ENERGY COST MODELLING OF NEW TECHNOLOGY ADOPTION FOR RUSSIAN REGIONAL POWER AND HEAT GENERATION. Alexandra Bratanova

Transcription

1 School of Economics Energy Economics and Management Group ENERGY COST MODELLING OF NEW TECHNOLOGY ADOPTION FOR RUSSIAN REGIONAL POWER AND HEAT GENERATION Alexandra Bratanova Dr Jacqueline Robinson Dr Liam Wagner USAEE/IAEE 6 November, 2012

2 Outline I. Overview of energy in Russia and Moscow - Energy efficiency potential and goals - Cogeneration and conventional generation technologies - Renewable energy targets II. Energy costs modelling (Moscow case study) - Model outline for heat and electricity generation - Financial parameters of the model - Technological parameters of the model - Analysis III. Conclusions and further research 2

3 The research scope Size: 17.1 mln km 2 Population: mln Russia TJ of electricity (53%) and heat (47%) consumption 0.27% the size 13% the population 11.7% energy consumption 3 Moscow and Moscow Region Size: km 2 (Moscow km 2 ) Population: 18.6 mln (Moscow mln) TJ of electricity (35%) and heat (65%) consumption

4 Energy efficiency in Russia: energy-gdp ratios improved by 5% annually ( ) times higher than in Canada, Finland, Germany GDP per energy use (2005 PPP $/kgoe) ,5 1% improvement is accounted for technological progress 6,4 6,8 6,9 6,9 7,4 8,1 8,2 8,3 4,1 4,6 5,0 5,5 5,5 5,7 5,9 3,0 2,3 2,5 1,7 9,5 10,1 4 Data source: WB

5 Energy efficiency in Moscow: energy-gdp ratios 5 Data source: Federal State Statistics Service of the Russian Federation

6 Energy efficiency potential in Russia: 45% of primary energy use EE Potential Estimate Example Energy consumption 154 mtoe 8-10 years without additional primary energy Electricity consumption 340 billion kwh Reduction of electricity consumption by 36% Distribution losses 844 mln Gcal Reduction of heat consumption by 53% Natural gas consumption Carbon gas emission Inefficiency in district heating 240 billion m 3 50% for domestic consumption natural gas export 793 million tons of CO 2 UK and the Netherlands oint annual emissions, = 2.9% of global energy-related CO 2 emission billion cubic metres of natural gas per year 6 Data source: IEA, CENEF, Government of the Russian Federation.

7 Energy efficiency and renewable energy: goals and programs in Russia and Moscow Energy efficiency goals National goal of Russia: 40% reduction of GDP energy intensity by 2020 in comparison with 2007 (GRF 2010c) Regional goal of Moscow: 40% reduction of regional GDP energy intensity by 40% by 2020 (DFERM 2011). Renewable energy targets National: RES shares %; % (excluding large hydroelectric power plants with above 25MW installed capacity) Regional: 4.5% by

8 Energy efficiency and renewable energy: Electricity consumption in Moscow proections 8 Data source: Federal State Statistics Service of the Russian Federation

9 Energy production in Russia and Moscow Data source: NP Market Council In Russia Total generating capacity installed, including 214 GW thermal and co-generation plants 148 GW (69%) hydroelectric plants 44 GW (21%) nuclear plants 22 GW (10%) Combined-heat-and-power generation (CHP) Existing share of renewable energy (ex. large hydro) Electricity supply in Moscow region: nearly 100% gas fired Value 30% installed capacity RES share 0% 0.1% 9 Data source: IEA and OECD (2005), (2008)

10 Infrastructure 90% of operating power stations, 83% of houses, 70% of boilers, 66% of the heating network built before

11 Research obective, questions, expected contribution Research questions: Costs of renewable energy generation? Are state goals feasible? Cost of natural gas dependency for the region? Stimulate technology and infrastructure development? Obective: Develop a tool for energy sector economic analysis for technological development planning to support policy decision making in Russian regions 11

