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Energy Security Assessment Framework to Support Energy Policy Decisions

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Energy Security Assessment Framework to Support Energy Policy Decisions Energy security assessment framework to support energy policy decisions Juozas Augutis, Ričardas Krikštolaitis, Linas Martišauskas Lithuanian Energy Institute, Kaunas, Lithuania Vytautas Magnus University, Kaunas, Lithuania EU Conference on modelling for policy support Brussels (Belgium), 26-27 November 2019 Presentation outline • Energy security definition • Methodology framework • Results of Lithuanian energy system 2 Energy security definition Energy Security Energy system Energy supply Energy price resilience to reliability acceptability disruptions Energy security* – the ability of the energy system: • to uninterruptedly supply energy to consumers under acceptable prices, • to resist potential disruptions arising due to technical, natural, economic, socio- political and geopolitical threats. *Vytautas Magnus University and Lithuanian Energy Institute, Energy Security Research Centre, 2013–2019 3 Methodology framework for the energy security analysis Threats • Identification and analysis of threats to energy security Disruptions • Formation of internal and external disruptions to energy system Energy system modelling • Model for energy system development implemented in OSeMOSYS tool • Energy system modelling with stochastic disruption scenarios or pathways Consequence analysis • Disruption consequences: energy cost increase and unsupplied energy Energy security metric Methodological approach • Energy security coefficient – quantitative level of energy security 4 Threats to energy security • A threat to energy security is defined as any potential danger that exists within or outside the energy system and that has a potential to result in some kind of disruption of that system. Category Threats • technical problems or accidents in the energy production, resource extractions and transportation, Technical energy transmission infrastructure and processing enterprises, • attacks on supply infrastructure. • extreme temperature, wind, rainfall and other extreme meteorological phenomena or natural Natural disasters. • corruption, poor or inadequate management and investments in energy sector, • low government effectiveness and regulations, • high energy market concentration or formation of monopolies, • political instability of the consumer, supplier and transit countries, • the coercive manipulation of energy supplies, Socio-political • increasing possibility of terrorist attacks and cyber-attacks, • increasing probability of armed conflict in the region, • international political and economic crises, • security concerns affecting the future of renewable energy, • shift in stability of European Union affecting the investments on strategic large-scale energy projects. 5 Disruptions of energy system Probability Value Disruption Parameter distribution qualitative quantitative From modelling Start Uniform – first year to end year Internal: Short-term ≤ 1 year • Restriction of technology Duration Exponential Medium-term > 1, ≤ 3 year availability due to technology Long-term > 3 year reliability and outage rate Small ≤ 33% Normal, • Change of technology or energy Extent Medium > 33, ≤ 66% lognormal projectThreats initial investment (capital Large > 66% cost) due to risk of investment for Frequency Low ≤ 0.01 the candidate technology of – Medium > 0.01, ≤ 0.1 External: occurrence High > 0.1 • Interruption or complete cut-off of energy supply Any energy supply, • Price increase of energy sources Technology production or or energy – transportation technology – source or any energy source in the energy system 6 Modelling of disrupted energy system • OSeMOSYS* (Open Source Energy Modelling System) tool is used and applied for the modelling of energy system development with stochastic disruptions. • Method – linear programming (optimization). Open source – code available in 3 languages (Gnu MathProg, GAMS, Python). Solver – glpk (open-source). • The objective function of the OSeMOSYS is to minimize the total discounted cost of energy systems to meet the given demand(s) for the energy system. • Model is applied for modelling of energy system in multiple (n) runs with various stochastic disruption scenarios using Monte-Carlo method. Reference Energy System OSeMOSYS blocks of functionality: • Objective • Costs • Capacity adequacy • Energy balance • Constraints • Emissions *http://www.osemosys.org/ 7 Energy security coefficient (ESC) • Disruption consequences: ▪ unsupplied energy UE (from 0% to 100%): UE = (1 – (Served / Demand)) x 100%; ▪ energy cost increase ECI (from 0% to 100% and more): ECI = ((FC – BC) / BC) x 100%. • Energy security coefficient (ESC) – quantitative measure of energy security. • ESC aims to evaluate the consequences of disruption scenarios in the energy system in terms of energy system resilience to disruptions: ESC = exp(– a1 × UE × exp(YS) – a2 × ECI × exp(YS)) 8 Application to Lithuanian energy system • Sectors: electricity, district heating. • Fuels: natural gas, oil, coal, waste, biomass, biogas, nuclear fuel. • Technologies (approx. 100): PP, CHP, HOB, CC, hydro, wind, solar, electricity and fuel import, T&D network, unserved energy (dummy). • Analysis period: 2015–2035. • Time step: 1 year. • Time slices per one year: 12 (4 seasons x 3 time slices per day). • Scenarios: Scenario 1 (SC1) – NPP is constructed; Scenario 2 (SC2) – NPP is not constructed, 50% of installed guaranteed capacity of electricity; Scenario 3 (SC3) – NPP is not constructed, 100% of installed guaranteed capacity of electricity. 9 Results of ESC 0,85 0,8 in LNG terminal (all) 0,75 0,7 0,65 0,6 Energy security coefficient 0,55 Scenario 1 in GIPL (all) 0,5 Scenario 2 Scenario 3 0,45 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 in NordBalt (all) in LitPol Link first phase (all) in Synchronization with CEN (all) in – starts exploitation out LPP 5-6 units (all) in LitPol Link second phase (all) out – ends exploitation out LPP 7-8 units (SC1, SC2) in New NPP (SC1) (all) – in all scenarios out VCHP-3 (SC1, SC2) (SC1) – in Scenario 1 out KCHP (all) (SC2) – in Scenario 2 in CCGT 455 MW (SC3) (SC3) – in Scenario 3 10 Conclusions The energy security assessment framework: • allows to evaluate the energy system’s resilience to disruptions and compare different energy system development scenarios in terms of the energy security quantitative measure; • might be seen as a supporting tool for energy policy makers to see an integrated picture of necessary energy security measures to be implemented. The modelling exercise on evaluation of energy security coefficient (ESC) for Lithuania revealed that: • ESC performance is highly dependent on generation adequacy. Lack of capacity might lead energy system being not sufficient to cope with technical or other disruptions. • Too large penetration of new capacities in short-term period leads to a huge economic burden for energy system to cope with severe consequences of economic disruptions due to over-investment risk. • Diversification of energy supply sources is a significant measure to increase energy security. • Energy policy and market mechanisms have to be looked through in order to find a way for implementation of the foreseen energy security measures in practice. 11 Thank you for your attention Linas Martišauskas [email protected] Lithuanian Energy Institute Breslaujos str. 3, LT-44403, Kaunas Lithuania www.lei.lt 12.
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