Control of Smart Buildings

In today’s fast growing life, buildings must have an effective energy management system to make compact and convenient atmosphere with low investment and high utilizations. A smart building energy management system (BEMS) would regulate the heating system, boilers, and pumps as a fundamental function, then locally control the thermal regulation to reach the optimal needed room temperature. BEMS would regulate air conditioning in building by using cooling system. Cooling systems distributes cold air throughout the building by using fans and dampers. BEMS can also be used to regulate lights or other energy-consuming devices, as well as to log data from energy meters.

Power system simulations should be adapted to be applicable to the trends that are currently evoked by the energy transition. This transition is pushing our power system from a traditional hierarchical system to a modern interactive system. In order to keep the supply and transport of energy safe and reliant, we need to change the way we perform power system simulations. This requires a comprehensive framework in which both transmission and distribution systems are simultaneously analyzed. This chapter describes how transmission and distribution networks are modeled together as an integrated network and used to do steady-state operation analysis in order to assess the interaction of these two networks. Furthermore, we investigate the influence of the increasing amount of imbalance at distribution level on the transmission network that is evoked by the increase of highly variable resources and loads at distribution level. This influence is not taken into account in traditional power system simulations as power networks are analyzed on its own. We show that the hybrid network representation is a powerful tool to analyze modern power systems and that the effects of increased PV penetration under normal operating conditions are limited.

Statistics show that approximate energy usage in a building is 10–20 times more than residential which is around 70–300 kWh/m². The electricity demand is expected to increase triple than current demand in 2030. It is found that total energy demand and produced are not balanced whereby there will be not enough energy to supply for higher demand in the future. This why we need to manage energy properly especially for commercial building. Thanks to technology, now there is no need for building owners to hire energy auditor in order to know how to manage energy in their building. Technology has evolved commercial building into smart building. By installing sensors in the building and make use of Internet of Things technology, the energy can be managed through web or mobile apps. In this chapter, we are going to explain on how building evolved from commercial building to smart building and the development of building energy management by using machine learning and big data analytic approach.

The real-time generation/load balance of power system determines the frequency stability of the system, which is the key to ensure the safe and stable operation of the system. In the traditional power system, due to the lack of effective control means for end-user behavior, the system power balance can only be guaranteed by adjusting the generation power. At this time, in the peak period of power consumption, if the user's power consumption behavior is changed through demand response, the load in the peak period can be reduced, so as to realize the real-time power balance of the system. Therefore, this chapter proposes a transaction mode of the multi-user demand response market based on controllable peak load capacity. Firstly, combined with the user big data generated by the smart grid, this chapter perceives the user's power consumption behavior and demand response potential. Secondly, in order to maximize the economic, security, and carbon reduction benefits brought by demand response, this chapter proposes a multi-stage market trading mode of medium-term and long-term monthly trading market and special market for peak demand response, studies the market mechanism of demand response, and analyzes the market and technical functions of load aggregators.

The demand-side management scheme, one of the smart grid’s distribution side features, is well accepted due to its role in controlling energy consumption in residential buildings by ensuring sustainability, leading to the concept of smart buildings. The consumers’ appropriate response without compromising the comfort, referred to as demand response, helps decrease their electricity consumption bill. Further, the flexibility and controllability in the power consumption patterns of the end user due to demand response has gained tremendous research interest in the smart grid research domain. Smart appliances’ demand pattern alternation, battery back-up during peak load period, and partial load demand fulfilment through renewable generations are instrumental in reducing the electricity bill. Besides, short-term forecasting plays a vital role in power system scheduling and management. This chapter attempts to summarize the concept of demand response strategies in smart buildings. Further, it discusses the importance of adapting point and probabilistic forecasting methods to yield efficient demand response strategies in a smart building environment.

The concept of local energy community (LEC) has been well received and caught on globally in various segments of the population from Rural setting–Remote villages, to Blue Economies—getting integrated with Green Power and to Urban housing societies as well, safeguarding from the present non-resilient energy infrastructure as of now. Demand-side management, reduction in energy consumption by intelligent governance of intelligent buildings has also caught society's attention. Surplus Power generated in the buildings at different times led to the development of grid virtual power plants. Community energy storage battery, topping on the Concept of LEC grid-connected or even off-grid with smarter buildings occupied by the smarter occupants, thus creating a ‘Human in the Loop system’- (Comptes Rendus Physique Volume 18, Issues 7–8, September–October 2017, Pages 428–444) rather than all auto-controlled. With higher per capita energy consumption in countries, the importance of intelligent buildings establishes still higher priority considering heating loads and other luxury requirements. Further, the buildings are now considered energy-positive to generate power from solar rooftops for building's consumption and sharing with the community. Energy flow is two-way between the community and the buildings through micro-grids or nano-grids, thus offering flexibility and energy demand response while acting as a significant reserve. The intelligent behaviour of the structures on getting integrated with the intelligent grid helps make the LEC smarter. The wise governance of the buildings by optimising the power generation management, its storage for meeting the self-load demand that anticipates and matches the load for optimisation and then shares the energy under Peer-to-Peer (P2P) trading in the local community.

