Dynamic modeling of thermal and electrical microgrid of multiapartment in different European locations

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1 Dynamic modeling of thermal and electrical microgrid of multiapartment in different European locations Luca Cioccolanti a, *, Alessandro FONTI b, Gabriele COMODI b a Università Telematica e-campus, Via Isimbardi 1, Novedrate (CO), Italy, luca.cioccolanti@uniecampus.it b Università Politecnica delle Marche, Department of Industrial Engineering and Mathematical Sciences, Via Brecce Bianche 1, Ancona, Italy, a.fonti@univpm.it, g.comodi@univpm.it *Corresponding author: luca.cioccolanti@uniecampus.it Topic/s: Electrical Network integration of Stirling based energy systems Keywords: Stirling engine; micro combined heat and power; photovoltaic; electrical energy storage system; demand side management; DesignBuilder. ABSTRACT In this paper authors investigate the dynamic behaviour of a potential residential microgrid which includes four apartments, a natural gas fuelled m-chp Stirling engine, a 4 kwp photovoltaic plant, an electrical energy storage consisting of. kwhe batteries and a natural gas back-up boiler. The thermal and electrical microgrid performance are investigated for three different European locations, namely Brescia, Bruxelles and Oslo. In general, the analysis shows that coupling a m-chp Stirling engine with a PV plant allows to sensibly extend the selfsufficiency of the microgrid throughout the year. In addition, the use of an electrical energy storage device is of fundamental importance to extend the self-consumption of the electrical energy produced especially in warm and hot seasons. The storage system indeed allows to match the multi-apartment residential consumptions and the renewable production profile. In particular, the electrical energy collected by the PV system during the day-time is partially released during the night-time thus reducing the fossil fuel energy demand. Moreover, the electrical storage system allows to extend the profitability of the Stirling engine operation at nominal load thus increasing the m-chp efficiency and the self-sufficiency of the microgrid in cold climates. In general, the analysis proves that the developed model can be used as a useful design tool to assess the opportunity of employing alternative energy technologies and different management system strategies for small residential users. 1. INTRODUCTION The rising global energy demand and the realization that fossil fuels consumption entails serious environmental impact and consequent climate change effects have increased the interest on energy efficiency and renewable technologies to face this challenges. A renewable non-fossil fuelled heat and electricity supply is certainly part of the future implementation of a sustainable development. Despite the use of renewables even at small or domestic scale allows to reduce the environmental impact of energy consumption, it presents important drawbacks, such as the discontinuity and reliability of generation, due to the * Corresponding author. Luca Cioccolanti Copyright 216 by Northumbria University at Newcastle upon Tyne and ISEC International Stirling Engine Committee. All right reserved.

