Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

The requirements on fuel consumption and emissions for passenger cars are getting stricter every year. This has forced the vehicle industry to look for ways to improve the performance of the driveline. With the increasing focus on electrification, a common method is to combine an electrical driveline with a conventional driveline that uses a petrol or diesel engine, thus creating a hybrid electric vehicle. To fully be able to utilise the potential of the driveline in such a vehicle, an efficient energy management strategy is needed. This thesis describes the development of an efficient route-based energy management strategy. Three different optimisation strategies are combined, deterministic dynamic programming, equivalent consumption minimisation strategy and convex optimisation, together with segmentation of the input data. The developed strategy shows a decrease in computational time with up to more than one hundred times compared to a benchmark algorithm. When implemented in Volvo's simulation tool, VSim, substantial fuel savings of up to ten percent is shown compared to a charge-depleting charge-sustain strategy.

The increasing levels of renewable generation and the electrification of transport and heating as parts of the movement towards low-carbon energy systems to cope with climate change will place significant challenges on the electricity system to facilitate the way towards future low carbon energy systems in a cost effective way and ensure secure power delivery. New solutions and higher levels of flexibility are required than currently exist in order to reduce the integration costs of low carbon generation and demand technologies. Price-based demand response in residential sector is considered as one of these potential solutions. However, a certain level of automation is required to reduce both the uncertainty in the consumer response and the complexity for consumers to react to the price signal. This thesis presents a comprehensive and general residential optimization-based Automated Demand Response (ADR). The modelling of home appliances has been extensively developed to include all the classifications proposed in the literature, namely, deferrable and thermal in addition to new groups of critical and fully curtailable loads. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user’s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different case studies show the effectiveness of the proposed controller to minimize the household’s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks. This thesis also proposes a two-stage distribution-planning framework to assess the benefits of the proposed ADR models in response to a location-specific time of use Distribution Use of Systems Charge (DUoSC) on the required investments to connect future low-carbon technologies. The network investments and the satisfaction of consumers in terms of energy curtailment are both quantified. The first stage aims to generate location-specific time of use price signals for all users in the network, which represents their contributions in future network investments due to congestion and security constraints. The second stage relates to a group of ADR controllers at residential premises that aims to minimise the daily energy payment whilst maximising consumer comfort in response to the corresponding price signal produced from the first stage.

Decentralised power systems with a high portion of power generated from renewable energy sources and cogeneration units (CHP) are emerging worldwide. Optimising the energy usage of such systems is a difficult task as the stochastic fluctuations of generation from renewable sources, the coupling of electrical and thermal power generation by CHP and the time dependence of necessary storage devices require new approaches. Evolutionary algorithms are able to solve the optimisation task of the energy management. They use the principles of erroneous replication and cumulative selection that can be observed in biological processes, too. Very often recombination is included in the optimisation process. Using these quite simple principles the algorithm is able to explore difficult, large and high dimensional solution spaces. It will converge to the optimal solution in most of the cases quite fast, compared to other types of optimisation algorithms. At the example of an one dimensional replicator it is derived that the convergence speed in optimising convex functions increases by several orders of magnitude even after a few cycles compared to Monte-Carlo-simulation. For several types of equipment models are developed in this work. The cost to operate a given power system for a given time span is chosen as objective function. There is a variety of parameters (more than 15) that can be set in the algorithm. With quite extensive investigations it could be shown that the product of number of replicators and the number of calculated cycles has the most important influence on the quality of the solution but the calculation time is also proportional to this number. If there are reasonable values chosen for the remaining parameters the algorithm will find appropriate solutions in adequate time in most of the cases. Although a pure evolutionary algorithm will converge to a solution the convergence speed can be greatly enhanced by extending it to a hybrid algorithm. Grouping the replicators of the first cycle in suggestive regions of the solution space by an intelligent initialisation algorithm and repairing bad solutions by introducing a Lamarckian repair algorithm makes the optimisation converge fast to good optima. The algorithm was tested using data of several existing energy systems of different structure. To optimise the energy usage in a power system with 15 different types of units the required computation time is in the range of 15 minutes. The results of this work show that extended hybrid evolutionary algorithms are suitable for integrated optimisation of energy usage in combined local energy systems. They reach better results with the same or less effort than many other optimisation methods. The developed method of optimisation of energy usage can be applied in energy systems of small and large size and complexity as optimisation computations of energy systems on the island of Cape Clear, at FH Offenburg and in the Allgäu demonstrate.

