A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty

Giannelos, Spyros; Borozan, Stefan; Štrbac, Goran

doi:10.3390/en15093334

Cited by 14 publications

(12 citation statements)

References 29 publications

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“…Moreover, it is of interest to the authors to compare the economic benefits resulting from the presented analysis to benefits arising from the application of other frameworks such as Backwards Induction methodology [35], machine-learning approaches [36] and stochastic optimisation [37]. Finally, the authors are interested in expanding the sensitivity analysis to include more parameters, such as changes in consumer behaviour, external economic factors and different geographical regions with varying energy demands and backgrounds.…”

Section: Discussionmentioning

confidence: 99%

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Giannelos,

Zhang,

Pudjianto

et al. 2024

Energies

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The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings.

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Section: Discussionmentioning

confidence: 99%

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Giannelos,

Zhang,

Pudjianto

et al. 2024

Energies

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“…In addition, it is of interest to the authors to focus on optimizing the value of hyperparameters. Methods that can be used for this purpose include heuristics such as backwards induction [42] and uncertainty analysis methods based on the combination of machine learning with reliability theory [43] and artificial neural networks [44]. The authors are also interested in evaluating the effect of external factors, such as the level of technological development and GDP, on the forecasts in these regions.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

et al. 2023

Self Cite

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The building sector has traditionally accounted for about 40% of global energy-related carbon dioxide (CO2) emissions, as compared to other end-use sectors. Due to this fact, as part of the global effort towards decarbonization, significant resources have been placed on the development of technologies, such as active buildings, in an attempt to achieve reductions in the respective CO2 emissions. Given the uncertainty around the future level of the corresponding CO2 emissions, this work presents an approach based on machine learning to generate forecasts until the year 2050. Several algorithms, such as linear regression, ARIMA, and shallow and deep neural networks, can be used with this approach. In this context, forecasts are produced for different regions across the world, including Brazil, India, China, South Africa, the United States, Great Britain, the world average, and the European Union. Finally, an extensive sensitivity analysis on hyperparameter values as well as the application of a wide variety of metrics are used for evaluating the algorithmic performance.

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“…All in all, the most pressing need for future work is to increase the amount of available experimental data. Uncertainty of existing data can be addressed by different techniques already successfully used in artificial intelligence and deep learning, such as clustering, backpropagation, Benders decomposition or option value [54][55][56]. Furthermore, variants of the procedure used in this article for eight-input models could be interesting for other energy conversion systems, since Equation ( 2) is applicable to other thermodynamic cycles [27].…”

Section: Discussion and Future Workmentioning

confidence: 99%

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

2023

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Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using three-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.

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A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty

Cited by 14 publications

References 29 publications

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

A Revision of Empirical Models of Stirling Engine Performance Using Simple Artificial Neural Networks

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