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Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
Mariia Kozlova; Julian Scott Yeomans. Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines. INFORMS Transactions on Education 2020, 1 .
AMA StyleMariia Kozlova, Julian Scott Yeomans. Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines. INFORMS Transactions on Education. 2020; ():1.
Chicago/Turabian StyleMariia Kozlova; Julian Scott Yeomans. 2020. "Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines." INFORMS Transactions on Education , no. : 1.
Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.
Ivan Deviatkin; Mariia Kozlova; Julian Scott Yeomans. Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis. Socio-Economic Planning Sciences 2020, 75, 100837 .
AMA StyleIvan Deviatkin, Mariia Kozlova, Julian Scott Yeomans. Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis. Socio-Economic Planning Sciences. 2020; 75 ():100837.
Chicago/Turabian StyleIvan Deviatkin; Mariia Kozlova; Julian Scott Yeomans. 2020. "Simulation decomposition for environmental sustainability: Enhanced decision-making in carbon footprint analysis." Socio-Economic Planning Sciences 75, no. : 100837.
This article presents a stylized renewable energy (RE) investment project profitability analysis under a rate-of-return RE support type. We use a dynamic programming approach to value the real options. While the method is widely used in RE policy analysis, the rate-of-return support is presented in this framework for the first time. We formulate a stylized RE project under the rate-of-return regulation in the dynamic programming framework and solve for optimal investment timing and project size. A stylized renewable energy (RE) investment under rate-of-return RE support is presented in the dynamic programming framework; The system is solved for optimal capacity choice in the presence of the electricity price uncertainty. We also comment on the optimal investment timing, which turns out to be a now-or-never decision in this case.
Mariia Kozlova; Stein-Erik Fleten; Verena Hagspiel. Optimal timing and capacity choice under the rate-of-return renewable energy support. MethodsX 2020, 7, 100828 .
AMA StyleMariia Kozlova, Stein-Erik Fleten, Verena Hagspiel. Optimal timing and capacity choice under the rate-of-return renewable energy support. MethodsX. 2020; 7 ():100828.
Chicago/Turabian StyleMariia Kozlova; Stein-Erik Fleten; Verena Hagspiel. 2020. "Optimal timing and capacity choice under the rate-of-return renewable energy support." MethodsX 7, no. : 100828.
A review of economic geography studies on renewable energy showed a lack of the investors’ perspective in such an analysis that is crucial for both a single investment planning and policy development. This paper introduces a framework for a cross-regional analysis of renewable energy investment attractiveness and illustrates its use on the case of Russia. The attractiveness of each Russian region is analyzed based on four main variables that are used in the construction of an attractiveness indicator. In addition, the indicator takes into consideration the effect of the different renewable energy investment support mechanisms presented in the country. The results allow the comparative analysis of different regions in terms of renewable energy investment attractiveness. The graphical representation of the results enables intuitive understanding and facilitates decision making. Apart from the direct usefulness of the results to Russian renewable energy market actors, such as investors and policy-makers, the introduced framework can be utilized by the research community and policy-makers for any country with geographical variability to better inform the transition to a low carbon future.
Mariia Kozlova; Mikael Collan. Renewable energy investment attractiveness: Enabling multi-criteria cross-regional analysis from the investors’ perspective. Renewable Energy 2020, 150, 382 -400.
AMA StyleMariia Kozlova, Mikael Collan. Renewable energy investment attractiveness: Enabling multi-criteria cross-regional analysis from the investors’ perspective. Renewable Energy. 2020; 150 ():382-400.
Chicago/Turabian StyleMariia Kozlova; Mikael Collan. 2020. "Renewable energy investment attractiveness: Enabling multi-criteria cross-regional analysis from the investors’ perspective." Renewable Energy 150, no. : 382-400.
Design of support-mechanisms is an important component of renewable energy policy. In order to be able to choose desirable designs one must have a good understanding of the most likely outcomes from different policy designs – this calls for proper before-implementation policy analysis and especially for analysis the results of which are intuitively understandable for the decision makers. We propose a simple process, based on the fuzzy pay-off method, for the purpose of analyzing renewable energy support designs in the context of auction-based support mechanism implementation. A numerical case from Finland is used to illustrate the proposed process. The results show that the process is relatively simple to use and able to produce intuitively understandable relevant information for design selection.
