Design Optimization of Hybrid Renewable Energy Systems for Sustainable Building Development based on Energy-Hub

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10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Design Optimization of Hybrid Renewable Energy Systems for Sustainable The 15th International on District Heating and Cooling Design Optimization of HybridSymposium Renewable Energy Systems for Sustainable Building Development based on Energy-Hub Building Development based on Energy-Hub

Assessing theShuangjun feasibility of using the heat demand-outdoor Xu, Chengchu Yan*, Chen Jin Shuangjun Chengchu Yan*, Chenheat Jin demand forecast temperature function for Xu, a long-term district College of Urban Construction, Nanjing Tech University, No.200, North Zhongshan Road, Nanjing, 210009, China College of Urban Construction, Nanjing Tech University, No.200, North Zhongshan Road, Nanjing, 210009, China

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract Building energy systems are gradually changing from a single form of conventional energies to a multi-energy system that Building energy multiple systems renewable are gradually changing from singlereliability form of and conventional energies to a multi-energy system usually includes energy sources. Thea poor controllability of renewable energies are likelythat to usually includes multiple sources. poor reliability andenergy controllability ofofrenewable likely to cause mismatches betweenrenewable supply andenergy demand, whichThe significantly affect the efficiency the wholeenergies building.are This paper Abstract cause mismatches between supply and which significantly the energy energy systems efficiency of the whole paper proposes a systematic methodology fordemand, the design optimization of affect building integrated by abuilding. multiple This renewable proposes a systematic methodology for the design optimization systems of building energy systems by a multiple renewable energy source. A unified mathematical model of multi-energy is established basedintegrated on the concept of energy-hub, in District heating networks area commonly addressed in the literature as isone of the most effective for decreasing the energy source. unified mathematical model oftomulti-energy systems established on the concept energy-hub, in which the core A idea is to use matrix approach standardize the unified modeling ofbased a variety ofsolutions building of energy processes greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat which the energy core idea is to useenergy a matrix approachand to energy standardize the The unified modeling of a variety building processes including generation, utilization storage. operating performance of of system withenergy different energy sales. Due toand the changed climateutilization conditions andenergy building renovation policies, heat demand in the future couldconsisting decrease, including energy generation, energy storage. The operating performance of system withmainly different energy sources mixes system configurations can beand effectively predicted. Lifecycle total cost of the entire system, prolonging investment return period. sources and system configurations be effectively predicted. Lifecyclethe total of thescenarios entire system, mainly consisting of initialmixes costthe and operating cost, is used can as the objective function to identify bestcost design for sustainable building The maincost scope ofoperating this paper toisassess the feasibility offunction using demand –best outdoor function heatbuilding demand of initial and cost, used as toheat identify the design scenarios for sustainable development. A case study isisconducted onthe theobjective application of the the proposed method for temperature optimal design of a for typical hybrid forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 development. A case study is conducted on the application of the proposed method for optimal design of a typical hybrid renewable energy system in Beijing. buildings energy that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renewable system in Beijing. renovation© scenarios wereLtd. developed (shallow, Copyright 2018 Elsevier All rights reserved.intermediate, deep). To estimate the error, obtained heat demand values were © 2019 The Authors. Published by Elsevier Ltd. compared with results from a dynamic heat demand model, previously developed and validated by the authors. Copyright © 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the 10th International Conference on Applied This isresults an open accessthat article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) The showed when only weather change is considered, the margin of error could be acceptable for someon applications th International Conference Applied Selection and peer-review under responsibility of the scientific committee of the 10 Energy (ICAE2018). Peer-review responsibility of lower the scientific committee of ICAE2018 – Theconsidered). 10th International Conference on Appliedrenovation Energy. (the error inunder annual demand was than 20% for all weather scenarios However, after introducing Energy (ICAE2018). scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: : Renewable energy systems; Energy-hub; Lifecycle cost; Optimal design The value of slope energy coefficient increased on average the range Keywords: : Renewable systems; Energy-hub; Lifecyclewithin cost; Optimal designof 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-025-83239533; fax: +86-025-83239964. Cooling. E-mail address: [email protected] * Corresponding author. Tel.: +86-025-83239533; fax: +86-025-83239964. E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied (ICAE2018). SelectionEnergy and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.246

Shuangjun Xu et al. / Energy Procedia 158 (2019) 1015–1020 Author name / Energy Procedia 00 (2018) 000–000

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Nomenclature A ck cv COP di,j Dcon Dpro Ein Eout f Isol m /m’ n Pdem Pin Pload Psup yk

sun catching area, m2 specific cost of the hub component K, €/kW or €/m2 specific cost of the energy-ware n, €/kWh coefficient of performance of the converter coupling factor energy-use coupling matrix energy-production coupling matrix vector of the hub energy input vector of the hub energy output function solar radiation, kWh/m2 number of loads number of energy-wares/energy sources system energy demand, kW building energy input, kW building energy-use system building load, kW building energy-production system energy output, kW life time of the hub component K, y

