- Email: [email protected]
Assessment of renewable energy and energy efficiency plans in Thailand’s industrial sector
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
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Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000 Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2017) 000–000 841–846 EnergyProcedia Procedia138 00 (2017) www.elsevier.com/locate/procedia
2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 AEDCEE, 25‐26 May 2017, Bangkok, Thailand 2017 AEDCEE, 25‐26 May 2017, Bangkok, Thailand
The 15th International Symposium on District Heating and Cooling Assessment of energy energy efficiency plans Assessment of renewable renewable energy and and energy efficiency plans in in Thailand’s sector Thailand’sofindustrial industrial Assessing the feasibility using thesector heat demand-outdoor a a,* a, Pornphimol a,* Achiraya Chaichaloempreecha Winyuchakrit Bundit Limmeechokchai temperature function for a long-term districtaa,,heat demand forecast Achiraya Chaichaloempreecha , Pornphimol Winyuchakrit Bundit Limmeechokchai a aSirindhorn
International Institute of Technology, Thammasat University, Pathumthani, 12120, Thailand
a,b,c International Institute a Thammasat University, c Thailand Sirindhorn of Technology, 12120, I. Andrić *, A. Pinaa, P. Ferrão , J. Fournierb., B.Pathumthani, Lacarrière , 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 c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
The industrial sector is one of the main energy consuming sector in Thailand, and accounted for 36.2% of total energy The industrial sector is one of the main energy consuming sector in Thailand, and accounted for 36.2% of total energy consumption in 2013. The increase of population and economic growth affected energy demand and greenhouse gas (GHG) consumption in 2013. The increase of population and economic growth affected energy demand and greenhouse gas (GHG) emissions. Thailand has the long-term climate plans related to energy saving and GHG mitigation in the industrial sector namely; emissions. Abstract Thailand has the long-term climate plans related to energy saving and GHG mitigation in the industrial sector namely; Energy Efficiency Plan 2015 (EEP2015) and Alternative Energy Development Plan 2015 (AEDP2015). Both plans have the Energy Efficiency Plan 2015 (EEP2015) and Alternative Energy Development Plan 2015 (AEDP2015). Both plans have the same periods and end at 2036. This paper analyzes the changes in GHG emissions during 2005-2036. Energy demand depends on same periods and networks end at 2036. paper analyzes the in changes in GHGas emissions during 2005-2036. Energy demand depends on District heating areThis commonly addressed the literature one of the most effective solutions for decreasing the population, gross domestic product, and fuel prices. The GHG mitigation can be evaluated by using Long-range Energy population, domesticfrom product, and fuel prices. Thesystems GHG mitigation be evaluated byare using Long-range greenhouse gross gas emissions the building sector. These require highcan investments which returned through Energy the heat Alternative Planning system (LEAP) under various scenarios. The business-as-usual (BAU) scenario can be expressed as a Alternative system climate (LEAP) conditions under various The business-as-usual scenario be expressed as a sales. Due Planning to the changed and scenarios. building renovation policies, heat(BAU) demand in the can future could decrease, frozen-scenario where new technology deployment and energy efficiency improvement are excluded. However, mitigation (MIT) frozen-scenario new technology deployment and energy efficiency improvement are excluded. However, mitigation (MIT) prolonging the where investment return period. scenarios are introduced by using the existing measures in the EEP2015 (MIT_EE scenario) and AEDP2015 (MIT_RE scenario). scenarios introduced by using existing measures in EEP2015 and AEDP2015 (MIT_RE scenario). The mainare scope of this paper is tothe assess the feasibility of the using the heat(MIT_EE demand –scenario) outdoor temperature function for heat demand In this study, Thailand’s industries are categorized into eleven categories: food and beverage, textile, wood and furniture, paper Inforecast. this study, categorized into eleven categories: foodasand beverage, wood and furniture, of paper TheThailand’s district ofindustries Alvalade,are located in Lisbon (Portugal), was used a case study.textile, The district is consisted 665 and pulp, chemical, nonmetallic, basic metal, fabricated metal, construction, mining, and other industries. Fossil fuels are the and pulp, chemical, basic metal, fabricated metal, Three construction, and(low, othermedium, industries. Fossil the buildings that vary nonmetallic, in both construction period and typology. weathermining, scenarios high) and fuels threeare district main source of GHG emission accounted for 86% of total GHG emission in this industry. GHG emission will increase from 77.6 main source scenarios of GHG emission accounted for 86%intermediate, of total GHGdeep). emission this industry. GHG emission willdemand increasevalues from 77.6 renovation were developed (shallow, To in estimate the error, obtained heat were Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036. This study found that nonmetallic industry is the major GHG emitters. Full 2005 to 222.5 in 2036. Thismodel, studypreviously found thatdeveloped nonmetallic GHG emitters. Full Mt-CO compared with results from aMt-CO dynamic demand andindustry validatedisbythe themajor authors. 2eq in 2eq heat implementation of the EEP2015 and AEDP2015 can reduce GHG emissions by 36% and 9% in 2036 under MIT_EE and implementation of the andweather AEDP2015 reduce GHG 36% andbe9% in 2036 for under MIT_EE and The results showed thatEEP2015 when only changecan is considered, theemissions margin ofby error could acceptable some applications MIT_RE scenarios, respectively. Such GHG reductions can be achieved from the implementation of EEP2015, due to the MIT_RE reductions be achieved the implementation of introducing EEP2015, due to the (the errorscenarios, in annual respectively. demand was Such lower GHG than 20% for all can weather scenariosfrom considered). However, after renovation financial incentives for energy performance achievement and enforcement of energy standards in the designated factories. financial incentives energy performance achievement and enforcement of energy standards scenarios in the designated factories. scenarios, the errorfor value increased up to 59.5% (depending on the weather and renovation combination considered). ©The 2017 The of Authors. Elsevier on Ltd.average within the range of 3.8% up to 8% per decade, that corresponds to the value slope Published coefficientbyincreased © 2017 The under Authors. Published by Ltd. committee of the 2017 International Conference on Alternative Energy in Peer-review responsibility of Elsevier the scientific in Authors. the number of heating hours ofLtd. 22-139h during heating season (depending on the combination of weather and ©decrease 2017 The Published by Elsevier Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE. D eveloping Countries and Emerging Economies. renovation scenarios considered). the other hand, function increased for 7.8-12.7% per decade (depending on the Peer-review under responsibility of On the Organizing Committee ofintercept 2017 AEDCEE. Keywords: Thailand industry; GHG emissions, EEP2015; AEDP2015; LEAP model coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Keywords: Thailand industry; GHG emissions, EEP2015; AEDP2015; LEAP model 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.: +66-2-986-9009; fax: +66-2-986-9112. Cooling. * Corresponding author. Tel.: +66-2-986-9009; fax: +66-2-986-9112.
