Potential of energy flexible buildings: Evaluation of DSM strategies using building thermal mass
Introduction
In this era of uncertainty, when speaking of energy planning consumer demand, has emerged as a central figure, since it can be seen as a means of balancing the energy supply and demand of electricity, by providing the system with flexibility whose responsibility no longer falls only on the generation infrastructure. Traditionally, the demand has been addressed by requiring green-rated buildings and energy efficient equipment. Conversely, retrofitting solutions for existing buildings can be a more costly and challenging task [1]. Around 40% of the energy is consumed by the buildings and they are responsible for 36% of greenhouse gas emissions [2]. The ultimate aim is buildings with a balance of zero energy or net positive energy [3], buildings that produce at least as much energy as they consume, involve high energy efficiency and on-site renewable energy (RE) generation [4].
The energy flexibility of a building could significantly contribute to the minimization of tempory mismatches between generation and demand, caused by intermittent renewable generation. It is defined as the ability to manage its demand and generation according to local climatic conditions, user needs and energy networks requirements [5]. Faced with these requirements, the concept of Demand Side Management (DSM) [6] appears as a proactive way to increase the energy efficiency among users in the long-term [7], and can reduce both the electricity peak power demand and the electricity consumption [[8], [9]].
The most prominent DSM methods include reducing peak loads (peak clipping or peak shaving), shifting load from on-peak to off-peak (load-shifting), increasing the flexibility of the load (flexible load shape), and reducing energy consumption in general strategic conservation), as stated by Müller et al. [10]. Besides, it is necessary to analyze how it is possible to improve energy storage strategies to make them more efficient and, to reach low cost alternatives. Additionally, demand side management provides an active integration of the user into the market by influencing its load profile, making more conscious and efficient use of energy. For example, Müller et al. [11] and Gelazanskas et al. [12] proposed more interesting strategies.
There are countless publications on DSM and there is a noticeable increase in recent years on the number of published papers about this issue. However, they emphasize different aspects. For example, many studies focus on price based DSM, quantifying the suitability and impact of DSM approaches under variable prices or real-time pricing [13], [14], [15], [16], [17]. Conversely, other publications emphasize the influence of DSM on Smart Grids, developing different frameworks for its integration into micro grids or Smart Grid environments [12,[18], [19], [20], [21], [22], [23], [24], [25], [26]], based on research and practice [27] addressing the behavioural changes of energy end-users.
On the other hand, Fernández et al. [28] make a comparison between renewable generation and DSM, suggesting that DSM exhibits the best performance in terms of economic efficiency and environmental sustainability, reducing peak loads and losses in the system. The estimation of this paper are: electric appliances consume around 62% of the electricity of a home in Spain, where unattended appliances such as washing machines are responsible for 21%. As a result, they consume 13% of the total electricity demand. This means that being able to program these appliances to work at non-peak times could achieve considerable savings at no cost.
In fact it has been shown that the impact of DSM programs is significant: it can be appreciated mostly in aggregated households [29]. CO2 emissions could also give customers an environmental motivation to shift loads during peak hours. Also, an empiric estimation of three different DSM measures is developed by Khanna et al. [30]: electricity pricing, energy label programs and information feedback mechanisms. As an alternative, the approach discussed by Khoury et al. [31]. It determines an optimal schedule of operation for predictable devices. At the beginning of each day, the energy flows are forecasted, modifying the consumption of the house accordingly. Furthermore, significant savings can be achieved with actions such as changing the settings of the thermostats or retrofitting projects. Shiftable loads are heating, cooling air conditioning, washing machines, dryers and dishwashers [32]. Command and control of heating and cooling systems are becoming a cost-effective viable option, particularly applicable to the existing building stock [1]. So, one of the major topics to be investigated in this field is the potential of DSM in existing buildings, and the requirements to achieve the maximum savings.
In addition, all programs intended to influence the customer's use of energy are considered DSM and can be addressed to reduce demand at peak times, seasonal consumption or alter the time of use [33]. In general, they encourage the end user to be more energy efficient. DSM can also help to reduce network congestion and the need for investment in new generation equipment [28]. Then, this field closely follows the paradigm of what type of characteristic should have user behavior of buildings to invest in these programs.
DSM often works best when there is storage available for the user. The storage in these demand management strategies is especially important as it is mainly about the movement of loads and uses of energy at periods other than production. Applicability of each one of them has been analyzed according to certain conditions, in particular the cost of installation and operation. There are three types, basically: electrical batteries; thermal energy storage TES using water tanks; and thermal energy storage using thermal inertia of buildings [34], [35], [36], [37]. The first one remains an expensive option but it is the most used. The second one takes advantage of domestic heat water needs to become an easy and viable solution [[11], [35], [38]], through pre heating and cooling, or storing the energy in tanks [1]. TES systems have shown a capability to shift electrical loads from high-peak to off-peak hours, which is the reason why they are a powerful instrument in DSM, especially in the presence of renewable energies [33]. Storage can also help to flatten the customer's load profile. Therefore, effective TES can potentially impact several categories of DSM, including peak load shifting, valley filling and strategic conservation [39]. And finally, the most innovative is the use of the internal thermal mass of buildings [[40], [41]]. In this work, it is studied the latter.
