Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 61 (2014) 2164 – 2167
The 6th International Conference on Applied Energy – ICAE2014
System dynamic modeling of low carbon strategy in Latvia Dagnija Blumbergaa*, Andra Blumbergaa, Aiga Barisaa, Marika Rosaa a
Riga Technical University, Kronvalda Boulevard 1, Riga, LV1010, Latvia
Abstract In this paper we study development patterns of Latvian electricity market based on a newly developed computeraided simulation model. A system dynamics approach was applied to simulate long-term development of electricity sector in the region under the existing policy design. Results show that despite the relatively large share of renewable energy sources in Latvian electricity generation mix, natural gas will remain its significant role in national electricity supply. Alternative policy strategies should be developed to allow achieving CO 2 emission reductions greater than 30% by 2050. © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of ICAE Peer-review under responsibility of the Organizing Committee of ICAE2014
Keywords: Modeling; power; renewable energy; system dynamics
1. Introduction Electricity generation in Latvia is based on both fossil fuels, and renewable energy sources. Neither Latvia, nor any of the Baltic States owns a natural gas field. Moreover, the use of fossil fuel is the primary source of greenhouse gas (GHG) emissions. In response to environmental, social and economic pressures governments are facing a continuous challenge to develop transition pathways to make their electricity systems more sustainable [1]. In this paper we study development patterns of Latvian electricity market based on a newly developed computer-aided simulation model. System dynamics modelling approach was applied to analyze electricity supply pattern in Latvia by 2050. System dynamics is a computer-aided modelling approach to understanding the behaviour of complex systems over time. A comprehensive literature review of system dynamics applications in the past is given in [2-3]. More recently system dynamics modelling in electricity sector has been used: to analyse generation expansion alternatives in Portuguese/Spanish electricity system [4] and in Switzerland [5]; to examine
* Corresponding author. Tel.: +371-670-899-08; fax: +371-670-899-08. E-mail address:
[email protected].
1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICAE2014 doi:10.1016/j.egypro.2014.12.100
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various investment initiatives in Iranian electricity market [6]; to analyse options for carbon mitigation in Turkish electric power industry [7] and to assess the fluctuation of energy supply and demand in Australia [8]. Finally, energy security issues in Finland’s energy system were investigated in [9]. The overall target of our system dynamics model is to simulate the long-term development of electricity sector in the region under current policy design to evaluate potential for GHG emission reduction. 2. Modelling Latvian electricity sector The model presented in this paper was developed and validated based on Latvian case study applying historical data from 1995 to 2012. Simulation is carried out on a yearly basis up to 2050. A perfect competitive electricity market is assumed. All generating units with the same type of technology are accumulated to represent individual generation technology, so that the competition is reflected among primary electricity generators. As the model is an energy-economic model, it was assumed that the capacity of installed facilities is influenced by two factors: investment and the depreciation of the equipment over the time. The installed capacity stock (Q, GW) is therefore a function of an incoming investment flow (I, GW/year) and an outgoing depreciation flow (D, GW/year): Q=f(I, D). Total annual investments are assumed to be equal to the total annual depreciation of equipment. Electricity shortage Electricity import
Ordering rate RES
GDP Demand Import electricity price
Ratio domestic import price
Investment decision RES Electricity price RES
Domestic electricity generation price Electricity price Fossil Investment fraction Fossil
Ordering rate Fossil
Fossil electricity capacity ordered Time to install Fossil
Energy efficiency improvement
Capacity installation Gas
Ratio capacity demand RES risk
Ratio Fossil RES electricity price Fossil electricity generation
Investment fraction RES Fossil electrictricity generation capacity Lifetime Fossil
Cummulative RES-E production
Total anual investment
Rate production RES
Experience of RES use
RES-E capacity ordered
Operating hours
Time to RES-E install RES generation
Capacity installation RES
RES-E generation capacity
Total domestic generation Total annual depreciation
Depreciation RES capacity Lifetime RES
Depreciation Fossil capacity
Fig. 1. Key stocks and flows of the Latvian electricity sector model
Key stocks and flows of the proposed system dynamics model are presented in Fig. 1. For the sake of simplicity only two resource flows, renewable and fossil energy respectively, are shown. However, the real model consists of several resource flows corresponding to national electricity generation mix in Latvia: natural gas, large hydro power (>10 MW), wind power, biomass, biogas, and photovoltaic power systems. Investment decisions are fundamentally based on total revenues gained by investors [4] and electricity price is an important factor determining the willingness to invest in new electricity generation capacity [10]. Therefore which of all fuels electricity producer will choose, depends on the relative costs of the alternatives. The average cost of a fuel option is determined as the sum of four components: 1) technology investment costs (EUR/MWh); 2) operation and maintenance costs (EUR/MWh); 3) fuel cost (EUR/MWh), and; 4) a relevant premium (EUR/MWh), e.g. tax burden (negative) or state support incentives (positive) [11]. In addition, risk factor associated with the use of technology (EUR/MWh) [12] is considered. List of modelling assumptions is presented in Table 1. Initial data on installed capacity of each technology corresponds to national electricity balance in 2012 [13]. Future development in electricity demand due to
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industrial growth is represented by an exogenous demand growth rate which is assumed to correlate with the gross domestic production. In addition, European Union requirement regarding end-user energy efficiency improvement is considered. Table 1. Initial data and modeling assumptions Natural gas
Wind power
HPP
Biomass
Biogas
Solar
23
43
0
Installed capacity, MW
953
59
1150
Life time, years
15
20
30
20
15
25
O&M costs, EUR/MWh
21
17
24
5% of c.c
3% of c.c.