E-mail address: [email protected] E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE. Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE.
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 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies. 10.1016/j.egypro.2017.10.105
842 2
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
1. Introduction Thailand, as a developing country, is the second largest economy in Southeast Asian Nation [1]. It not only consumes an enormous energy but also releases a large amount of GHG, especially in the energy sector including the power sector, the transportation sector, the industrial sector, the commercial sector and the residential sector. Thailand’s industrial sector is the major transformation due to rapid economic growth and development. Thus, this sector obviously demonstrates as an energy intensive sector for driving economic activities. The industrial sector includes manufacturing industries, mining, and construction. This sector consumes the largest final energy consumption accounted for 27.2 Mtoe or 36.2% of total final energy consumption in 2013 [2]. In addition, GHG emissions released 78.9 Mt-CO2eq which is higher than 2012 by 1.5%. The major emission originated from the electricity consumption. Furthermore, fossil fuel combustion such as coal, oil, and natural gas also shows a numerous GHG emission. A significant GHG emission proves that the atmosphere will be damaged. Therefore, Thailand proposes plans to mitigate GHG emission directly related to the industrial sector namely; Thailand energy efficiency plan 2015 (EEP2015) and Thailand alternative energy development plan 2015 (AEDP2015). 1.1. Thailand’s energy efficiency plan 2015 (EEP2015) According to the energy demands continuously increase, Thailand establishes targets for reducing future energy demands. The EEP2015 is energy conservation plan, it includes a demand side management and a deployment of advanced energy efficiency technologies, in terms of appliances, machinery and energy consumption behaviors change, as the end year at 2036 [3]. The EEP2015 introduces significant five measures related to the industrial sector namely: designated industry, energy labelling, energy efficiency resource standard (EERS), lighting emitted diode (LED) promotion, and monetary incentive. The designated industry is the industry enforced by energy conservation measure which is driven by government regulations. Energy labelling is the measure to establish the Minimum Energy Performance Stands (MEPs) and High Energy Performance (HEPs) for appliances in cooling system. The EERS measure or the minimum standard use to implement energy conservation measures by encouraging customers to use energy efficiently. LED promotion is the measure to encourage the industries to use LED light bulb. Monetary incentive obviously provides funds to industries which eventually desire to conserve the energy. 1.2. Thailand’s alternative energy development plan 2015 (AEDP2015) Alternative energy and renewable energy play a great role for GHG mitigation. The government has been making an effort to encourage the AEDP2015 in order to become a Low Carbon Society. Targets of AEDP2015 are to substitute fossil fuel consumption and to address social problems particularly municipal solid waste (MSW) and agricultural waste [4]. In the industrial sector, AEDP2015 focuses on solar, biomass and MSW. Most of these renewable energies are consumed in the heating system which is the importance system in the industrial sector. Thus, the aim of this paper is to assess the long-term GHG emission targets by the implementation of EEP2015 and AEDP2015 using the Long-range Energy Alternative Planning system (LEAP). 2. Methodology 2.1. Calculation of future energy demands The industrial sector has been categorized into eleven categories: food and beverage, textile, wood and furniture, paper and pulp, chemical, nonmetallic, basic metal, fabricated metal, construction, mining, and other industries. These categories are classified base on Thailand national energy situation report by Department of Alternative Energy Development and Efficiency (DEDE) [5]. In this study, all subindustries are assumed to be independent of each other.
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000 Data collection of • Energy demand • Population • GDP • Fuel prices Classification by subindustries
Future population GDP and fuel prices
Regression equation
Future energy demand of subindustries
843 3
Future energy demand in total energy industry sector
Regression analysis
Fig. 1. Flow diagram of estimation of future energy consumption.
