Special Session on Industry 4.0

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Investigation into the impact of wind power generation on demand side management (DSM) practices

The construction of a number of wind farms in South Africa will lay the foundation for the country to embrace the generation of greener energy into the National Grid. Despite the benefits derived from introducing wind power generation into the grid, this source encompasses adverse effects which need to be managed. These adverse effects include the intermittency and lack of predictability of wind. In power systems with a high penetration of wind energy, these effects can severely affect the power system’s security and reliability in the event of significant rapid ramp rates. Recently, many utilities around the world have been exploring the use of Demand Side Management (DSM) and Demand Response (DR) initiatives and programmes to support and manage the intermittency of wind power generation. This report outlines the programmes and benefits of DSM/DR and provides a critical analysis of the challenges facing South Africa with implementing these initiatives. Introducing these programmes necessitates the employment of a number of Smart Grid technologies including Advanced Metering Infrastructure (AMI), next generation telecommunications technologies, smart meters, enterprise system integration and dynamic pricing. These tools and techniques are discussed and their challenges described within the context of South Africa’s current state of the power system. The current practices for DSM/DR in South Africa have been evaluated in this report. Despite, the success of many DSM/DR initiatives in the commercial, industrial and agricultural sectors, it is found that much work is still required in the residential sectors as the current DSM initiatives are not adequate for managing wind power generation. A detailed analysis and recommendations for South Africa’s DR program is then presented based on industry best practices and experiences from other utilities who are currently exploring DSM/DR in the residential sector using Smart Grid technologies

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

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Älykkäiden rakennusten investointilogiikka kiinteistö- ja rakennusalalla

Smart buildings have been described as the embodiment of digitalisation in the real estate and construction (REC) sector. These buildings typically utilise a variety of interactive technical building systems, which operate autonomously through a smart grid without constant input from users. A clear definition of a smart building is, however, missing. Moreover, the added investment value of a smart building is not well measurable through the traditional real estate investment logic, which is based on the universal property value equation. The objectives of this thesis are to review the definition of a smart building through the Smart readiness indicator (SRI) introduced by the European Commission, and to observe the added value of a smart building from the investment point of view. The research was accomplished through two research methods, a case study and interviews. In the SRI-methodology, the definition of a smart building and high SRI-score has been tied together. Therefore, in the case study the SRI-framework was applied to a multi-purpose campus building to evaluate, how well it takes into account the building’s smartness. The interviews were carried out with Finnish REC-sector specialists to identify the key investment drivers, which are adding value to a smart building. Additionally, their effect on the investment logic was identified. The case study showed that the smart readiness of the campus building was approximately 58% from the maximum obtainable SRI-score, but the framework did not take into consideration all the smart technical building systems implemented in the building. From the interviews it was discovered that the traditional methods of calculating a property’s value, where the investment logic is based on the property level drivers of rental income, operating expenses and required yield, do not explicitly show the added investment value of a smart building. Instead, the added investment value of smart buildings is perceived to be related to the synergistic benefits in smart communities. Thus, a revision of the regular real estate property value equation and investment logic are considered as a prerequisite to be able to explicitly represent the added investment value of a smart building.Älykkäät rakennukset kuvaavat digitalisaation ilmentymää kiinteistö- ja rakennus (KIRA) sektorilla. Nämä rakennukset käyttävät tyypillisesti monia vuorovaikutteisia taloteknisiä järjestelmiä, jotka toimivat itsenäisesti älykkään verkon kautta ilman käyttäjien ohjausta. Älykkäälle rakennukselle ei ole kuitenkaan vielä kehittynyt yleisesti hyväksyttyä määritelmää. Lisäksi älykkään rakennuksen lisäarvo ei ole mitattavissa perinteisen kiinteistöinvestointilogiikan kautta, joka pohjautuu yleiseen kiinteistön arvon laskukaavaan. Tämän työn tavoitteena on tarkastella älykkään rakennuksen määritelmää Euroopan Komission ehdottaman ’Smart readiness indicator’ (SRI) työkalun kautta sekä tunnistaa älykkään rakennuksen lisäarvo investointina. Tutkimus toteutettiin tapaustutkimuksen ja haastatteluiden avulla. SRI-metodologiassa esitetyn ehdotuksen mukaisesti korkea SRI-pistemäärä vastaa älykkään rakennuksen määritelmää. Sen takia SRI-kehikkoa sovellettiin monimuotoisen kampusrakennuksen älykkyyden arvioinnin tapaustutkimuksessa. Haastattelut toteutettiin suomalaisten KIRA-sektorin asiantuntijoiden kanssa. Tavoitteena oli tunnistaa älykkään rakennuksen investointiarvoa lisäävät tekijät sekä havainnoida tunnistettujen tekijöiden vaikutusta investointilogiikkaan. Tapaustutkimus osoitti, että kampusrakennuksen älykkyysvalmius oli 58% määritellystä SRI-maksimiarvosta, mutta kehikko ei ottanut huomioon kaikkia rakennuksessa toteutettuja älykkäitä taloteknisiä järjestelmiä. Haastatteluissa havaittiin, että perinteisen kiinteistön arvon laskukaavan kautta ei ole mahdollista yksiselitteisesti osoittaa älykkään rakennuksen lisäarvoa, missä vuokratuotto, kiinteistökustannukset ja tuottovaatimus mittaavat investointia kiinteistötasolla. Sen sijaan älykkään rakennuksen lisäarvon havaittiin liittyvän älykkään yhteisön synergian tuottamaan hyötyyn. Näin ollen perinteisen kiinteistön arvon laskukaavan ja kiinteistöinvestointilogiikan muuttaminen havaittiin edellytykseksi älykkään rakennuksen lisäarvon yksiselitteiselle perustelulle

