Combining energy efficiency at source and at consumer to reach 4th generation district heating: Economic and system dynamics analysis

Accepted Manuscript Combining energy efficiency at source and at consumer to reach 4th generation district heating: economic and system dynamics analysis

Jelena Ziemele, Armands Gravelsins, Andra Blumberga, Dagnija Blumberga PII:

S0360-5442(17)30689-8

DOI:

10.1016/j.energy.2017.04.123

Reference:

EGY 10764

To appear in:

Energy

Received Date:

31 October 2016

Revised Date:

15 April 2017

Accepted Date:

23 April 2017

Please cite this article as: Jelena Ziemele, Armands Gravelsins, Andra Blumberga, Dagnija Blumberga, Combining energy efficiency at source and at consumer to reach 4th generation district heating: economic and system dynamics analysis, Energy (2017), doi: 10.1016/j.energy. 2017.04.123

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 

District heating transition to 4GDH system in Latvia is modelled



Economic and system dynamics analysis of transition existing DH system toward 4GDH



Balance between heat savings and heat supply for assessing economic feasibility



Applied policies accelerate transition to renewable energy and reduce CO2 emissions

ACCEPTED MANUSCRIPT 1 2 3

4th

Combining energy efficiency at source and at consumer to reach generation district heating: economic and system dynamics analysis Jelena Ziemele*, Armands Gravelsins, Andra Blumberga, Dagnija Blumberga

4 5

Institute of Energy Systems and Environment, Address: Riga Technical University, Azenes iela 12/1, LV-1048, Latvia

6

*Corresponding author, e-mail: [email protected]

7

Abstract

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

The article examines the transition from conventional district heating (DH) system to a 4th generation DH (4GDH) system using system dynamics modeling and economic feasibility analysis. Six alternative scenarios are studied. Energy saving measures reduce energy consumption, CO2 emissions and installed capacity of heating equipment thus facilitating the transition of the DH system towards zero emission system. Reduction of the required installed heating capacity allows implementing a low-temperature regime for heat distribution networks. For four of the analyzed scenarios this regime was achieved without increase of electricity consumption for heat supply. Results show that the implementation of 4GDH depends on the policies applied. The research identifies a balance point between the implementation of energy efficiency measures at the source and at heat consumers’ side. The article shows how the price of a fossil fuel influence the share of heat energy production and the balance point between investment at the source and heat consumers side.

23

1. Introduction

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Efficient use of energy in district heating (DH) is one of the main challenges in the European Union (EU). Tackling this challenge would have a positive impact on the security of energy supply, climate change mitigation and the country’s economic competitiveness. The EU Strategy on Heating and Cooling foresees transition of DH systems towards low carbon systems with an emphasis on building decarburization [1]. With 45% share the residential sector is the largest DH system user, followed by the industrial consumers (37%) and energy consumption by services (18%) [1]. Hence, the buildings have the highest energy-saving potential, which is closely connected with the required installed capacity of the heat source. During DH modernization the involved parties must coordinate partly conflicting goals of heat consumption and demand [2]. Modernization projects of DH include: integration of renewable energy sources [3], a shift to a low-temperature regime [4] and extension of networks [5], recovery of waste heat from industry [6] and other options. The heat energy price is a decisive factor for economic effectiveness of energy efficiency measures [7] but a fossil fuel price influences renewable heat generation technologies installation [8]. At the same time, the transformation of the whole DH system must be economically justified to preserve competitiveness [9]. Another important component is the institutional framework and regulation that needs to be adjusted in each country in accordance with the selected direction of development [10]. The modernization of DH system must coordinate the supply and demand sides with system’s flexibility by implementing Smart Energy System [11] and moving towards 4th generation DH (4GDH) [10]. Numerous software tools and approaches used for the planning of DH system are summarized by Allegrini et al. [12]. Residential energy

Keywords: district heating, 4GDH, system dynamics modelling, renewable energy, sustainable energy

1

ACCEPTED MANUSCRIPT 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

consumption may be determined using two approaches – top-down and bottom-up modeling [13]. When creating different development scenarios, the energy savings measures at end-users are divided in various levels from high specific heat consumption to passive buildings level [14]. At the same time energy savings measures must be consistent with the development scenarios at energy source [15]. Depending on the desired level of efficiency and the degree of integration of renewable energy technologies, various policy tools may be used [13]. Despite several available studies, the coherent modernization of DH system has achieved insufficient attention. The aim of the research is to examine the implementation of energy savings measures and evaluate their impact for the transition towards 4GDH system using system dynamic approach. Moreover, the goal of the present study is to access the economic feasibility of the energy savings measures by end-users and compare it with the necessary investments at the heat source while maintaining competitiveness of DH system. It is important to determine how to change the system's structure to the natural gas price variation. In addition, it was established, how policy instrument influences the transition to renewable energy sources.

62

2.1. District heating system in Latvia

63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85

DH system in Latvia supplies 67% of the population. Therefore, it is very well developed. The previous study by Lund et al. [16] justified a long-term strategy of development of the use of Latvian wood fuel in boiler houses and CHP, which is realized in DH development.. Currently, two fuels dominate in the DH system: natural gas -69.1% and biomass – 30.9%. Largest part of heat energy is produced in cogeneration process, for example, in 2014 it was 75%[17]. Systems operate with the 2nd and 3rd Generation temperature regime, respectively, 110/70 or 90/60 (supply/return). Now the development strategy for Latvian DH is based on the 4GDH concept.In Latvia, two stages of the DH network – the heat source and distribution networks – are owned by DH companies. Networks inside the buildings are property of the house owners. As a result, two sides rarely coordinate their activities regarding reconstruction or modernization. Therefore, the capacity of a newly renovated heat source may be too high after buildings’ renovation works. In addition, the system is further more complicated by the fact that the available co-financing from various funds is split between reconstruction measures at heat sources, distribution networks and heat consumers, as well, for each of these components the co-financing is available at different periods. Figure 1 compares the specific heat energy consumption for space heating for two cases – existing and required for the same European countries [18]. For Latvia the specific heat energy consumption for space heating is around 200 kWh/m2 which is significantly higher in comparison with Scandinavia where it accounts for 130 kWh/m2. In the new buildings in Latvia the specific heat consumption is around 80 kWh/m2. Low temperature heating systems are often installed in new buildings. To achieve the CO2 emission reduction goals, Energy development guidelines state a target of the specific heat energy consumption 150 kWh/m2 in 2020 [19].

