Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

AI and digitalization as enablers of flexible power system

Abstract. The Paris climate agreement obligate energy and power sector to reduce greenhouse gasses even though at the same time the global power demand increases. This leads to need to increase emission-free power generation with renewable energy sources (RES). Wind- and solar power technologies have developed significantly and price of power generated by them has decreased clearly in recent years. These factors have led to large-scale installations globally. However transitioning towards RES, such as wind and solar power, poses a challenge, since supply and demand in the electric power system must be equal at all times, but wind- and solar power are non-adjustable. These factors leads to need of finding flexibility from elsewhere e.g. from demand side, but also from storage systems. Purpose of this thesis is to analyze electric power system’s flexibility and how it can be increased by employing digital technologies including artificial intelligence (AI). This research was done by using qualitative conceptual research method, where data is collected until saturation point is reached. Data was collected from scientific journals and relevant sources to form conceptual understanding of current state and future possibilities. With digital technologies and artificial intelligence, companies can create new types of products, services and business models, which create more value for the customer. At the same time, these new solutions can improve the electric power system and create needed flexibility. The thesis studied these novel solutions and discussed practical implementation of three example cases in more detail. Digital solutions are rising into more significant role and they act as enablers for greener electric power system.Tekoäly ja digitalisaatio joustavan sähköjärjestelmän mahdollistajana. Tiivistelmä. Pariisin ilmastosopimus velvoittaa energia- ja sähkösektorit rajoittamaan kasvihuonepäästöjä, vaikka samaan aikaan sähkön kysyntä globaalisti kasvaa. Tämä johtaa tarpeeseen lisätä päästötöntä sähköntuotantoa uusiutuvilla energialähteillä. Tuuli- ja aurinkovoimateknologiat ovat kehittyneet ja niillä tuotetun sähkön hinta on laskenut selvästi viime vuosina. Nämä seikat ovat johtaneet niiden laajamittaiseen käyttöönottoon maailmanlaajuisesti. Siirtyminen näihin energiamuotoihin tuottaa haasteita sähköjärjestelmälle, sillä sähköjärjestelmässä tuotannon ja kulutuksen tulee olla tasapainossa koko ajan, mutta tuuli- aurinkovoiman sähköntuotantoa ei pystytä säätämään. Nämä seikat ovat johtaneet tarpeeseen löytää joustavuutta sähköjärjestelmän muista osista mm. kysynnästä, mutta myös varastoinnista. Tämän tutkimuksen tavoitteena on tutkia ja analysoida, miten sähköjärjestelmän joustavuutta voidaan lisätä digitaalisten teknologioiden, erityisesti tekoälyn avulla. Tutkimus on tehty laadullisella konseptuaalisella tutkimusmenetelmällä, jossa datan keräystä on jatkettu saturaatiopisteen saavuttamiseen asti. Data on kerätty tiedejulkaisuista ja muista tutkimuksen kannalta merkityksellisistä lähteistä, joiden pohjalta on voitu muodostaa konseptuaalinen ymmärrys tämän hetken tilasta ja tulevaisuuden mahdollisuuksista. Digitaalisten teknologioiden ja tekoälyn avulla yritykset voivat luoda uudenlaisia tuotteita, palveluita ja liiketoimintamalleja, jotka tuottavat aikaisempaa enemmän arvoa asiakkaalle. Samalla nämä uudet ratkaisut pystyvät parantamaan sähköjärjestelmää ja luomaan tarvittavaa joustavuutta. Tässä työssä tutustuttiin näihin uusiin ratkaisuihin ja tutkittiin myös niiden käytännön toimivuutta analysoimalla kolmea esimerkkitapausta tarkemmin. Digitaaliset ratkaisut ovat nousemassa merkittävään osaan sähköjärjestelmää ja niillä, kuten monella muullakin digitaalisiin teknologioihin pohjautuvilla ratkaisuilla voidaan mahdollistaa ympäristöystävällisempi sähköjärjestelmä

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches

FORECASTING SPOT ELECTRICITY PRICES WITH TIME SERIES MODELS

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.Electricity, price forecasting, ARMA model, seasonal component