The ISO 50001 standard on energy management systems was released in 2011 with the aim of providing organizations with a standardised guideline for the evaluation and continual improvement of energy performance. Complex structures such as the waste to energy (WTE) sector, which must comply with social, environmental, economic and productivity objectives, can benefit from the development of a compliant energy management system (EnMS). Energy efficiency and energy savings contribute to the mitigation of the heavy cost of such a facility, while the benchmark analysis allows verifying the state of the system and the potential areas for improvement. The energy performance improvements can be valorised on an environmental base, and proper promotion of the process can contribute to the mitigation of social opposition. This thesis reviews the energy performance of a waste to energy facility located in Lecco, Italy, using the information available from the control system already installed in the company. Regression analysis is used to fit linear and polynomial functional forms to the energy consumption data and create the energy baseline for the areas of significant energy use and production with a predictive power of 59 – 92%. The difference among the baseline and the actual energy consumption is proposed as energy performance indicator, in order to appreciate the results of the improvement opportunities, and a set of key performance indicators are evaluated for internal and external benchmarking of the facility. The economic feasibility of a selection of sectorial Best Available Technologies (BATs) and the related energy performance improvement is evaluated, revealing that 2 – 16 M€ can be invested in the system retrofit; the creation of synergies with the facility heating, cooling and electricity demand is also suggested. Lastly, an energy, economic and environmental analysis is suggested as a ranking system for the identified improvement opportunities. The methodology and results of this study are intended to provide a proper energy review for Silea Spa, suggest a review procedure for other WTE facilities willing to develop a compliant EnMS and enrich the literature with a case study on WTE. For the Italian context, which is currently facing difficulties in the modernization of the waste management system (WMS), the ISO 50001 application on WTE facilities offers a unified structure that can be used by the government to map the current status of each facility and develop the legislative framework in accordance to the needs of the WMS, the circular economy and the public opinion. / ISO 50001-standarden för energihanteringssystem släpptes 2011 för att ge organisationer en standardiserad riktlinje för utvärdering och kontinuerlig förbättring av energiprestanda. Komplexa strukturer som sektorn för avfall till energi (WTE), som måste uppfylla målen socialt, miljömässigt, ekonomiskt och produktivt, kan dra nytta av utvecklingen av ett kompatibelt energihanteringssystem (EnMS). Energieffektivitet och energibesparingar bidrar till att mildra den stora kostnaden för en sådan anläggning, medan jämförelseanalysen gör det möjligt att verifiera systemets tillstånd och de potentiella förbättringsområdena. Förbättringen av energiprestanda kan värderas på en miljöbas, och en korrekt främjande av processen kan bidra till att mildra social opposition. Denna avhandling granskar energiprestanda för ett slöseri till en energianläggning i Lecco, Italien, med hjälp av den information som finns tillgänglig från det styrsystem som redan är installerat i företaget. Regressionsanalys används för att passa linjära och polynomiska funktionsformer till energiförbrukningsdata och skapa energigrundnivå för områdena med betydande energianvändning och