Thermal and Electrical Cover Factors: Definition and Application for Net-Zero Energy Buildings

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1 Thermal and Electrical Cover Factors: efinition and Application for Net-Zero Energy Buildings Juan Van Roy #*1, Robbe alenbien *, irk Vanhoudt *, Johan esmedt *, Johan riesen # # KU Leuven, epartment of Electrical Engineering Kasteelpark Arenberg 10, PB 2445, 3001 Heverlee, Belgium * Flemish Institute for Technological Research (VITO), Unit Energy Technology Boeretang 200, 2400 Mol, Belgium #* EnergyVille ennenstraat 7, 3600 Genk, Belgium 1 Juan.VanRoy@esat.kuleuven.be Abstract The presented work focuses on the introduction of thermal cover factors and the assessment of their interaction with the electrical cover factors. Cover factors are used to quantify the mismatch or non-simultaneity of local production and consumption of electricity and heat. First, the electrical cover factors are redefined to include the losses related to the storage of electricity and other inherent losses. Thereafter, similar thermal cover factors are defined. This allows the assessment of the self-consumption and self-generation for locally generated heat by means of e.g. heat pumps. In this paper, a case study focuses on a net-zero energy building. Netzero energy require an increase in distributed energy resources (e.g. photovoltaic systems). To minimize the impact of the resulting bidirectional power flows on the distribution grid, demand side management and storage (thermal and electrical) are often mentioned as solutions; i.e. increasing the self-consumption of locally produced electricity. The building in this case study is heated by means of a heat pump. The case study is used to demonstrate the use of the thermal cover factors, as well as the interaction between electrical and thermal energy flows. Keywords electrical cover factors; thermal cover factors; residential building; demand side management; heat pump; photovoltaics. 1. Introduction World-wide, the residential and commercial sector account for about 32 % of the final energy use and for nearly one-third of the global CO 2 emissions [1]. As per recent European directive, by 2020 all new need to be nearly zero energy, leading to a high penetration of renewable energy resources (RE) to provide the required energy in the built environment. Nevertheless, nearly zero energy are not clearly defined [2]. Therefore, this paper will focus on net-zero energy (nzebs), defined by the assumption that the local yearly renewable energy

2 production covers the yearly energy consumption (net-zero site energy) [3]. Other types of zero energy are defined in literature [3-6]. An nzeb requires both an increased integration of local RE (e.g. photovoltaic systems) and energy efficiency (e.g. proper insulation). The latter also includes a further electrification through more efficient technologies, such as heat pumps (space heating and domestic hot water) and plug-in (hybrid) electric vehicles, both having the advantage of flexibility to shift the consumption in time [7, 8] and the advantage of a significantly reduced consumption of greenhouse gas emitting fuels [1]. However, these technologies have a significant impact on the electricity consumption in. For instance, full electric vehicles, which are only charged at home, nearly double the household electricity consumption [9] and heat pumps will increase the electricity consumption by an amount depending on the technology and the building energy performance, e.g. a one-third increase in [10]. econdly, the intermittent and seasonal production profile of RE may have an impact on the distribution grid since the local consumption and production may lack simultaneity. Injection of the local production surplus in the grid results in bidirectional power flows, which can lead to higher peak loads and voltage deviations [8, 11]. This requires a proper synchronization of consumption and production of heat and electricity, through demand side management (M), electrical and thermal storage and minimization of the energy consumption. In literature, several grid impact indicators are defined, quantifying the grid impact of electricity consumption and production [12-14]. Additionally, load matching indicators [12, 15] and electrical cover factors [12, 13] are defined, quantifying the mismatch or non-simultaneity between local electricity demand and supply. Equation (1) shows the electrical supply,, and demand,, cover factors for a period [t 1,t 2 ] as defined in [13], where P and P are respectively the local supply and demand of electricity. These factors respectively define how much of the generation is locally consumed and how much of the demand is covered by local production. However, (1) does not yet include the possible storage of e.g. locally produced electricity. E m in{ P, P } dt and P dt E m in{ P, P } dt P dt The nzeb level defined by (2), provides insight on the energy coverage. For a zero energy building is reached.