Design, Integration, and Evaluation of IoT-Based Electrochromic Building Envelopes for Visual Comfort and Energy Efficiency

Electrochromic glazing has been identified as the next-generation high-performance glazing material for building envelopes due to its dynamic properties, which allow the buildings to respond to various climate conditions. IoT technologies have improved the sensing, communication, and interactions of building environmental data. Few studies have been done to synthesize the advancements in EC materials and building IoT technologies for better building performance. The challenge remains in the lack of compatible design and simulation tools, limited understanding of integration, and a paucity of evaluation measures to support the convergence between the EC building envelopes and IoT technologies. This research first explores the existing challenges of using EC building envelopes using secondary data analysis and case studies. An IoT-based EC prototype system is developed to demonstrate the feasibility of IoT and EC integration. Functionalities, reliability, interoperability, and scalability are assessed with comparisons of four alternative building envelope systems. Nation-wide evaluations of EC building performance are conducted to show regional differences and trade-offs of visual comfort and energy efficiency. A machine learning approach is proposed to solve the predictive EC control problem under random weather conditions. The best prediction models achieve 91.08% mean accuracy with the 16-climate-zone data set. The importance of predictive variables is also measured in each climate zone to develop a better understanding of the effectiveness of climatic sensors. Additionally, a simulation study is conducted to investigate the relationships between design factors and EC building performance. An instantaneous daylight measure is developed to support active daylight control with IoT-based EC building envelopes

Systematic Analysis of Engineering Change Request Data - Applying Data Mining Tools to Gain New Fact-Based Insights

Large, complex system development projects take several years to execute. Such projects involve hundreds of engineers who develop thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases, but due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important in the product development process to enhance a product. The opportunity at hand is that vast amount of data on industrial changes are captured and stored, yet the present challenge is to systematically retrieve and use them in a purposeful way.This PhD thesis explores the growing need of product developers for data expertise and analysis. Product developers increasingly refer to analytics for improvement opportunities for business processes and products. For this reason, we examined the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge for ECR information needs, and applying mathematical tools for solution design and implementation.Results from extensive interviews generated a list of engineering information needs related to ECRs. When preparing for data mining, it is crucial to understand how the end user or the domain expert will and wants to use the extractable information. Results also show industrial case studies where complex product development processes are modeled using the Markov chain Design Structure Matrix to analyze and compare ECR sequences in four projects. In addition, the study investigates how advanced searches based on natural language processing techniques and clustering within engineering databases can help identify related content in documents. This can help product developers conduct better pre-studies as they can now evaluate a short list of the most relevant historical documents that might contain valuable knowledge.The main contribution is an application of data mining algorithms to a novel industrial domain. The state of the art is more up for the algorithms themselves. These proposed procedures and methods were evaluated using industrial data to show patterns for process improvements and cluster similar information. New information derived with data mining and analytics can help product developers make better decisions for new designs or re-designs of processes and products to ensure robust and superior products

An Overview of Demand Response : From its Origins to the Smart Energy Community

The need to improve power system performance, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants’ economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select an appropriate plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed