A. Amirreza Silani
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1
Achieving the Paris Agreement's goal necessitates not only reducing carbon dioxide emissions to net zero but also actively removing CO2 from the atmosphere. Direct Air Capture (DAC) emerges as a pivotal technology in this effort, offering a reliable, flexible, and scalable solution for negative emissions. However, DAC performance is highly sensitive to environmental factors such as temperature and humidity. Consequently, it is vital to develop dynamic control and optimization mechanisms that can enhance the cost-efficiency of DAC. Due to the complexity and lack of a comprehensive model for DAC systems, the need for expert knowledge for modeling, and high computational costs, traditional model-based methods are not feasible. Therefore, we suggest a model-free, data-driven optimization technique based on Bayesian optimization to enhance the productivity and cost-effectiveness of DAC.
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Achieving the Paris Agreement's goal necessitates not only reducing carbon dioxide emissions to net zero but also actively removing CO2 from the atmosphere. Direct Air Capture (DAC) emerges as a pivotal technology in this effort, offering a reliable, flexible, and scalable solution for negative emissions. However, DAC performance is highly sensitive to environmental factors such as temperature and humidity. Consequently, it is vital to develop dynamic control and optimization mechanisms that can enhance the cost-efficiency of DAC. Due to the complexity and lack of a comprehensive model for DAC systems, the need for expert knowledge for modeling, and high computational costs, traditional model-based methods are not feasible. Therefore, we suggest a model-free, data-driven optimization technique based on Bayesian optimization to enhance the productivity and cost-effectiveness of DAC.
The sudden proliferation of Electric Vehicles (EVs), batteries and photovoltaic cells in power networks can lead to congested distribution networks. A substitute for upgrading network capacity is a redispatch market that enables the Distribution System Operators (DSOs) to mitigate congested networks by requesting the energy consumers to modify their consumption schedules. However, energy consumers are able to strategically modify their day-ahead market bids in anticipation of the redispatch market outcomes. This behaviour, which is known as increase-decrease gaming, can exacerbate congestion and give arbitrage opportunities to the energy consumers for gaining windfall profits from the DSO. In this paper, we propose an algorithm based on mean-field Stackelberg game to mitigate the increase-decrease game for large populations of energy consumers. In this game, the energy consumers (followers) maximize their individual welfare on the day-ahead market with anticipation of the redispatch market outcomes while the leader maximizes the social welfare of all agents and minimizes the costs of DSO on the redispatch market. We show the convergence of this algorithm to the mean-field leader-follower εN-Nash equilibrium.
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The sudden proliferation of Electric Vehicles (EVs), batteries and photovoltaic cells in power networks can lead to congested distribution networks. A substitute for upgrading network capacity is a redispatch market that enables the Distribution System Operators (DSOs) to mitigate congested networks by requesting the energy consumers to modify their consumption schedules. However, energy consumers are able to strategically modify their day-ahead market bids in anticipation of the redispatch market outcomes. This behaviour, which is known as increase-decrease gaming, can exacerbate congestion and give arbitrage opportunities to the energy consumers for gaining windfall profits from the DSO. In this paper, we propose an algorithm based on mean-field Stackelberg game to mitigate the increase-decrease game for large populations of energy consumers. In this game, the energy consumers (followers) maximize their individual welfare on the day-ahead market with anticipation of the redispatch market outcomes while the leader maximizes the social welfare of all agents and minimizes the costs of DSO on the redispatch market. We show the convergence of this algorithm to the mean-field leader-follower εN-Nash equilibrium.
The proliferation of batteries, photovoltaic cells and Electric Vehicles (EVs) in electric power networks can result in network congestion. A redispatch market that allows the Distribution System Operators (DSOs) to relieve congested networks by asking the energy consumers to adjust their scheduled consumption is an alternative to upgrading network capacity. However, energy consumers can strategically increase their bids on the day-ahead market in anticipation of payouts from the redispatch market. This behaviour, which is called increase-decrease gaming, can aggravate congestion and allow the energy consumers to extract windfall profits from the DSO. In this paper, we model the increase-decrease game for large populations of energy consumers in power networks using a mean field game approach. The agents (energy consumers) maximize their individual welfare on the day-ahead market with anticipation of the redispatch market, coupled via the electricity price. We show that there exists a Nash equilibrium for this game and use an algorithm that converges to the Nash equilibrium for the infinite population case.
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The proliferation of batteries, photovoltaic cells and Electric Vehicles (EVs) in electric power networks can result in network congestion. A redispatch market that allows the Distribution System Operators (DSOs) to relieve congested networks by asking the energy consumers to adjust their scheduled consumption is an alternative to upgrading network capacity. However, energy consumers can strategically increase their bids on the day-ahead market in anticipation of payouts from the redispatch market. This behaviour, which is called increase-decrease gaming, can aggravate congestion and allow the energy consumers to extract windfall profits from the DSO. In this paper, we model the increase-decrease game for large populations of energy consumers in power networks using a mean field game approach. The agents (energy consumers) maximize their individual welfare on the day-ahead market with anticipation of the redispatch market, coupled via the electricity price. We show that there exists a Nash equilibrium for this game and use an algorithm that converges to the Nash equilibrium for the infinite population case.