Active Building Energy Systems

Due to the energy crisis and air pollution issues, energy-saving and demand flexibility in buildings have received significant attention in recent years since buildings are a large energy consumer sector in the today’s world. For this aim, the conventional buildings are moving towards active buildings to solve these challenges. Active buildings are flexible and energy-efficient buildings, which contribute to energy sustainability by providing energy services for the grid and sharing energy with neighboring buildings. The main characteristic of active buildings is their flexibility in viewpoint of energy consumption. However, the self-generation of electricity by buildings is also widely focused on by researchers. In this chapter, an overview on promising active buildings is outlined. Firstly, the concept and definition of such buildings are discussed. In addition, the energy services that can be provided by active buildings are described. The enabling technologies and challenges of such concept are also discussed. Finally, a review of previous works conducted on this subject is accomplished. The literature includes load forecasting, enabling technologies, energy storage systems, distributed generation, demand-side response programs, ancillary services, and so forth. Finally, the chapter is concluded by giving some remarks and recommendations.

As significant contributors to global emissions, energy and buildings sectors must decarbonise. Active Buildings present a contemporary conceptualisation for addressing environmental policy, technical and societal problems, incorporating low-carbon building fabric design, renewable energy production and energy storage capacity with intelligent digitalisation. Underrepresented in some existing concepts is an understanding of the diverse and valued roles that buildings play in society, how this influences the varying subjective and powerful values and meanings attached to them and consequently the everyday energy practices carried out within them. Adopting a social science lens, this chapter outlines key conceptualisations that are important in a contemporary understanding of Active Buildings as homes. We consider the dynamic role Active Homes are playing in some new build schemes that represent the foreseeable future of energy infrastructure. We argue that understanding the interplay between people, homes and energy is essential to Active Buildings fulfilling their many requirements.

The structure of traditional power systems is changing from passive to active due to the increased amount of renewable energy sources as well as the presence of microgrids, energy communities, active buildings, and active network management. Due to the stochastic nature of renewable energy sources, their intermittent output power can create stability issues within different levels of electrical systems. Therefore, as a potential solution, the integration of energy storage systems in different sizes/scales has become pivotal. In this chapter, different types of energy storage devices along with their applications and capabilities are discussed. The focus of this chapter is mostly on electrical and electrochemical energy storage that could be utilized in active buildings. These devices are categorized as static storages as well as mobile storages such as electric vehicles. The technologies, requirements, as well as the application of mentioned storages are presented in this chapter as well.

Concerns about sustainability, climate change, and energy efficiency have contributed to a more environmentally conscious society. Taking into account the targeting of sustainable smart grids, progressively, innovative technologies and control strategies related to renewable energy sources, energy storage systems, electric mobility, and controlled electrical appliances are emerging with enormous potential. Facing this new paradigm, disruptive power electronics technologies are welcome for the future electrical power grids, contributing to guarantee sustainability, stability, efficiency, and power quality. Furthermore, considering that some of the aforementioned technologies are natively DC, hybrid AC/DC electrical power grids have gained more importance. Moreover, based on the support granted by modern power electronics technologies, electrical power grids are facing a new scenario, where DC systems offer pertinent advantages in terms of simplicity, cost, and efficiency. In these circumstances, this book chapter presents power electronics perspectives for hybrid AC/DC electrical power grids, specifically for applications in active buildings, e.g., as the case of smart homes. Throughout the book chapter, the main structures of hybrid AC/DC electrical power grids and the associated power electronics topologies, as well as the power quality aspects from the main AC grid point of view, are presented and discussed. Additionally, representative experimental results obtained with laboratory prototypes are presented, highlighting the advantages of the proposed hybrid AC/DC electrical grids and introducing innovative functionalities.

Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a guide for practitioners in the residential sector. The workflow begins with physics-based modelling methods for analysis and evaluation. Following this, model-based and data-driven techniques for developing low-complexity, control-oriented models are outlined. Through sections detailing these different stages, a case study is constructed, concluding with a final section in which MPC strategies based on the proposed methods are evaluated, with a price-aware formulation producing a reduction in operational space-heating cost of 11%. The combination of simulation model development, control design and analysis in a single workflow can encourage a more rapid uptake of MPC in the sector.

