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Le bâtiment est un noeud énergétique important et un support idéal pour développer etanalyser les effets d’un système de gestion optimisée d’énergie (SGEB) tant son impactpotentiel sur la demande énergétique globale est important. Cependant, pour que ces objectifssoient atteints, plusieurs verrous doivent être levés. Au-delà des problématiques liées àl’architecture de distribution, aux modèles (y compris ceux relatifs au comportement desusagers), aux outils de dimensionnement, à la formalisation des paramètres, contraintes etcritères, aux systèmes de production et aux modes de connexions au réseau de distribution, lesproblèmes liés à la mise en oeuvre d’un outil de gestion décentralisée et à sa validation sontcentraux centrale. Ces travaux s’inscrivent directement dans cette optique. Ils portent enparticulier sur l’élaboration de modèles énergétiques, de stratégies de gestion d’énergie dansune configuration multi-sources et multi-charges et surtout de mise en oeuvre de méthodes etd’outils de validation au travers de bancs tests variés où certains composants peuvent êtreréels.Ce travail analyse le gestionnaire énergétique « G-homeTech » comprenant plusieursfonctionnalités de gestion testées sur des bancs d’essai virtuels et hybrides qui permettent decombiner à la fois des composants matériels et logiciels dans les simulations. Cela a permisd'insérer des actionneurs communicants pour tester leur pertinence. Les validations menéesmontrent que le gestionnaire énergétique permet l'effacement de pointes de consommation etdes économies sur la facture énergétique globale tout en respectant les contraintes techniqueset réglementaires.Les évènements prédits ne sont pas toujours ceux qui se produisent. Nous avons alorssimulé de telles situations. La radiation solaire et la consommation totale des services noncontrôlables sont différentes de celles prédites. Cette différence a conduit à des dépassementsde puissance électrique souscrite qui a activé le mécanisme de gestion réactive du gestionnaireénergétique. Des ordres de délestage sont alors dynamiquement envoyés à certainséquipements. Ces ordres alimentent directement les modèles des équipements électriques.Selon les importances relatives données au coût et au confort, nous avons montré que legestionnaire énergétique permet de faire des économies substantielles en évitant lesconsommations durant les pics de prix et évitant les dépassements de souscription pareffacement, par modulation du fonctionnement des systèmes de chauffage et par décalage defonctionnement des services temporaires dans les périodes plus intéressante énergétiquement. / The building is an important energy node and an ideal support to develop and analyzethe effects of an Energy Management System (EMS). Because of its potential impact, such amanagement of global energy demand is important. However, to achieve these goals, severallocks must be removed. Beyond issues related to the distribution architecture, to models(including those relating to user behavior), sizing tools, the formalization of parameters,constraints and criteria, production systems and methods of connection to the grid, problemrelated to implementation of decentralized management tool and its validation are central. Mywork is part of this context. It focuses in particular on the development of energy models,strategies for energy management in a multi-source and multi-load configuration, andespecially, the implementation methods and the validation tools through various test bencheswhere some components are real.This paper analyzes the energy manager “G-homeTech” including several managementfeatures tested on virtual and hybrid test benches that combine both hardware and softwarecomponents in the simulations. This has put communicating actuators to test their relevance.The validations show that the energy manager allows the deletion of peak demand andsavings on the overall energy bill while respecting the technical and regulatory constraints.Predicted events are not always those that occur. We then simulated such situations.Solar radiation and the total consumption of uncontrollable services are different from thosepredicted. This difference has led to over-subscribed electric power which has enabled themanagement mechanism of reactive energy manager. Load shedding orders are thendynamically sent to certain equipement. These orders directly supply models of electricalequipment.According to the relative importance given to cost and comfort, we have shown that theenergy manager can make substantial savings avoiding consumption during price peaks andavoiding over-subscription by erasure, by modulation of heating system operation and byshefting the timed service operation in the most interesting periods in energy.