3 P dt P dt An increased self-consumption of locally produced electricity can be reached with e.g. heat pumps in a M environment. However, this does not imply an optimal interaction between heat production and consumption and may lead to e.g. higher thermal losses compared to the non-m environment. In order to quantify this level of interaction, thermal cover factors will be defined, similar to the electric cover factors in (1). Moreover, the cover factors are defined to be applicable on multiple levels (single, energy exchanges between, etc.). An assessment for multiple residential in [10] demonstrated increased electrical cover factors. First, the (extended) electrical and thermal cover factors will be defined. Next, the use and the interactions between these factors will be demonstrated with a small case study. 2. Methods: efinition of cover factors Cover factors are defined to quantify the mismatch or non-simultaneity between local demand and production of a certain energy flow. They identify the ratio to which the local supply is covered by local demand (selfconsumption and vice versa (self-generation, ). The supply and demand cover factors given in (1) can be written as: m in{, } dt dt and m in{, } dt dt (3) where and are respectively the supply and demand of a power vector, which can be either electrical power P or thermal power Q. and are respectively the supply and demand cover factor during a period [t 1,t 2 ]. The electrical and thermal cover factors will be respectively denoted as and. The cover factors, as given in (3), do not include storage, various losses and are not applicable to a group of building. Therefore, they are adapted and rewritten as shown in (4) and (5). The storage losses are included via storage. storage is the discharge power of the storage unit, accounting for the

4 different storage losses: charge, self-discharge and discharge losses. Inherent losses of the system are represented by losses. m in{, } storage losses dt m in{, } storage losses ( ) dt losses dt dt (4) (5) The factor losses can represent various types of losses. These may include electrical losses (P losses ) in cables, power electronic converters, etc. and thermal losses (Q losses ) in water pipes, to the surroundings, etc. These losses are inherent to the system. However, a change in the efficiency will have an impact on the local consumption, the sizing of the local production units, and therefore on the cover factors. By incorporating these losses separately in the cover factors, e.g. the impact of several efficiencies on the cover factors can be investigated. In the case study of this paper, these losses are incorporated in the consumption. 3. Case tudy Here, a residential case study is used to demonstrate the use of the thermal cover factors and to assess the interaction of the electrical and thermal cover factors. The building is equipped with a photovoltaic (PV) system and a heat pump. The PV system is dimensioned so that the yearly local electricity production covers the yearly consumption. The heat pump is used for space heating and domestic hot water. A market-based multiagent control system is applied to the heat pump to assess the coordination impact on the interaction of the electrical and thermal cover factors. The performance of the heat pump and the coordination, for which a test setup was built, were assessed for an average winter week in [16]. The following sections discuss the different models, data and assumptions that will be used in this paper. a. Thermal building topology As a building simulation tool, TRNY was used for the simulation of the heat demand profile of the building [17]. TRNY type 56 was taken as

5 a reference building, i.e. a two storey semi-detached building with floor heating on both floors. The overall heat transfer coefficient is 0.40 W/m 2 K. The total annual space heating demand amounts to kwh, with a 4.5 kw power peak. For the average winter week being discussed, the demand is 332 kwh. The total annual energy demand for domestic hot water is 2963 kwh, with 55.7 kwh for the discussed winter week. A standardized demand profile for domestic hot water (200 l/day at 45 C) is used [18], which is common for an average Belgian four-person family. Fig. 1 and Fig. 2 show the heat demand profiles for space heating and domestic hot water. Fig. 1 Heat demand profile for space heating for the considered average winter week. Fig. 2 Heat demand profile for domestic hot water for the considered average winter week. b. Thermal building systems The heat demand of the building is met by means of a non-modulating water-to-water heat pump. A test setup was built and the results of the

