Oleg Zhdaneev
Head
Technological Development Centre of the Fuel and Energy Complex
Oleg Zhdaneev, D.Sc., Head of the Technological Development Centre of the Fuel and Energy Complex and Russian Hydrogen Council. He spent more than 15 years at Schlumberger, advancing from field engineer to engineering VP for Russia and Central Asia. He is an expert in the UN high level dialogue: Innovation, Technology and Data. A member of Ministry of Industry and Trade technical advisory board and Public Council. Secretary of the technical council of the Ministry of Energy. He is author of more than 100 research papers and 15 patents.
Participates in
TECHNICAL PROGRAMME | Energy Technologies
Research, Technology Start-ups and Funding
Forum 19 | Digital Poster Plaza 4
28
April
10:00
12:00
UTC+3
Energy systems face continual deterioration of the mineral resource base and recurrent technological or economic shocks. Conventional planning models treat innovation as an external adjustment and leave the value of superior information unquantified. For the first time, resource degradation, endogenous innovation and the quantitative Value of Information (VOI) are integrated in a single linear-quadratic optimal-control problem. The framework yields an explicit technological-capital substitution rate, captures investment lags and maps the nonlinear variation of marginal returns across factor levels, so that capital is channelled into research or extraction only when this is provably optimal.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
Co-author/s:
Ivan Ovsyannikov, Senior Expert, Technological Development Centre of the Fuel and Energy Complex.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
Co-author/s:
Ivan Ovsyannikov, Senior Expert, Technological Development Centre of the Fuel and Energy Complex.
