Production planning of energy systems: Cost and risk assessment for district heating

This dissertation is a collection of research articles that assess economic andoperational risk in production planning of district heating. District heatingsystems are typically coupled to the electricity system through cogenerationand power-to-heat technologies, and production planners must account foruncertainty stemming from changing weather, demands and prices. Years ofhigh-resolution data from the district heating system in Aarhus, Denmark havebeen used throughout the project to model the system and estimate uncertainties.Risk management tools have been developed to aid district heating operatorsand investment decision makers in short-, medium- and long-term productionplanning.Short-term production planning involves commitment of production unitsand trading on the electricity markets and relies on forecasts of the heat load.Weather predictions are a significant source of uncertainty for heat load forecasts,because the heat load is highly weather-dependent. I introduce the method ofensemble weather predictions from meteorology to heat load forecasting andcreate a probabilistic load forecast to estimate the weather-based uncertainty.Better estimates of the weather-based uncertainty can be applied to optimizesupply temperature control and reduce heat losses without compromising securityof supply in heat distribution systems.Consumer behavior is another substantial, but difficult to capture, source ofuncertainty in short-term heat load forecasts. I include local holiday data instate-of-the-art load forecasts to improve accuracy and capture how load patternschange depending on the behavior of the consumers. A small overall improvementin forecast accuracy is observed. The improvement is more significant on holidaysand special occasions that are difficult to forecast accurately.In medium-term production planning, there can be substantial economicpotential in performing summer shutdown of certain production units. Theshutdown decision carries significant risk, due to changing seasonal weatherpatterns. Based on 38 years of weather data, the uncertainty on the timing ofthe optimal decision is estimated. This information is used to develop practicaldecision rules that are robust to rare weather events and capable of realizingmore than 90% of the potential savings from summer shutdown.Long-term production planning decisions regarding investments in futuredistrict heating production systems are affected by uncertainty from changingelectricity prices, fuel prices and investment cost for technology. The effects ofthese uncertainties on a cost-optimal heat production system are explored, usingwell-established production and storage technologies and extensive multivariatesensitivity analysis. The optimal technology choices are highly stable and,taxes aside, large heat pumps and heat storages dominate the cost-optimal heatproduction systems. However, the uncertainty on the exact capacity allocationis substantial. Excluding heat production based on fossil fuels increases theuncertainty on the system cost, but drastically reduces the uncertainty on theoptimal capacity allocation

Short-term forecasting for the electrical demand of Heating, Ventilation, and Air Conditioning systems

The heating, ventilation, and air conditioning systems (HVAC) of large scale commercial and institutional buildings can have significant contributions to the buildings overall electric demand. During periods of peak demand, utilities are faced with a challenge of balancing supply and demand while the system is under stress. As such, utility companies began to operate demand response programs for large scale consumers. Participation in such programs requires the participant to shift their electric demand to off-peak hours in exchange for monetary compensation. In such a context, it is beneficial for large scale commercial and institutional buildings to participate in such programs. In order to effectively plan demand response based strategies, building energy managers and operators require accurate tools for the short-term forecasting of large scale components and systems within the building. This thesis contributes to the field of demand response research by proposing a method for the short-term forecasting for the electric demand of an HVAC system in an institutional building. Two machine learning based approaches are proposed in this work: a component method and a system based method. The component-level approach forecasts the electric demand of a component within the HVAC system (e.g. air supply fans) using an autoregressive neural network coupled with a physics based equation. The system-level approach uses deep learning models to forecast the overall electric demand of the HVAC system through forecasting the electric demand of the primary and secondary system. Both approaches leverage available data from the building automation system (BAS) without the need for additional sensors. The system based forecasting method is validated through a case study for a single building with two data sources: measurement data obtained from the BAS and from an eQuest simulation of the building. The building used as the case study for the work herein consists of the Genomic building of Concordia University Loyola campus