The effort to limit the usage of primary energies highly relevant and topical issue on both European and global level. The wide and diverse industry area, especially the technological equipment of operation, still offer a large potential for such savings. Some of the tools that can be used to lower the consumption and overall operational costs are the energy management system and the energy audit, which helps to identify working areas with a potential for savings and efficiency improvements. However, the current approach to energy audit is not sufficient when it comes to technologies. Which is why this thesis aims at designing a universal methodology for audits with a focus on manufacturing and processing equipment, which will be easily applicable in various types of industrial production.

Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electri ed powertrain, is a much simpler version of the theoretically developed EMS. Such simpli cation is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simpli cation should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simpli cation must be effective to create a versatile and utilitarian EMS. The reinforcement learning-based controllers feature pro table characteristics in optimizing the performance of controllable physical systems as they do not mandatorily require a mathematical model of system dynamics (i.e. they are model-free). Quite naturally, it can aspired to testify such prowess of reinforcement learning-based controllers in achieving near-global optimal performance for energy management system (supervisory) of electri ed powertrains. Before deployment of any supervisory controller as a mainstream controller, they should be essentially scrutinized through various levels of virtual simulation platforms with an ascending order of physical system emulating-capability. The controller evolves from a mathematical concept to an utilitarian embedded system through a series of these levels where it undergoes gradual transformation to finally become apposite for a real physical system. Implementation of the control strategy in a Simulink-based forward simulation model could be the first stage of the aforementioned evolution process. This brief will delineate all the steps required for implementing an reinforcement learning-based supervisory controller in a forward simulation model of a hybrid electric vehicle. A novel framework of loss-minimization based instantaneous optimal strategy is introduced for the energy management system of a multi-mode hybrid electric powertrain in this brief. The loss-minimization strategy is flexible enough to be implemented in any architecture of electrified powertrains. It is mathematically proven that the overall system loss minimization is equivalent to the minimization of fuel consumption. An online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and future prospects of such transmissions. It is noteworthy to mention that a novel architecture topology selected for this dissertation is engendered through a series of rigorous screening process whose workflow is presented here with brevity. One of the legitimate concern of multi-mode transmission is it's proclivity to contribute discontinuity of power-flow in the downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Advent of dynamic coordinated control as a technique for ameliorating such discontinuity has been substantiated by many scholars in literature. Hence, a system-level coordinated control is employed within the energy management system which governs the mode schedule of the multi-mode powertrain in real-time simulation. / Thesis / Doctor of Philosophy (PhD)

本論文では，電力利用率向上と需給バランス調整に寄与する蓄電池付太陽光PCSの新たな制御法について検討を行い，その有効性を確認している。受電点の潮流に応じてPCSの力率を制御することで，逆潮流時には系統電圧の上昇を抑制しつつ，順潮流時には負荷への電力利用率を向上する手法を提案し，フィールド試験により有効性を定量評価した。また，受電電力の制御目標値を可変とする制御を用い目標値を最適化することで，新たな機器を付加する必要なく上げ下げのDRに対応し需給バランスを調整する制御手法を提案し，その有効性をシミュレーションおよび実機により検証した。 / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

Цель исследования – разработка методических основ эффективного управления системой энергетического менеджмента на промышленном предприятии с учетом тенденций развития зеленой экономики и цифровизации производства в современных условиях. Объектом исследования является система энергетического менеджмента промышленного предприятия. Научная новизна исследования состоит в разработке теоретических основ и методических подходов на основе интеграции концепций экологической ответственности и цифровизации производства в систему энергетического менеджмента предприятия. Развита концепция дорожной карты внедрения системы энергетического менеджмента на предприятии в соответствии с принятой энергетической политикой и современными представлениями об энергоэффективности. Особенностью карты является планирование мероприятий, направленных на углубление цифровизации внутренней среды энергопотребления и соблюдение показателей результативности внедрения зеленой экономики, таких как снижение выбросов диоксида углерода, доля невозобновляемых ресурсов в структуре энергопотребления, доля повторного использования энергии в производстве. Предложенная концепция позволит достичь энергоэффективности бизнес-процессов с учетом современных подходов к ответственному энергоменеджменту на промышленном предприятии. / The purpose of the study is to develop methodological foundations for the effective management of the energy management system at an industrial enterprise, taking into account the trends in the development of a green economy and digitalization of production in modern conditions. The object of the study is the energy management system of an industrial enterprise. The scientific novelty of the research lies in the development of theoretical foundations and methodological approaches based on the integration of the concepts of environmental responsibility and digitalization of production into the energy management system of an enterprise. The concept of a roadmap for the implementation of an energy management system at an enterprise has been developed in accordance with the adopted energy policy and modern ideas about energy efficiency. A feature of the map is the planning of activities aimed at deepening the digitalization of the internal environment of energy consumption and compliance with the performance indicators of the implementation of the green economy, such as reducing carbon dioxide emissions, the share of non-renewable resources in the structure of energy consumption, the share of energy reuse in production. The proposed concept will allow achieving energy efficiency of business processes, taking into account modern approaches to responsible energy management at an industrial enterprise.

