On the economic optimization of national power generation mix in Iran: A Markowitz portfolio- based approach Arash FARNOOSH IFP Energies Nouvelles IFP School 1 Outline Introduction Methodology Modeling Framework Results & Analysis Conclusion 2 Introduction (economy and oil&gas sector) Source: Fnack Unattractive buy-back contracts -Huge domestic energy consumption -Lack of investment Oil & Gas Sector -2 nd Oil producer in the Middle East -1 st Gas proven reserves: 1200tcf 34tcm (BP Stat. 2015) -Suprise election of Dr. Rouhani (75% of 50 million voters) -Openning bilateral talks with the US - Very difficult time for Irans economy -GDP contracted for the first time in 2012 since 1990s -Rial depreciated by around 80% since 2011 Introduction (power sector situation) Irans power market was launched in October 2003 Mandatory pool: all producers/consumers should send their bids one day ahead before 10am to the market Electricity Market Regulatory Board surveys the market Ancillary services, primary frequency control along with voltage support (reactive power) and black start services markets were introduced on May 22 nd 2007 Electricity production composition in Iran 2014 (IEA&BP) -68GW of capacity in Gas and Hydro variations are considerable -TAVANIR operates and owns most of the plants -IPPs also with 25-year long-term contracts Introduction (electricity demand) 80% of demand increase over a decade between 2000 and 2010 2015 demand equals 272 TWh Electricity demand evolution in Iran (IEA&TAVANIR) 5 Outline Introduction Methodology Modeling Framework Results & Analysis Conclusion 6 Methodology In this study we attempted to construct an optimal decision-making model (optimization) based on the Markowitz's portfolio theory by which investors try to manage risk and maximize their portfolio performance under variety of volatile economic outcomes. We abandon the reliance on traditional minimized-cost detached technology cost estimates and instead apply portfolio optimization methods. Attempt to evaluate both conventional (fossil & nuclear) and renewable energy sources based on their portfolio costs. In other words, optimizing the costs of their portfolio relative to the risk associated to each mix of generating asset. Literature review: Example of such models: Bar-Lev & Katz (1976) for US electric utilities Humphreys & McClain (1998) optimal energy mix in the USA Awerbuch & Berger (2003) optimal European technology mix Jansen et al. (2006) electricity generation mix of Netherlands. And many other examples have also been developed by consultants, industrials and utilities themselves and are not therefore published. Our methodology is similar in a sense that we also analyse a national generation mix and our analysis are based on generation costs for each power unit. However, our focus is less on the fuel price volatility and more on the real international prices of fuels in the markets (based on their opportunity costs) and not domestic subsidies. Methodology (Markowitz Mean-Variance Portfolio Theory) E(Cp) = X 1 E(C 1 ) + X 2 E(C 2 ) E(p) = ( X 1 2 X 2 2 X 1 X 2 12 1 2 ) 0.5 C levelized costs X fractional shares standard deviation of costs correlation coefficient Risk Cost Profit Max Cost Min 8 Modeling Framework (cost-risk of the Iranian portfolio) Therefore the total expected portfolio cost of the Iranian mix is given by: E(C IranP ) = X oil E(C oil ) + X gas E(C gas ) + X coal E(C coal ) + X nuc E(C nuc ) + X hydro E(C hydro ) + X solar E(C solar ) + X wind E(C wind ) And the total expected standard deviation (risk) of the portfolio is: E( IranP ) = [ X oil 2 oil 2 + X gas 2 gas 2 + X coal 2 coal 2 + X nuc 2 nuc 2 + X hydro 2 hydro 2 + X solar 2 solar 2 + X wind 2 wind 2 + 2X oil X coal oil,coal oil oal + 2X oil X gas oil,gas oil gas + 2X oil X nuc oil,nuc oil nuc + 2X oil X hydro oil,hydro oil hydro + 2X oil X solar oil,solar oil solar + 2X oil X wind oil,wind oil wind + 2X gas X coal coal,gas gas coal + 2X gas X nuc nuc,gas nuc gas + 2X gas X hydro hydro,gas gas hydro + 2X gas X solar solar,gas gas solar + 2X gas X wind wind,gas gas wind + 2X nuc X coal coal,nuc nuc coal + 2X hydro X coal coal,hydro coal hydro + 2X solar X coal coal,solar coal solar + 2X wind X coal wind,coal wind coal + 2X nuc X hydro nuc,hydro hydro nuc + 2X nuc X solar nuc,solar nuc solar + 2X nuc X wind nuc,wind nuc wind 2X hydro X solar hydro,solar hydro solar + 2X hydro X wind hydro,wind hydro wind + 2X soalr X wind solar,wind solar wind ] 0.5 In which X i and C i are respectively the shares and costs of Iranian power generation technologies. The standard deviation associated with each technology is denoted by i and i illustrates the correlation coefficients between various fuels used in related power units. Modeling Framework (fossil-fuel costs correlation analysis) Modeling Framework (model inputs) Source: TAVANIR, Awerbuch et al. (2010) & Authors calculations Decision Variable Lower Bound Base Case Upper Bound Coal1%5%10% Gas30%55%80% Hydro10%15%20% Nuclear2%15%40% Oil10%20%40% Solar1%5%30% Wind1%5%50% 11 Modeling Framework (model flowchart) Modelling tools CB & OptQuest (Oracle Enterprise Performance Management System) 1. We forecast and simulate cost and risks under CB 2. Optimization under OQ based on the simulation results Stochastic Simulation-Optimization Model 12 Modeling Framework (models structure) Optimization Objectives: Elements that represents the target goal of the optimization, such as maximizing profit or minimizing cost (in our case), based on a forecast and related decision variables. Requirements: Optional restrictions placed on forecast statistics. All requirements must be satisfied before a solution can be considered feasible. Decision Variables: Variables over which you have control Constraints: Optional restrictions placed on decision variable values Modeling Framework (Cost distribution of each power generation unit => => => => => => => Total generation cost portfolio) 14 Outline Introduction Methodology Modeling Framework Results & Analysis Conclusion 15 Output Models convergence Efficient frontier Objective function Constraints & requirements Decision variables Number of trials 16 Output 17 Output 18 Output 19 Conclusion Increasing the non-fossil share, even if it is believed to cost more on a stand-alone basis, reduces portfolio cost-risk and enhances very high energy security. The results showed that the current Iranian generation mix is far from the optimality in terms of cost and diversity and there is a huge potential of improvement in costs and risks reductions (respectively 15 and 10 per cent) by going toward more non-fossil fuel based portfolio of power generation. However, any sort of aggressive strategy concerning both cost and risk reduction process, is not recommended as they are negatively correlated to each other. (Massive investment in nuclear and other non-fossil resources would highly increase the portfolios costs and can make the Iranian power sector very vulnerable against technological risks even if the impact on the energy security risk reduction could be very significant. Besides, relying on the current investment trend in the fossil power units can harm the Iranian power sector seriously by increasing the total risk of electricity generation portfolio. Moreover, this will also lead to substantial reduction of hydrocarbon export, as the domestic demand of oil and gas for power generation will continue to rise.) A compromise between fossil and non-fossil sources of power generation would be the most efficient solution for Iran. In the short/medium term Iran should continue to invest in both types of power units while gradually decrease the share of fossil units until reaching the optimal values. Both nuclear and renewable (wind and solar) power plants should gradually become more and more present in the national electricity portfolio of the country. 20 Dont forget! Subsidies issue ?! Values of fossil fuel subsidies Source: IEA (WEO 2014)