12 Levelised cost of energy (LCOE) model construction LCOE E = n t= 1 E TOC( t) + Capex t (1 WACC) + n E (SO(t) )* CPI( t) R t= 1 t (1 + WACC) E Source: Wagner, Foster 2011 Separation coefficient for costs k e = SO( t) SO( t) e k h h SO( t) = e + h e h k k = 1 E ( k, ) SO( t) k SO ( t) + SO( t) = SO( t) h h e k h k SO - separation coefficient for costs associated with electricity production; - separation coefficient for costs associated with heat production; - total output lever (in GJ) used for separation coefficients determination. 12

13 Levelised cost of energy (LCOE) model construction Technologies considered for LCOE model: thermal power generation (both natural-gas based and coal-based), including: Supercritical PC, CCGT, OCGT distributed generation, including: SolarThermal, Photovoltaic,Wind, Bio-fuel Existing conventional combined natural-gas based generation 14

14 WACC 15 Post TaxNo min al = E V LCOE model financial parameters * R e + WACC D * R V d (1 T Post Tax Re al e ) 1 TaxNo min al = e + WACCPost 1+ CPI R = R + b( R R ) + C f 1 Notation Parameter Assumed values E /V Equity capital share 80% D /V Debt capital share 20% R d Cost of debt 10% R f Risk free rate of return 8.5% R m Market rate of return 13.5% ( R Market risk premium 5% m R f ) T e Effective tax rate 24% R e Cost of equity 15.5% β Equity beta 1 R C Country risk 2% CPI Price indexes 6% WACC Post tax nominal WACC 13.92% Post TaxNomin al WACC Post tax real WACC 7.47% Post Tax Real m f R c

15 16 LCOE model technological parameters

16 LCOE model: data availability and scenarios Data required for EE indicator development and its availability in Russia End-use sector Data for EE measures Required Available Industry Residential sector Service sector 12 3 Transport 29 8 Modelling scenarios Scenario 1 Domestic natural gas prices (RUR85/GJ) Scenario 2 Natural gas priced at opportunity cost (RUR210/GJ ) 17

17 Scenario 1 Domestic natural gas prices LCOE, RUR/MWh

18 Scenario 1 Domestic natural gas prices LCOE, RUR/MWh CCGT technologies (RUR1419) outperform existing technologies (RUR 2488) PV and solar thermal technologies are unreasonably costly Less costly RES options bio-fuel and wind based Is state RES target (4.5% energy generation based on RES by 2020) feasible? 19

19 Scenario 2 Natural gas priced at opportunity cost (shadow price)

20 Scenario 2 Natural gas priced at opportunity cost shadow pricing of natural gas increased gas prices by 150%, conventional existing technologies by nearly 36.5% regional energy supply system is heavily dependent on gas price accounting for opportunity costs give priority to alternative technologies solar technologies remain too costly to be considered as an option 21 Technological diversity should be considered for public programs

21 LCOE model scenario comparison Wind - Small scale (50 MW) Wind - Medium scale (200 MW) Supercritical PC - Brown coal Wind - Large scale (500 MW) Small Supercritical PC - Black coal Supercritical PC - Black coal Existing Mosenergo Gas Average OCGT Biomass Very Small CCGT Small CCGT 300MW CCGT 0,0 500,0 1000,0 1500,0 2000,0 2500,0 3000,0 3500,0 4000,0 22 LCOE, Scenario 1 LCOE, Scenario 2

22 Conclusions and discussion 1. The model provides an interesting insight into the generation costs for the Moscow region. 2. The model recommends CCGT with a priority for small scale plants as the most cost-efficient new energy generation technology. 3. If the domestic gas prices in Russia reach parity with international prices: - Significant increase in gas-based generating costs - Generation from biomass and supercritical PC technologies would outperform CCGT s - Wind options improved position becoming among the best RES solutions. 23

23 Conclusions and discussion 4. Market mechanisms in the energy sector in Russia are not yet suited to create incentives for new technologies which include RES development and their implementation. 5. The regional authorities of Russia and CIS countries could benefit from the application of this model for the planning of their energy system development and public programs management. Further research directions Heat generation costs modelling Energy efficiency improvement costs modelling Renewable technology capacity introduction to the model 24

24 Thank you!