Fuel Cell Hybrid Electric vehicles are hybrid vehicles that consist of both fuel cells and batteries as energy conversion systems. The Energy Management System plays an important role in the operation of the fuel cell hybrid system, as it helps in reducing the hydrogen consumption of the system. This study investigates an optimal control algorithm with an aim to reduce the hydrogen consumption of the fuel cell system for five different drive cycles operating in Europe. Model Predictive Control(MPC) is used to solve the optimal control problem, by formalizing a look ahead controller, utilizing its receding horizon approach. The optimal controller analysis is compared with a conventional rule-based controller, by analysing the hybrid system over various battery and fuel cell sizes, on the basis of the overall hydrogen consumption. Firstly, a simplified system model is developed, by modelling the fuel cell system with respect to the efficiency curve of the hydrogen power and fuel cell power. The battery system model with its State of Charge(SOC) is coupled with the fuel cell model to form an objective function satisfying the power demand from the drive cycles. The MPC controller and the rule-based controller are implemented in MATLAB and the powersplit analysis is simulated for all five routes. The results show that the energy management system with the MPC controller optimizes the powertrain configuration efficiently, with preparing for the uphill or downhill, such that the battery SOC stays in its limits and the fuel cell operates in the most efficient range. This ensures operating over different types of drive cycles with the most efficient battery and fuel cell size, hence concluding with the MPC controller outperforming the rule-based one. / Fuel Cell Hybrid Electric Vehicle (FCHEV) är hybridfordon som består av både bränsleceller och batterier som energiomvandlingssystem. Energy ManagementSystem (EMS) spelar en viktig roll i driften av bränslecellshybridsystemet, eftersom det hjälper till att minska systemets vätgasförbrukning. Denna studie undersöker en optimal styralgoritm framtagen i syfte att minska syfte att minska vätgasförbrukningen i bränslecellssystemet. Algoritmen testas på fem olika körcykler, baserade på verkliga Europeiska vägsträckor. Model Predictive Controller (MPC) används för att lösa det optimala styrproblemet, genom att formalisera en framåtblickskontroller med hjälp av dess vikande horisont. Den optimala kontroller jämförs med en konventionell regelbaserad kontroller, genom att analysera hybridsystemet över olika batteri- och bränslecellstorlekar, baserat på den totala väteförbrukningen. Först utvecklas en förenklad systemmodell, som modellerar bränslecellssystemet med avseende på effektivitetskurvan för vätgaskraften och bränslecellseffekten. Batterisystemmodellen med dess State of Charge (SOC) är kopplad till bränslecellsmodellen för att bilda en målfunktion som tillfredsställer kraftbehovet från drivcyklerna. MPC-styrningen och den regelbaserade styrningen är implementerade i matlab och effektdelningsanalysen simuleras för alla fem rutterna. Resultaten visar att energihanteringssystemet medMPC-styrningen optimerar drivlinans konfiguration effektivt, med förberedelser för uppförsbacke eller nedförsbacke, så att batteriets SOC håller sig inom sina gränser och bränslecellen arbetar i mest optimala räckvidden. Detta säkerställer drift över olika typer av körcykler med den mest effektiva batteri- och bränslecellsstorleken, och avslutar därför med att MPC-styrenheten överträffar den regelbaserade.