Fig. 1. presents the steps for considering the future energy demand in the industrial sector. The first step is to collect the historical energy demand and socio-economic information such as population, value added and fuel prices. The future of population has projected by using National Economic and Social Development Board (NESDB) data (average growth rate: 0.03% annually) [6]. The GDP has projected with the growth rates illustrated in Thailand Power Development Plan 2015 (PDP2015) by Electricity Generation Authority of Thailand (EGAT) (average growth rate: 3.94% annually) [7]. In addition, coal price, oil price, natural gas price and renewable energy price are also estimated. The future of coal price, oil price and natural gas price has estimated by collecting significant prices from the World Bank [8,9,10]. While, renewable energy price including wood, paddy husk and biomass has estimated by the linear regression from the historical data of Energy Policy and Planning Office (EPPO) [11]. The energy demand projection had eventually been carried out by using the multiple variable regression and linear regression analysis. These methods apparently reveal the future energy demand of the subindustries. Likewise, the energy demand projection for the respective sub-industries are given in Table 1. Table 1. Energy demand in industries. Sub-industry
Energy demand in the industrial sector (ktoe) 2005
2010
2020
2030
2035
2036
Food and Beverage
6,353
8,017
13,387
19,832
24,069
25,166
Textile
1,007
783
1,202
1,771
2,102
2,254
Wood and Furniture
192
231
460
724
898
959
Paper and Pulp
815
1,412
2,895
4,484
5,401
5,626
Chemical
2,746
2,221
4,610
7,108
8,511
8,935
Nonmetallic
7,573
7,782
10,150
14,047
16,667
17,325
Basic Metal
1,049
1,503
2,238
3,570
4,292
4,527
Fabricated Metal
1,500
1,503
2,636
4,027
4,791
5,148
Construction
152
167
149
248
323
341
Mining
125
123
185
272
322
349
Other
1,363
1,856
2,681
4,333
5,365
5,604
Total
22,875
25,598
40,595
60,416
72,741
76,234
2.2. Long-range Energy Alternative Planning system (LEAP) The GHG emissions and mitigations in industries are analyzed by using an end-use model, called LEAP [12]. LEAP is an energy environment modeling tool developed by Stockholm Environment Institute (SEI). LEAP is a widely-used software tool for energy policy analysis and climate change mitigation assessments. Additionally, the GHG mitigation aspects can be calculated by using the emission factors from IPCC [13]. This study creates the demand side model of the industrial sector and separates the structure into eleven subindustries. Then, energy consumption was separated into two types: electricity consumption and non-electricity consumption. Electricity consumption consists of four service systems which are lighting system, cooling system, motor system and other system. Non-electricity consumption is consumed only in heating system. In each service system, the appliances can be divided into existing appliances and efficient appliances.
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
844 4
2.3. Scenario description In this study, two difference scenarios are created. Business-as-usual (BAU) scenario is used as a reference case without any policy intervention. Mitigation (MIT) scenario applies aforementioned measures in both EEP2015 and AEDP2015. The BAU scenario, as a reference case, illustrates current activities without any climate policy intervention. This scenario can be expressed as a frozen-scenario which new technologies deployment and energy efficiency improvement are excluded. The energy demand in this case is provided in Table1. To reduce GHG emissions, this study introduced five measures in EEP2015 focusing on energy conservations such as energy efficiency improvement and economic use (reduce expendable use of energy). In addition, the AEDP2015 provides three measures related to the renewable energy such as MSW, biomass and solar. The MIT scenario are divided into three sub-scenarios including mitigation by EEP2015, called MIT_EE scenario, mitigation by AEDP2015, called MIT_RE scenario, and mitigation by both EEP2015 and AEDP2015, called MIT_EE+RE scenario. A relative change of GHG emission are calculated compared to the BAU scenario. 3. Results This section presents the significant results of expected GHG emission, obtained from the LEAP model. It also presents the GHG mitigation by implementation of the EEP2015 and AEDP 2015. 3.1. GHG emission in BAU scenario The results in this scenario are presented as reference results during 2005-2036. GHG emission will increase from 77.6 Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036 or increasing by 3.5% annually. The main GHG originates from electricity consumption followed by the briquettes combustion and diesel usage accounted for 38.9%, 19.9% and 13.8% of total GHG emission in 2036 (see Fig. 2(a).). The nonmetallic industry reveals the highest GHG emission followed by food and beverage industry and chemical industry accounted for 27.8%, 15.8% and 14.0% of total GHG emission, respectively (see Fig. 2(b).). The nonmetallic industry has the proportion of GHG emission from fossil fuel combustion accounted for 85.5% of GHG emission in this sector. Thus, this industry is a significant industry to be concerned and it is the major of Thailand’s industry as a cement industry. 3.2. GHG emission and mitigation in MIT scenario The first mitigation year is 2011 in this study. By implementation of the EEP2015 and AEDP2015 measures, the GHG emission will be decreased by 67.0 Mt-CO2eq in 2011 and 98.7 Mt-CO2eq in 2036, or it can mitigate 11.4% annually, compared with the BAU scenario. The GHG emissions in the BAU scenario and the MIT_EE+RE scenario are shown in the Fig. 3. 200 150
Electricity Diesel LPG Lignite Oil Others
Briquettes Natural Gas Bituminous Gasoline Anthracite
100 50 0
2005 2010 2015 2020 2025 2030 2035 2036
250
GHG Emission (kt-CO2eq)
GHG emission (kt-CO2eq)
250
200 150 100
Food Wood Chemical Basic metal Other Construction
Textiles Paper Non metallic Fabricated Mining
50 0
2005 2010 2015 2020 2025 2030 2035 2036
Fig. 2. (a) GHG emission by fuel types; (b) GHG emission by sub-industries in the BAU scenario.
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
GHG Emission (kt-CO2eq)
250
845 5
BAU scenario MIT_EE+RE scenario
200 150 100 50 0 2005
2010
2015
2020
2025
2030
2035
Fig. 3. GHG emission.