6 performance and coordination strategy assessment are used for this case study. The test setup and control strategies are discussed in [16]. The heat pump has a rated power of 11 kw th and two thermal storage buffers. The first open water buffer is used for space heating and has a volume of 400 l. The second buffer (300 l) has an internal coil for indirect heating of the domestic hot water. Two heat pump control strategies are compared: a conventional heat driven control and a market-based multiagent control, further denoted as respectively no control and smart control. c. Market-based multi-agent control of the heat pump In order to minimize the impact of the dwelling on the distribution grid (peak load reduction) and to maximize the self-consumption of locally produced electricity by means of PV system, a market-based multi-agent system (Intelligator) is applied to the heat pump. Intelligator is previously used for different applications [19, 20]. The power consumption is shifted to moments with a low electricity consumption of the other loads and/or moments with PV production (see Fig. 3). In this control system, all devices are represented by a consumer or producer agent. These agents exchange bid functions with the market. A bid function represents the desired supply and demand powers in function of the willingness (priority) to produce or consume. The bid functions are aggregated in an aggregator agent, which then sends back allocations to each agent. This process is illustrated in Fig. 4. d. Building electricity consumption Fig. 5 shows the power consumption profile of a measured four-person Belgian household [16]. The total annual electricity use amounts to 3973 kwh (76.4 kwh for the considered week), which is in line with an average electricity consumption of 3500 kwh for an average Flemish family [21]. Note that this profile does not include the power demand of the heat pump (see Fig. 3). e. Photovoltaic system A PV system covers the yearly electricity consumption. Using meteorological data, the PV power output is simulated in TRNY and scaled to cover the annual consumption of 6673 kwh. uring the investigated week, the PV system produces kwh (see Fig. 6), corresponding to a PV system of 7.7 kw p.

7 Fig. 3 Power consumption profiles for the heat driven (left) and market-based controlled heat pump (right) for the considered average winter week. For the coordinated case, the power consumption is shifted to moments with a low electricity consumption of the other loads and/or moments with PV production. Fig. 4 Overview of the market-based multi-agent system. The aggregator agent collects the agent bid functions and allocates power to device agents based on their respective bid functions [16].

8 Fig. 5 Measured household power consumption profile for a four-person Belgian household, excluding the heat pump power demand, for the considered average winter week [16]. Fig. 6 PV production profile of a 7.7 kw p PV system for the considered average winter week [16]. 4. Results In this section, the results on the interaction between thermal and electrical energy flows will be discussed with the preceding factors. In Table 1, the thermal cover factors are shown with the no control and smart control scenario. ince the thermal comfort in the building is guaranteed, the self-generation is 100 % in both cases, i.e. the required heating demand is produced by the heat pump. However, the smart control has an impact on the self-consumption, lowering the latter. When the heat pump is enabled more often, this results in higher thermal losses in the heating circuit and possible higher average temperatures in the buffers lead to higher storage losses. In the no control case the heat pump is only enabled when one of the buffers is empty. As a consequence, more heat has to be generated by the heat pump in the smart case, resulting in a decreased self-consumption. Vanhoudt et al. calculated an increase in both electrical selfconsumption and self-generation [16], proving the effectiveness of the market-based multi-agent control to maximize the self-consumption of the locally produced PV power. The PV system is sized to produce the same amount of electricity as is consumed per year in the no control case. Therefore, the building reaches

9 the nzeb level during one year ( = 1). For the considered week, is equal to 0.705, while in the smart case, decreases to This means less of the electricity demand could be covered by locally produced electricity, so on a yearly basis the total local production will be lower than the total consumption; with smart control the building can no longer be classified as nzeb. This is the result of the increased power consumption of the heat pump [16]. Table 1. Thermal cover factors for the uncontrolled and smart controlled heat pump. No control mart control elf-consumption, [%] Q elf-generation, [%] Q Attaining the nzeb level for a building with a smart controlled heat pump would therefore require a larger PV system. In turn, the selfconsumption increase can be lower, which will result in a higher grid impact since more electricity will be injected in the grid with this new PV system sizing. o, it is clear that maximizing the electrical cover factors, by applying a certain control strategy, will have an impact on the thermal cover factor and vice versa. It is for that reason that the thermal cover factors are introduced in this paper to easily assess the interaction between electrical and thermal energy flows. 5. Conclusions In this paper, electrical and thermal cover factors are respectively redefined and defined. Cover factors are used to quantify the mismatch or non-simultaneity between local production and consumption of a power vector. The redefinition of the electrical cover factors includes the losses during the storage of electricity and other inherent losses. Thereafter, similar thermal cover factors are defined. This allows the assessment of the selfconsumption and self-generation for locally generated heat by means of e.g. heat pumps, as well as the interaction between electrical and thermal energy flows in or between. emand side management strategies are often mentioned to increase the self-consumption of electricity produced locally by means of photovoltaic (PV) systems and thus to limit injections of electricity into the electrical distribution grid. However, maximizing the electrical cover factors may impact the thermal cover factors due to e.g. higher storage losses. And vice