Residential buildings account for about a quarter of the global energy use. As such, residential buildings can play a vital role in achieving net-zero carbon emissions through efficient use of energy and balance of intermittent renewable generation. This chapter presents a co-design framework for simultaneous optimisation of the design and operation of residential buildings using Model Predictive Control (MPC). The adopted optimality criterion maximises cost savings under time-varying electricity prices. By formulating the co-design problem using model predictive control, we then show a way to exploit the use of seasonal storage elements operating on a yearly timescale. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating on multiple timescales. In particular, numerical results from a low-fidelity model report approximately doubled bill savings and carbon emission reduction compared to the a priori sizing approach.

Smart grid control and management is a growing research area that affects the global goals of net-zero emissions, increased renewable energy generation, and efficient energy management. Active buildings are the building blocks of the smart grid of the future. Their passive role as energy consumer buildings is replaced in the new paradigm as active components, which are not only the energy consumers but also provide energy services to the net- work or neighbouring areas in time of need. This chapter puts a spotlight on the control of a community of active buildings and considers high-level smart grid control approaches that use active buildings; aggregation, frequency and voltage regulation as well as security considerations. The chapter also provides a detailed case study that considers demand-side frequency regulation, power tracking and formal control synthesis methods using energy storage systems (ESSs) and thermostatically controlled loads (TCLs).

An active building typically owns some flexible energy resources, such as controllable loads, energy storages, or distributed generation units, that can be controlled to reduce the building’s total cost or benefit from providing the grid with required services. In this regard, the energy management system is responsible for scheduling/controlling these resources based on the targets of the active building. In addition, the energy management system needs to take into account the operational cost of each resource, the limits imposed by the owner, and those associated with the power system. This chapter will focus on these targets, objectives, and constraints. Moreover, the chapter reviews some existing algorithms utilized by the building’s energy management methods such as optimization-based, rule-based, and artificial intelligence-based approaches in order to control the flexible appliances of the building optimally.

A growing number of distributed generation units in power systems along with the characteristics of intermittent renewable resources have increased the` need for flexibility in electrical networks. Flexible energy resources can assist system operators to operate their networks more efficiently. Active buildings can provide services to distribution and transmission systems through their flexible energy resources such as storage-based resources and controllable appliances. To analyze the flexibility potential of active buildings, this chapter firstly categorizes flexible appliances/devices of the building in terms of their degree of flexibility. Moreover, different flexibility services and their technical characteristics are introduced. Based on technical considerations, the contribution of active buildings to the provision of these services is discussed as well. Finally, the chapter analyzes the role of active building’s energy management system in this process.

Energy management has become the main focus of researchers to propose efficient strategies to use available resources and reduce CO2 emissions, thus preserving the environment and creating smarter cities. One of the most efficient energy management tools in this sector is non-intrusive load monitoring (NILM). NILM aims to extract the power consumption of each appliance from the given total consumption through purely analytical methods. This information about the appliances can be sent as feedback to the consumers, which increases their knowledge about their consumption behavior and helps them make the right decisions to reduce their consumption and cost while maintaining their comfort. In this chapter, we present the basic concepts about NILM, various algorithms that have been explored to develop more practical and accurate NILM techniques and their challenges. Finally, we illustrate the application of NILM in determining the energy flexibility potential of each consumer.

Nowadays, the world is facing energy crisis and environmental issues. This is why the energy demand is increasing in different energy sections. The buildings as a large energy consumer are critical to face with these issues. To overcome these challenges, the conventional active buildings are moving toward the active building. Demand flexibility and self-generation are two characteristics of the active building. However, the main feature of such emerging buildings is related to their flexibility demand. Demand flexibility in active buildings is enabled by demand response programs. The change in energy consumption pattern by residents of buildings is the aim of implementing such programs. Demand response programs are designed and managed by aggregators in retail markets. In addition, the enabling technologies for implementing such programs are provided by aggregators. Therefore, the aggregators are important for developing acting buildings. Accordingly, in this chapter, the role of aggregators in demand flexibility of active buildings is outlined. Firstly, the concept of aggregators and retail electricity markets are presented. Then, the benefits, barriers, motivators, and challenges of demand response programs are discussed. Moreover, the existing demand response programs and enabling technologies for implementing such programs are described in this chapter.