Planning and Integrated Design of Urban Heat-Sharing Networks

Résumé Ces dernières années, les villes ont dû renforcer leurs obligations en matière de réduction de leur impact sur l’environnement. Heureusement, les villes relèvent ce défi et sont déterminées à trouver des solutions. La consommation d’énergie des bâtiments est l’un des principaux obstacles au développement durable. Les villes sont construites pour fournir des espaces confortables et habitables à leurs habitants, ainsi que pour assurer un environnement résistant aux événements météorologiques et climatiques. Cependant, fournir ce confort nécessite de grandes quantités d’énergie, ce qui participe aux changements climatiques. Les besoins en énergie des bâtiments étant en grande partie le résultat de décisions en matière de conception, les constructeurs des villes de demain ont un contrôle sur les différentes solutions proposées dans le domaine de l’environnement bâti. Des solutions technologiques existent, mais la bonne solution doit être mise en œuvre dans le bon contexte. Cette thèse porte sur une solution technique clé : l’utilisation de réseaux urbains d’énergie pour répartir — ou partager — la chaleur entre les bâtiments et équilibrer les charges de chauffage et de refroidissement restantes avec des sources de chaleur ou des puits hautement efficaces et à faible émission de carbone. Cette thèse est également motivée par les relations interdisciplinaires complexes entre les concepteurs participant à l’urbanisme, à l’architecture et à l’ingénierie de l’environnement bâti. Elle plaide en faveur d’un processus de conception intégrée piloté par les données et propose des méthodologies et des outils pour informer et activer ce processus de conception. Un indicateur de performance, l’indice de diversité thermique, est proposée pour localiser et évaluer la compatibilité thermique entre des bâtiments présentant différents niveaux de filtrage spatio-temporel. La thèse apporte ensuite des contributions aux différentes étapes nécessaires à la conception et à l’évaluation de réseaux de partage de chaleur, faisant souvent partie de la 5e génération de systèmes de chauffage et de refroidissement urbains : évaluer la demande thermique des bâtiments à l’échelle de la ville en optimisant la topologie des réseaux urbains d’énergie et l’intégration de sources d’énergie efficaces et à faibles émissions de carbone. Les archétypes, ou représentations typiques des bâtiments sont les fondements de nombreux outils de modélisation énergétique des bâtiments urbains (UBEM). Une méthodologie est proposée pour générer automatiquement des modèles d’archétype adaptés aux méthodes de modélisation contextuelles telles que celle implémentée dans UMI, l’un des principaux outils UBEM. La thèse aborde ensuite la complexité de la combinaison de sources de données partiellement complètes et parfois contradictoires pour obtenir une carte dynamique de la demande de chaleur pour une ville telle que Montréal. La méthodologie proposée comprend l’utilisation d’empreintes de bâtiment virtuelles basées sur des données ALS (Airborne Laser Scanning) (également appelées données LiDAR) pour estimer les empreintes au sol et les hauteurs de bâtiment. Elle est appliquée pour obtenir une carte dynamique de la demande de chaleur de bâtiments résidentiels, commerciaux et institutionnels pour l’ensemble de la ville de Montréal. Pour compléter le processus de conception des réseaux de partage de chaleur, cette thèse propose une méthodologie qui étend la capacité des algorithmes d’optimisation de la littérature utilisés pour les réseaux de chauffage et de refroidissement urbains : elle permet des flux de puissance bidirectionnels inhérents au partage de chaleur et optimise la compétitivité à long terme de l’approvisionnement en chaleur en équilibrant les coûts totaux d’exploitation et les coûts totaux d’investissement des différentes unités d’alimentation en chaleur. L’algorithme proposé, avec les autres contributions de la thèse, ouvre la porte à un cadre d’optimisation visant à peser l’impact des choix de conception inhérents à la sélection de la densité de construction, de la forme du bâtiment et de ses performances. Cette thèse affirme que l’intégration de cette optimisation des réseaux de partage de chaleur dans la phase de planification peut avoir une incidence nouvelle et imprévue sur la performance environnementale des futurs quartiers. Conformément à cet objectif à long terme, les contributions méthodologiques ont été mises en œuvre dans des outils contribuant à l’expansion rapide du corpus de logiciels en code ouvert. Avec les contributions à la littérature et aux pratiques de planification des réseaux urbains d’énergie, ces outils offrent une solution à la planification et à la conception intégrée de réseaux de partage de chaleur en milieu urbain. ---------- Abstract In recent years, cities have had to step up their obligations to reducing their impact on the environment. Fortunately, cities are rising to this challenge and are determined to find solutions. One piece of the larger sustainability problem is the energy use of buildings. Cities are built to provide comfortable and livable spaces to their inhabitants as well as ensure a resilient environment towards meteorological and climatic events. However, providing this comfort requires large amounts of energy, which exacerbates climate change. Since the energy requirements of buildings are in large part the result of design decisions, the builders of cities have an innate control over the various solutions in the built environment problem space. Technological solutions exist, but the right solution must be implemented in the right context. This thesis focuses on one key technical solution: the use of district energy systems to distribute—or share—heat between buildings and balance the remaining heating and cooling loads with highly efficient, low-carbon heat sources or sinks. This thesis is also motivated by the complex interdisciplinary relationships between designers participating in the urban planning, the architecture and the engineering of the built environment. It makes the case for a data-driven Integrated Design process and proposes methodologies and tools to inform and enable this design process. An urban planning metric, the thermal diversity index, is proposed to locate and assess the thermal compatibility between buildings with various levels of spatial and temporal filtering. The thesis then makes contributions to the different steps required in designing and assessing heat-sharing networks, often part of the 5th generation district heating and cooling (5GDHC): assessing the thermal demand of buildings at the city scale, optimizing the topology of district systems, and integrating efficient and low-carbon energy sources within an overall optimization process. Archetypes, or typical representations of buildings, are the foundation stones of many Urban Building Energy Modelling (UBEM) tools. A methodology is proposed to automatically generate archetype templates adapted to context-aware modelling methods such as the one implemented in UMI, one of the prominent UBEM tools. The thesis then addresses the complexity of combining partially complete and sometimes contradictory data sources to obtain a dynamic heat demand map for a city such as Montréal,Canada. The proposed methodology includes the use of virtual building footprints based on Airborne Laser Scanning (ALS) data (also known as LiDAR data) to estimate building footprint areas and building heights. It is applied to obtain a dynamic heat demand map of residential, commercial and institutional buildings for the whole city of Montréal. To complete the design process of heat-sharing networks, this dissertation proposes a methodology that expands the capability of state-of-the-art optimization algorithms used for district heating and cooling networks: it allows bidirectional power flows that are inherent to heat-sharing networks and optimizes the long-term competitiveness of heat supply by balancing the total operating costs and the total investment costs of different heat supply units. The proposed algorithm, with the other contributions of the thesis, opens the door to an optimization framework aiming to weigh in the impact of design choices inherent to the selection of built density, building form and building systems performance. This thesis proclaims that bringing this optimization of heat-sharing networks inside the planning phase can impact the environmental performance of future districts in new and unforeseen ways. In line with this long-term goal, the methodological contributions were implemented in tools contributing to the rapidly expanding body of open source software. Together with the contributions to the literature and the district energy planning practice, these tools offer one solution to the planning and integrated design of urban heat-sharing networks