The current global scenario is such where impact on the environment is becoming a rising concern. Global automotive manufacturers have focused more towards hybrid and electric vehicles as both more aware customers and governmental legislation have begun demanding higher emission standards. One of the many ways that Volvo Car Group approaches this trend is by mild hybridization which is by assisting the combustion engine by a small electric motor and a battery pack. A smart energy management strategy is needed in order to get the most out of the benefits that hybrid electric vehicles offer. The main objective of this strategy is to utilize the electrical energy on-board in such a manner that the overall efficiency of the hybrid powertrain becomes as high as possible. The current implementation is such that the decision for using the on-board battery is non-predictive. This results in a sub-optimal utilization of the hybrid powertrain. In this thesis, a predictive energy optimization tool is developed to maximize the utility of hybridization and the practical implementation of this tool is investigated. The optimization considers both the capacity as well as the thermal loadconstraints of the battery. The developed optimization tool uses information about the route ahead together with convex optimization to produce optimal reference trajectories of the battery states. These trajectories are used in a real-time controller to determine the battery use by controlling the adjoint states in the Equivalent Consumption Minimization Strategy equation. This optimization tool is validated and compared with the baseline controller in a simulation environment based on Simulink. When perfect information about the road ahead is known, the average reduction in fuel consumption is 0.99% relative the baseline controller. Several issues occurring in the real implementation are explored, such as the limited computational speed and the length of the route ahead that can be predicted. For this reason the information input to the optimization tool is segmented and the resulting performance is investigated. For a 30 second segmentation of the future route information, the average saving in fuel consumption is 0.13% relative to the baseline controller. It is shown that the main factor limiting the amount of savings in fuel consumption is the introduction of the thermal load constraints on the battery. / Det nuvarande globala scenariot är sådant där miljöpåverkan håller på att bli en växande angelägenhet. Globala fordonstillverkare har fokuserat mer på hybrid- och elfordon, eftersom både mer medvetna kunder och statlig lagstiftning har börjat kräva högre emissionskrav. Ett av de många sätt som Volvo Car Group närmar sig denna trend är genom mild hybridisering genom att bistå förbränningsmotorn med en liten elmotor och ett batteripaket. En smart strategi för energihantering behövs för att få ut det mesta av de fördelar som hybrida elfordon erbjuder. Huvudsyftet med denna strategi är att utnyttja den elektriska energin ombord på ett sådant sätt att den totala effektiviteten hos hybriddrivlinan blir så hög som möjligt.Den nuvarande implementeringen är sådan att beslutet att använda det fordonsbaserade batteriet är inte-förutsägbart. Detta resulterar i en suboptimal användning av hybriddrivlinan. I denna avhandling är ett prediktivt Energioptimeringsverktyg utvecklat för att maximera nyttan av hybridisering och det praktiska implementerandet av detta verktyg undersöks. Optimeringen beaktar både kapaciteten och de termiska belastningsbegränsningarna hos batteriet. Det utvecklade optimeringsverktyg använder information om vägen framåt tillsammans medkonvex optimering för att producera optimala referenstrajektorier av batteritillståndet. Dessa trajektorier används i en realtidsstyrenhet för att bestämma batterianvändningen genom att kontrollera adjungerade tillstånden strategiekvationen för den ekvivalenta förbrukningsminimiseringen. Optimeringsverktyget verifieras och jämförs med den ursprungliga styrenheten i en simuleringsmiljö baserad på Simulink. När perfekt information om vägen framåt är känd, är den genomsnittliga minskningen av bränsleförbrukningen 0,99 % relativt den ursprungliga styrenheten. Flera frågor som uppstår i den verkliga implementeringen undersöks, såsom den begränsade beräkningshastigheten och längden på den väg framåt som kan förutses. Av denna anledning är segmenteras informationen till optimeringsverktyget och den resulterande prestandan undersöks. För en 30 sekunders segmentering av framtida väginformation är den genomsnittliga besparingen i bränsleförbrukningen 0,13 % i förhållande till den ursprungligastyrenheten. Resultaten visar att den viktigaste faktorn som begränsar bränsleförbrukningsbesparingen är införandet av de termiska belastningsbegränsningarna på batteriet.