14
Capital costs M.EUR/MW
1.0
1.5
4.9
7.1
5.8
2.8
Efficiency, %
85
50
90
80
60
10
Fuel costs, EUR/MWh
46
0
0
21
34
0
CO2 tax, EUR/tCO2
3
0
0
0
0
0
Excise, EUR/MWh
1.8
0
0
0
0
0
145.1
105.3
0.0
185.0
194.9
426.9
-
1.5
1.5
No limit
No limit
3
Feed-in tariff, EUR/MWh RES potential, GW
It is assumed that if there is under-capacity at a given modeling step, then imports will be increased; otherwise, the decision is based on comparison between the imports price and the inland generation cost similarly as it was done in [6]. Electricity import price is modeled as an exogenous variable based on the average electricity price in Nord Pool Spot (32 EUR/MWh in 2012 [14]). Annual electricity price increase of 3% is assumed for the coming years. Construction time of a new facility is assumed two years. 3. Results and discussion Figure 1 shows modeling results for installed electricity generation capacities and related CO2 emissions by 2050. Hydro 1.8
CO2 emissions, mil.t
Installed capacity, GW
1.0
Natural gas
0.5 Biomass
1.7 1.6 1.5 1.4
Wind 0.0
2020
Biogas 2030
Solar 2040
1.3 2050
2020
2030
2040
2050
Fig. 1. (a) Installed electricity generation capacity; (b) CO2 emission from electricity generation
According to modeling results with current policy measures (feed-in tariffs and tax policy) natural gas and hydropower will remain their leading positions in Latvian electricity balance. With an increasing natural gas
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price, the use of other resources will become more attractive. Especially biomass is expected to compete with electricity production from natural gas. Modeling results also demonstrate that decreasing natural gas share in electricity production will allow reducing CO2 emissions from electricity sector. However, this decrease will not allow reaching zero-emission level. The results show that alternative policy strategies should be developed to allow achieving CO 2 emission reductions greater than 30% by 2050. Acknowledgements This work was supported by Nordic Energy Research NORSTRAT project. References [1] Pina A, Silva CA, Ferrćo P. High-resolution modeling framework for planning electricity systems with high penetration of renewables. Appl Energ 2013; 112:215–23. [2] Kilanc GP, Or I. A decision support tool for the analysis of pricing, investment and regulatory processes in a decentralized electricity market. Energ Policy 2008; 36:3036-44. [3] Hasani M, Hosseini SH. Dynamic assessment of capacity investment in electricity market considering complementary capacity mechanisms. Energy 2011; 36:277-93. [4] Pereira AJC, Tomé Saraiva J. A long term generation expansion planning model using system dynamics – Case study using data from the Portuguese/Spanish generation system. Electr Pow Syst Res 2013; 97:41– 50. [5] Ochoa P, Van Ackere A. Policy changes and the dynamics of capacity expansion in the Swiss electricity market. Energ Policy 2009; 37:1983–98. [6] Hasani-Marzooni M, Hosseini SH. Dynamic analysis of various investment incentives and regional capacity assignment in Iranian electricity market. Energ Policy 2013; 56:271–84. [7] Saysel AK, Hekimoglu M. Exploring the options for carbon dioxide mitigation in Turkish electric power industry: System dynamics approach. Energ Policy 2013; 60:675–86. [8] Rasjidin R, Kumar A, Alam F, Abosuliman S. A system dynamics conceptual model on retail electricity supply and demand system to minimize retailer’s cost in eastern Australia. Procedia Eng 2012; 49:330-37. [9] Aslani A, Helo P, Naaranoja M. Role of renewable energy policies in energy dependency in Finland: System dynamics approach. Appl Energ 2014; 113:758–65. [10] Nielsen S., Sorknæs P, Ostergaard PA. Electricity market auction settings in a future Danish electricity system with a high penetration of renewable energy sources – A comparison of marginal pricing and pay-as-bid. Energy 2011; 36:4434-44. [11] Moxnes E. Interfuel substitution in OECD-European electricity production. Syst Dynam Rev 1990; 6:44-65. [12] Blumberga A., Blumberga D., Bazbauers G., Davidsen P., Moxnes E., Dzene I., Barisa A., Zogla G., Dace E., Berzina A. System dynamics for environmental engineering students. – Madona: Madonas Poligrafists, 2010. – 318 p. [13] Central Statistical Bureau of Latvia. Statistics Database [14] Nord Pool Spot market data. Available online: http://www.nordpoolspot.com/Market-data1/Elspot/Area-Prices/ALL1/Hourly/ [Accessed : 20/12/2013].
Biography Prof. Dr.hab.sc.ing, Dagnija Blumberga is author of more than 200 publications and 14 books. Her main research area is renewable energy resources. Prof. Dr..sc.ing.Andra Blumberga is author of more than 50 publications and 8 books. Her main research area is energy efficiency and system dynamic modelling. Aiga Barisa is second year PhD student. Her main research is in system dynamic modeling in green energy sector including transport Prof. Dr..sc.ing. Marika Rosa is author of more than 56 publications and 6 books. Her main research area is energy planning and emission trading.
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