2 availability of most renewable energy resources, and the integration of distributed generation into existing grids. Also m-chp systems can significantly contribute to reduce the energy consumptions at domestic scale but their widespread adoption is still low. Several reasons contribute to this result such as the lack of an adequate regulatory framework or more often technical and economic reasons. The recent regulation on Near Zero Buildings (NZEB) [1] reinforced the role of distributed renewable generation to meet the important environmental targets set by EU. However, the higher investment cost of m-chps or renewables is justified only if their potential is completely exploited. Therefore, the widespread adoption of renewable energy technologies and m-chp systems requires complex design, planning and control optimization methods to provide a solution to several energy problems. Banos et al. [2] presented an extensive review of the optimization methods and algorithms applied to renewable and sustainable energy systems. In future sustainable energy systems district heating will also play a key role. Lim et al. [3] developed a numerical model for estimating heat flow and temperature variation in a complex energy network system using Simulink. The model was then validated and authors found that the amount of energy lost through the pipe network is not negligible when dealing with large pipe diameters and district networks. However, the present district heating systems should change into low-temperature district heating networks, interacting with low energy buildings and thus becoming a part of the so called smart energy systems [4]. In such system smart thermal and electrical grid are combined to identify common strategies in order to achieve an optimal solution. A comprehensive review of modelling approaches and associated software tools that address district-level energy systems has been provided by Allegrini et al.[]. Shabanpour-Haghighi and Seifi [6] studied an integrated approach to optimize electrical, natural gas, and district heating networks taking into account several interdependencies between these infrastructures. In particular, authors made use of the modified teaching learning based optimization algorithm to solve the multi-period optimal power flow problem of multi-carrier energy networks. Indeed, the widespread use of decentralised multi-energy generation technologies such as boilers, Combined Heat and Power systems (CHP), heat pumps and so forth increases the linkages between the different distribution networks. For this reason Liu and Mancarella [7] developed a multi-temporal simulation model in MATLAB- Excel VBA, which was tested in a one day case study application of a real district at the University of Manchester. According to the U.S. Department of Microgrid Exchange Group, a microgrid is a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. Besides the advantages above, as locally controlled systems microgrids allow to: (i) make wise balanced choices, such as between investments in efficiency and supply technologies; and (ii) avoid major new investments that are needed to integrate emerging decentralized resources. In [8] some of the authors investigated the performance of a real life residential multiapartment microgrid also by accounting different operating conditions and energy management policies. They found that higher the integration level of electrical and thermal storage, higher the degree of self-sufficiency. However, the high investment cost of the energy storage system made them not really profitable at the current price conditions. In this work, a numerical model is developed for a thermal and electrical microgrid serving multi-apartment. The main focus is to investigate and compare the considered energy vectors consumption and the technologies operating hours in different European location. In particular, the city of Brescia, Bruxelles and Oslo have been considered for reference. * Corresponding author. Luca Cioccolanti Copyright 216 by Northumbria University at Newcastle upon Tyne and ISEC International Stirling Engine Committee. All right reserved.

3 Therefore, the paper is organized as follows: Section 2 presents the methodology of the work while Section 3 describes the dynamic modeling of the thermal and electrical microgrid. Section 4 reports the results of the examined scenarios, Section the main discussions of the analysis and Section 6 draws the conclusions of the work. 2. METHODOLOGY In general, a proper numerical tool offers the opportunity to support the design of an energy system whose configuration can be varied to suit the user s needs through a more costeffective manner. The comprehensive understanding of the performance of a microgrid requires complex analytical models which operate according to multi-objective optimization criteria. Systems configuration and their management strategies indeed depend on several parameters related both to techno-economic reasons and environmental constraints. However, in this work the economic aspects and the environmental constraints have been neglected and the performance of a residential microgrid consisting of four apartments have been analysed according to energy optimization criteria only. The objective of this work indeed is to investigate the dynamic behaviour of the residential microgrid in different European locations, namely Brescia, Bruxelles and Oslo, in order to evaluate the energy vectors consumptions and technologies operating hours with climate. As regards the microgrid under analysis, the thermal energy demand and the thermal plant model have been developed using DesignBuilder [9]. It is a state-of-the-art software tool for checking building environmental performance such as energy, comfort and cost performance. However, it does not allow to model the electrical energy demand of a building. For this reason, the electrical building load has been designed in Matlab [1]. Despite the use of two different software tools this modeling analysis still presents high accuracy and precision since the thermal and the electrical models do not depend on each other. Finally, the results obtained are then compared in order to understand the feasibility of the distributed generation and the changing management strategy requested with different climate in Europe. At the moment, the proposed numerical model is under development. Thanks to the use of proper software packages, DesignBuilder could directly interact with Matlab thus creating a unique software tool. The ultimate goal of this research is indeed to provide a comprehensive energy control mechanism of an entire building complex that can be applicable in different situations. 3. THE DYNAMIC MODELING OF THERMAL AND ELECTRICAL MICROGRID In this paragraph the main components of the simulator are described. The thermal model of the building is defined by means of DesignBuilder [9]. DesignBuilder is the most established and advanced user interface to Plus which is an extremely powerful simulation engine considered as the industry standard Building Simulation tool. In DesignBuilder the indoor temperature, air humidity and energy use for heating and cooling purposes of a multi-zone building can be simulated using a multi-zonal approach. The apartments differ slightly from each other for their energy consumptions while they have the same size and orientation as depicted in Figure 1 for a single apartment: * Corresponding author. Luca Cioccolanti Copyright 216 by Northumbria University at Newcastle upon Tyne and ISEC International Stirling Engine Committee. All right reserved.