L. Hietanen; M. Kozlova; M. Collan. Analyzing Renewable Energy Policies – Using the Pay-Off Method to Study the Finnish Auction-Based Renewable Energy Policy. Journal of Environmental Informatics Letters 2020, 4, 50-56 .
AMA StyleL. Hietanen, M. Kozlova, M. Collan. Analyzing Renewable Energy Policies – Using the Pay-Off Method to Study the Finnish Auction-Based Renewable Energy Policy. Journal of Environmental Informatics Letters. 2020; 4 (2):50-56.
Chicago/Turabian StyleL. Hietanen; M. Kozlova; M. Collan. 2020. "Analyzing Renewable Energy Policies – Using the Pay-Off Method to Study the Finnish Auction-Based Renewable Energy Policy." Journal of Environmental Informatics Letters 4, no. 2: 50-56.
This study analyzes a renewable energy (RE) support scheme recently introduced in Russia and compares it to the most frequently applied policy measures, feed-in tariff (FiT) and feed-in premium (FiP) schemes. In particular, we present an analytical formulation of the problem set-up and study optimal investment timing and capacity choice employing a real options approach. In addition, we conduct detailed sensitivity analyses to highlight how different policies shape investment behavior. The contributions of this paper include modeling the Russian RE support mechanism in a dynamic programming framework that allows us to show that such a RE support design offers a strong case for transferring market risks away from the investor and has potential for a unique combination of effectiveness and cost efficiency.
Mariia Kozlova; Stein-Erik Fleten; Verena Hagspiel. Investment timing and capacity choice under rate-of-return regulation for renewable energy support. Energy 2019, 174, 591 -601.
AMA StyleMariia Kozlova, Stein-Erik Fleten, Verena Hagspiel. Investment timing and capacity choice under rate-of-return regulation for renewable energy support. Energy. 2019; 174 ():591-601.
Chicago/Turabian StyleMariia Kozlova; Stein-Erik Fleten; Verena Hagspiel. 2019. "Investment timing and capacity choice under rate-of-return regulation for renewable energy support." Energy 174, no. : 591-601.
Design concept selection lacks economic evaluation in the early stages of the design process. This chapter introduces the levelized function cost for express design evaluation, adapted from the power-generation sector. A single indicator represents all concept-related life-cycle costs per a unit of function produced, reflecting also the lifetime and productivity. The indicator allows comparing fundamentally different designs, and handles different sets of function objects. After a brief overview and comparison of potential indicators for economic assessment of design concepts, this chapter introduces the levelized function cost providing its derivation and definition, analyzes its sensitivity to the input variables, depicts the range of problems that can be addressed with the levelized function cost estimate, and finally illustrates its application in a flow meter design case.
Mariia Kozlova; Leonid Chechurin; Nikolai Efimov-Soini. Levelized Function Cost: Economic Consideration for Design Concept Evaluation. Advances in Systematic Creativity 2018, 267 -297.
AMA StyleMariia Kozlova, Leonid Chechurin, Nikolai Efimov-Soini. Levelized Function Cost: Economic Consideration for Design Concept Evaluation. Advances in Systematic Creativity. 2018; ():267-297.
Chicago/Turabian StyleMariia Kozlova; Leonid Chechurin; Nikolai Efimov-Soini. 2018. "Levelized Function Cost: Economic Consideration for Design Concept Evaluation." Advances in Systematic Creativity , no. : 267-297.
This chapter presents an analysis of how the new Russian support policy for renewable energy investments changes the expected profitability of renewable energy investments in Russia. A comparative analysis of investment profitability in the before and after support policy cases is presented for a wind farm investment to illustrate the effect of the policy. This chapter is among the first to comparatively analyze the effect of the Russian renewable energy support mechanism on investment project profitability.