2. Background information on case study

2

Specific heat energy consumption, kWh/m2

ACCEPTED MANUSCRIPT 250

200 200 150

80

100 50 0 AT

CZ

DK

EE

LV

LT

SE

Performance-based reguirements for new buildings

86

Specific heat energy consumption in existing buildings

87

Fig. 1. Specific heat energy consumption for space heating [18].

88 89 90 91 92 93 94 95 96 97

In Latvia for each DH company the heat tariffs have to be approved by Public Utility Commission of Latvia (the Regulator). In March 2016 the heat energy tariffs were in range from 43.47 €/MWh to 77 €/MWh. These tariff differences can be explained by numerous arguments – different technological solutions at heat sources, different efficiency level of heat production and distribution, different fuel sources etc. The fuel costs may vary even twice in case if natural gas boilers are used (about 35 €/MWh) or if biomass is used as fuel (about 15 €/MWh). Thus, the integration of renewable resources (especially, biomass) in DH system is intensively on-going in the recent years and the share of renewable resources has increased from 11.9% in 2010 to 30.9% in 2016. Other renewable energy sources such as solar collectors and heat pumps are poorly represented in the DH system in Latvia [19].

98 99 100 101 102 103 104

2.2. Case study object in Riga, Latvia The research is based on the case study of DH area in Riga. For heat production two natural gas boilers with capacity of 2.5 MW for base load are used; and additional 1 MW natural gas boiler is used to cover peak loads only. Heat from flue gas is recovered using flue gas economizer. Heat is distributed using 115 °C supply and 70 °C return water temperature (115/70 temperature regime). Heat source has two type of consumers – apartment building area and industrial area (see Fig. 2).

3

ACCEPTED MANUSCRIPT

k10 k-4, k-11, k-12

k9

Pipeline DN 200

k6

Pipeline DN 100

Boiler house

Pipeline DN 80

k7

Pipeline DN 50

k8

Pipeline DN 32

9

Industrial area 1 k1-2

Apartment buildings area

8

k-3

2

k-5

3

5 7

Fig. 2. Location of case study DH area.

107 108 109 110 111

Energy data from boiler house and buildings are used for the period 2012-2014. Thermal energy is used to heat buildings’ (18195 m2) and industrial area (18271 m2). Hot water load is covered throughout the year for buildings’ only. The average amount of heat produced during the summer period is 116 MWh/month and heating season – 900 MWh/month; 60 % of the produced heat in heating season is consumed in buildings and 40 % by industry.

Heat load, MW

105 106

4 3.5 3 2.5 2 1.5 1 0.5 0

Existing heat load

0

112 113 114 115 116 117 118 119 120

2,000

4,000

6,000

8,000

Hours, h

Fig. 3. Existing heat load for case study object. The usable capacity of the object 3.6 MW (see Fig.3) is 1.7 times less than the installed capacity of 6 MW. The difference between the installed and used capacity can be explained by the projected design of the DH area 20 years before, which were taken into account for development, but the area has not been developed accordingly. In addition, in recent years, both consumers groups began to take the first energy efficiency measures, which did not require large investments, what decreased the payment for heat energy. The heat energy is generated not by a sustainable way – using natural gas. The normalized thermal energy consumption for 4

ACCEPTED MANUSCRIPT 121 122 123

heating is 6100 MWh/year. During the heating season, the transmission heat losses are 110-125 MWh or 12-14 % of total consumption, but in the summer losses are 13-16 MWh or 15-20 % of total consumption.

124

3. Methodology

125

3.1. Algorithm of methodology

126 127

To develop system dynamics model problem was formulated and target was set (see Fig. 4, Module 9) and dynamic hypothesis was created (Module 10). SYSTEM DYNAMICS

EMPIRICAL STUDY DH system audit

9.Problem formulation

Database

Network layout

Heat load

Pipe design

Efficiency Platform

Efficiency of heat sources equipments

Building construction and position Walls Roof Windows Floor Ventilation

3.Data of heat sources 6.Assumption 7.Defenition of independent variables

Industrial consumption 15.Refine the model

2.Statistical Database

4.Data of heat network

11.Model formulation and simulation

Indoor and outdoor temperature regime

Operational parameters

1.Technology Database

10.Creating a dynamic hypothesis

5.Data of heat consumers Not acceptable

Correlation analysis Correlation analysis 8.Regression function determination for independent variables

12.Model testing

13.Historical data validation and evaluation

Acceptable 20. Policy instruments

14.Scenarios simulation

Alternative route

DECISION MAKERS

19.DH companies development strategies

17.Expert workshop and system improvement

Not acceptable 16.Expert evaluation of simulation results

18. Additional economic indicators assessment for scenarios

Acceptable

128 129 130

Fig. 4. Algorithm for model development.

