Analysing decarbonizing strategies in the European power system applying stochastic dominance constraints

Abstract In this paper we develop an analysis of the efficiency of the expansion strategies to be followed to attain the emissions targets established by the European Commission in the Energy Roadmap 2050. A multi-stage investment model in generating and storage capacity from the point of view of a central planner is presented, considering long-term uncertainties in the decision-making process, such as the demand growth and the investment and fuel costs, and short-term variability. To evaluate the wellness of the expansion strategies according to the CO2 emissions generated and the total cost, second-order stochastic dominance constraints are introduced in the model. This approach allows to obtain better expansion strategies enforcing acceptable distributions of CO2 emissions. The numerical study is carried out considering the case of the European power system. The predictions and suggestions made by the European Commission towards 2050 are the basis to define the benchmark solutions, whose outcomes are analysed. The results obtained from this study highlight that a renewable capacity of at least 2900 GW is needed to attain a net zero CO2 emission European power system. The strategy based on carbon capture and storage does not reduce effectively CO2 emissions while it represents an expensive alternative. Including stochastic dominance in the optimization model allows to obtain less expensive alternative expansion strategies with comparatively lower CO2 emissions in the worst scenarios.

[1]  Antonio J. Conejo,et al.  Correlated wind-power production and electric load scenarios for investment decisions , 2013 .

[2]  Antonio J. Conejo,et al.  Multistage Stochastic Investment Planning With Multiscale Representation of Uncertainties and Decisions , 2018, IEEE Transactions on Power Systems.

[3]  Juan M. Morales,et al.  Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage , 2018, IEEE Transactions on Power Systems.

[4]  Milos Kopa,et al.  Individual optimal pension allocation under stochastic dominance constraints , 2018, Ann. Oper. Res..

[5]  Trine Krogh Boomsma,et al.  Impact of forecast errors on expansion planning of power systems with a renewables target , 2014, Eur. J. Oper. Res..

[6]  Rüdiger Schultz,et al.  Risk aversion for an electricity retailer with second-order stochastic dominance constraints , 2009, Comput. Manag. Sci..

[7]  Hamed Kebriaei,et al.  An interval-based stochastic dominance approach for decision making in forward contracts of electricity market , 2018, Energy.

[8]  A. J. Conejo,et al.  Transmission and Wind Power Investment , 2012, IEEE Transactions on Power Systems.

[9]  Timo Kuosmanen,et al.  Efficient Diversification According to Stochastic Dominance Criteria , 2004, Manag. Sci..

[10]  Thierry Post,et al.  A general test for SSD portfolio efficiency , 2015, OR Spectr..

[11]  M. H. Javidi,et al.  Self-Scheduling of Large Consumers With Second-Order Stochastic Dominance Constraints , 2013, IEEE Transactions on Power Systems.

[12]  Nadia Maïzi,et al.  Fukushima's impact on the European power sector: The key role of CCS technologies , 2013 .

[13]  H. Levy Stochastic Dominance: Investment Decision Making under Uncertainty , 2010 .

[14]  Antonio J. Conejo,et al.  Influence of the number of decision stages on multi-stage renewable generation expansion models , 2021 .

[15]  Michal Kaut,et al.  Multi-horizon stochastic programming , 2014, Comput. Manag. Sci..

[16]  Jitka Dupacová,et al.  Robustness in stochastic programs with risk constraints , 2012, Ann. Oper. Res..

[17]  Martin Greiner,et al.  Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints , 2018, Energy.

[18]  G. Oggioni,et al.  Planning and operating a renewable-dominated European power system under uncertainty , 2020 .

[19]  Vittorio Moriggia,et al.  Pension fund management with hedging derivatives, stochastic dominance and nodal contamination , 2019, Omega.

[20]  Thierry Post,et al.  Empirical Tests for Stochastic Dominance Efficiency , 2003 .

[21]  Laureano F. Escudero,et al.  On capacity expansion planning under strategic and operational uncertainties based on stochastic dominance risk averse management , 2018, Comput. Manag. Sci..

[22]  A. Conejo,et al.  Toward Fully Renewable Electric Energy Systems , 2015, IEEE Transactions on Power Systems.

[23]  Martin Junginger,et al.  Is a 100% renewable European power system feasible by 2050? , 2019, Applied Energy.

[24]  J. Lesser Application of stochastic dominance tests to utility resource planning under uncertainty , 1990 .

[25]  H. Rudnick,et al.  CVaR Constrained Planning of Renewable Generation with Consideration of System Inertial Response, Reserve Services and Demand Participation , 2016 .

[26]  Enzo Sauma,et al.  Effect of Climate Change on wind speed and its impact on optimal power system expansion planning: The case of Chile , 2019, Energy Economics.

[27]  F. D. Munoz,et al.  Efficient proactive transmission planning to accommodate renewables , 2012, 2012 IEEE Power and Energy Society General Meeting.

[28]  Enzo Sauma,et al.  If you build it, he will come: Anticipative power transmission planning , 2013 .

[29]  G.B. Sheble,et al.  Second Order Stochastic Dominance Portfolio Optimization for an Electric Energy Company , 2007, 2007 IEEE Lausanne Power Tech.

[30]  Wing-Keung Wong,et al.  Stochastic Dominance and Risk Measure: A Decision-Theoretic Foundation for VAR and C-Var , 2006, Eur. J. Oper. Res..

[31]  Darinka Dentcheva,et al.  Optimization with Stochastic Dominance Constraints , 2003, SIAM J. Optim..

[32]  J. Quirk,et al.  Admissibility and Measurable Utility Functions , 1962 .

[33]  Andrew Angus,et al.  Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling , 2019, Energy Economics.

[34]  Sebastiano Vitali,et al.  Comparing stage-scenario with nodal formulation for multistage stochastic problems , 2020, 4OR.

[35]  John R. Birge,et al.  The value of the stochastic solution in stochastic linear programs with fixed recourse , 1982, Math. Program..

[36]  Werner Römisch,et al.  Scenario Reduction Algorithms in Stochastic Programming , 2003, Comput. Optim. Appl..

[37]  Daniel S. Kirschen,et al.  Stochastic Multistage Coplanning of Transmission Expansion and Energy Storage , 2017, IEEE Transactions on Power Systems.

[38]  Daniel Kirschen,et al.  Optimal energy storage siting and sizing: A WECC case study , 2017, 2017 IEEE Power & Energy Society General Meeting.

[39]  Rafael Zárate-Miñano,et al.  Influence of the controllability of electric vehicles on generation and storage capacity expansion decisions , 2019 .

[40]  Enzo Sauma,et al.  Approximations in power transmission planning: implications for the cost and performance of renewable portfolio standards , 2013 .

[41]  Michaela Fürsch,et al.  Decarbonizing Europe's power sector by 2050 — Analyzing the economic implications of alternative decarbonization pathways , 2013 .

[42]  Sebastiano Vitali,et al.  Long-term individual financial planning under stochastic dominance constraints , 2020, Ann. Oper. Res..