4 Figure 1. apartments orientation The model description of a building is usually very complex and it takes into account the main parameters of the building and the weather conditions. Hence, the operation of the different energy systems changes during the time to assure a proper thermal comfort indoor according to Fanger theory [11]. As regards the thermal plant, it consists of a natural gas fuelled m-chp Stirling engine and a back-up boiler. On the contrary, solar thermal technologies and thermal energy storage devices have been not included in the model since the hot water demand has been neglected. As previously mentioned, the electric building load has been designed in Matlab considering a stochastic distribution of the energy consumptions related to the main electric loads. In this paper, the air conditioning has been neglected and the electric building load depends on lighting, standby and most common household equipment [12] as reported in Table 1. Table 1. electric building load Equipment Maximum Power hours/day Dishwasher 2.1 kwe <1 h Fridge 13 We.2h Lighting ~ W 24 h Occasional Load 1 kwe. h Oven 1.1 kwe <1 h PC 12 W 1 h Standby 2 W 24 h TV 1 W 1 h Washing Machine 1.6 kwe <1 h For what concerns the electric energy supply, in addition to the 3 kwe m-chp a 4 kwp photovoltaic plant and a. kwhe electric energy storage system have been considered. Moreover, the building is connected to the grid in order to purchase or sell the excess electricity back at any time depending on users need. Table 2 summarizes the main features of the considered energy supply technologies while Figure 2 shows a schematic illustration of the energy vectors of the building: * Corresponding author. Luca Cioccolanti Copyright 216 by Northumbria University at Newcastle upon Tyne and ISEC International Stirling Engine Committee. All right reserved.

5 Table 2. the main features of the energy supply technologies Back-up boiler m-chp Stirling engine Photovoltaics Electrical energy storage device Electrical Power 3 kwe 4 kwe Thermal Power 9% 1 kwt Electrical Efficiency 1% 9% 9% * Thermal Efficiency 7% Minimum Thermal Power 6% Capacity kwh Minimum Discharge Threshold 2% * During both the charge and the discharge process Figure 2. a schematic illustration of the energy vectors of the building In summary, two different software tools have been used to model the thermal and the electrical microgrid of the four apartments. However, since heat pumps are not taken into account for heating or cooling purposes the thermal and electrical grids do not depend on each other. In general, the whole simulator is able to evaluate the performance of the electrical and thermal plant models and the performance unit of the building. 4. RESULTS The dynamic response of the thermal and electrical microgrid of the considered multiapartment has been analysed in the three different European locations. In particular, a hourly time interval operation has been considered in this analysis and the energy vectors consumptions and technologies operating hours extensively evaluated. The results reported in Figures 3a-c show the simulated electrical and thermal power demand for a whole year: * Corresponding author. Luca Cioccolanti Copyright 216 by Northumbria University at Newcastle upon Tyne and ISEC International Stirling Engine Committee. All right reserved.