131 132 133 134 135 136 137 138 139 140 141 142 143

Firstly, the initial data is collected. Data is divided into five Modules: (Module 1) dynamics of technology costs; (Module 2) statistics, e.g., climatology, generation and consumption of thermal energy, fuel and electricity prices etc.; (Module 3) characterization of heating source; (Module 4) characterization of heating network; (Module 5) characterization of the final consumer. In addition, the methodology can include energy savings measures in buildings (installation of insulation, replacement of windows and doors, modernization of ventilation system and introduction of heat recovery) and in industrial area, given as “Efficiency Platform”. The mathematical equations are developed using deterministic approach in the “Empirical study” block. The initial data is analyzed using regression equations (Module 8), then obtained regression equations and assumptions (Module 7) form the structure of system dynamics model (Module 11). The model undergoes structural (Module 12) and behavioral (Module 13) validation. If the model is inaccurate and inadequate results are achieved, the model is further improved (Module 15) and tested again. The experts evaluate reached results with the aim to 5

ACCEPTED MANUSCRIPT 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161

reach low carbon system and 4GDH, if target is not reached the model is adjusted with other provided technologies or fuel price (Module 16,17). The natural gas price has been changed by 30 ±30% €/MWh with an aim to identify dynamic of DH structure changing. To study specific DH development scenarios policy instruments are defined (Module 20). When desired results are achieved, the created scenarios are presented to the decision-makers, and for selected scenarios economic performance is calculated - specific investment for project, etc. (Module 19). 3.2. Causal loop diagram The mutual links and connections between the elements of the DH system are depicted in the causal loop diagram (Fig. 5), showing how various elements could contribute to the transition to 4GDH system [20]. Within the current research, the previous research made by the authors [21] is supplemented with implementation of the heat saving measures in buildings. The energy saving measures contribute to a reduction in heat consumption in buildings and industrial area, thus contributing to the opportunity for more swift transition to a lowtemperature mode, and together with the development of renewable energy technologies promote achieving 4GDH conditions. The structure of the systems model was further developed based on these relationships. The dynamic hypothesis is that 4GDH can be reached in near or distant future, depending on used policy instruments. The share of fossil fuel technology Heat tariff of fossil fuel

+

Investment in fossil fuel technologies

Heat losses in network

+

Capacity of fossil fuel energy sources

162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178

Heat tariff of renewable energy sources

+

+

Heat network temperature

New technology inconvenience cost

+

+

Heat consumption

B

+

+

The share of renewable energy sources R

+

+

Energy savings measures in Investment in buildings renewable energy technologies

+

Capacity of renewable energy sources

+

-

Access to finance

+

Energy savings measures in buildings

+

+

Standards and normative

Energy efficiency requirements in legislation

Fig. 5. Causal loops diagram for the development of 4GDH with energy savings measures in buildings (R – reinforcing, B – balancing). The causal loop diagram consists of one positive (R) and one negative or balancing (B) loop. The positive loop R promotes the development of renewable energy technologies, thus bringing the system closer to the 4GDH concept, and hence closer to implementation of the low-temperature regime. If the tariffs for renewable energy technologies are lower than the tariffs of fossil technologies, then the positive loop develops and contributes to further replacing of fossil fuels technologies. The system’s transition towards 4GDH is promoted. However, the system also has a balancing loop B, which tries to maintain the fossil resource technologies in the system, thus resisting the transition to 4GDH system. The mutual competition between the loops in this model is based on economic considerations, i.e., where the selection of the most appropriate technology is based on a lower tariff. If the tariff for renewable energy technologies will be lower than the tariff for fossil technologies, the positive loop will be stronger than the balancing loop, and it will promote the development of renewable 6

ACCEPTED MANUSCRIPT 179 180 181

technologies accordingly to the S-shape growth principle. Otherwise, the fossil technologies will keep their current positions. 3.3. Stock and flow diagram

182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207

The model used in this research is extension of authors’ previous research [21], where the model was built to predict how four types of DH technologies – natural gas boilers, biomass boilers, solar collectors with a seasonal accumulation and heat pumps – would develop in the future [21]. These four technologies are the main stocks of the model. Changes in stocks are based on how new technologies are installed after old ones reach the end of their technical lifetime. The selection of which new technology will be installed is determined by comparing heat energy tariffs of all four technologies at the time when change should happen. Calculation of the heat energy tariff is based on the methodology developed by the Regulator [22]. The heat tariff consists of 3 parts – production, transmission and sales tariff. Each part of the heat tariff consists of both variable and fixed costs. In addition, a parameter called “risk” (or “inconvenience costs”) is also included in the tariff calculation. Risk represent costs that emerge when implementing new technologies, for which we do not have enough knowledge about proper installation and operation. Tariff is calculated separately for each of the technologies. Specific values for different technologies, like COP for heat pumps or fuel costs and efficiency for solar collectors are also taken into account when calculating heat energy tariffs for particular technologies. More in-detail description can be found article [21]. Capacity of the new technologies depends not only on depreciation of old technologies, but also on heat consumption at the end users, which means that if heat consumption at the end users either increases or decreases, the capacity to-be-installed also gets adjusted. Therefore, in the current article, previous model structure is supplemented with more accurate heat network structure for the specific case scenario and also with energy savings measures for buildings and industrial area. Within this study, the model was adapted for a particular DH area with a 3.6 MW heat load. Calculations were done on the specific DH network heat losses (Qlos, kWh/year) and specific heat energy consumption at end-users (Qcon, MWh/year). The required amount of heat energy (Qprod, kWh per year) is determined as follows: 𝑄𝑝𝑟𝑜𝑑 = 𝑄𝑐𝑜𝑛 + 𝑄𝑙𝑜𝑠 + 𝑄𝐻𝑊

(1) (2)

𝑄𝑝𝑟𝑜𝑑 = 𝜂𝐵𝑄𝑑𝑧

208 209 210 211 212

213 214 215 216 217 218 219 220 221

where 𝜂 efficiency coefficient; B – fuel consumption, kg/year or m3/year; 𝑄𝑑𝑧 – lower calorific value of the fuel, kWh/kg or kWh/m3; 𝑄𝐻𝑊 – heat energy amount required for preparation of hot water, kWh/kg or kWh/m3. The model also accounts for the specific transmission network parameters, i.e., mass flow rate (G, kg/s) and pressure loss: 𝐺 = 𝑁𝑝𝑟𝑜𝑑 (𝑐𝑝(𝑇𝑠 ‒ 𝑇𝑟))