ε η

factor ε efficiency of converter

Subscripts CD cooling device E electricity ED electric drive dehumidifier HP heat pump PV photovoltaic SC solar collector WH water heater Superscripts c cooling energy e electricity hw hot water energy nc natural cold energy wl wet load

1. Introduction Building energy systems are gradually changing from a single form of conventional energies to a multi-energy system that usually includes multiple renewable energy sources. Generally, the reliability and controllability of such renewable energies are usually not as good as that of conventional energies, which usually cause mismatches between supply side and demand side, and then affect the energy efficiency of the whole building. In order to solve these problems, extensive studies have been conducted in literatures. For instance, researchers have proposed concepts such as energy hub [1], smart energy system [2], and integrated energy system [3] to coordinated operation of multiple renewable energy sources. A simulation model for an energy hub consisting of natural gas, wind energy and photovoltaic solar energy has been established by Sharif et al. to simulate and replace coal-fired power plants for power generation [4]. Chicco [5] proposed an input-output matrix method for describing the energy flow between small triple plants and external energy networks. Mancarella [6] reviewed the current status of research on multi-energy system models and the evaluation methods for distributed renewable energy in buildings. Deshmukh et al. introduced a mathematical modelling method for multiple hybrid renewable energy systems, focusing mainly on the performance of renewable energy generation systems [7]. Although significant achievements have been obtained in previous studies, the problems of energy imbalance and low efficiency are still a big challenge for building multi-energy systems, particularly when the system consisting of hybrid renewable energy resources. This paper therefore proposes an optimal design method for multi-energy systems, which addresses to minimize the lifecycle total cost and energy mismatching between the supply side and demand side of the integrated building energy systems. 2. Hybrid renewable energy systems modelling In order to include different types of energy into the optimization scope during the conceptual design phase, it is necessary to establish a unified energy model for describing the energy flow of multi-energy systems in buildings. In this paper, the concept of “Energy Hub” is introduced to uniformly describe the energy coupling relationship between multiple renewable energy sources and loads [8]. The energy flow and conversion relationship of all types of energies and systems within a building is established through a universal matrix mathematical model. A building multi-energy system is decoupled into three sub-systems, i.e., energy generation systems, energy utilization systems and energy storage systems.



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2.1. Energy generation systems The model of energy generation system is used to describe the relationship between energy supplies and energy inputs. The energy system of “micro-energy building”, which will be introduced with more details as a case study in section 4, is used as an example for system modelling. The system consists of n types of energy inputs, including solar energy, wind energy, and natural cold energy (or seasonal cold storage) as well as electricity and gas. Through energy conversion by energy generation equipment, the system can provide m different forms of energy supplies to the building, including electricity, thermal energy, cold energy and other forms of energy. Using the matrix modelling concept, the relationship between energy inputs (energy-ware) and supplies can be T expressed in an m×n matrix of Psup  Dpro Pin , as shown in Eq.1. Where, Pin   Pin1 , Pin2 , Pin3 , , Pinn  and T 1 2 3 m  represent energy input and energy supply matrices respectively. Dpro represents the Psup   Psup , Psup , Psup , , Psup coupling matrix of generation systems, which defines the mathematical mapping from the input to the output. It is worth noting that the generation coupling matrix is generally irreversible, whose physical meaning is that the exact same output can be obtained through multiple sets of different inputs, which also reflects the fact that the generation system can be optimized. 1  Psup  1,11,1  2,12,1  3,13,1  2   0  3,23,2 P  sup   0 3   Psup 0 0  0        Pm   0 0 0  sup  

0

0

0

 4,34,3  i , ji , j 0

0

 0  0   0   n,mn,m 

 Pin1   2  Pin   Pin3   4  Pin       Pinn 

(1)

Each basic element di, j of the generation coupling matrix is called a coupling factor, which is determined by the energy distribution coefficient εi, j and the generation efficiency ηi, j as di, j =εi, j ·ηi, j. The energy distribution coefficient ε is used to describe how much (i.e., the ratio) of an energy-ware is used to fed one or more energy supplies. The value 1 is used in the case where an energy-ware only provides one supply, while any value between 0 and 1 is used in the case where an energy-ware provides more than one supplies [8]. Therefore, determining the proper value of ε is one major goal of the optimization of the energy generation system. 2.2. Energy utilization systems The model of the energy utilization system is used to describe of the relationship between energy demands and 1 2 3 1 2 3 m' T m T  and Pdem   Pdem  represent , Pload , Pload , , Pload , Pdem , Pdem , , Pdem building loads, as expressed in Eq.2. Where Pload   Pload building load and energy demand respectively. Dcon is called as the coupling matrix of utilization system, which defines the mathematical mapping from building loads to building demands.  1,1  COP 1,1   1  Pdem   0  2    Pdem   3   Pdem  0        Pm    dem      0 