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

Selected Papers from SDEWES 2017: The 12th Conference on Sustainable Development of Energy, Water and Environment Systems

EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

This open access book presents the exciting research results of the BMBF funded project iCity carried out at University of Applied Science Stuttgart to help cities to become more liveable, intelligent and sustainable, to become a LIScity. The research has been pursued with industry partners and NGOs from 2017 to 2020. A LIScity is increasingly digitally networked, uses resources efficiently, and implements intelligent mobility concepts. It guarantees the supply of its grid-bound infrastructure with a high proportion of renewable energy. Intelligent cities are increasingly human-centered, integrative, and flexible, thus placing the well-being of the citizens at the center of developments to increase the quality of life. The articles in this book cover research aimed to meet these criteria. The book covers research in the fields of energy (i.e. algorithms for heating and energy storage systems, simulation programs for thermal local heating supply, runtime optimization of combined heat and power (CHP), natural ventilation), mobility (i.e. charging distribution and deep learning, innovative emission-friendly mobility, routing apps, zero-emission urban logistics, augmented reality, artificial intelligence for individual route planning, mobility behavior), information platforms (i.e. 3DCity models in city planning: sunny places visualization, augmented reality for windy cities, internet of things (IoT) monitoring to visualize device performance, storing and visualizing dynamic energy data of smart cities), and buildings and city planning (i.e. sound insulation of sustainable facades and balconies, multi-camera mobile systems for inspection of tunnels, building-integrated photovoltaics (BIPV) as active façade elements, common space, the building envelopes potential in smart sustainable cities)

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Optimal Control of Hybrid Systems and Renewable Energies

This book is a collection of papers covering various aspects of the optimal control of power and energy production from renewable resources (wind, PV, biomass, hydrogen, etc.). In particular, attention is focused both on the optimal control of new technologies and on their integration in buildings, microgrids, and energy markets. The examples presented in this book are among the most promising technologies for satisfying an increasing share of thermal and electrical demands with renewable sources: from solar cooling plants to offshore wind generation; hybrid plants, combining traditional and renewable sources, are also considered, as well as traditional and innovative storage systems. Innovative solutions for transportation systems are also explored for both railway infrastructures and advanced light rail vehicles. The optimization and control of new solutions for the power network are addressed in detail: specifically, special attention is paid to microgrids as new paradigms for distribution networks, but also in other applications (e.g., shipboards). Finally, optimization and simulation models within SCADA and energy management systems are considered. This book is intended for engineers, researchers, and practitioners that work in the field of energy, smart grid, renewable resources, and their optimization and control

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)