(3)

Δ𝑝 =‒ 𝜆𝐿𝜌𝑉2 2𝑑

(4)

where 𝑁𝑝𝑟𝑜𝑑 – heat power, kW; 𝑐𝑝 – specific heat, kJ/(kg°C); G – mass flow rate, kg/s; Ts – supply temperature, °C; Tr – return temperature, °C; Δ𝑝 – differential of pressure drop, Pa; 𝜆 – friction factor; L – length of pipe, m; 𝜌 – heat carrier density, kg/m3; V – velocity, m/s; d – diameter of pipe, m. The development of the 4GDH model is performed in the program “Powersim Studio 8”. In addition, three policy instruments were implemented in system dynamics model – subsidies, risk reduction and efficiency tool [21]. 3.4. The Energy Efficiency platform 7

ACCEPTED MANUSCRIPT 222 223 224 225 226 227 228 229 230 231

The Energy Efficiency platform incorporates a number of energy savings measures for the residential area: insulation of walls, roof (attic) and floors, installation of more energy efficient windows and doors, and mechanical balanced ventilation with recovery. The energy savings measures for the industrial area were assessed according to the literature by the percentage reductions and their costs [23]. The costs of energy savings measures were taken from the report of Ministry of Economics of Latvia, which determines the costs for installing the energy efficiency measures as well as the necessary material costs and tax expenses [24]. The efficiency characteristics for various scenarios, their cost range and project lifetimes are summarized in Table 1. For each upcoming scenario an increasing level of energy performance are stated.

232 233

Characteristic of scenarios in the model.

Table 1 Scenario

234 235 236 237 238 239 240 241 242 243

Building Resulting U-value Additional insulation Heat saving cost element (W/(m∙K)) thickness (mm) (€/m2) Base Wall 1.07 scenario Floor 0.82 (BSc) Roof 0.42 Window 2.0 Ventilation Industrial area Scenario Wall 0.19 150 70.1-85.5a 1(Sc1) Floor 0.30 100 15.2-18.7a Roof 0.15 200 7.1-8.5a Window 1.5 55.5-70.4a Ventilation Industrial area Scenario Wall 0.15 200 85.5-95.5a 2(Sc2) Floor 0.22 100 18.7-22.5a Roof 0.11 300 8.5-12.2a Window 1.1 70.4-94.9a Ventilation 0.75** 25.5-40.0b Industrial area 20* 87.0-142.0c Scenario Wall 0.08 400 100.0-180.0a 3(Sc3) Floor 0.22 100 18.7-22.5a Roof 0.09 400 14.5-16.5a Window 0.80 110.0-150.0a Ventilation 0.75** 25.5-40.0b Industrial area 40* 87.0-142.0c a – heat saving cost per appropriate building element area (wall, floor, roof, windows m2); b - heat saving cost per heating area; c – heat saving cost per MWh; * - heat saving percentage; ** - efficiency of heat recovery.

The energy amount required for the heating of each building is calculated according to the following equation: 𝑄𝑐𝑜𝑛 = 𝑄ℎ𝑙𝑜𝑠 ‒ 𝜂ℎ𝑔𝑄ℎ𝑔 (5) where 𝑄ℎ𝑙𝑜𝑠 – total heat losses through constructions, kWh/year; 𝜂ℎ𝑔 – utilization factor for heat gains (calculated according ISO 13790:2009); 𝑄ℎ𝑔 – total heat gains, kWh/year.

8

ACCEPTED MANUSCRIPT 244 245 246 247 248 249 250 251 252 253 254 255

The building heat consumption that corresponds to the efficiency parameters for each scenario is determined by building energy efficiency calculation program “HeatMod”, which is based on Latvian Construction Code and the climatic conditions. The calculation results are integrated into the system dynamics model, which allows determining the necessary heat amount for heating according to Equation 1. If a low temperature mode is introduced into the DH system without implementation of energy saving measures at the end-user side, then to provide the necessary heat energy amount the mass flow rate G2 (see Eq. 3) and the power for heat carrier distribution must be increased. The system dynamic model includes possibility to change the temperature regimes. It will change from the current condition 69/47 °C (𝑇𝑠1 ‒ 𝑇𝑅1= 22 °C, supply and return temperature at the outdoor temperature 0 °C) to 50/35 °C (𝑇𝑠2 ‒ 𝑇𝑅2= 15 °C at the outdoor temperature 0 °C). 𝑁𝑝𝑟𝑜𝑑1 = 𝐺1𝑐𝑝1(𝑇𝑠1 ‒ 𝑇𝑅1) 𝑁𝑝𝑟𝑜𝑑2 = 𝐺2𝑐𝑝2(𝑇𝑠2 ‒ 𝑇𝑅2) 𝑁𝑝𝑟𝑜𝑑1↓ ⟹ 𝐺1 = 𝐺2 benchmark level

(6) (7) (8)

256 257 258 259 260 261

where 𝑁𝑝𝑟𝑜𝑑1- heat power for high temperature regime, kW; 𝑁𝑝𝑟𝑜𝑑2 - heat power for low temperature regime, kW. The transition to a low-temperature heating systems happens if the required heat energy consumption has reached the level of benchmark (see Eq.8), when due to the implementation of energy savings measures at the end-user the consumption of heat carrier is reduced and there is no need to increase the consumption of electricity for pumping the heat carrier.

262

3.5. The assessment of economic feasibility

263 264 265 266 267 268 269

During the reconstruction of the DH system, the heat savings and heat generation must be balanced and the investments in both phases of the DH should be economically justified. Therefore, it is an optimization task, where on one side are the investments in the source that allow the installation of new renewable energy technologies, and on the other hand are the investment costs for energy saving measures. Both investments are interdependent and influenced on the price of fuel. The article investigate optimal DH system’s development solution by changing the natural gas price by 30 ± 30% €/MWh.