In Sweden, the building sector accounts for about 35% of the total energy consumption. Some of the major contributors to energy consumption are the urban educational buildings, such as schools and universities which have considerable potential for improved energy efficiency. Furthermore, it is Sweden’s goal to mitigate climate change and set a zero net target for greenhouse gas emissions by 2045 at the latest. To meet this goal, it is essential to design building energy management with advanced optimization algorithms and data science to ensure renewable sources integration and strategical management for loads and storage. This thesis designs an Energy Management System (EMS) Optimization model that combines Mixed-Integer Linear Programming (MILP) and PV-battery sizing to satisfy energy consumption with the least energy bills and carbon emissions in urban educational buildings. A case study of two educational buildings in Stockholm will be used to simulate and evaluate the effectiveness of the proposed EMS model. Three main studies were made under the current electricity contract and a pre-defined PV capacity for buildings. The first study shows the MILP-based EMS enables optimized decisions of solar production curtailment, smart grid consumption, and smart battery usage while satisfying the building load with the lowest possible energy cost. The MILP-based EMS model achieves more flexible scheduling for batteries and PV integration than traditional rule-based EMS, but the annual saving difference is minimal. With a 25kWp PV system and the proposed EMS, the electric-heated case building saves 21.49% of energy bills annually, while the case building with district heating can save 23.35% of energy bills annually. Secondly, the best optimal Battery Energy Storage System (BESS) sizing is determined with findings that increasing BESS sizing can bring a higher saving but the increase is less than 0.5% due to limited solar energy production and low feed-in income. Under current energy contracts and building conditions, results justify the installation of PV systems but do not support the investment of a BESS. Energy cost saving doesn’t have more potential in electric-heated buildings compared to traditional district-heated buildings. Finally, the third study conducts a sensitivity analysis of the BESS’s Levelized Cost of Energy (LCOE), providing the threshold LCOE for the system with PV-BESS to be economically beneficial, which is 0.27 SEK/kWh. / Byggsektorn står för cirka 35% av Sveriges totala energiförbrukning. Bland de främsta bidragsgivarna återfinns stadsutbildningsbyggnader, såsom skolor och universitet, som har stor potential för förbättrad energieffektivitet. Dessutom strävar Sverige efter att mildra klimatförändringar och sätta upp klimatneutrala mål för byggnader. För att nå dessa mål krävs smart energihantering. Denna avhandling presenterar en modell för optimering av energihanteringssystem (EMS) som kombinerar blandad heltalslinjär programmering (MILP) och dimensionering av solcellsbatterier. Syftet är att minimera elkostnader och koldioxidutsläpp i urbana utbildningsbyggnader och därigenom förbättra hållbarheten. En fallstudie av två utbildningsbyggnader i Stockholm används för att utvärdera EMS-modellens effektivitet. Tre huvudstudier genomfördes inom ramen för det befintliga elavtalet och med en fördefinierad solcellskapacitet för byggnaderna. I den första studien framkommer att EMS baserad på MILP möjliggör mer flexibel schemaläggning för batterier och integration av solceller jämfört med en regelbaserad EMS. Trots detta är skillnaden i årliga besparingar mycket liten. Med ett 25 kWp solcellssystem och den föreslagna EMS sparar en eluppvärmd byggnad 21,49% av elkostnaderna årligen, medan en byggnad med fjärrvärme kan spara 23.35% av elkostnaderna årligen. I den andra studien bestäms optimal storlek för batterilagringsystemet (BESS). Resultaten visar att en ökad storlek på BESS kan ge högre besparingar, men ökningen är mindre än 0,5% på grund av begränsad produktion av solenergi och låga intäkter från nätmatning. Under nuvarande avtal och byggnadsförhållanden motiverar resultaten installationen av solcellssystem, men stöder inte investeringen i BESS. Slutligen genomför den tredje studien en känslighetsanalys av nivåniserad energikostnad (LCOE) för BESS och ger tröskel-LCOE för att systemet med solceller och BESS ska vara ekonomiskt fördelaktigt, vilket är 0,27 SEK/kWh.

