Seminar « Project for Kazakhstan energy system modeling: results and plans »

1. Experience of development and application of the optimization models for strategic planning in the power sector under the recent technological, economic and environmental challenges Fedor Veselov Alla Makarova Andery Khorshev Energy Research Institute Russian Academy of Sciences Seminar «Project for Kazakhstan energy system modeling: results and plans» Kiev, May 16, 2012

2. ERI RAS – experience in system energy studies • Energy Research Institute of the Russian Academy of Sciences (ERI RAS) was established in 1985 for the fundamental studies in the area of national energy policy development and implementation: • state level - methodological, modeling and analytical support for the energy policy priorities and implementation mechanisms (incl. macroeconomic, technological, pricing, environmental and other aspects), quantitative elaboration of the economy and energy sector scenarios in the context of Energy Strategy • Ministry of Energy, Ministry ofeconomic development, Ministry ofnatural resources, Federal Antimonopoly Service • corporate level – capacity building, modeling and information support of the strategic planning system of leading energy companies, justification of investment and market policy under the energy markets transformation processes • Gazprom, Gazexport, NovaTEK, Mezhregiongas, Wintershall, Roneft, TNK-ВР, SUEK, RAO EES Rossii, Rosenergoatom, Fortum Energy Research Institute RAS

3. SCANER – multi-functional system of models for the investigation of the global and Russian energy sector development • «SCANER» is a tool for the system analysis of the Russian energy sector development for the mid- and long-term prospects (to 2030-50) as an important part of national economy and global energy markets. Integrating the powerful modeling and informational resources, SCANER provides: • Unique informationalsupport to the analysis and forecasts (regularly updated databases on the national and regional economy, energy sector, energy balances and markets) • Multi-level co-ordination system of energy forecastsfocused on the formulation of rational variants of the economy, energy sector and energy companies’ development • Huge flexibilityand fast adaptation of the models and their calculation modes under the separate forecasting requirements Energy Research Institute RAS

4. Parameters of the global economy and energy sector development scenarios Parameters of the Russian economy and energy sector development scenarios Forecast of global energy markets development and regulation environment Formulation of integrated target parameters for the economy and energy sector development National and regional forecast of the economic and technological development National and regional fuel and energy demand forecast with estimation of energy saving and efficiency improvements Estimation of resources and reserves for fuel supply industries Forecast of energy demand and prices by countries Estimation of the impact of pricing and investment policy in energy sector on the economy Estimation of priorities for new technologies Forecast of production and financial programs by energy industries Power sector Oil and gas sector Coal sector National and regional energy balances Forecast of the financial feasibility by energy industries and companies Recommendations for the national energy policy improvements Forecasted investment programs by energy industries and companies Domestic fuel and energy prices’ forecast SCANER – multi-functional system of models for the investigation of the global and Russian energy sector development Energy Research Institute RAS

5. Gas sector development optimization system Coal sector development simulation system Oil sector development optimization system Final energy demand simulation models Gas production and transportation Fuel consumption at power plants and boilers Other domestic fuel consump-tion and export Fuel demand models Fuel demand models Fuel demand models Gas balances Steam coal balances Coal production and transportation Fuel oil production and transportation Fuel oil balances Cond. Thermal CHP Hydro, Nuclear, RES Electricity demand Electricity and electric capacity balances Electricity demand model Electricity demand model Electricity demand model Boilers Heat demand Centralized heat supply balances Heat demand model Heat demand model Power sector structure optimization as a part of national energy sector development • Joint optimization of power sector structure with the gas and coal production and transportation system development provides opportunities: • To form the core of the national and regional energy balances, incl. electricity, heat, gas and coal supply and demand. • To obtain the system of equilibrium wholesale prices by the regions of Russia on the basis of long-term fuel and electricity supply costs • To assess the long-term elasticity of fuel demand in electricity and heat production affected by the prices, cost and environmental factors Energy Research Institute RAS

6. Dynamic aspect and area of optimization (EPOS) Capacity, electricity and heat production structure is formed under the optimization of capital and operational costs (or revenue requirement) of different types of investment alternatives (excl. distributed generation): • life extension and rehabilitation of the existing capacities • new CHP and/or boilers • intersystem grid reinforcement • new gas, coal and nuclear technologies • renewable technologies System assessment of the investment decisions is preformed under the uncertainities of: • electricity and centralized heat demand • fuel supply infrastructure development • fuel pricing mechanisms (coat plus, netback of inter-fuel competition) • GHG emission limitations and carbon prices • Capital expenditure limitations In the optimization model investment and production decisions are defined by the system of balances: • electricity • centralized heat (by types of towns and cities) • installed capacityrequirements (for peak and off-peak hour) • fuel supply and demand Dynamic links ensure to assess the efficiency of these decisions on the whole planning horizon (taking into account the 10-15 years end-effect in costs) Energy Research Institute RAS