270

3.6. Scenario analysis with policy instruments

271 272 273

The research studies six scenarios that forecast transition to the 4GDH. In addition to the introduction of low temperature regime, three different policy measures were implemented to promote faster transition to renewable energy technologies, see Table 2.

274 275

Table 2 Description of scenarios Scenario

Technology life time (years)

Policy instruments Subsidies (percent of Investment)

Risk reduction

Efficient improvement

Base scenario, BSc

NG-25; S-20; B-25; HP-20

0

0

0

Scenario 1, Sc1

NG-20; S-20; B-20; HP-20

0

0

0

9

ACCEPTED MANUSCRIPT

276 277 278 279 280 281 282 283 284 285 286 287 288 289 290

First of the policies were subsidies: for Scenario 2a - 25 % from investment, for Scenario 3a – 50 %. In order to install new technologies, service period of other technologies in operation were reduced [25]. The second policy was reduction of the risk parameter, which was used in order to reduce costs emerging from use of the unknown technologies. This policy can be implemented as informative campaign, in which knowledge about appropriate and safe use of new technologies is gained. By implementing more of the new technologies, risk slowly diminishes. The third policy measure is intended on increasing energy efficiency of renewable energy technologies. Same as with risk reduction, energy efficiency measure is also connected to the obtaining of experience. By increasing use of renewable technology, knowledge about using it more efficient is also acquired.

4. Results and discussion 4.1. Scenario analysis of energy efficiency level The realization of various scenarios determines the energy efficiency levels in buildings and in the industrial area, which are characterized by specific heat energy consumption and building classes according to the “Regulations on the energy certification of buildings” [26] (see Fig. 6). Specific heat energy consumption, kWh/m2 per year

291 292 293

NG-15; S-20; B-15; Scenario 2, 0/1(25%) 0/1 0/1 HP-20 Sc2 / Sc2a NG-10; S-20; B-10; Scenario 3, 0/1(50%) 0/1 0/1 HP-20 Sc3 / Sc3a * - heat saving percentage; ** - efficiency of heat recovery; 0 – non active, 1 – active; NG – natural gas boiler; S – solar collector with accumulation; B – biomass boiler; HP – heat pump.

200 180 160 140 120 100 80 60 40 20 0

Apartment buildings classes Industrial area classes

187.0 139.0

139.0 109.6

95.4

83.5 60.5

BSc Sc 1 Apartment buildings

51.5

Sc 2 Sc 3 Industrial area

F

D

C

B

E

E

D

C

294 295 296

Fig. 6. Specific heat energy consumption by different scenarios and building classes in different scenarios.

297 298

The heat energy consumption decreases 2 times and the heat losses decreases 1.4 times when comparing the Scenario 3 to the Base scenario (see Table 3).

299 300

Table 3 Description of parameters for different scenarios. 10

ACCEPTED MANUSCRIPT Parameters Produced heat energy, MWh/year Heat energy consumption, MWh/year Losses, MWh/year

301 302 303 304

Base

Scenario 1

Scenario2

Scenario3

6914.14

5414.72

4020.86

3470.51

6193.39

4693.97

3510.90

2960.55

720.75

720.75

509.96

509.96

Apartment buildings consumption, MWh/year

3652.39

2152.97

1508.13

1435.01

Industrial area, MWh/year

2541.00

2541.00

2002.77

1525.54

From the viewpoint of the Latvian legislation, the specific consumption of heat energy in renovated buildings should not exceed 150 kWh/m2 [19]; this corresponds to the upper limits of 4GDH requirements [10]. Significant investments at the source would require for ensuring the necessary capacity for the demand by transition to renewable energy sources. Heat energy consumption per energy produced in base scenario

1 0.8 0.6 0.4 0.2 0 BSc

Sc 1

Sc 2

Sc 3

Specific heat losses Specific heat energy consumption of apartment buildings Specific heat energy consumption of industrial area Low temperature level benchmark

305 306

Fig. 7. Low temperature benchmarking evaluation by different scenarios.

307 308 309 310 311 312

We also determine how the energy saving measures affects the introduction of low temperature regime and which scenario conditions allow to implement low temperature. Figure 7 shows the heat energy consumption compared to energy produced at the Base scenario. Only two of the examined scenarios (Sc1 and Sc2) could ensure a low temperature system without increasing electricity consumption for heat carrier pumping. In order to achieve this, the required installed heating capacity has to be reduced to 67 % benchmark from the Base scenario.

313 314 315 316

4.2. Impact of energy savings measures for reaching 4GDH system The implementation of energy saving measures at the end-user allows increasing the share of renewables in the long term. Figure 8 shows the results for two scenarios – the Base scenario and Scenario 2, which includes implementation of energy saving measures.

11

Heat energy production share, %

ACCEPTED MANUSCRIPT

317

100 90 80 70 60 50 40 30 20 10 0 2015

Short term perspective

Long term perspective

2020 2025 Base sc. NG

2030 2035 Base sc. Solar

2040 2045 Base sc. Biomass

Sc. 2 NG

Sc. 2 Solar

Sc. 2 Biomass

Sc 2a Solar

Sc 3a Solar

2050

318 319

Fig. 8. The results of system dynamics modeling regarding the impact of energy savings measures for reaching 4GDH.

320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336

The graph shows that in short term perspective (until 2030) the difference between share of technology (natural gas and biomass) is only 7 %, but in long term perspective difference doubles reaching 15 %. In the short term it is not possible to install solar technologies, without the policy instruments (see Fig.8 Sc2a with 25 % and Sc3a with 50 % subsidies for Solar technology). Scenario 2a allows reaching 10 % share but Scenario 3a 34 % for solar collectors with accumulation in 2030. There are several barriers for the integration of renewable technologies within DH system: the price of fossil fuels, the heat source reconstructions that have been carried out in the last 5 years and consequently the lifetime of the installed technologies is long, and relatively high investment costs for solar collectors and heat pumps [27]. All these factors hinder the transition towards the 4GDH. The price of natural gas has a particular impact on the integration of renewable energy sources [28]. The average natural gas price in 2016 (30.0 €/MWh) was about 26 % lower than in 2013 (40.6 €/MWh) [17]. The fossil fuel price reduction not only hinders the integration of renewable energy sources, but due to the reduction of heat tariff, also hinders the implementation of heat savings measures. This affect also, heat consumer’s behavior, but these aspects and consequences are not in the scope of the current study.