0 0 0

1,3 COP1,3 0

0

 2,4 COP2,4

 3,3

 3,4

COP3,3

COP3,4

 i, j COPi , j 0

0

0

    0    0        m ,m '   COPm,m '  0

0

1  Pload   2  P  load  3   Pload  4   Pload      m'  Pload 

(2)

Each coupling factor di, j is determined by the load distribution coefficient εi, j and the energy efficiency COPi, j, i.e., di, j =εi, j / COPi, j. The load distribution coefficient ε is used to handle a situation where one load may be undertaken by multiple utilization equipment. Once building loads and ε are given, the corresponding energy demands can be calculated by Eq. (2).

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2.3. Energy storage systems Achieving energy balance between supply side and demand side is actually required by all energy systems. However, considering the intermittent nature of renewable energy sources, it is very difficult to achieve balances at all times. In this paper, a certain degree of energy imbalance ΔP during a certain period is allowed between demand side and supply side. The definition of ΔP is given as follows. 1 1 1 1 P1  Psup  Pdem  Psup  Pdem      2  P2  P2   2  P2 P   sup  Pdem   sup dem  3  3   3   3 P  P 3   Psup   Pdem   Psup  Pdem             P m   m  P m   m m  Psup   dem  Psup  Pdem  

(3)

By using the concept of energy imbalance, the entire building energy system can be decoupled into three subsystems, i.e., energy generation systems, energy utilization systems and energy storage systems. The most important parameters of an energy storage system include the storage capacity, the charging and discharging rate to and from the system. The storage capacity is determined by the cumulative energy imbalance Σ (ΔP) over a period of time (e.g. one year). The charging and discharging rate are determined by the maximum (i.e., Max (ΔP)) and minimum (i.e., Min (ΔP)) power imbalances, respectively. 3. Lifecycle total cost optimization The lifecycle total cost of the entire building energy system is used as the objective function to optimize system design, which mainly consists of the operational cost and initial capital cost [8], as shown in Eq. (4). Where, the subscripts “heat” and “cool” represent the heating and cooling season respectively.



 c k max  Pk ,heat , Pk ,cool  f   cv  Einv ,heat  Einv ,cool       yk K 

   

(4)

4. Application to a case study The proposed design optimization method is implemented in a “micro-energy building” in Beijing (cold climate). The energy system of this building is a typical hybrid renewable energy system, which mainly consists of a PV and wind turbine power generation system, a solar water heating system, a ground source heat pump system and a seasonal storage system (i.e., using the stored winter cold energy in the form of ice for cooling in summer) [9]. The corresponding energy-wares including solar energy, wind energy, geothermal energy and nature cold energy are feed into this hybrid energy system as energy inputs to provide electricity, heat and cold energy as energy supplies. Such supplied are used to undertake the electric load (lighting, etc.), heating load (space heating, dehumidifying heat and domestic water, etc.) and cooling load (space cooling, etc.), respectively. The involved energy flows through each subsystem are shown in Fig.1. The annual or seasonal average energy efficiency of main energy conversion equipment/devices are summarized in Table 1. The daily building cooling and heating load profiles are shown in Fig. 2. Since the air-conditioning system adopts an independent temperature and humidity control method, the space cooling load is only attributed by sensible load while the latent load is handled by a liquid desiccant air-conditioning system. Three types of heating load are involved in this building, which are the load for space heating in winter, dehumidifying heat (as the heat source of liquid desiccant air-conditioning system) in summer and domestic hot water throughout the year. In addition, the basic building electricity demand of other electricity consumers rather than air-conditioning equipment (e.g., pumps and heat pump) is calculated as 2500kWh per month.

Shuangjun Xu et al. / Energy Procedia 158 (2019) 1015–1020 Author name / Energy Procedia 00 (2018) 000–000

Geothermal energy

Natural cold energy

collector

Heat pump Seasonal cold storage

Space heating Dehumidify Domestic water ……

Thermal storage Cold energy

Cooling load Load 1 Load 2 ……

Cold storage

0 -500

-1000 -1500

Date

Fig. 1. Schematic of the hybrid renewable energy system

12/1

Solar energy

11/1

Heat energy

500

10/1

Heating load

1000

9/1

PV panel

8/1

……

7/1

Appliances

Battery

Space heating Domestic hot water

6/1

Lighting，

Space cooling Dehumidifying heat

5/1

wind turbine

1500

1019 5

4/1

Electric load

3/1

Electric energy

Wind energy

Building loads

Electric energy

2/1

Building energy systems

Daily cooling/heating demand（kWh）

Energy resources

1/1



Fig. 2. Annual heating and cooling load of the building

Table 1. Average efficiency of energy conversion devices Energy efficiency

CD

Mean value

Energy efficiency

Mean value

0.88

wl (heat)  ED

5

5.75

6.5

0.69

COP

4



3

wl (cool)  ED  PV E

 SC (heat)

