthermore, the results indicate that the forecasting accuracy strongly correlates with the forecast horizon. The 4-time step prediction yields considerably superior results compared with the 96-time step forecast

Analysing electric vehicle charging power quality in large-scale charging sites - a data-driven approach

Peer reviewe

Tulevaisuuden kaupunkiympäristön energiaratkaisut : Tulosraportti

Korkeakouluille, niin yliopistoille kuin ammattikorkeakouluille, on asetettu koulutustehtävän ja tutkimuksen lisäsi tehtäväksi toimia vuorovaikutuksessa yhteiskunnan ja elinkeinoelämän kanssa. Niiden perusrahoitus tai tutkimusprojektit antavat kuitenkin hyvin rajallisesti mahdollisuuksia osallistua oman ammattialan käytännön kehittämiseen erilaisissa yhteistyöverkostoissa. Sähkötekniikan ja energiatehokkuuden edistämiskeskus STEK ry:n monivuotinen hankeyhteistyörahoitus on monipuolinen resurssi korkeakoulujen toiminnan tukemiseen ja kehittämiseen. Tampereen ammattikorkeakoulun ja Tampereen yliopiston saama rahoitus vuosille 2018 - 2022 mahdollisti joustavan ja monipuolisen kehitystoiminnan hyvin ajankohtaisen teeman, sähköistyvä energiajärjestelmä, aihealueella. Hankeyhteistyön keskeisenä tavoitteena oli vahvistaa ammattikorkeakoulun ja yliopiston yhteistyötä liittyen aihealueen koulutukseen, tutkimushankkeiden kehittämiseen sekä osallistumiseen yhteiskunnalliseen keskusteluun ja erilaisiin asiantuntijaryhmiin. Hankeyhteistyörahoitus mahdollisti merkittävästi resursseja sellaiseen kehitystoimintaan, johon korkeakoulujen perusrahoitusta vähän ohjataan ja jolla kuitenkin olisi merkitystä sekä opettajien ja tutkijoiden ammattitaidon ylläpitämiseen sekä verkostojen luomiseen elinkeinoelämän kanssa. Sen avulla osallistuttiin asiantuntija- ja kehitysryhmiin, laadittiin lausuntoja ja kannanottoja alan lainsäädäntöön, tehtiin tieteellisiä julkaisuja, kirjoitettiin lehtiartikkeleita, annettiin haastattelua tai asiantuntijalausuntoa lehdistölle, tehtiin useita opinnäytetöitä sekä osallistuttiin seminaariesityksiin ja paneelikeskusteluihin. Lisäksi pilotoitiin 2.asteen koulutuksen kanssa yhteistyötä projektikurssien ja teemaluentojen avulla. Yhteistyössä tehtiin useita yhteisiä TKI-hankehakuja, joiden taustatyöhön ja toisaalta tulosten jalkauttamiseen käytettiin saatua kehitysresurssia. Hankekauden aikana tulleet yllättävät toimintaympäristön muutokset, ennen kaikkea pandemia ja energiakriisi, vaativat pikaisia muutoksia suunniteltuihin toimenpiteisiin ja toimintamuotoihin. Vuoden 2022 aikana energiakriisi nosti ennennäkemättömällä tavalla sähköenergian ja sen merkityksen esiin yhteiskunnallisessa keskustelussa. Hankeyhteistyö antoi hyvät mahdollisuudet osallistua ja vaikuttaa aktiivisesti sekä jakaa laajasti aiheeseen liittyviä tutkimus- ja selvitystuloksia, joita erillisissä hankkeissa oli saatu tuloksina

Customized Retail Pricing Scheme Design with a Hybrid Data-driven Method

Rapid growth of smart metering data in smart grids provides great opportunities for the retailer to design customized price schemes and demand side management (DSM) programs for different customer groups. This paper proposes a hybrid data-driven method of clustering customers' daily load profiles and optimizing different electricity retail plan recommendations for electricity retailers. By combing the user-side information with the risk-aware decision-making framework, specifically using conditional value-at-risk (CVaR) modeling method, the retailer could guarantee its accumulated revenue without doing any harm to the customers' benefit, while guiding their energy consumption behavior instead. Through large-scale experiments, it is observed that a slight increase in the customers' possible payment would be compensated by their big gain in more demand response opportunities. The retailers' profit could also be increased by roughly 49%-51% and 33%-38% with or without enabling demand response programs.acceptedVersionPeer reviewe

Overcoming non-idealities in electric vehicle charging management

The inconvenient nature of non-ideal charging characteristics is demonstrated from a power system point of view. A new adaptive charging algorithm that accounts for non-ideal charging characteristics is introduced. The proposed algorithm increases the local network capacity utilization rate and reduces charging times. The first unique element of the charging algorithm is exploitation of the measured charging currents instead of ideal or predefined values. The second novelty is the introduction of a short-term memory called expected charging currents. This makes the algorithm capable of adapting to the unique charging characteristics of each vehicle individually without the necessity to obtain any information from the vehicle or the user. The proposed algorithm caters to various non-idealities, such as phase unbalances or the offset between the current set point and the real charging current but is still relatively simple and computationally light. The algorithm is compatible with charging standard IEC 61851 and is validated under different test cases with commercial electric vehicles

Network-adaptive and capacity-efficient electric vehicle charging site

The adaptive charging algorithms of today divide the available charging capacity of a charging site between the electric vehicles without knowing how much current each vehicle draws in reality. Thus, they are not able to detect deviations between the current set point at the charging station and the real charging current. This leads to a situation where the charging capacity of the charging site is not used optimally. This paper presents an algorithm including a novel feature, Expected Characteristic Expectation and tested under realistic circumstances. It is demonstrated that the proposed algorithm enhances the adaptability of the charging site, increasing the efficiency of the used network capacity up to about 2 kWh per charging point per day in comparison with the previous benchmark algorithm. The algorithm is able to increase the average monetary benefits of the charging operators by up to around 5.8%, that is 0.6 € per charging point per day. No input, such as departure time, is required from the user. The proposed algorithm has been tested with real electric vehicles and charging stations and is compatible with the IEC 61851 charging standard. The charging algorithm is applicable in practice as it is described in this paper

The Use of Typical User Load Profiles to Energy Communities in Finland - Aspects on Distribution Tariff Design and Regulation

The recent developments in the energy sector have promoted the role of citizen as active actors in the energy market in the form of energy communities (ECs). However, it is not clear how ECs should be treated in the pricing of electricity distribution and the price regulation. This paper discusses the definitions and load profiles of typical users used in the electricity distribution price regulation in Finland. The definitions are compared to different combinations of small-scale customers who could form different types of ECs. The comparison shows that the typical user definitions of larger commercial and industrial users would be of the same size as the studied ECs. The conclusion is that the typical user definitions should be kept up to date and updated in the future to account for ECs to ensure that the legislative requirements are met, and accurate statistics can be provided to the public.Peer reviewe

Aspects of development needs for distribution network tariffs in the case of energy communities as new emerging user types

publishedVersionPeer reviewe