Smart Zero-energy Buildings and Communities for Smart Grids. Группа авторов

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СКАЧАТЬ century is ill-suited to the needs of the 21st century. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability. The role of buildings in this framework is crucial. This chapter addresses critical issues related to smart grid technologies and the integration of buildings in this new power grid framework (Güngör et al. 2011). The main objective of this chapter is to provide a contemporary view of the current state of the art in the potential of buildings and communities to be integrated in smart grids, as well as to discuss the still-open research issues in this field. Since the vast majority of smart grids’ potential customers are buildings (residential, commercial, retail and industrial) and communities, the chapter addresses the challenges posed by smart grids on the building and community level.

1.1. Smart and zero-energy buildings

      The energy consumption for buildings accounts for 40% of the energy used worldwide. It has become a widely-accepted fact that measures and changes in the building modus operandi can yield substantial energy savings, minimizing the buildings’ carbon footprint (Santamouris and Kolokotsa 2013; Deng et al. 2014). Moreover, buildings in the near future should be able to produce the amount of energy they consume, that is, become zero or nearly zero-energy buildings (ZEBs) (Kolokotsa et al. 2011; Pyloudi et al. 2015). This is a mandatory requirement based on the fact that by December 31, 2020, all new buildings were nearly zero-energy consumption buildings. New buildings occupied and owned by public authorities needed to comply with the same criteria by December 31, 2018 (Kapsalaki and Leal 2011; Kolokotsa et al. 2011).

      ZEBs are buildings that work in synergy with the grid, avoiding putting additional stress on the power infrastructure (Li et al. 2013). Achieving a ZEB includes, apart from minimizing the required energy through efficient measures and covering the minimized energy needs by adopting renewable sources, a series of optimized and well-balanced operations between consumption and production, coupled with successful grid integration (Carlisle et al. 2009).

      Information and computer enabled technologies (ICT) and smart grids implementation are the keys to achieve the aforementioned zero energy goals (Privat 2013). ICT for energy management in buildings has evolved considerably in the last decades, leading to a better understanding and usage of the term “smart buildings” (Nikolaou et al. 2012). Advances in the design, operation optimization and control of energy-influencing building elements (e.g. HVAC, solar, fuel cells (FC), CHP, shading, natural ventilation, etc.) unleashed the potential for the realization of significant energy savings and efficiencies in the operation of both new and existing dwellings worldwide. Smart buildings ready to be interconnected with smart grids should comply with the following requirements:

       a) incorporation of smart metering;

       b) demand response capabilities;

       c) distributed architecture;

       d) interoperability.

      1.1.1. Smart metering

      Smart metering is a prerequisite and starting point for the effective implementation of smart grids and zero-energy buildings. In Finland, the usage of smart metering encouraged consumers to increase energy efficiency by 7%. In order for electricity providers to deliver intelligent services for customers, bidirectional metering interfaces should be used to obtain customers’ energy demand information (Bae 2014). Moreover, through the advances of smart metering, sensors-based approaches can be exploited to provide power load forecasting (Jain et al. 2014). Data collected from smart meters, building management systems and weather stations can be used by advanced artificial intelligent techniques and machine learning algorithms to infer the complex relationships between energy consumption and various variables such as temperature, solar radiation, time of day and occupancy (Mellit and Pavan 2010; Gobakis et al. 2011; Zhao and Magoulès 2012; Jain et al. 2014; Jetcheva et al. 2014; Papantoniou et al. 2016). Due to the fast development and application of low cost options for energy metering in recent years, power load prediction is becoming increasingly relevant and cost effective (Fan 2014; Jain et al. 2014).

      Smart metering with sensor-based approaches was exploited in the framework of the Green@Hospital project (www.greenhospital-project.eu/). In this project, the outdoor temperatures and hospitals’ energy demand were predicted for 4, 8, 12 and 24 hours ahead (Papantoniou et al. 2015). This prediction is then used for optimal control of the hospitals’ air handling units, leading to an almost 20% reduction of the energy used. Other researchers exploit neural networks’ capabilities for 24 h-ahead building-level electricity load forecasting, using data collected from various operational commercial and industrial building sites (Jetcheva 2014). Data mining-based approaches which collate models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy, are also developed. This approach was adopted to analyze the large energy consumption data of the tallest building in Hong Kong (Fan 2014) with very satisfactory results. These ensemble models can be valuable tools for developing strategies of fault detection and diagnosis, operation optimization and interactions between buildings and smart grids. Moreover, data processing and interpretations extracted by the smart meter can provide useful information for the buildings’ energy behavior. Advanced techniques such as cluster analysis are used by various researchers (Nikolaou et al. 2012; Panapakidis 2014), leading to the determination of optimum clustering procedures as well as building benchmarking.

      1.1.2. Demand response (DR)

      DR (Bartusch and Alvehag 2014; Li and Hong 2014) offers the ability to apply changes in the electricity usage by the consumers from their normal consumption patterns in response to changes in electricity pricing over time (Bradley et al. 2013). This leads to lower energy demand during peak hours or during periods that the electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be a more cost-effective alternative than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. DR is expected to increase energy market efficiency and security of supply, which will ultimately benefit customers by way of providing options for managing their electricity costs, which leads to a reduced environmental impact.

The already available DR programs are generally categorized into incentive and price-based programs. Incentive-based programs provide economic incentives for customers to reduce demand at times of capacity shortage or exceptionally high electricity prices, whereas price-based demand response programs involve dynamic tariff rates that promote general changes in patterns of electricity use. Time-of-use tariffs, which are one of the major price-based DR programs in use involve different unit prices within different blocks of time, and reflect the average cost of utilities during these periods (Bartusch and Alvehag 2014).

      There are some efforts at the country level that show the benefits of DR in electricity supply. The policy discussions in the UK on the economic case for DR are analyzed by Bradley et al. (2013). A cost/benefit analysis is performed in a quantitative manner showing that the benefits on a country level are clearly very significant, that is, there was a 2.8% reduction in overall electricity use and a 1.3% shift in peak demand. Moreover, the economic viability of the DR mainly depends on ensuring participation by the end users, that is, the building sector. An increase in participation СКАЧАТЬ