3.2.1. GHG Mitigation by implementation of EEP2015 Fig. 4(a). presents the GHG mitigation by the implementation of five measures in EEP2015. These measures can be reduced the GHG emission about 79.8 Mt-CO2eq or accounted for 35.8% reduction in 2036. The GHG emission reduction in this plan mainly comes from electricity savings. Nowadays, the industries required a high investment for energy transformation. Therefore, monetary incentive is the most effective measure. This measure can reduce the overall energy consumption, especially electricity consumption and reducecGHG emissions by 58.0% of total GHG emission reduction. However, designated industry and energy labelling measures is the important measure to achieve the target. The designated energy conservation industries and energy labelling can reduce GHG emission about 31.2% and 5.8% of total GHG emission reduction, respectively. While the GHG emissions can be reduced by 2.9% by the implementation and deployment of LED light bulbs in the lighting system. The remaining of 2.1% is in the EERS implementation. 3.2.2. GHG Mitigation by implementation of AEDP2015 Fig. 4(b). presents the GHG mitigation by the implementation of three measures in the AEDP2015. These measures substitute fossil fuel combustion by renewable energy in the heating system. Thus, Most of the GHG emission reduction comes from the reduction of fossils fuel combustion. Results indicate that three measures are effective for GHG reduction. GHG emission can be reduced by 19.0 Mt-CO2eq, and accounted for 8.5% reduction in 2036. The highest reduction measure apparently achieves by MSW and accounted for 74.0% of total GHG reduction followed by the implementation of solar energy (14.4% of total GHG reduction). MSW not only reduces the GHG emission but also help the management of domestic waste. The remaining is the use of biomass, which is accounted for 11.6% of total GHG reduction.
80 70 60 50
EERS LED Labelling Desingnated Industry Monetory incentive
GHG mitigation (Mt-CO2eq)
GHG mitigation (Mt-CO2eq)
90
40 30 20 10 0
2005
2010
2015
2020
2025
2030
2035
2036
20 18 16 14 12 10 8 6 4 2 0
Biomass Solar MSW
2005
2010
2015
2020
2025
2030
Fig. 4. (a) GHG mitigation by implementation of EEP2015; (b) GHG mitigation by implementation of AEDP2015.
2035
2036
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Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
4. Conclusion This study assesses the long-term targets of EEP2015 and AEDP2015 plans in terms of GHG mitigation in the industrial sector during 2005–2036 by using LEAP. The measures in industries can reduce GHG emissions. By adopting all measures in the EEP2015 and AEDP2015 plans, GHG emissions can be reduced by 98.7 Mt-CO2eq in 2036. GHG emissions in industries will increase from 77.6 Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036 accounting for 3.5% per year. This study found that nonmetallic industry is the major GHG emitters. Full implementation of the EEP2015 and AEDP2015 can reduce GHG emissions by 35.8% and 8.5% in 2036 in the MIT_EE and MIT_RE scenarios, respectively. Such GHG reductions can be achieved mainly from the implementation of EEP2015, due to the introduction of monetary incentives and energy conservation in designated industries for energy performance achievement. Acknowledgements The authors would like to gratefully acknowledge the contribution of Sirindhorn International Institute of Technology of Thammasat University (SIIT-TU) for support of this research. The authors would also like to thank the Low carbon research funds for the financial support. References [1] World Bank. (2011). Thailand Now an Upper Middle Income Economy. Retrieved from http://www.worldbank.org/en/news/pressrelease/2011/08/02/thailand-now-upper-middle-income-economy. [2] Department of Alternative Energy Development and Efficiency, Ministry of Energy. (2013). Energy Balance of Thailand 2013, Bangkok: Department of Alternative Energy Development and Efficiency, Ministry of Energy. [3] Energy Policy and Planning Office, Ministry of Energy. (2015). Energy Efficiency Plan 2015-2036 (EEP 2015). Retrieved from http://www.eppo.go.th/encon/EEP2015/Draft-EEP2015.pdf [4] Energy Policy and Planning Office, Ministry of Energy. (2015). Alternative Energy Development Plan (AEDP 2015–2036). Retrieved from http://www.dede.go.th/download/files/AEDP2015_Final_version.pdf [5] Department of Alternative Energy Development and Efficiency, Ministry of Energy. (2007). Energy Balance of Thailand 2007. Bangkok: Department of Alternative Energy Development and Efficiency, Ministry of Energy. [6] Office of The National Economic and Social Development Board. (2016). Population total fertility rate 2010 – 2040. Retrieved from http://social.nesdb.go.th/SocialStat/StatReport_Final.aspx?reportid=697&template=2R1C&yeartype=M&subcatid=1. [7] Energy Policy and Planning Office, Ministry of Energy. (2015). Power Development Plan 2015–2036 (PDP 2015). Retrieved from http://www.eppo.go.th/power/PDP2015/PDP2015.pdf [8] World Bank. (2016). Crude Oil Price Forecast: Long Term 2016 to 2025. Retrieved from https://knoema.com/WBCFPD2015Oct/worldbank-commodity-forecast-price-data-July 2016. [9] World Bank. (2016). Coal Prices: Long Term Forecast to 2020. Retrieved from https://knoema.com/xfakeuc/coal-prices-long-term-forecastto-2020-data-and-charts. [10] World Bank. (2016). World Bank Commodities Price Forecast. Retrieved from http://pubdocs.worldbank.org/en/764161469470731154/ CMO-2016-July-forecasts.pdf. [11] Energy Policy and Planning Office, Ministry of Energy. (2016). Economic Data and Analysis. Retrieved from http://www2.eppo.go.th/info/7economic_stat.htm [12] Heaps, C. (2008). An Introduction to LEAP, Stockholm Environment Institute. Retrieved from https://www.energycommunity.org/default.asp?action=introduction [13] Intergovernmental Panel on Climate Change (IPCC). (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Retrieved from http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf
ScienceDirect ScienceDirect
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available Energy Procedia 00 (2017) 000–000 Energy Procedia 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2017) 000–000 841–846 EnergyProcedia Procedia138 00 (2017) www.elsevier.com/locate/procedia
2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies 2017 AEDCEE, 25‐26 May 2017, Bangkok, Thailand 2017 AEDCEE, 25‐26 May 2017, Bangkok, Thailand
The 15th International Symposium on District Heating and Cooling Assessment of energy energy efficiency plans Assessment of renewable renewable energy and and energy efficiency plans in in Thailand’s sector Thailand’sofindustrial industrial Assessing the feasibility using thesector heat demand-outdoor a a,* a, Pornphimol a,* Achiraya Chaichaloempreecha Winyuchakrit Bundit Limmeechokchai temperature function for a long-term districtaa,,heat demand forecast Achiraya Chaichaloempreecha , Pornphimol Winyuchakrit Bundit Limmeechokchai a aSirindhorn