0.67

 SC (cool)

c COPHP hw HP

hw WH

0.14 1

10,000 9,000 8,000

Cost (€)

7,000

6,000

Above: Operating cost Below : Initial cost Left : Original design Right: Optimum design

5,000 4,000

3,000 2,000 1,000 0

Cooling system

Heating system

Electric system

Entire system

Fig. 3. Annualized lifecycle total cost of the original and optimum energy system

The actual building energy system, which was originally designed based on conventional design methods (donated as original design), has been put into operation since 2008. In order to validate the effectiveness of the proposed method, we re-design the entire energy system using the design optimization method as described in section 2 and 3. In this study, three main design parameters including the area of solar collector, the size of PV panel and the volume of seasonal cold storage are optimized simultaneous. The energy supplies and energy demands are calculated in Eq.1 and Eq.2 respectively, once the size of energy generation systems, the building loads and the

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Shuangjun Xu et al. / Energy Procedia 158 (2019) 1015–1020 Author name / Energy Procedia 00 (2018) 000–000

energy efficiencies are given. The annual operational cost and annualized initial investment cost of the three subsystem (i.e., cooling system, heating system and electric system) and the entire energy system under a large number of candidate design combinations are then calculated. Table 2. Key parameters of the original and optimum building energy system Area of solar

Area of

Volume of seasonal

collector (m2)

PV(m2)

cold storage system (m3)

Original design

200

69

450

Optimum design

170

220

300

The design combination with the minimum lifecycle total cost of the entire energy system is selected as the optimum design. The key parameters of the original and optimum building energy system are shown in Table 2. The annualized lifecycle total cost, operational cost and initial cost of the optimized systems are compared with that of original design, as shown in Fig.3. As can be observed, the annualized lifecycle total cost of the optimized building energy system is about 14.9% lower than that of the original building energy system. The total cost saving is mainly contributed by the electrical system, which reduces 49.6% of the sum of annual operational cost and annualized initial cost, compared to the original electrical system. 5. Conclusion In order to solve the problems of energy imbalance and low energy efficiency of hybrid renewable energy system, this paper proposes an optimal design method for building multi-energy systems. A unified energy model based on matrix modeling is established for describing the energy flow of multi-energy systems, including the energy generation, energy utilization and energy storage. The building energy system integrated by hybrid renewable energy systems can be optimized by minimizing the lifecycle total cost of the entire system. The proposed energy modelling and design optimization method have been applied in a typical hybrid renewable energy system in Beijing. The design parameters of three major sub-systems including the solar water heating system, the PV power generation system and seasonal cold storage are optimized. Compared with the original building energy system, the lifecycle total cost of the optimized system is reduced by 14.9%. Acknowledgements The research presented in this paper is financially supported by a grant of the National Natural Science Foundation of China (51708287) and a grant of the Natural Science Foundation of Jiangsu Province (BK20171003). References [1] Geidl M，Koeppel G，Favre-Perrod P, et al. Energy hubs for the future．IEEE Power and Energy Magazine, 2007, 5(1):24-30． [2] Mathiesen B V，Lund H，Connolly D, et al．Smart energy systems for coherent 100% renewable energy and transport solutions. Applied Energy, 2015, 145: 139-154. [3] Lund H, Munster E. Integrated energy systems and local energy markets. Energy Policy, 2006 34(10) :1152-1160. [4] Sharif, A., et al., Design of an energy hub based on natural gas and renewable energy sources. International Journal of Energy Research, 2014. 38(3): p. 363-373. [5] Chicco, G. and P. Mancarella, Matrix modelling of small-scale trigeneration systems and application to operational optimization. Energy, 2009. 34(3): p. 261-273. [6] Mancarella, P., MES (multi-energy systems): An overview of concepts and evaluation models. Energy, 2014. 65(2): p. 1-17. [7] Deshmukh, M.K. and S.S. Deshmukh, Modeling of hybrid renewable energy systems. Renewable & Sustainable Energy Reviews, 2008. 12(1): p. 235-249. [8] Fabrizio, E., V. Corrado and M. Filippi, A model to design and optimize multi-energy systems in buildings at the design concept stage. Renewable Energy, 2010. 35(3): p. 644-655. [9] Chengchu Yan, Wenxing Shi, Xianting Li, Shengwei Wang. (2016). A seasonal cold storage system based on separate type heat pipe for sustainable building cooling. Renewable Energy, 85, 880-889.