6 Power [kw] Power [kw] Multi-apartment energy demand in Brescia Thermal Electrical 1 4 Multi-apartment energy demand in Bruxelles Thermal Electrical 1

7 Power [kw] Multi-apartment energy demand in Oslo Thermal Electrical 1 1 Figure 3a-c. the simulated electric and thermal power demand for the three locations As clearly shown, the thermal energy demand depends on the considered location and varies with seasonality while the electrical energy demand is almost constant independently from the location. As regards the energy supply technologies, the m-chp Stirling engine operates whenever the thermal power demand of the multi-apartment microgrid is 9 kwt. If the thermal energy requested by the users is < 9 kwt or exceeds 1 kwt, which corresponds to the maximum thermal power generated by the considered m-chp unit, the back-up boiler starts its operation. The PV production depends on weather conditions while the electrical storage system operates down to a minimum discharge level of 1 kwh. Figures 4a-c report the electrical energy self-production from the m-chp Stirling engine, the PV plant and the electrical storage device with respect to the electrical energy demand for the different locations:

8 [kwh] [kwh] Electrical Demand and Production in Brescia Electrical Demand Stirling Engine PV Electrical Storage System 6 Electrical Demand and Production in Bruxelles Electrical Demand Stirling Engine PV Electrical Storage System

9 [kwh] Electrical Demand and Production in Oslo Figure 4a-c. the electrical energy demand and production in the different EU locations Due to the higher thermal power request, the operating hours of the m-chp unit in Oslo are sensibly higher than in the other locations. Therefore, also the electrical energy production from the m-chp Stirling engine is higher in Oslo. On the contrary, the electrical energy production by PV is higher in Brescia due to the higher solar irradiance compared to the other locations. Moreover, since the PV electrical energy production is higher in summer when the m-chp is shut off this contributes to substantially extend the self-sufficiency of the microgrid during the whole year. Table 3 reports the annual energy balance for the three different locations: Table 3. the annual energy balance Brescia Bruxelles Oslo Thermal Demand 3232 kwht 3738 kwht 7939 kwht Electrical Demand 149 kwhe 149 kwhe 149 kwhe Thermal Production from SE * kwht 3683 kwht 33 kwht Thermal Production from boiler 1631 kwht 1697 kwht 263 kwht Electrical Production from SE * 7386 kwhe 7366 kwhe 16 kwhe Electrical Production from PV 3792 kwhe 3281 kwhe 3493 kwhe Excess Electrical to the grid 2186 kwhe 1914 kwhe 3713 kwhe Electrical from Batteries 979 kwhe 16 kwhe 917 kwhe * SE=Stirling Engine PV=PhotoVoltaic Electrical Demand Stirling Engine PV Electrical Storage System. DISCUSSION The simulation of the thermal and electrical microgrid in different European locations allows to appreciate the contribution of different distributed generation technologies to the energy supply of small residential users with climate.