10 versa these higher losses may require a larger PV system to attain e.g. a netzero energy building, which may increase the grid impact. To illustrate this interaction between electrical and thermal energy flows, a small case study has been investigated. A residential building is used, which is equipped with a PV system and heat pump. The impact on the interaction of the electrical and thermal cover factors of a market-based multi-agent control system on the heat pump is assessed. The multi-agent system of the heat pump is applied to maximize the self-consumption of PV power and to limit the grid peak power. The results showed that by increasing this self-consumption, the thermal self-consumption decreased; more heat had to be generated because of the higher thermal losses. Acknowledgment J. Van Roy is funded by a VITO doctoral scholarship. KU Leuven and VITO are jointly collaborating in the EnergyVille initiative. References [1] International Energy Agency. Energy Technology Perspectives 2012: Pathways to a Clean Energy ystem. June [2] The European Parliament. irective 2010/31/EC of the European Parliament and the Council on 19 May 2010 on the energy performance of (recast). [3]. Pless and P. Torcellini. Net-Zero Energy Buildings: A Classification ystem Based on Renewable Energy upply Options. National Renewable Energy Laboratory. Report NREL/TP , June [4]. Crawley,. Pless and P. Torcellini. Getting to Net Zero. AHRAE J., eptember [5] A. Marszal, P. Heiselberg, J.. Bourrelle, E. Musall, K. Voss, I. artori and A. Napolitano. Zero Energy Building: A review of definitions and calculation methodologies. Energy and Buildings. Vol. 43 (4), April 2011, pp [6] I. artori, A. Napolitano, and K. Voss. Net zero energy : A consistent definition framework. Energy and Buildings. Vol. 48, May 2012, pp [7]. ix, J. esmedt,. Van Houdt, and J. Van Bael. Exploring the flexibility potential of residential heat pumps combined with thermal storage for smart grids. In: 21st International Conference on Electricity istribution, Frankfurt, Germany, 6-9 June [8] K. Clement, E. Haesen, and J. riesen. The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential istribution Grid. IEEE Transactions on Power ystems. Vol. 25 (1), February 2010, pp [9] J. Van Roy,. e Breucker, and J. riesen. Analysis of the Optimal Battery izing for Plug-in Hybrid and Battery Electric Vehicles on the Power Consumption and V2G Availability. In: 16th International Conference on Intelligent ystem Applications to Power ystems, Hersonnissos (Crete), Greece, eptember [10] R. Baetens, R. e Coninck, J. Van Roy, B. Verbruggen, J. riesen, L. Helsen, and. aelens. Assessing Electrical Bottlenecks at Feeder Level for Residential Net Zero-Energy Buildings by Integrated ystem imulation. Applied Energy. Vol. 96, August 2012, pp [11] A. Woyte, T. Vu Van, R. Belmans, and J. Nijs. Voltage Fluctuations on istribution Level Introduced by Photovoltaic ystems. IEEE Transactions on Energy Conversion. Vol. 21 (1), March 2006, pp [12] J. alom, J. Widén, J. Candanedo, I. artori, K. Voss, and A. Marszal. Understanding Net Zero Energy Buildings: Evaluation of Load Matching and Grid Interaction Indicators. In: Proceedings of Building imulation 2011, ydney, Australia, November 2011.

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