337 338 339 340 341 342 343 344 345

4.3. Modelling of heat generation technologies The installed capacity for natural gas heating equipment in case study area is 3.6 MW. To increase the share of renewable energy it is necessary to replace the fossil fuel based heating equipment with renewable energy technologies, e.g., biomass based and solar energy technologies (solar collectors with seasonal accumulation) as well as heat pump technologies. By simulating, all scenarios the heat source designs were created based on the share of technologies in 2050 (see Fig. 9). The share of renewable energy sources (solar collectors with the accumulation and biomass) increases from 75 % in Scenario 1 to 93 % in Scenario 3, thus promote the transition of the whole system towards a low-carbon system.

12

Heat energy production share, %

ACCEPTED MANUSCRIPT

346 347 348 349 350

100 90 80 70 60 50 40 30 20 10 0 2015

2020

2025

2030

2035

2040

2045

Sc 1 NG

Sc 1 Solar

Sc 1 Biomass

Sc 2 NG

Sc 2 Solar

Sc 2 Biomass

Sc 3 NG

Sc 3 Solar

Sc 3 Biomass

2050

Fig.9. The dynamics of heat energy production share for different scenarios. The descriptions of the created technological solutions are provided in Table 4. Table 4 Heat source design. Parameters Heat energy share produced by NG, % Heat energy share produced by solar collectors , % Heat energy share produced by biomass % Solar collectors area, m2 Heat storage volume, m3 Specific investment by heat source (€/MWhsaved) Specific investment by heat source (€/MWhproduced)

Scenario 1 25.17

Scenario 2 17.76

Scenario 3 6.94

57.67

62.02

78.39

16.98

19.83

14.16

7367 15170 1467.2

5120 11584 691.3

5429 16434 554.4

406.3

497.4

550.1

351 352 353 354 355 356 357 358

If energy saving measures are implemented, the necessary investments in the heat source in the respect to the saved amount of heat would decrease 2.65 times (Sc1 against Sc3, see Table 4). The analysis of the investments in respect to the produced heat unit indicates that the investments are increasing; however, their relative increase (1.35) is lower in comparison with specific investment per saved energy. This is explained by the increase of the share of solar collectors with seasonal accumulation (Sc2 collector’s flats 5120 m2 but Sc3 – 5429 m2) in the heating system and that their costs are decisive for the total investment.

359

4.4. Impact of DH system development by natural gas price changing

360 361 362 363

The existing DH system is operated by natural gas combustion technology. Figure 10 compares dynamic of heat energy production share for Scenario 2 by changing the natural gas price by 30 ±30% €/MWh. Figure 10 reveals that system is more sensitive to the decrease in natural gas price rather than increase. For a long time natural gas retained the leading role, 13

ACCEPTED MANUSCRIPT and even has a long term perspective. This technology share is 36.4 % (17.7% in Sc.2 without NG price changing). In case of a decrease of gas prices, mostly technologies retain their role for a longer period of a time (Fig.10). Firstly, because technology is well known and “convenient”, it helps producers to manage business according to the principle - “as usually”.

Biomass

Solar

Biomass

Natural gas

Solar

2049

2046

2043

2040

2037

2034

2031

2028

2019

2049

2016

0% 2046

0% 2043

20% 2040

20% 2037

40%

2034

40%

2031

60%

2028

60%

2025

80%

2022

80%

2019

100%

2016

100%

2025

Natural gas price +30%

Natural gas price -30%

2022

364 365 366 367

Natural gas

368

Fig. 10. Heat energy production share for Scenario 2 by changing natural gas price 30%.

369 370 371 372 373 374 375 376 377 378 379

Secondly, now installed capacity has a longer lifetime for investment payback. Otherwise, Figure 10 show that even though the gas price reductions of the solar technology share is relatively high 46.7% (62.0% in Sc.2 without NG price changing). It means that the renewable energy technology will be installed later by natural gas price decrease and 4GDH system conditions will be achieved in distant future. Heat energy production share is less sensitive by increase of the natural gas price. The solar collectors with accumulation share increase to 65.1% compared to 62.0% in Scenario 2. Biomass technology shares change significantly in short-term perspective: 51.7% (in 2032) by NG price increasing and 27.6% (in 2039) by NG price decreasing. It has a stable position in long-term perspective (2050): Scenario 2 -19.9%; 19.2% by NG price increasing; 16.8% by NG price decreasing. All these trends remain by Scenario 1 and 3 too.

380

4.5. Levelised investment approach for heat consumers and heat sources projects

381 382 383 384 385

In order to identify the optimum between heat saving measures at the consumer’s side and in connection with heat source reconstruction, the research also concerns specific investment costs at both consumer and heat source sides.