produktion med en prediktiv effekt på 59-92%. Skillnaden mellan baslinjen och den faktiska energiförbrukningen föreslås som indikator för energiprestanda för att uppskatta resultaten av förbättringsmöjligheterna och en uppsättning nyckelresultatindikatorer utvärderas för intern och extern riktmärkning av anläggningen. Den ekonomiska möjligheten för ett urval av sektorns bästa tillgängliga teknik (BAT) och den därmed sammanhängande förbättringen av energieffektiviteten utvärderas, vilket visar att 2 - 16 miljoner euro kan investeras i systemets eftermontering. skapandet av synergier med anläggningens uppvärmning, kylning och el efterfrågan föreslås också. Slutligen föreslås en energi-, ekonomisk och miljöanalys som ett rankningssystem för de identifierade förbättringsmöjligheterna. Metoden och resultaten av denna studie är avsedda att ge en ordentlig energianalyse för Silea Spa, föreslå ett granskningsförfarande för andra WTE-anläggningar som är villiga att utveckla en kompatibel ENMS och berika litteraturen med en fallstudie om WTE. För det italienska sammanhanget, som för närvarande står inför svårigheter i moderniseringen av avfallshanteringssystemet (WMS), erbjuder ISO 50001-tillämpningen på WTE-anläggningar en enhetlig struktur som kan användas av regeringen för att kartlägga nuvarande status för varje anläggning och utveckla den rättsliga ramen i enlighet med WMS, den cirkulära ekonomins och den allmänna opinionens behov.

Denna studie undersöker hur ett smart energihanteringssystem påverkar användarbeteende och vanor hos användaren när det kommer till energianvändning. Med dagens fokus på energi och dess viktighet i samhället utvecklas tjänster som tillåter individen att övervaka sin energiförbrukning. Exempel på sådana tjänster är E.ON Elna™ och Tibber, där användaren kan se och övervaka sin energiförbrukning i hemmet i realtid. Tidigare forskning inom användarbeteende i relation till energianvändning visar att medvetenhet, feedback och egenkontroll är viktiga faktorer för ett förändrat användarbeteende. För att besvara studiens frågeställning genomfördes ett samarbete med E.ON och deras tjänst Elna™ där intervjuer utfördes med användare av Elna™-tjänsten samt icke-användare.  Studiens resultat visade att användningen av ett smart energihanteringssystem har medfört en positiv påverkan på användarens användarbeteende och medvetenhet kring deras energiförbrukning. Vidare visade resultaten från intervjuerna med respondenterna att ett smart energihanteringssystem har potentialen att främja energieffektivt beteende och öka användarnas medvetenhet om elförbrukning. / This study examines how smart energy management systems influence user behavior and habits of the user when it comes to energy use. With today's focus on energy and its importance in society, services are being developed that allow the individual to monitor their energy consumption. Examples of such services are E.ON Elna™ and Tibber, where the user can see and monitor their energy consumption in the home in real time. Previous research in user behavior in relation to energy use shows that awareness, feedback and self-control are important factors for a change in user behavior. To answer the study's question, a collaboration was carried out with E.ON and their service Elna™ where interviews were conducted with users of the Elna™ service and non-users. The results of the study showed that the use of a smart energy management system has had a positive impact on the user's user behavior and awareness of their energy consumption. Furthermore, the results from the interviews with the respondents showed that a smart energy management system has the potential to promote energy efficient behavior and increase users' awareness of electricity consumption.