Forecasting wind energy for a data center

Abstract. Data centers are increasingly using renewables such as wind and solar energy. RISE’s ICE data center has already solar panels and is now studying impact of adding a wind turbine into their microgrid. In this thesis, a machine learning model was developed to forecast wind power production for the data center. Data center in Luleå has several applications to utilize wind power forecasting. Renewable energy sources are intermittent, so accurate forecasting of output power reduces a need for additional balancing of energy and reserve power in an electricity grid. Renewable energy can be reserved from market for next hour or next day to maximize its use. Forecasting from 30 min to 6 hours ahead allows job scheduling to optimize usage of renewables and to reduce power consumption. Data center may target to minimize electricity cost or maximize usage of renewables for lower greenhouse gas emissions. Smart microgrid based on artificial intelligence is the way to implement the applications. Two open data sets from India and Sweden have been used in the research. The data available supports choosing of a statistical model. Random forest regression was the model used in the research. Data from India enabled to develop a model for one wind turbine. Developed model forecasted output power well. Swedish data set is from EEM20 competition, it included total wind power production in Sweden and had to be applied to approximate production of one wind turbine in Luleå. To achieve the goal output power of Luleå price region was averaged, and location for the simulation was chosen to be near Luleå. As expected, the accuracy of forecasting with Swedish data was reasonable, but approximations done reduced it. The developed model was applied to RISE’s ICE data center. Validation has been done, but final testing will take place in RISE’s simulation environment. In general, data from northern Sweden is not openly available for wind power forecasting. In addition, any scientific articles covering the geographical area were not found while working on literature review. The study with Swedish competition data gave understanding, which variables are significant in northern Sweden and about their relative importances. Wind gust is such a variable. Using two data sets from different geographical locations proved that climate has a major impact on performance of the trained model. Thus, it is reasonable to use the trained model in locations with similar weather conditions only.Tuulienergian ennustaminen datakeskusta varten. Tiivistelmä. Datakeskukset käyttävät uusiutuvia energialähteitä yhä enemmän. Tällaisia lähteitä ovat mm. tuuli- ja aurinkoenergia. RISE:n ICE datakeskuksella Luulajassa on jo aurinkopaneelit käytössä, ja nyt tutkitaan tuulimyllyn lisäämisen vaikutusta mikroverkkoon. Tässä työssä kehitettiin koneoppimismalli tuulivoiman tuotannon ennustamiseksi datakeskusta varten. Datakeskuksella on useita sovelluksia tuulienergian ennustamisen hyödyntämiseksi. Uusiutuvat energialähteet ovat luonteeltaan vaihtelevia, joten tuotetun tehon tarkka ennustaminen vähentää ylimääräisen säätämisen ja reservitehon tarvetta sähköverkossa yleensäkin. Datakeskus voi varata uusiutuvaa energiaa markkinoilta seuraavaksi tunniksi tai päiväksi uusiutuvan energian käytön maksimoimiseksi. Ennustaminen 30 minuutista 6 tuntiin etukäteen mahdollistaa työjonon aikatauluttamisen uusiutuvien käytön optimoimiseksi ja vähentää tehonkulutusta. Datakeskus voi pyrkiä minimoimaan sähkön käytön kustannuksia, tai pienentämään kasvihuonekaasujen päästöjä käyttämällä mahdollisimman paljon uusiutuvaa energiaa. Tekoälyyn perustuva älykäs mikroverkko on tapa toteuttaa edellä mainitut sovellukset. Tutkimuksessa on käytetty kahta avointa tietoainestoa Intiasta ja Ruotsista. Saatavilla oleva data tukee tilastollisen ennustemallin valintaa. Tässä työssä käytettiin satunnaismetsämenetelmää. Intian dataa käytettiin mallin kehityksessä yhtä tuulimyllyä varten. Kehitetty malli ennusti tuotetun tehon hyvin. Ruotsalainen data perustuu EEM20-kilpailuun, jossa arvioitiin koko Ruotsin tuulivoiman tuotantoa. Sitä olikin sovellettava Luulajassa olevan yhden tuulimyllyn tuotannon arvioimiseksi. Luulajan hinta-alueen tuottama teho keskiarvoistettiin, ja ennustamista varten valittiin maantieteellinen paikka läheltä Luulajaa. Kuten oli odotettavissa, soveltamisessa tehdyt likiarvoistukset pienensivät ennustamisen tarkkuutta, jota voidaan kuitenkin pitää kohtuullisena. Kehitettyä mallia sovellettiin RISE:n ICE datakeskusta varten. Algoritmin validointi on suoritettu, mutta lopullinen testaus tehdään RISE:n simulointiympäristössä. Yleisesti ennustamiseen soveltuvaa dataa ei ole Pohjois-Ruotsista tarjolla. Tieteellisiä artikkeleita ko. maantieteelliseltä alueelta ei löytynyt kirjallisuustutkimusta tehtäessä. Tutkimus ruotsalaisella datalla toi ymmärrystä siihen, mitkä muuttujat ovat merkittäviä Pohjois-Ruotsin alueella sekä niiden suhteellisesta merkityksestä. Kahden eri maantieteellisen alueen tietoaineiston käyttö osoitti, että ilmastolla on huomattava vaikutus koulutetun mallin suorituskykyyn. Näin onkin mielekästä käyttää koulutettua mallia vain sellaisilla alueilla, joiden sääolosuhteet ovat samankaltaiset