Mariia Kozlova; Mikael Collan; Pasi Luukka. Russian Mechanism to Support Renewable Energy Investments: Before and After Analysis. Computational Methods in Applied Sciences 2017, 45, 243 -252.
AMA StyleMariia Kozlova, Mikael Collan, Pasi Luukka. Russian Mechanism to Support Renewable Energy Investments: Before and After Analysis. Computational Methods in Applied Sciences. 2017; 45 ():243-252.
Chicago/Turabian StyleMariia Kozlova; Mikael Collan; Pasi Luukka. 2017. "Russian Mechanism to Support Renewable Energy Investments: Before and After Analysis." Computational Methods in Applied Sciences 45, no. : 243-252.
Natalya Antonova; Ural Federal University; Iuliia Shnai; Mariia Kozlova; Lappeenranta University of Technology. Flipped Classroom in the Higher Education System: a Pilot Study in Finland and Russia. The New Educational Review 2017, 48, 17 -27.
AMA StyleNatalya Antonova, Ural Federal University, Iuliia Shnai, Mariia Kozlova, Lappeenranta University of Technology. Flipped Classroom in the Higher Education System: a Pilot Study in Finland and Russia. The New Educational Review. 2017; 48 (2):17-27.
Chicago/Turabian StyleNatalya Antonova; Ural Federal University; Iuliia Shnai; Mariia Kozlova; Lappeenranta University of Technology. 2017. "Flipped Classroom in the Higher Education System: a Pilot Study in Finland and Russia." The New Educational Review 48, no. 2: 17-27.
The paper compares numerically the results from two real option valuation methods, the Datar-Mathews method and the fuzzy pay-off method. Datar-Mathews method is based on using Monte Carlo simulation within a probabilistic valuation framework, while the fuzzy pay-off method relies on modeling the real option valuation by using fuzzy numbers in a possibilistic space. The results show that real option valuation results from the two methods seem to be consistent with each other. The fuzzy pay-off method is more robust and is also usable when not enough information is available for a construction of a simulation model.
Mariia Kozlova; Mikael Collan; Pasi Luukka. Comparison of the Datar-Mathews Method and the Fuzzy Pay-Off Method through Numerical Results. Advances in Decision Sciences 2016, 2016, 1 -7.
AMA StyleMariia Kozlova, Mikael Collan, Pasi Luukka. Comparison of the Datar-Mathews Method and the Fuzzy Pay-Off Method through Numerical Results. Advances in Decision Sciences. 2016; 2016 ():1-7.
Chicago/Turabian StyleMariia Kozlova; Mikael Collan; Pasi Luukka. 2016. "Comparison of the Datar-Mathews Method and the Fuzzy Pay-Off Method through Numerical Results." Advances in Decision Sciences 2016, no. : 1-7.
Russian renewable energy policy, introduced in May 2013, is a capacity mechanism-based approach to support wind, solar, and small hydro power development in Russia. This paper explores the effect of the new mechanism on the profitability of new renewable energy investments with a numerical example. The sensitivity of project profitability to selected factors is studied and the results are compared ceteris paribus to results from a generic feed-in premium case. Furthermore, the paper gives a complete and detailed presentation of the capacity price calculation procedure tied to the support mechanism. The results show that the new Russian renewable energy capacity mechanism offers a significant risk reduction to the investor in the form of dampening the sensitivity to external market factors. At the same time it shields the energy market system from excessive burden of renewable energy support. Even if the complexity of the method is a clear drawback to the detailed understanding of how the mechanism works, the design of the incentive policy could be an appealing alternative also for other emerging economies.
Mariia Kozlova; Mikael Collan. Modeling the effects of the new Russian capacity mechanism on renewable energy investments. Energy Policy 2016, 95, 350 -360.
AMA StyleMariia Kozlova, Mikael Collan. Modeling the effects of the new Russian capacity mechanism on renewable energy investments. Energy Policy. 2016; 95 ():350-360.
Chicago/Turabian StyleMariia Kozlova; Mikael Collan. 2016. "Modeling the effects of the new Russian capacity mechanism on renewable energy investments." Energy Policy 95, no. : 350-360.