<p>  </p> <p>Heating and cooling (HC) energy use account for about 40% of the total annual energy consumption and cost of an average household in the U.S and it is significantly affected by residents’ energy-related behavior. This is particularly important for low-income residents in the U.S. who spend a larger portion of their income (i.e., about 16%) on home energy costs compared to average-income households (i.e., 4%). To address opportunities for reducing residential HC energy usage without requiring physical building upgrades, this thesis presents a new paradigm for smart and connected energy-aware communities that leverage smart eco-feedback devices and social games to engage residents in understanding and reducing their home energy use.</p> <p><br></p> <p>First, this Thesis presents a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents to enhance their thermostat use while promoting community-level energy savings. The modeling framework is integrated into a cloud-based application, MySmartE, with visual (wall-mounted tablet) and voice (Alexa) user interfaces to facilitate behavioral changes in a user-centric approach. The platform is deployed in a multi-unit residential community in Fort Wayne, IN and the experimental data are used to investigate: (i) how occupants’ thermostat behaviors changed after using the MySmartE app; (ii) how users interacted with the app during the game; and (iii) how was users’ experience with the developed platform. Despite the heterogeneous characteristics of households, the results from the field study show the positive effect of the intervention in the thermostat-adjustment behaviors, which results in an increase in the indoor temperature during the cooling season compared to the baseline period. Findings from the user interaction analysis and post-experiment interviews also reveal the significant potential to nudge households’ energy conservation behaviors with the developed platform along with the challenges that should be tackled to derive long-term behavior changes. </p> <p><br></p> <p>Second, this Thesis introduces a sociotechnical modeling approach based on utility theory to reveal causal effects in human decision-making and infer attributes affecting households’ thermostat responses during an eco-feedback intervention. This modeling approach (i) is based on a utility model that quantifies residents’ preferences over indoor temperatures given decision attributes related to their thermal environment and eco-feedback and (ii) incorporates latent parameters that are inferred to determine the unique behavioral characteristics of each household. For parameter learning, a hierarchical Bayesian model is developed with a non-centered parameterization and calibrated to the field data. Based on the calibration results, the proposed model quantifies the impact of the eco-feedback on households’ thermostat-adjustment behaviors and serves as a foundation for analyzing resident behavior in connected residential communities with eco-feedback energy-saving programs.</p> <p><br></p> <p>Finally, this Thesis presents a modeling approach for investigating the decision trends of residents in goal-oriented collaborative social games while considering their decision preferences and goal achievement capabilities. The proposed approach involves a mechanism design method that derives optimal decisions by conducting counterfactual simulations given various scenarios of goal and reward sets. This modeling approach (i) re-defines utility functions to include decision attributes that reflect user preferences on the game status; (ii) calibrates the model to learn the decision preferences of the residents; (iii) simulates the decision-making process of residents by solving the Nash Equilibrium for a given set of game scenarios. The results revealed the decision trends of the residents given the various goals and rewards along with the potential goal achievement trends and the resulting variations in the marginal community utility.</p>

In early design phase for new hybrid electric vehicle (HEV) powertrains, simulation isused for the estimation of vehicle fuel consumption. For hybrid electric powertrains,fuel consumption is highly related to powertrain efficiency. While powertrainefficiency of hybrid electric powertrain is not a linear product of efficiencies ofcomponents, it has to be analysed as a sequence of energy conversions includingcomponent losses and energy interaction among components.This thesis is aimed at studying the energy losses and flows and present them in theform of Sankey diagram, later, an adaptive energy management system is developedbased on current rule-based control strategy. The first part involves developing energycalculation block in GT-SUITE corresponding to the vehicle model, calculating allthe energy losses and flows and presenting them in Sankey diagram. The secondpart involves optimizing energy management system control parameters according todifferent representative driving cycles. The third part involves developing adaptiveenergy management system by deploying optimal control parameter based on drivingpattern recognition with the help of SVM (support vector machine).In