International Institute of Technology, Thammasat University, Pathumthani, 12120, Thailand
a,b,c International Institute a Thammasat University, c Thailand Sirindhorn of Technology, 12120, I. Andrić *, A. Pinaa, P. Ferrão , J. Fournierb., B.Pathumthani, Lacarrière , 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 c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract Abstract
The industrial sector is one of the main energy consuming sector in Thailand, and accounted for 36.2% of total energy The industrial sector is one of the main energy consuming sector in Thailand, and accounted for 36.2% of total energy consumption in 2013. The increase of population and economic growth affected energy demand and greenhouse gas (GHG) consumption in 2013. The increase of population and economic growth affected energy demand and greenhouse gas (GHG) emissions. Thailand has the long-term climate plans related to energy saving and GHG mitigation in the industrial sector namely; emissions. Abstract Thailand has the long-term climate plans related to energy saving and GHG mitigation in the industrial sector namely; Energy Efficiency Plan 2015 (EEP2015) and Alternative Energy Development Plan 2015 (AEDP2015). Both plans have the Energy Efficiency Plan 2015 (EEP2015) and Alternative Energy Development Plan 2015 (AEDP2015). Both plans have the same periods and end at 2036. This paper analyzes the changes in GHG emissions during 2005-2036. Energy demand depends on same periods and networks end at 2036. paper analyzes the in changes in GHGas emissions during 2005-2036. Energy demand depends on District heating areThis commonly addressed the literature one of the most effective solutions for decreasing the population, gross domestic product, and fuel prices. The GHG mitigation can be evaluated by using Long-range Energy population, domesticfrom product, and fuel prices. Thesystems GHG mitigation be evaluated byare using Long-range greenhouse gross gas emissions the building sector. These require highcan investments which returned through Energy the heat Alternative Planning system (LEAP) under various scenarios. The business-as-usual (BAU) scenario can be expressed as a Alternative system climate (LEAP) conditions under various The business-as-usual scenario be expressed as a sales. Due Planning to the changed and scenarios. building renovation policies, heat(BAU) demand in the can future could decrease, frozen-scenario where new technology deployment and energy efficiency improvement are excluded. However, mitigation (MIT) frozen-scenario new technology deployment and energy efficiency improvement are excluded. However, mitigation (MIT) prolonging the where investment return period. scenarios are introduced by using the existing measures in the EEP2015 (MIT_EE scenario) and AEDP2015 (MIT_RE scenario). scenarios introduced by using existing measures in EEP2015 and AEDP2015 (MIT_RE scenario). The mainare scope of this paper is tothe assess the feasibility of the using the heat(MIT_EE demand –scenario) outdoor temperature function for heat demand In this study, Thailand’s industries are categorized into eleven categories: food and beverage, textile, wood and furniture, paper Inforecast. this study, categorized into eleven categories: foodasand beverage, wood and furniture, of paper TheThailand’s district ofindustries Alvalade,are located in Lisbon (Portugal), was used a case study.textile, The district is consisted 665 and pulp, chemical, nonmetallic, basic metal, fabricated metal, construction, mining, and other industries. Fossil fuels are the and pulp, chemical, basic metal, fabricated metal, Three construction, and(low, othermedium, industries. Fossil the buildings that vary nonmetallic, in both construction period and typology. weathermining, scenarios high) and fuels threeare district main source of GHG emission accounted for 86% of total GHG emission in this industry. GHG emission will increase from 77.6 main source scenarios of GHG emission accounted for 86%intermediate, of total GHGdeep). emission this industry. GHG emission willdemand increasevalues from 77.6 renovation were developed (shallow, To in estimate the error, obtained heat were Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036. This study found that nonmetallic industry is the major GHG emitters. Full 2005 to 222.5 in 2036. Thismodel, studypreviously found thatdeveloped nonmetallic GHG emitters. Full Mt-CO compared with results from aMt-CO dynamic demand andindustry validatedisbythe themajor authors. 2eq in 2eq heat implementation of the EEP2015 and AEDP2015 can reduce GHG emissions by 36% and 9% in 2036 under MIT_EE and implementation of the andweather AEDP2015 reduce GHG 36% andbe9% in 2036 for under MIT_EE and The results showed thatEEP2015 when only changecan is considered, theemissions margin ofby error could acceptable some applications MIT_RE scenarios, respectively. Such GHG reductions can be achieved from the implementation of EEP2015, due to the MIT_RE reductions be achieved the implementation of introducing EEP2015, due to the (the errorscenarios, in annual respectively. demand was Such lower GHG than 20% for all can weather scenariosfrom considered). However, after renovation financial incentives for energy performance achievement and enforcement of energy standards in the designated factories. financial incentives energy performance achievement and enforcement of energy standards scenarios in the designated factories. scenarios, the errorfor value increased up to 59.5% (depending on the weather and renovation combination considered). ©The 2017 The of Authors. Elsevier on Ltd.average within the range of 3.8% up to 8% per decade, that corresponds to the value slope Published coefficientbyincreased © 2017 The under Authors. Published by Ltd. committee of the 2017 International Conference on Alternative Energy in Peer-review responsibility of Elsevier the scientific in Authors. the number of heating hours ofLtd. 22-139h during heating season (depending on the combination of weather and ©decrease 2017 The Published by Elsevier Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE. D eveloping Countries and Emerging Economies. renovation scenarios considered). the other hand, function increased for 7.8-12.7% per decade (depending on the Peer-review under responsibility of On the Organizing Committee ofintercept 2017 AEDCEE. Keywords: Thailand industry; GHG emissions, EEP2015; AEDP2015; LEAP model coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Keywords: Thailand industry; GHG emissions, EEP2015; AEDP2015; LEAP model 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.: +66-2-986-9009; fax: +66-2-986-9112. Cooling. * Corresponding author. Tel.: +66-2-986-9009; fax: +66-2-986-9112.