7. Technological and regional representation of the power sector (EPOS) Representation of the Unified Power System (UPS) by integrated power systems (IPS) and balancing zones Optimization and simulation models can operate with different regional detailing level of the Russian Unified Electricity System: • 7 Integrated Power System • 42 balancing zones based on the main grid congestions • 29 Free Capacity Flow zones assigned by the System Operator for the competitive capacity market • results are additionally detailed by the simulation model (ELIS) to the level of administrative units and converted in the form of regional electricity and centralized heat balances Free capacity flow zones (ZSPM) Generating capacities are modeled with the different detailing: • aggregated technologies: 10-15 types of existing and 15-30 types of new technologies • power plants: over 400 existing plants and over 150 investment projects of repowering, brown- and green-field unit construction Energy Research Institute RAS

8. Representation of power plant (i) operation modes (j) Installed capacity Xit Installed capacity requirements Operation modes Xi1t Xi2t … XiJt h1 b1*h1 a11*b1*h1 … aN1*b1*h1 h2 b2*h2 a12*b2*h2 … aN2*b2*h2 hJ bJ*hJ a1J*bJ*hJ … aNJ*bJ*hJ Electricity supply/demand balance Electricity production Total fuel consumption Fuel 1 supply/demand balance … Fuel 1 consumption … . . . Fuel N consumption Fuel N supply/demand balance Snanj = 1 for each fuel mix Energy Research Institute RAS

9. Representation of capacity development Existing capacities Xit+1 = Xit - Decommit,t+1 New & rehabilitated capacities Xit+1 = Xit + Additionit,t+1 Condition for total capacity of rehabilitated alternatives (k) for existing plant (i) Sk b*Additionkt,t+1≤ Decomm it,t+1 Energy Research Institute RAS

10. Capacity constraints Installed capacity requirement Nreq = Pmax + ReserveMargin Nreq Reserve margin Pmax Rated capacity requirement for a peaking hour (max load requirement) Existing hydro and other peaking capacities New peaking capacity requirement Rated capacity requirement for an off-peak hour (min load requirement) Pmin Energy Research Institute RAS

11. Representation of heat supply • different heal load classes (separate balances) • different set of heat supply alternatives (CHP and boiler technologies) for each heat load class • heat supply alternatives can be used for different heat load classes, but with different heat distribution costs • heat-driven rehabilitation for existing CHP – electricity is considered as by-product of the required heat production Energy Research Institute RAS

12. Analysis of dual solution Primal LP task solution capacity and generation structure, supply/demand balances of all considered energy resources Dual LP task solution system of “shadow” prices of energy resources (electricity, capacity, heat, fuels) Static LP task - short-term (spot) marginal pricing Dynamic LP task - long-term (spot) marginal pricing Post-optimization analysis of dual solution and “shadow” prices’ structure required the investigation of basic variables, their impact on matrix of LP task and cost function: j = cb B-1 Energy Research Institute RAS

13. Modeling pricing rules at the energy markets EPOS allows to investigate different pricing models for fuel industries and power sector based on the long-term marginal costs Power plants operates at several markets with different pricing models Long-term efficiency of plant operation (revenues-costs) can be evaluated on the basis of variable reduced costs Energy Research Institute RAS

14. System of automatic formulation and information support of the optimization models and multi-case runs (EPOS family) Automatization of interaction between post-relatinal and relational databases and an interface openessensure the high flexibility and efficiency of new models and solution reports creation Subject area Hierarchical stratified database Knowledge base Library of the formation methods (modules) of the LP model matrix elements Industry Country 7 IPS(42 nodes, 29 free flow zones) Linear Programming (LP) Model Database of the mathematical LP model coefficients(by LP matrix fragments and variants) Energy companies (22GenCos, indep. producers and utilities) ~ 80 regional systems(RF administrative units) Generating technologies (~20per region) Power plants (~600existing and new plants) LP task Matrix and RHS coefficients of the liner programming task (# of equatuins – up to 150 000 # of variables- up to 300 000 # of non-zero elements – up to 1 500 000) Equipment groups (~2500groups of units with the same equipment type and existing/new state) Operation and fuel supply modes (~6500variants of the capacity factor and fuel supply mix modes) Energy Research Institute RAS

15. Technology of prompt matrix generation / transformationfor EPOS family EPOS modeling system • Types of LP matrix transformation • recalculation of coefficients (RHS) for existing variables (constraints) without changes of dimension • simple changes of dimension without changes of the matrix structure - addition of variables (constraints) of existing types • complex changes of dimension with the changes of the matrix structure - addition of variables (constraints) of new types Opened for used and time saving technology will not require regular general recalculations of input data Alternative algorithms for separate matrix coeffs, bounds and RHS calculation from initial input data Free fragmentation of LP matrix elements in the database Automatic assembling of the whole LP matrix from its fragments in the database Energy Research Institute RAS Energy Research Institute RAS

16. Conclusion. Financial aspects of power sector optimization Technological structure and fuel mix At present Joint (iterative) analysis of EPOS results and financial models of energy companies Market structure and pricing rules Profitability of investments and capitalization growth in the sector In the future Agent-based approach to simulation of the investment decisions and market evolution Energy Research Institute RAS Energy Research Institute RAS

17. Energy Research Institute of RAS www.eriras.ru info@eriras.ru Veselov Fedor, PhD economics, Head of Department info@eriras.ru, erifedor@rambler.ru Makariva Alla, PhD economics, Head of Laboratory info@eriras.ru Khorshev Andrey, PhD economics, Head of Laboratory epos@eriras.ru Thanks for attention