conclusion, a sturctured way to generate the Sankey diagram has been successfullygenerated and it turns out to be an effective tool to study HEV powertrain efficiencyand fuel economy. In addition, the combination of driving pattern recognition andoptimized control parameters also show a significant potential improvement in fuelconsumption. / Under den tidiga utvecklingsfasen av nya elektrifieradedrivlinor for hybridapplikationer (HEV) används simulering för uppskattning avfordonets bränsleförbrukning. För dess drivlinor är bränsleförbrukningen i hög gradkopplad till drivlinans verkningsgrad. Även om drivlinans verkningsgrad inte ären linjär prokukt av komponenternas verkningsgrad behöve rden analyseras somen sekvens av energiomvandlingar, inklusive förluster och energipåverkan mellankomponenter.Detta examensarbete syftar till att undersöka energiförluster och flöden samtpresentera dessa i form av sankey diagram. Senare utvecklas ett anpassningsbartenergihanteringssystem baserat på nuvarande regelbaserad kontrollstrategi. Deninledande delen involverar utvecklandet av energianalys i GT-SUITE som motsvararfordonsmodellen, beräkningar av totala energiförluster och flöden samt presentationav dessa i ett sankey diagram. Den andra delen innefattar optimering avenergihanteringssystems kontrollparametrar enligt olika representativa körcykler.Den tredje delen involverar utveckling av anpassningsbara energihanteringssystemgenom användning av optimala kontrollparameterar baserad på detektering avkörbeteende med hjälp av SVM ( stödvektormaskin).Slutligen, ett strukturerat sätt att generera sankey diagrammet har med framgånggenererats och visat sig vara ett effektivt verktyg för studier av HEV drivlinorseffektivitet och bränsleekonomi. Dessutom visar kombinationen av detektering avkörbeteende och optimerade kontrollparametrar på en markant potentiell förbättringi bränsleförbrukning.

SCADA-system (Supervisory Control and Data Acquisition) har under de senaste decennierna använts i stor utsträckning, med utmärkta resultat för nätverksdrift och -förvaltning. Kunder ställer emellertid krav på att SCADA-system ska kunna integrera externa komponenter för att möjliggöra utveckling av befintliga och nya affärsprocesser. Det innebär att dessa system utvecklas från en monolitisk infrastruktur till en löst kopplad och flexibel arkitektur. Således har nya behov uppstått för att förbättra systemets interoperabilitet, minska komplexiteten och förbättra underhållet. Föreliggande masterprojekt presenterar ett ramverk för att förutsäga systems interoperabilitetet (IPF); ett ramverk som stöder arkitekturprocessen under de tidiga stadierna av produktutveckling. Vidare har arbetet undersökt några alternativa arkitekturer, vilka har modellerats och verifierats med hjälp av ovannämnda ramverk. En första konceptuell arkitektur har utvecklats för att förbättra interoperabiliteten hos interna system, för att reducera kopplingen mellan det grundläggande SCADA-systemet och Energy Management-systemet (EMS). Därefter genererades en andra arkitektur som möjliggör integration av externa komponenter för att främja den externa interoperabiliteten. Resultat visar att de föreslagna arkitekturerna är korrekta (enligt IPF) och systemets driftskompatibilitet förbättras. Vidare förefaller den slutligt föreslagna lösningen vara mindre komplex än den nuvarande arkitekturen på lång sikt, men det skulle behövas en större insats och väsentliga förändringar för att uppgradera systemarkitekturen. / SCADA (Supervisory Control and Data Acquisition) systems have been widely used during the last decades delivering excellent results for the power network operation and management. However, some current customer requirements are for SCADA systems to integrate external components in order to perform advanced power network studies and develop both existing and new business processes. This novel viewpoint will make these systems evolve from a monolithic infrastructure towards a loosely coupled and flexible architecture. Hence, new needs have arisen with the aim of improving the system interoperability, reducing the complexity and enhancing the maintainability. This master´s thesis project presents an Interoperability Prediction Framework (IPF), that supports the architecture design process during the early stages of product development. In addition, this work has also investigated some alternative architectures, which have been modelled and verified using the previously mentioned framework. A first conceptual architecture has been designed to improve the internal system interoperability, reducing the coupling between the basic SCADA and the Energy Management System (EMS). Later, a second architecture that allows the integration of external components has been introduced to promote the external interoperability. Results show that the proposed architectures are correct (according to the IPF) and the interoperability of the system is improved. Furthermore, initial conclusions suggest that the final proposed solution would be less complex than the current architecture in the long term, although a large effort and substantial changes would be needed to upgrade the system architecture.