E-mail address: [email protected] E-mail address: [email protected] Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE. Peer-review under responsibility of the Organizing Committee of 2017 AEDCEE.
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 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 2017 International Conference on Alternative Energy in Developing Countries and Emerging Economies. 10.1016/j.egypro.2017.10.105
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Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
1. Introduction Thailand, as a developing country, is the second largest economy in Southeast Asian Nation [1]. It not only consumes an enormous energy but also releases a large amount of GHG, especially in the energy sector including the power sector, the transportation sector, the industrial sector, the commercial sector and the residential sector. Thailand’s industrial sector is the major transformation due to rapid economic growth and development. Thus, this sector obviously demonstrates as an energy intensive sector for driving economic activities. The industrial sector includes manufacturing industries, mining, and construction. This sector consumes the largest final energy consumption accounted for 27.2 Mtoe or 36.2% of total final energy consumption in 2013 [2]. In addition, GHG emissions released 78.9 Mt-CO2eq which is higher than 2012 by 1.5%. The major emission originated from the electricity consumption. Furthermore, fossil fuel combustion such as coal, oil, and natural gas also shows a numerous GHG emission. A significant GHG emission proves that the atmosphere will be damaged. Therefore, Thailand proposes plans to mitigate GHG emission directly related to the industrial sector namely; Thailand energy efficiency plan 2015 (EEP2015) and Thailand alternative energy development plan 2015 (AEDP2015). 1.1. Thailand’s energy efficiency plan 2015 (EEP2015) According to the energy demands continuously increase, Thailand establishes targets for reducing future energy demands. The EEP2015 is energy conservation plan, it includes a demand side management and a deployment of advanced energy efficiency technologies, in terms of appliances, machinery and energy consumption behaviors change, as the end year at 2036 [3]. The EEP2015 introduces significant five measures related to the industrial sector namely: designated industry, energy labelling, energy efficiency resource standard (EERS), lighting emitted diode (LED) promotion, and monetary incentive. The designated industry is the industry enforced by energy conservation measure which is driven by government regulations. Energy labelling is the measure to establish the Minimum Energy Performance Stands (MEPs) and High Energy Performance (HEPs) for appliances in cooling system. The EERS measure or the minimum standard use to implement energy conservation measures by encouraging customers to use energy efficiently. LED promotion is the measure to encourage the industries to use LED light bulb. Monetary incentive obviously provides funds to industries which eventually desire to conserve the energy. 1.2. Thailand’s alternative energy development plan 2015 (AEDP2015) Alternative energy and renewable energy play a great role for GHG mitigation. The government has been making an effort to encourage the AEDP2015 in order to become a Low Carbon Society. Targets of AEDP2015 are to substitute fossil fuel consumption and to address social problems particularly municipal solid waste (MSW) and agricultural waste [4]. In the industrial sector, AEDP2015 focuses on solar, biomass and MSW. Most of these renewable energies are consumed in the heating system which is the importance system in the industrial sector. Thus, the aim of this paper is to assess the long-term GHG emission targets by the implementation of EEP2015 and AEDP2015 using the Long-range Energy Alternative Planning system (LEAP). 2. Methodology 2.1. Calculation of future energy demands The industrial sector has been categorized into eleven categories: food and beverage, textile, wood and furniture, paper and pulp, chemical, nonmetallic, basic metal, fabricated metal, construction, mining, and other industries. These categories are classified base on Thailand national energy situation report by Department of Alternative Energy Development and Efficiency (DEDE) [5]. In this study, all subindustries are assumed to be independent of each other.
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000 Data collection of • Energy demand • Population • GDP • Fuel prices Classification by subindustries
Future population GDP and fuel prices
Regression equation
Future energy demand of subindustries
843 3
Future energy demand in total energy industry sector
Regression analysis
Fig. 1. Flow diagram of estimation of future energy consumption.
