Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

SPACERGY:

SPACERGY builds upon the need for planning authorities to develop new models to implement energy transition strategies in the urban environment, departing from the exploitation or reciprocity between space and energy systems. Several policies have been made by each EU nation, but effective and practical tools to guide the urban transformations towards a carbon-neutral future present several challenges. The first challenge is to confront long term changes in envisioning how a specific socio-cultural context can respond to the application of solutions for energy efficiency. Secondly, the engagement of communities in bottom-up approaches mainly includes the sphere of urban planning that underestimates the importance of relating spatial transformations with the energy performances generated in the urban environment. The third challenge regards the tools used for the assessment of the energy performance and the necessity of enlarging the scale in which energy demand is analyzed, from the scale of the building to that of the district. In this context, the project explores the role of mobility, spatial morphologies, infrastructural elements and local community participation in regards to the smart use of local resources. The project addresses a knowledge gap in relation to interactions and synergies between spatial programming, energy and mobility systems planning and stakeholder involvement necessary to improve models of development and governance of urban transformations. Based on detailed spatial morphology and energy use modeling, SPACERGY develops new toolsets and guidelines necessary to advance the implementation of energy-efficient urban districts. New toolsets are tested in three urban areas under development in the cities of Zurich, Almere, and Bergen, acting as living laboratories for real-time research and action in collaboration with local stakeholders. The results of this research project support planners and decision-makers to facilitate the transition of their communities to more efficient, livable and thus prosperous urban environments

Dynamic simulation of Swedish residential building renovations and its impact on the district heating network

As urbanization continues to rise, cities now account for two-thirds of the world's total energy consumption. The built environment alone contributes to 40\% of total energy consumption and a third of total greenhouse gas emissions. With an additional 2.5 billion people projected to inhabit cities by 2050, efficient use of available resources is a critical aspect of climate action. One such resource is the low-temperature waste heat from growing industries such as data centers. However, harnessing this resource requires modifications to the existing building stock and networks. Urban Building Energy Models (UBEM) is an available tool with the capabilities of performing dynamic simulations for assessing different technical scenarios and providing information for a solution to the mentioned challenges. It allows for comprehensive analysis and optimization of energy usage in urban environments, providing a pathway towards more sustainable and efficient cities. In this study, a district in Stockholm is simulated using the open-source software City Energy Analyst (CEA), evaluating the limitations and adaptations required when using this tool, and investigating how building renovations and other technical adaptations can increase the integration of low-temperature waste heat systems into the district heating network. It is concluded that the level of detail (LOD) of the building's physical characteristics has a fundamental role in the accuracy and validation of the output data and it must be defined depending on the scale of the simulation. Besides, the implementation of building renovations decreases the energy demand, specifically for space heating demand, and enhances the reduction of the supply and return temperatures of the district heating network, providing technical conditions for the integration of low-temperature waste heat recovery systems

Bridging the Flexibility Concepts in the Buildings and Multi-energy Domains

paper aims to stimulate a discussion on how to create a bridge between the concept of flexibility used in power and energy systems and the flexibility that buildings can offer for providing services to the electrical system. The paper recalls the main concepts and approaches considered in the power systems and multi-energy systems, and summarises some aspects of flexibility in buildings. The overview shows that there is room to strengthen the contacts among the scientists operating in these fields. The common aim is to identify the complementary aspects and provide inputs to enhance the methodologies and models to enable and support an effective energy and ecologic transition