The energy neutrality of buildings is subject to different interpretations and in the context of a building or neighborhood (or, equivalently, district), it can be understood to mean “the generation of equal electricity, as it consumes”. Nevertheless, it is found out that even though buildings can achieve energy neutrality annually, the energy self-sufficiency achieved is not satisfactory. Most of the locally produced electricity is fed back to the grid because of the mismatch of supply and demand. However, nearly self-sufficient buildings and neighborhoods will be a future requirement for newly arising smart buildings and neighborhoods with the electrification of energy systems. Therefore, in this chapter, with the aim of improving energy self-sufficiency, an analysis was conducted for a renovated smart neighborhood. In this context, a smart neighborhood is a group of highly performing houses, equipped with enhanced energy efficiency measures. These houses include improved insulation and high-efficiency heat pumps, electric water heaters, domestic appliances, and photovoltaic panels. In this neighborhood, to tackle the challenges associated with multiple distributed energy resources, and to design (size) and control them simultaneously, an optimization problem is developed and partitioned into sub-problems in a distributed manner. The study showed with the introduction of different storage systems and optimal smart control of these systems, the energy self-sufficiency of the neighborhood can be increased from the current 10% value to 71.5%.

With over 4.4 billion people living in urban areas, buildings are currently the greatest energy consumers on a worldwide scale, accounting for 40% of the global energy consumption and using 50% of the world’s final electrical energy. The built environment, as being the focus of numerous types of research, is the key platform for introducing technologies and regulations that can contribute to decreasing energy consumption and improving the energy efficiency of accessible services. Technological advancements have made it possible to build more autonomous and efficient buildings capable of managing their infrastructures and services, such as lighting or airconditioning systems, using a number of predefined patterns via software as a decision-making basis. Indeed, the diagnosis of the new applications allowed by the advancement of research in Information and Communication Technologies, and especially in the area of the Internet of Things (IoT) makes it possible to observe that the concept of “Smart Building” has also evolved. Hence, through this chapter, we examine how the introduction of the new technologies in the building sector can be used to improve the requirements of environmental quality and energy use. In this context, we included a case study with the purpose to show the importance of using building-related data analysis and Machine Learning techniques to define the energy inefficiencies in highly efficient Leadership in Energy and Environment Design (LEED), Energy Star, and Net Zero-certified building in Houston, Texas, USA.

This chapter proposes an optimal scheduling approach for multi-energy grids (MEGs) integrated with the grid-interactive buildings. To optimally coordinate the grid-interactive buildings and the MEGs, this chapter develops a bi-level optimization method. The MEGs operator is able to activate the heating demand response (HDR) from grid-interactive buildings, while the grid-interactive buildings can enjoy heating cost saving with the proposed bi-level optimization method. At the upper level, the MEG operator optimizes the heating sale price (HSP) to the buildings and the energy schedules by dispatching the multi-energy devices. In the lower level, in order to reduce consumers’ heating costs, indoor radiators’ water flow rates are optimally adjusted according to the HSPs. To efficiently solve the bi-level optimization problem, we reformulate the original problem as a mixed-integer linear programming (MILP). We also use the piecewise linearization method to treat the nonlinearity of constraints of the heating distribution network. Case study results demonstrate that the proposed method can optimally coordinate the grid-interactive buildings and the MEGs. Consequently, the flexibility of the grid-interactive buildings can be fully used in the optimization of the multi-energy grids. Moreover, the heating costs of the buildings can be significantly reduced with the proposed bi-level optimization method.

A recent progressing trend on grid-connected residential photovoltaic (PV) system has been reported all around the world with the focuses on various aspects, mainly on the reduction of grid consumption and demand-side management with peak shaving and peak shifting. Subsequently, a huge impact on the distribution network is caused due to the power quality problems arising from grid-connected PV systems. As the most promising solution, the integration of battery energy storage with the PV system is going to improve the operation of distribution network by enhancing self-consumption as well as in demand side management. The purpose of this study is to investigate the technical performance of a battery-coupled grid-connected PV system and to assess the economic viability of such integrated renewable energy systems in Sri Lanka. In this work, a residential PV-battery energy system is designed and developed considering a control algorithm for energy efficient system operation at conditions to maximize the PV self-consumption through battery energy throughput and to reduce the utility grid consumption accordingly. The system is analysed for dynamic load profile under variable PV conditions with dynamic battery state of charge (SOC) conditions. The results are analysed for system performance and stability conditions. Overall system performance indicated a stable system operation for each investigated condition by effectively managing the PV, battery and grid systems to meet the real-time residential demand. The economic viability of the proposed PV system is analysed using the discounted cash flow (DCF) approach using the indices of benefit-cost ratio (B/C), net present value (NPV), internal rate of return (IRR), and payback period, which revealed to be viable in regular Sri Lankan grid connected PV-battery energy residential system.