Fig. 1. presents the steps for considering the future energy demand in the industrial sector. The first step is to collect the historical energy demand and socio-economic information such as population, value added and fuel prices. The future of population has projected by using National Economic and Social Development Board (NESDB) data (average growth rate: 0.03% annually) [6]. The GDP has projected with the growth rates illustrated in Thailand Power Development Plan 2015 (PDP2015) by Electricity Generation Authority of Thailand (EGAT) (average growth rate: 3.94% annually) [7]. In addition, coal price, oil price, natural gas price and renewable energy price are also estimated. The future of coal price, oil price and natural gas price has estimated by collecting significant prices from the World Bank [8,9,10]. While, renewable energy price including wood, paddy husk and biomass has estimated by the linear regression from the historical data of Energy Policy and Planning Office (EPPO) [11]. The energy demand projection had eventually been carried out by using the multiple variable regression and linear regression analysis. These methods apparently reveal the future energy demand of the subindustries. Likewise, the energy demand projection for the respective sub-industries are given in Table 1. Table 1. Energy demand in industries. Sub-industry
Energy demand in the industrial sector (ktoe) 2005
2010
2020
2030
2035
2036
Food and Beverage
6,353
8,017
13,387
19,832
24,069
25,166
Textile
1,007
783
1,202
1,771
2,102
2,254
Wood and Furniture
192
231
460
724
898
959
Paper and Pulp
815
1,412
2,895
4,484
5,401
5,626
Chemical
2,746
2,221
4,610
7,108
8,511
8,935
Nonmetallic
7,573
7,782
10,150
14,047
16,667
17,325
Basic Metal
1,049
1,503
2,238
3,570
4,292
4,527
Fabricated Metal
1,500
1,503
2,636
4,027
4,791
5,148
Construction
152
167
149
248
323
341
Mining
125
123
185
272
322
349
Other
1,363
1,856
2,681
4,333
5,365
5,604
Total
22,875
25,598
40,595
60,416
72,741
76,234
2.2. Long-range Energy Alternative Planning system (LEAP) The GHG emissions and mitigations in industries are analyzed by using an end-use model, called LEAP [12]. LEAP is an energy environment modeling tool developed by Stockholm Environment Institute (SEI). LEAP is a widely-used software tool for energy policy analysis and climate change mitigation assessments. Additionally, the GHG mitigation aspects can be calculated by using the emission factors from IPCC [13]. This study creates the demand side model of the industrial sector and separates the structure into eleven subindustries. Then, energy consumption was separated into two types: electricity consumption and non-electricity consumption. Electricity consumption consists of four service systems which are lighting system, cooling system, motor system and other system. Non-electricity consumption is consumed only in heating system. In each service system, the appliances can be divided into existing appliances and efficient appliances.
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2.3. Scenario description In this study, two difference scenarios are created. Business-as-usual (BAU) scenario is used as a reference case without any policy intervention. Mitigation (MIT) scenario applies aforementioned measures in both EEP2015 and AEDP2015. The BAU scenario, as a reference case, illustrates current activities without any climate policy intervention. This scenario can be expressed as a frozen-scenario which new technologies deployment and energy efficiency improvement are excluded. The energy demand in this case is provided in Table1. To reduce GHG emissions, this study introduced five measures in EEP2015 focusing on energy conservations such as energy efficiency improvement and economic use (reduce expendable use of energy). In addition, the AEDP2015 provides three measures related to the renewable energy such as MSW, biomass and solar. The MIT scenario are divided into three sub-scenarios including mitigation by EEP2015, called MIT_EE scenario, mitigation by AEDP2015, called MIT_RE scenario, and mitigation by both EEP2015 and AEDP2015, called MIT_EE+RE scenario. A relative change of GHG emission are calculated compared to the BAU scenario. 3. Results This section presents the significant results of expected GHG emission, obtained from the LEAP model. It also presents the GHG mitigation by implementation of the EEP2015 and AEDP 2015. 3.1. GHG emission in BAU scenario The results in this scenario are presented as reference results during 2005-2036. GHG emission will increase from 77.6 Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036 or increasing by 3.5% annually. The main GHG originates from electricity consumption followed by the briquettes combustion and diesel usage accounted for 38.9%, 19.9% and 13.8% of total GHG emission in 2036 (see Fig. 2(a).). The nonmetallic industry reveals the highest GHG emission followed by food and beverage industry and chemical industry accounted for 27.8%, 15.8% and 14.0% of total GHG emission, respectively (see Fig. 2(b).). The nonmetallic industry has the proportion of GHG emission from fossil fuel combustion accounted for 85.5% of GHG emission in this sector. Thus, this industry is a significant industry to be concerned and it is the major of Thailand’s industry as a cement industry. 3.2. GHG emission and mitigation in MIT scenario The first mitigation year is 2011 in this study. By implementation of the EEP2015 and AEDP2015 measures, the GHG emission will be decreased by 67.0 Mt-CO2eq in 2011 and 98.7 Mt-CO2eq in 2036, or it can mitigate 11.4% annually, compared with the BAU scenario. The GHG emissions in the BAU scenario and the MIT_EE+RE scenario are shown in the Fig. 3. 200 150
Electricity Diesel LPG Lignite Oil Others
Briquettes Natural Gas Bituminous Gasoline Anthracite
100 50 0
2005 2010 2015 2020 2025 2030 2035 2036
250
GHG Emission (kt-CO2eq)
GHG emission (kt-CO2eq)
250
200 150 100
Food Wood Chemical Basic metal Other Construction
Textiles Paper Non metallic Fabricated Mining
50 0
2005 2010 2015 2020 2025 2030 2035 2036
Fig. 2. (a) GHG emission by fuel types; (b) GHG emission by sub-industries in the BAU scenario.
Achiraya Chaichaloempreecha et al. / Energy Procedia 138 (2017) 841–846 Achiraya Chaichaloempreecha et al./ Energy Procedia 00 (2017) 000–000
GHG Emission (kt-CO2eq)
250
845 5
BAU scenario MIT_EE+RE scenario
200 150 100 50 0 2005
2010
2015
2020
2025
2030
2035
Fig. 3. GHG emission.