Urban building energy modelling for retrofit analysis under uncertainty

Urban building energy modelling (UBEM) is a growing research field that seeks to expand conventional building energy modelling to the realm of neighbourhoods, cities or even entire building stocks. The aim is to establish frameworks for analysing combined urban e˙ects rather than those of individual buildings, which city governments, utilities and other energy policy stakeholders can use to assess the current environmental impact of our buildings, and, maybe more importantly, the future e˙ects that energy renovation programmes and energy supply infrastructure changes might have. However, the task of creating reliable models of new or existing urban areas is diÿcult, as it requires an enormous amount of detailed input data – data which is rarely available. A solution to this problem is the introduction of archetype modelling, which is used to break down the building stock into a manageable subset of semantic building archetypes, for which, it is possible to characterize their parameters. It is the focus of this thesis to explore and develop new methods for stochastic archetype characterization that can enable archetype-based UBEM to be used for accurate urban-scale time series analysis.The thesis is divided into three parts. The first part acts as an introduction to case study data of the residential building stock of detached single-family houses (SFHs) in Aarhus, Denmark, which is used throughout the thesis for demonstration purposes.The second part concerns the development of methods for archetype modelling. Bayesian methods for archetype parameter calibration are presented that incorporates the variability of the underlying cluster of buildings, and correlation between parameters, to enable informed predictions of unseen buildings from the archetype under uncertainty. The capabilities of archetype-based UBEM are further widened through the introduction of dynamic building energy modelling that allows for time series analysis.The third part of the thesis is devoted to demonstrating the usefulness of the proposed archetype formulation as a building block for urban-scale applications. An exhaustive test scheme is employed to validate the predictive performance of the framework before establishing a city-scale UBEM of approx. 23,000 SFHs in Aarhus. It is used to forecast citywide heating energy use from 2017 up until 2050 under uncertainty of energy renovations and climate change.Overall, the proposed archetype-based UBEM framework promises very useful for fast, flexible and reliable urban-scale time series analysis, including forecasting the effects of energy renovation or city densification, to establish an informed basis for energy policy decision-making

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University

Prediction of power and energy use in dwellings : Addressing apects of thermal mass and occupant behaviour

Households are responsible for approximately 26 % of the annual energy use in the EU. Following the EU-directives regarding energy performance in buildings, international initiatives have been taken in Europe to help countries to define and include guidelines in their own building codes, for example, to establish the concept of zero energy buildings, ZEBs. This concept includes passive building energy-saving technologies, energy-efficient building services systems and renewable energy generation technologies. It is usually very difficult for a building to use zero energy and the concept has therefore been developed to include so-called net-zero energy buildings, or nearly zero energy buildings. These are usually defined as having a net-zero energy use on an annual basis and a nearly-zero energy use if they have a significantly lower use than stipulated in the respective national building codes. Technological advances have resulted in new buildings being very well insulated and, subsequently, using very little energy. However, the focus has now moved towards the use of renewable energy rather than only looking at the amount of energy used. Energy production can be achieved via numerous different arrangements and can be utilized in ways that are dependent on the time of day and the weather. Taking these different aspects into consideration, it can be assumed that the temporal variations regarding production can vary significantly. The heating demand of a building depends on the outdoor climate and the occupants’ behaviour, which leads to an uncertain situation with regard to matching the renewable production and demand, and even more so when the occupants’ behaviour is subject to temporal variations. In addition to the temporal variations, occupant actions or preferences are subject to large stochastic variations within a population. Thus, when designing to meet these challenges, the temporal resolution would have to be higher with regard not only to demand but also to the renewable energy production, in order to provide general benefits as well as covering a larger part of the possible future scenarios.This thesis aims show how the use of household electricity and domestic hot water varies and how these variations impact the energy and power demand of buildings. Additionally, in order to achieve a higher possible level of load matching there is a need to time shift power loads. This is another building operation process that has been investigated. The primary method in both cases has been to use building simulations with large amounts of measurement data for occupant behaviour as input to the simulation models. By randomly inserting different measurement data sets, and running simulations repeatedly, the outcomes were hundreds of annual energy and power demands that varied with the variation of the input.Furthermore, load shifting was investigated by abruptly reducing the heating power supply to buildings. The heat stored in the building envelope and furniture was then used to reduce the effects on the indoor temperature. This thesis examines the temperature drops caused by such power reductions and the various factors that affect the size of the temperature drops, such as the thermal mass and the properties of the building envelope, as well as the stochastic behaviour of the occupants that creates the internal heat load