14

Specific investment for project, Eur/m2

ACCEPTED MANUSCRIPT 170 160 150 140 130 Sc 1

120 Sc 2

110 100 Sc 3

90 80 70 60 40

50

60

70

80

90

100

Specific heat consumption for apartment building area, kWh/m2

Specific investment by consumers side, EUR/m2

Specific investment by heat source side for base NG price , EUR/m2 Specific investment by heat source side by -30% NG price, EUR/m2

386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416

Specific investment by heat source side by +30% NG price, EUR/m2

Fig. 11. Specific investment by consumers and heat sources sides for studied case. The specific investments at the heat source side in respect to the specific heat consumption for apartment buildings are increasing slowly; in Scenario 1 they are 12 % higher than in Scenario 3 (Fig.11). Both lines that characterize the interrelated specific investment costs intersect at specific heat consumption 60 kWh/m2, corresponding to Scenario 2, which can be considered as the optimal solution. This is due to the increase of the share of solar collectors with seasonal accumulation and their cost. For Scenario 3 the specific investments at the consumer’s side are 140 €/m2, which is about 52 % greater than the specific investments for Scenario 1. The relatively rapid growth is related to the costs of energy efficiency constructive solutions for buildings (windows with U-value of 0.8 (W/(m∙K)), ventilation reconstruction cost etc.). Specific investment by heat source side increase average by 6% by changing up to 30% more of NG price because growing share of solar technology for all scenario. It intersect with specific investments at the consumer’s side by specific heat consumption 50 kWh/m2, corresponding to Scenario 3, which can be considered as the optimal solution. More significant decreasing of specific investment by heat source side (average 22.3%) can be identified by decreasing 30% of NG price. This can be explained by the fact that natural gas combustion technology is well known, relatively cheaper and is used in this DH system now. Both specific investments intersect by 87 kWh/m2 that is nearer to Scenario 1 and specific heat energy consumption 31.0% less that in Scenario 2. Several authors in their studies underscore the need to harmonize heat saving measures with heat generation technologies [15]. However, the question regarding how high level of efficiency has to be reached by implementation of energy savings measures at the end-users is still debatable. Within an article that formulates the concept of 4GDH, Lund et al. [10] defines the specific heat energy consumption for existing building in a wide range from 50 to 150 kWh/m2. The results of the current research varies depending on the fuel price from 50 kWh/m2 (which is close to the lowest limit of the previously defined range) to 87 kWh/m2. Amstalden et al. [7] underlined a significant influence of energy price and fuel price to energy efficiency measures. Their research result shows that 40% reduction in heat energy consumption is profitable. The

15

ACCEPTED MANUSCRIPT 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438

reduction in heat energy consumption is 43.3% in Scenario 2 that correspond with Amstalden et al. [7] result. Zvingilaite &Jacobsen [8] pay attention on necessity to find optimal point between investment into energy efficiency measure on consumer’s side and heat generation costs, which depend on fuel price. This research provides the heat energy savings 18.3-23.8% that is nearer to Scenario 1 in presented article. Scenario 1 is a more appropriate solution by decrease of the NG price. Scenario 2 identifies the potential optimal solution by existing NG price that incorporates implementation of energy savings measures at the end user, maximum integration of renewable energy sources and ensuring that the systems energy consumption level corresponds to the low temperature level benchmark. Based on the above arguments, the reduction of heat energy consumption by 43.3%, which is ensured by both the implementation of energy saving measures at the end-user and the transition to low-temperature regime, is an optimal solution for the existing DH system. Another significant aspect is how much the heat demand could be reduced in buildings in 2050. In their study, Zvingilaite & Balyk indicates that 40 % could be achieved if all studied heat saving measures are implemented in Danish buildings [14]. Our obtained results – 42 % – correspond to the above mentioned number. However, the scope of this article is much smaller (installed capacity of 3.6 MW). Zvingilaite & Balyk points that it is cost effective to reduce approximately 12 % to 17 % of future heat demand in buildings depending on assumed lifetime and cost of heat saving measures. Similarly, the results of this study point to the need to evaluate the costs of heat saving measures, but in addition to interconnect them with the costs at the heat source side.

439

5. Conclusions

440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465

The article presented the application of the methodology created by authors, which consists of three main interlinked parts: empirical study, system dynamics modelling and decision makers section. The

article analyzes the transition from conventional DH system towards 4GDH system by using system dynamics modeling, economic feasibility analysis by using levelised investment approach and simultaneously evaluating all stages of the DH – the source, networks and endusers, therefore ensuring coherent modernization approach. The developed by authors system dynamic model allows analysing the changing behaviour of a complex DH system by identifying and defining its elements and their interactions. The article identifies the optimal solution between the

investments in the source that allow the installation of new renewable energy technologies, and the investment costs for energy savings measures by using levelised investment approach. Both investments are interdependent and influenced on fuel price.Energy saving measures with various levels of efficiency were incorporated into the modeled scenarios: Scenario 1 – 24 % from the heat energy consumption in the Base scenario, Scenario 2 – 43 %, Scenario 3 – 52 %. This allowed to reduce the installed capacity of heating equipment, the consumption of energy resources and thus CO2 emissions, as well as to facilitate the transition of the DH system towards zero emission system while in increasing the efficiency of DH. The article evaluates energy saving measures and compares the specific investment costs for all scenarios according to their heat energy savings. Lower heat energy savings (and thus higher demand) correspond to higher specific investments at the source and contrary – higher specific heat energy savings (lower demand) correspond to lower specific investments at the source. At specific heat consumption 60 kWh/m2 (Scenario 2) an optimal solution is achieved which is characterized by the lowest investment and by the lowest heat energy consumption by end users. The DH system behaviour is not symmetric by changing fuel price by ±30%. More significant decreasing of specific investment by heat source side (average 22.3%) can be identified by decreasing by -30% of NG price because natural gas technology retains its leading position and is well known technology. The optimal system design correspond to 87 kWh/m2 specific heat 16

ACCEPTED MANUSCRIPT 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482

consumption. Both facts slow the pace at which the system will reach the 4GDH conditions. Specific investment by heat source side increase average only by 6% by changing by +30% of NG price, therefore solar collector’s technologies are installed and the optimal system design is at specific heat consumption 50 kWh/m2 (Scenario 1). Heat energy production share is less sensitive by increasing of the natural gas price. Implementation of heat savings measures at the end-user decrease and demands to link consumers and heat source in the long term. In the short term it is not possible to install solar technology without using the policy instruments. Using the policy tools in the short term until the year 2030, the share solar collectors with accumulation may reach 10 % and up to 76.4 % share in long term perspective until year 2050. Using the system dynamics model, heat source designs were created for all scenarios on the basis on the share of technologies in 2050. The results show that by reducing the heat consumption, the share of renewable energy sources increases (solar collectors with the accumulation and biomass) up to 92.55 % in Scenario 3. The developed system dynamic model with efficiency platform could be applied to other DH systems if corresponding initial data were added. The proposed methodology allows evaluating the opportunities to transition from an existing DH system to 4GDH system, while maintaining the competitiveness of the DH system.