3.2.1. GHG Mitigation by implementation of EEP2015 Fig. 4(a). presents the GHG mitigation by the implementation of five measures in EEP2015. These measures can be reduced the GHG emission about 79.8 Mt-CO2eq or accounted for 35.8% reduction in 2036. The GHG emission reduction in this plan mainly comes from electricity savings. Nowadays, the industries required a high investment for energy transformation. Therefore, monetary incentive is the most effective measure. This measure can reduce the overall energy consumption, especially electricity consumption and reducecGHG emissions by 58.0% of total GHG emission reduction. However, designated industry and energy labelling measures is the important measure to achieve the target. The designated energy conservation industries and energy labelling can reduce GHG emission about 31.2% and 5.8% of total GHG emission reduction, respectively. While the GHG emissions can be reduced by 2.9% by the implementation and deployment of LED light bulbs in the lighting system. The remaining of 2.1% is in the EERS implementation. 3.2.2. GHG Mitigation by implementation of AEDP2015 Fig. 4(b). presents the GHG mitigation by the implementation of three measures in the AEDP2015. These measures substitute fossil fuel combustion by renewable energy in the heating system. Thus, Most of the GHG emission reduction comes from the reduction of fossils fuel combustion. Results indicate that three measures are effective for GHG reduction. GHG emission can be reduced by 19.0 Mt-CO2eq, and accounted for 8.5% reduction in 2036. The highest reduction measure apparently achieves by MSW and accounted for 74.0% of total GHG reduction followed by the implementation of solar energy (14.4% of total GHG reduction). MSW not only reduces the GHG emission but also help the management of domestic waste. The remaining is the use of biomass, which is accounted for 11.6% of total GHG reduction.
80 70 60 50
EERS LED Labelling Desingnated Industry Monetory incentive
GHG mitigation (Mt-CO2eq)
GHG mitigation (Mt-CO2eq)
90
40 30 20 10 0
2005
2010
2015
2020
2025
2030
2035
2036
20 18 16 14 12 10 8 6 4 2 0
Biomass Solar MSW
2005
2010
2015
2020
2025
2030
Fig. 4. (a) GHG mitigation by implementation of EEP2015; (b) GHG mitigation by implementation of AEDP2015.
2035
2036
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4. Conclusion This study assesses the long-term targets of EEP2015 and AEDP2015 plans in terms of GHG mitigation in the industrial sector during 2005–2036 by using LEAP. The measures in industries can reduce GHG emissions. By adopting all measures in the EEP2015 and AEDP2015 plans, GHG emissions can be reduced by 98.7 Mt-CO2eq in 2036. GHG emissions in industries will increase from 77.6 Mt-CO2eq in 2005 to 222.5 Mt-CO2eq in 2036 accounting for 3.5% per year. This study found that nonmetallic industry is the major GHG emitters. Full implementation of the EEP2015 and AEDP2015 can reduce GHG emissions by 35.8% and 8.5% in 2036 in the MIT_EE and MIT_RE scenarios, respectively. Such GHG reductions can be achieved mainly from the implementation of EEP2015, due to the introduction of monetary incentives and energy conservation in designated industries for energy performance achievement. Acknowledgements The authors would like to gratefully acknowledge the contribution of Sirindhorn International Institute of Technology of Thammasat University (SIIT-TU) for support of this research. The authors would also like to thank the Low carbon research funds for the financial support. References [1] World Bank. (2011). Thailand Now an Upper Middle Income Economy. Retrieved from http://www.worldbank.org/en/news/pressrelease/2011/08/02/thailand-now-upper-middle-income-economy. [2] Department of Alternative Energy Development and Efficiency, Ministry of Energy. (2013). Energy Balance of Thailand 2013, Bangkok: Department of Alternative Energy Development and Efficiency, Ministry of Energy. [3] Energy Policy and Planning Office, Ministry of Energy. (2015). Energy Efficiency Plan 2015-2036 (EEP 2015). Retrieved from http://www.eppo.go.th/encon/EEP2015/Draft-EEP2015.pdf [4] Energy Policy and Planning Office, Ministry of Energy. (2015). Alternative Energy Development Plan (AEDP 2015–2036). Retrieved from http://www.dede.go.th/download/files/AEDP2015_Final_version.pdf [5] Department of Alternative Energy Development and Efficiency, Ministry of Energy. (2007). Energy Balance of Thailand 2007. Bangkok: Department of Alternative Energy Development and Efficiency, Ministry of Energy. [6] Office of The National Economic and Social Development Board. (2016). Population total fertility rate 2010 – 2040. Retrieved from http://social.nesdb.go.th/SocialStat/StatReport_Final.aspx?reportid=697&template=2R1C&yeartype=M&subcatid=1. [7] Energy Policy and Planning Office, Ministry of Energy. (2015). Power Development Plan 2015–2036 (PDP 2015). Retrieved from http://www.eppo.go.th/power/PDP2015/PDP2015.pdf [8] World Bank. (2016). Crude Oil Price Forecast: Long Term 2016 to 2025. Retrieved from https://knoema.com/WBCFPD2015Oct/worldbank-commodity-forecast-price-data-July 2016. [9] World Bank. (2016). Coal Prices: Long Term Forecast to 2020. Retrieved from https://knoema.com/xfakeuc/coal-prices-long-term-forecastto-2020-data-and-charts. [10] World Bank. (2016). World Bank Commodities Price Forecast. Retrieved from http://pubdocs.worldbank.org/en/764161469470731154/ CMO-2016-July-forecasts.pdf. [11] Energy Policy and Planning Office, Ministry of Energy. (2016). Economic Data and Analysis. Retrieved from http://www2.eppo.go.th/info/7economic_stat.htm [12] Heaps, C. (2008). An Introduction to LEAP, Stockholm Environment Institute. Retrieved from https://www.energycommunity.org/default.asp?action=introduction [13] Intergovernmental Panel on Climate Change (IPCC). (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Retrieved from http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/2_Volume2/V2_2_Ch2_Stationary_Combustion.pdf