Les réseaux électriques actuels connaîtront un profond changement dans les années à venir. La nouvelle génération est le Smart Grid (SG) ou le réseau électrique intelligent qui se caractérise par une couche d'information et de communication qui permet aux différents composants du réseau de communiquer. Il doit considérer tous les aspects du réseau électrique, le rendant plus intelligent et flexible. Cette notion est présentée comme une réponse à l'évolution du marché de l'électricité, visant à gérer l’augmentation de la demande tout en assurant une meilleure qualité de service et plus de sécurité.Premièrement, nous présentons une formulation de programmation linéaire mixte en entier pour optimiser les systèmes de production et de consommation d'énergie dans une maison intelligente avec un déploiement efficace de plusieurs ressources énergétiques distribuées. Ensuite, à travers la conception d'expériences avec la méthode de Taguchi, divers scénarios sont introduits en faisant varier des facteurs significatifs. Par la suite, une technique heuristique est proposée pour résoudre le problème de la gestion de l'énergie résidentielle en trouvant la solution optimale globale pendant plusieurs jours consécutifs avec une réduction significative du temps d'exécution.Deuxièmement, un modèle de gestion de l'énergie est assuré grâce à des modèles mathématiques pour optimiser l’utilisation du réseau, des ressources énergétiques renouvelables, des véhicules électriques et de la batterie, ainsi que pour différents types d'appareils thermiques et électriques. Une méthode de solution exacte est mise en œuvre pour réduire le coût de l'électricité dans une maison intelligente et pour trouver des modes de fonctionnement de différentes charges. Ensuite, un algorithme d'optimisation math-heuristique est proposé pour résoudre le problème avec un temps de simulation étendu.Enfin, nous étudions le problème de gestion de l'énergie dans un microréseau constitué de plusieurs maisons intelligentes. Chacune d'elles dépose de ressources énergétiques renouvelables, d’un véhicule électrique et d’appareils intelligents. Les ressources d'énergie renouvelable injectent l’excès de l'énergie dans un système de stockage d'énergie partagé. Un modèle mathématique linéaire mixte en entier pour la gestion d'énergie est proposé pour réduire le coût total de fonctionnement du microréseau. Des comparaisons avec des scénarios conventionnels où chaque maison intelligente possède son propre système de stockage d'énergie sont effectuées pour démontrer l’efficacité de la démarche proposée. / The current electricity grids will experience a profound change in the coming years. The new generation is the Smart Grid (SG) which is characterized by information and communication layer enabling the communication between the different components of the grid. It needs to consider all sides of power grid, making it more intelligent and flexible. This notion is presented as an answer to changes in the electricity market, aiming to manage the increased demand while ensuring a better quality of service and more safety.First, we present a mixed integer linear programming formulation to optimize the energy production and consumption systems in a smart home with an effective deployment of several distributed energy resources. Then through the design of experiments with the Taguchi method, diverse scenarios are introduced by varying significant factors. Afterward, a heuristic technique is proposed to solve the problem of residential energy management by finding the global optimum solution for many consecutive days with significant reduction of execution time.Second, an energy management model is proposed thanks to mathematical models to optimize the grid, renewable energy resources, battery and electric vehicles are presented as well as for different type of thermal and electrical appliances. An exact solution method is implemented to reduce the electricity cost in a smart home and find out operation modes of different loads. Then a math-heuristic optimization algorithm is proposed to solve the problem with extended simulation time horizon.Finally, we study a microgrid energy management problem which comprises multiple smart homes. Each of them owns renewable energy resources, one electric vehicle and smart appliances. The renewable energy resources inject the excess energy in the shared energy storage system. An optimized energy management model using mixed integer linear programming is proposed to reduce the total electricity cost in the microgrid. Comparisons with conventional scenarios where each smart home has its individual small energy storage system without sharing energy with their neighbors are done to ensure that the proposed formulation is well efficient.