483

Acknowledgements

484 485 486 487 488

The work has been supported by the National Research Program “Energy efficient and lowcarbon solutions for a secure, sustainable and climate variability reducing energy supply (LATENERGI)”. We would also like to show our gratitude to the Ms. Lelde Timma, Dr. Anna Kubule and Ms. Lasma Chernovska from Institute of Energy Systems and Environment for sharing valuable comments during the creation of this article.

489

References [1]

[2] [3] [4] [5] [6] [7]

[8]

European Commission. An EU Strategy on Heating and Cooling. Communication from the Committee to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. COM(2016) 51 Final 2016; European Commission, Brussels. Lundström L, Wallin F. Heat demand profiles of energy savings measures in buildings and their impact on a district heating system. Applied Energy 2016; 161: 290-9. Lund H, Möller B, Mathiesen B, Dyrelund A. The role of district heating in future renewable energy systems. Energy 2014; 35:1381-90. Brand M, Svendsen S. Renewable-based low-temperature district heating for existing buildings in various stages of refurbishment. Energy 2013; 62: 311-19. Köfinger M, Basciotti D, Schmidt R, Meissner E, Doczekal C, Giovannin A. Low temperature district heating in Austria: Energetic, ecologic and economic comparison of four case studies. Energy 2016; 110: 95-104. Persson U, Möller B, Werner S. Heat Roadmap Europe: Identifying strategic heat synergy regions. Energy Policy 2014; 74: 663-81. Amstalder R.W, Kost M, Nathani C, Imboden D.M. Economic potential of energy-efficient retrofitting in the Swiss residential building sector: The effects of policy instruments and energy price expectations. Energy Policy 2007; 35:1819-1829. Zvingilaite E, Jacobsen H.K. Heat savings and heat generation technologies: Modelling of Residential investment behaviour with local health costs. Energy Policy 2015; 77: 31-45. 17

ACCEPTED MANUSCRIPT [9] [10] [11] [12]

[13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

[25]

Person U, Werner S. Heat distribution and the future competitiveness of district heating. Applied Energy 2011; 88:568-76. Lund H, Werner S, Wiltshire R, Svendsen S, Thorsen J, Hvelplund F, Mathiesen B. 4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems. Energy 2014; 68: 1-11. Mathiesen B,Lund H, Connolly D, Wenzel H, Østergaard P, Möller B et al. Smart Energy Systems for coherent 100% renewable energy and transport solutions. Applied Energy 2015; 145:139-54. Allegrini J, Orehounig K, Mavromatidis G, Ruesch F, Dorer V, Evins R. A review of modelling approaches and tools for the simulation of district-scale energy systems. Renewable and Sustainable Energy Reviews 2015; 52: 13911404. Swan L, Ugursal V. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews 2009; 13: 1819 - 35. Zvingilaite E, Balyk O. Heat saving in building in a 100% renewable heat and power system in Denmark with different share of district heating. Energy and Buildings 2014; 82:173-86. Hansen K, Connolly D, Lund H, Drysdale D, Thellufsen J. Heat Roadmap Europe: Identifying the balance between saving heat and supplying heat. Energy 2016; in Press Lund H, Hvelplund F, Kass I, Ducalskis E, Blumberga D. District heating and market economy in Latvia. Energy 1999; 24:549-59. Central Statistical Bureau of Latvia. 2010. http://www.csb.gov.lv/. EUROPE’S BUILDINGS UNDER THE MICROSCOPE. A country-by-country review of the energy performance of buildings. Buildings Performance Institute Europe (BPIE) 2011. Ministry of Economics of Latvia. Long-Term Strategy for Building Renovation 2014. http://ec.europa.eu/energy/sites/ener/files/documents/2014_article4_en_latvia.pdf Blumberga A, Blumberga D, Bazbauers G, Davidsen P, Moxnes E, Dzene I. et al. System Dynamics for Environmental Engineering Students. Riga: Riga Technical University; 2011. Ziemele J, Gravelsins A, Blumberga A, Vigants G, Blumberga D. System dynamics model analysis of pathway to 4th generation district heating in Latvia. Energy 2016; 110: 85-94. Methodology for Calculation of Heat Supply Tariff. http://likumi.lv/doc.php?id=208283. Backlund S, Thollander P. Impact after three years of the Swedish energy audit program. Energy 2015; 82; 54-60. Ministry of Economics of Latvia. Report on energy performance requirements in Latvia for new buildings and buildings to be reconstructed in compliance with cost-optimal level pursuant to Article 5 of Directive 2010/31/EU on the energy performance of building. 2013. Romagnoli F, Barisa A, Dzene I, Blumberga A, Blumberga D.Implementation of Different Policy Strategies Promoting the Use of Wood Fuel in the Latvian District Heating System: Impact Evaluation through a System Dynamic Model. Energy 2014; 76: 210-22.

18

ACCEPTED MANUSCRIPT

490

[26] Regulations on the energy certification of buildings. Regulation of the Republic of Latvia Nr.383. Riga: The official Publisher of the Republic of Latvia "Latvijas Vēstnesis"; 2014. [27] Ziemele J, Gravelsins A, Blumberga A, Blumberga D. The effect of energy efficiency improvements on the development of 4th generation district heating. Energy Procedia 2016; 95: 522-27.

19