There are also many studies which stress the importance of storage within a DSM framework. For instance, the study presented by Quareshi et al. [39] assesses the impact of using Phase Change Materials (PCM) in buildings to leverage its thermal energy storage capability, claiming that significant advantages can be obtained for space heating applications. On the other hand, Wolisz et al. [42] analysed the potential of Thermal Energy Systems (TES) in buildings, integrating conventional storage technologies like hot water tanks as well as the structural thermal storage capacity of a building itself. The calculations are based on a thermal building simulation in Modelica. The study developed by Arteconi et al. [43] presents an existing installation of a TES system coupled with heat pumps, performing simulations to show the load shifting potential of the storage while assessing energy and cost savings. [32] Also, showing some impressive results regarding load shifting, arguing that the peak load of a dwelling can be reduced on average by 24% and 13.5% as a result of washing machine and dishwasher load shifting respectively. Last, the potential to improve the balancing between electricity used for heating and local production of a Net Zero Energy Building (nZEB) by active used of the structural thermal storage capacity of the building is analyzed by Reynders et al. [44]. This study shows the possibilities of structural thermal storage but, results are not replicable in existing building.
Literature shows that the DSM potential is function of the availability of thermal mass and the geometry of the building [44]. However, the use of the thermal mass only enables short-term storage. Therefore it is not able to reduce seasonal mismatches of energy production. The DSM potential is higher for the massive buildings compared to lightweight buildings, and it could be expected that the efficiency of the structural storage is higher for well insulated buildings. Le Dreau et al. [[34], [45]] analyze the influence of interior mass and envelope quality of buildings in heating energy flexibility. However, cooling energy is not studied, and the decision of refurbishment yes or not is not clear. There are many works about thermal storage in the building structure [[35], [40], [44], [46], [47]]. The most discussed topics are: benefits in thermal comfort and a small reduction of building demand [[46], [47]]; effect of intermittent operation schemes [47]; district heating approach [35]; and change in user behavior [40].
Activating the structural thermal storage demands for the active control of the indoor temperatures and the total energy use increases [44], since the use of structural storage might result in increased transmission and ventilation losses. The building could be preheated and its storage capacity activated by increasing the indoor temperature set-point when the price of the electricity is low. Alternatively, the set-point could be lowered with high electricity prices, releasing the stored energy and thus reducing the electricity demand.
Another way in which DSM can work is by relying on the inherent property of buildings, for example by changing the temperature thanks to their thermal mass or disengaging heating or cooling for short periods, especially when the air flow within the building is maintained [1]. So, there are some rules which should be considered related to DSM [1], but it is not about weather and building thermal behavior aspects. For example, improving the performance of a building through building automation requires adequate measurements and justification of the economic gains it could offer before its realization, which could differ depending on the user behavior. The study presented in [48] suggests that results from observations and product research for residential homes indicates that the investment cost of building automation ranges from 500 to 2000 Euros, depending on the building type.
The literature review revealed a significant knowledge gap in this area: there are not many studies on the performance of thermal storage systems in residential buildings (heating & cooling), user behavior or thermal characteristics of buildings valuing the potential savings obtained both economic and energetic. Moreover from the existing research initiatives, most do not talk about energy or economic savings, studies tend to analyze the storage performance, but not linked to savings obtained or their dependences.
This study addresses this knowledge gap by presenting systems used for DSM thermal energy storage in five different buildings, in different climatic conditions, with the refurbishment of the thermal envelope of the buildings or not, and the study of the most relevant parameters (electricity tariffs, nightcooling, preheating, precooling…).
Additionally, it proposes a new energy controller to optimize and analyze:
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Effect of tariffs: a study of different electricity tariffs in Spain
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Operation of heat pumps (duration and setpoints)
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Equilibrium between energy savings and costs.
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Importance of rehabilitation in DSM solutions (thermal energy storage using buildings as a battery).
The objective of the work is evaluating an algorithm that automatically manages the activation of a heat pump in response to the most appropriate strategies according to pricing and operating conditions. It is interesting to see if a balance can be reached between the cost savings, the increase in energy consumed, the thermal comfort of the occupants and the contribution to the reduction of the peak loads. The study shows different results and conclusions, highlighting the important influence of various factors on the results obtained, such as user behavior, constructive quality of the building and electricity pricing.
The literature review revealed a possible knowledge gap in this area: there are not many studies on the thermal storage systems performance in residential buildings (heating & cooling) and the thermal characteristics of buildings, valuing the potential savings obtained for both energy and economics. And those that exist, do not talk about energy or economic savings, only analyze the storage performance, but the works do not link to the achieved savings or their dependences.
Section snippets
Overview
The implemented methodology allows analizing the potential use of the buildings' thermal mass as an energy storage system ('building as a battery') through the application of different measures of demand management. For this purpose, an intelligent manager has been implemented to decide the optimal operating scenario based on the climatic conditions, the use given by the users and the different measures to analyze (preheating, precooling and night ventilation). These measures are based on the
Evaluation of the strategies in the reference scenario
The demand-management system is evaluated in five existing residential buildings for the so-called reference scenario. This reference situation is detailed in Table 4, and consists on the selection of study rates and study strategies. The objective is to analyze the implications that occur in the choice of preheating, precooling and night ventilation strategies defined previously in Section 3.2.2.
Conclusion
Buildings can act as active elements in innovative city systems where it is possible to connect vehicles, utilities, renewable energy sources and energy storage for sustainable growth and development. In this same matter, buildings could be energy exchange hubs with generation, storage and conversion capabilities, if they provide energy demand flexibility, but they require smart technologies and energy management. This work is focused on the study of the most common energy demand management
Declaration of competing interest
None.
Acknowledgements
The authors would like to take this opportunity to thank the DACAR project “Zero-Energy Balance Districts Through Algorithms of Adaptive Comfort and Optimal Management of Energy Networks” (BIA2016-77431-C2-2-R) funded by Ministry of Economy and Competitiveness (Government of Spain) and European Regional Development's Funds (ERDF) for its partial support. And the University of Seville under its Research Plan VI (VPPI-US).
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