Abstract Achieving the objective of a decarbonised economy by 2050 will require massive efforts in the energy sector. Emissions from residential houses will have to be almost completely cut, by around 90% by 2050. Home automation is a potential tool for achieving this goal. However, the environmental and economic benefits of automation technologies first need to be assessed. This thesis evaluates the impact of home automation for electricity management in the residential sector using environmental and economic indicators. To this end, a life cycle assessment was performed to evaluate the impacts of the manufacturing, use and disposal phases. The influences of end-user behaviour, household size and multiple levels of technological deployment were also investigated. A Markov chain simulation tool, built on the MatLab platform, was developed to assess all possible combinations of impacting factors. Dynamic environmental indicators were developed based on the ReCiPe method for aggregating the impacts of processes. All these indicators were then combined to form a single index based on multi-criteria acceptability analysis. The results suggest that home automation can decrease peak load, but that overall electricity consumption may increase due to electricity use by the actual automation system. The effect of home automation was more noticeable in larger households than in one-person households. In addition, use of dynamic environmental indicators proved more relevant than fixed indicators to represent the environmental impact of home automation. Within the life cycle of automation technology, the manufacturing phase had the highest impact, but most of the CO2 emissions originated from the use phase. In conclusion, the most important environmental benefit of home automation is reducing CO2 emissions during peak time by load shifting. / Tiivistelmä Vähähiilisen talouden saavuttaminen vuoteen 2050 mennessä edellyttää valtavia ponnisteluja energia-alalla. Rakennuksista aiheutuvia päästöjä on vähennettävä radikaalisti, jopa 90 % vuoteen 2050 mennessä. Rakennusten energiatehokkuutta edistävä automaatiotekniikka on yksi keino tämän päämäärän saavuttamiseen. Kotiautomaation kautta voidaan sekä vähentää energian kokonaiskulutusta että tasoittaa energiankäyttöprofiilia. On kuitenkin tutkittava myös, mitkä ovat automaatiotekniikan ympäristö- ja taloudelliset vaikutukset. Tässä työssä käsitellään kotiautomaation vaikutusta sähkön kulutuksen hallintaan asuinrakennuksissa käyttämällä ympäristö- ja talousindikaattoreita. Tätä varten suoritettiin kotiautomaation elinkaariarviointi selvittämällä laitteiden valmistus-, käyttö- ja hävittämisvaiheiden ympäristövaikutukset. Työssä tarkasteltiin myös asukkaiden käyttäytymisen, kotitalouden koon ja eri teknologiavaihtoehtojen vaikutuksia ympäristö- ja talousvaikutuksiin. Arviointi suoritettiin Markovin ketjun simulointityökalulla, joka rakennettiin Matlab-alustalle. Dynaamisia ympäristömittareita kehitettiin ReCiPe-menetelmää käyttäen. Indikaattorit on edelleen yhdistetty yhdeksi indeksiksi käyttäen monikriteeriarviointia. Tulokset viittaavat siihen, että huippukuormitusta voidaan vähentää käyttämällä kotiautomaatiota, mutta sähkön kokonaiskulutus voi kasvaa automaatiojärjestelmän sähkönkulutuksen takia. Kotiautomaation vaikutukset ovat eniten havaittavissa suurissa kotitalouksissa. Lisäksi, dynaamiset indikaattorit edustavat paremmin kotiautomaation vaikutusta ympäristöön kuin staattiset indikaattorit. Automaatioteknologian elinkaaressa suurimmat ympäristövaikutukset ovat valmistusvaiheessa, mutta CO2-päästöjä syntyy eniten käyttövaiheessa. Lopuksi voidaan todeta, että kotiautomaation merkittävin ympäristöhyöty on CO2-päästöjen vähentäminen huippukulutuksen aikana siirtämällä kuormitusta toiseen ajankohtaan.