10 In particular, independently from the locations a 3 kwe/1kwt m-chp Stirling engine proves to be adequate to satisfy more than 6% of the thermal energy demand of the considered multi-apartment. However, the m-chp operation is limited to cold seasons and it does not contribute to the self-sufficiency of the microgrid throughout the year. In order to extend the self-sufficiency of a microgrid also during the hot seasons, a m-chp unit can be coupled with renewables. As regards renewable technologies, photovoltaic is by far one of the most promising solutions since the electrical energy production from PV is maximized in summer season when m-chp is shut off. The simultaneous operation of a 3 kwe m-chp Stirling engine and a 4 kwp PV system usually exceeds the electrical energy demand of the users during the cold seasons. For this reason, the use of an electrical storage system is of fundamental importance to reduce grid interactions thus extending the self-sufficiency of the microgrid. As reported in Figures 4a-c the contribution of the electrical storage system mainly occurs during the winter season while it is limited in summer. However, the capacity of the batteries limited to kwh reduces the amount of the self-consumed electrical energy produced by the m-chp and PV units. In winter indeed part of the exceeding electrical energy is sold to the grid. On the contrary, during the hot seasons the electrical energy production from PV is usually lower than the electrical energy demand of the users and the additional electrical energy is purchased from the grid. With respect to the considered locations, in Oslo the microgrid has an electrical energy excess equal to 3713 kwhe which is sensibly higher than that of the other locations. Although the higher electrical energy production from PV both in Brescia and in Bruxelles, the electrical energy excess to the grid is indeed about 2 kwhe. Moreover, in Bruxelles the electrical energy from the batteries is about 16 kwhe thus proving the potential of an energy storage system to increase the self-sufficiency of a microgrid. 6. CONCLUSIONS In this work, the dynamic behaviour of a thermal and electrical microgrid for a residential user consisting of four apartments is analysed for different European locations. More precisely, a 3 kwe natural gas fuelled m-chp Stirling engine, a 4 kwp photovoltaic plant, a. kwhe electrical energy storage system and a natural gas back-up boiler have been included into the model. The thermal and the electrical microgrids have been modelled in DesignBuilder and Matlab respectively. Since the thermal and the electrical grids do not depend on each other the the use of two different software tools does not affect the simulation results. In general, the simulation analysis has allowed to assess the contribution of the different distributed generation technologies to the energy supply of the multi-apartment with climate. In terms of results, the simulation proves the interesting potential of a 3 kwe/1kwt m-chp Stirling engine to satisfy more than 6% of the thermal energy demand of the four apartments independently from the locations. With respect to the locations, the m-chp unit reaches the maximum annual operating hours in Oslo where is maximum also the electrical energy excess sold to the grid. Coupling the 3 kwe m-chp unit with a 4 kwp PV plant allows to sensibly extend the self-sufficiency of the microgrid also in hot seasons. Moreover, the use of an electrical energy storage device represents a fundamental element in the management of the residential electrical energy demand especially in warm and hot climate zones. Indeed the renewable solar electrical energy collected during the day-time is partially released during the night-time thus reducing the fossil fuel energy demand. Moreover, the electrical storage system allows to

11 extend the profitability of the Stirling engine operation at nominal load thus increasing the m- CHP efficiency and the self-sufficiency of the microgrid especially in cold climates. ACKNOWLEGEMENT The authors wish to thanks Eng. Sebastiano Tomasetti for his support. REFERENCES [1] Directive 21/31/EU of the European Parliament and of the Council of 19 May 21 on the energy performance of buildings, last visited on 1/6/216. [2] R. Banos, F. Manzano-Agugliaro, F.G. Montoya, C. Gil, A. Alcayde, J. Gómez, Optimization methods applied to renewable and sustainable energy: A review, Renewable and Sustainable Reviews, 211; (1): [3] S. Lim, S. Park, H. Chung, M. Kim, Y.J. Baik, S. Shin, Dynamic modeling of building heat network system using Simulink, Applied Thermal Engineering, 21; (84): [4] H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J.E. Thorsen, F. Hvelplund, B. Vad Mathiesen, 4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems,, 214; (68): [] J. Allegrini, K. Orehounig, G. Mavromatidis, F. Ruesch, V. Dorer, R. Evins, A review of modelling approaches and tools for the simulation of district-scale energy systems, Renewable and Sustainable Reviews, 21; (2): [6] A. Shabanpour-Haghighi, A.R. Seifi, Simultaneous integrated optimal energy flow of electricity, gas, and heat, Conversion and Management, 21; (11): [7] X. Liu, P. Mancarella, Modelling, assessment and Sankey diagrams of integrated electricity-heat-gas networks in multi-vector district energy systems, Applied, 21, In press. [8] G. Comodi, A. Giantomassi, M. Severini, S. Squartini, F. Ferracuti, A. Fonti, D. Nardi Cesarini, M. Morodo, F. Polonara, Multi-apartment residential microgrid with electrical and thermal storage devices: experimental analysis and simulation of energy management strategies, Applied, 21, (137): [9] DesignBuilder, last visited on 1/6/216. [1] Mathworks MATLAB, last visited on 1/6/216. [11] P.O. Fanger, Thermal Comfort: Analysis and applications in environmental engineering, McGraw-Hill, 197. [12] G. Comodi, L. Cioccolanti, M. Renzi, Modelling the household sector at the municipal scale: micro-chp, renewables and energy efficiency,, 214. (68):