Simultaneously optimizing bidding strategy in pay-as-bid-markets and production scheduling

Varelmann, Tim; Erwes, Nils; Scäfer, Pascal; Mitsos, Alexander

doi:10.1016/j.compchemeng.2021.107610

Cited by 13 publications

(7 citation statements)

References 51 publications

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“…The model considers uncertainties in production from the wind turbine and conditional value at risk (CVaR) as a measure of financial risks. Simultaneous participation in an ancillary service market with a pay-as-bid mechanism and a day-ahead market has been considered in [42]. This can be used to monetize the flexibility of an electricity consumer.…”

Section: Literature Review Of State-of-the-art Optimization Modelsmentioning

confidence: 99%

Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X

2023

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Power-to-X is one of the most attention-grabbing topics in the energy sector. Researchers are exploring the potential of harnessing power from renewable technologies and converting it into fuels used in various industries and the transportation sector. With the current market and research emphasis on Power-to-X and the accompanying substantial investments, a review of Power-to-X is becoming essential. Optimization will be a crucial aspect of managing an energy portfolio that includes Power-to-X and electrolysis systems, as the electrolyzer can participate in multiple markets. Based on the current literature and published reviews, none of them adequately showcase the state-of-the-art optimization algorithms for energy portfolios focusing on Power-to-X. Therefore, this paper provides an in-depth review of the optimization algorithms applied to energy portfolios with a specific emphasis on Power-to-X, aiming to uncover the current state-of-the-art in the field.

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Section: Literature Review Of State-of-the-art Optimization Modelsmentioning

confidence: 99%

Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X

2023

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“…Following Schäfer et al, 21,32 we embed efficiency losses into the process model by means of a piecewise linear approximation with a set of equidistant support points {( p i , r i )}. Instead of using the formulation of Schäfer et al 21,32 with equality constraints and binary variables, we apply a reformulation into a set of inequality constraints without binary variables as suggested by Varelmann et al 48 Thus, for a process with efficiency losses, Equation () is replaced by

r_{s, t} \leq b_{i} goodbreak+ s_{i} p_{s, t} \forall ((), i, s, t) \in scriptI goodbreak\times double-struckS goodbreak\times ({},,, 1, \dots, T)

with b_{i} ≔ r_{i} - s_{i} p_{i},

s_{i} ≔ \frac{r_{i + 1} - r_{i}}{p_{i + 1} - p_{i}}, and

r_{i} ≔ (1 - ζ {((), \frac{P_{nom} - p_{i}}{P_{nom} - P_{nom} θ_{\min}})}^{2}) p_{i},

where b i and s i denote the linearization parameters intercept and slope, respectively, for the set of linearization intervals

I

. Note that the reformulation Equations () only holds due to the concave curvature of Equation () in the interval p s , t ∈ [ P nom θ min , P nom (1 + θ add )] 48 .…”

Section: Stochastic Scheduling With Generalized Process Modelmentioning

confidence: 99%

“…Instead of using the formulation of Schäfer et al 21,32 with equality constraints and binary variables, we apply a reformulation into a set of inequality constraints without binary variables as suggested by Varelmann et al 48 Thus, for a process with efficiency losses, Equation () is replaced by

r_{s, t} \leq b_{i} goodbreak+ s_{i} p_{s, t} \forall ((), i, s, t) \in scriptI goodbreak\times double-struckS goodbreak\times ({},,, 1, \dots, T)

with b_{i} ≔ r_{i} - s_{i} p_{i},

s_{i} ≔ \frac{r_{i + 1} - r_{i}}{p_{i + 1} - p_{i}}, and

r_{i} ≔ (1 - ζ {((), \frac{P_{nom} - p_{i}}{P_{nom} - P_{nom} θ_{\min}})}^{2}) p_{i},

where b i and s i denote the linearization parameters intercept and slope, respectively, for the set of linearization intervals

I

. Note that the reformulation Equations () only holds due to the concave curvature of Equation () in the interval p s , t ∈ [ P nom θ min , P nom (1 + θ add )] 48 . When considering efficiency losses, we therefore limit the maximum operational range to ±50% P nom by means of θ min and θ add to ensure an adequate linearization by five linearization points.…”

Section: Stochastic Scheduling With Generalized Process Modelmentioning

confidence: 99%

“…Following Schäfer et al, 21,32 we embed efficiency losses into the process model by means of a piecewise linear approximation with a set of equidistant support points {(p i , r i )}. Instead of using the formulation of Schäfer et al 21,32 with equality constraints and binary variables, we apply a reformulation into a set of inequality constraints without binary variables as suggested by Varelmann et al 48 Thus, for a process with efficiency losses, Equation ( 4) is replaced by…”

Section: Generalized Process Modelmentioning

confidence: 99%

“…where b i and s i denote the linearization parameters intercept and slope, respectively, for the set of linearization intervals I. Note that the reformulation Equations ( 11)-( 14) only holds due to the concave curvature of Equation ( 10) in the interval p s,t [P nom θ min , 48 When considering efficiency losses, we therefore limit the maximum operational range to ±50% P nom by means of θ min and θ add to ensure an adequate linearization by five linearization points.…”

Section: Generalized Process Modelmentioning

confidence: 99%

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Demand response potential of industrial processes considering uncertain short‐term electricity prices

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Time-varying electricity prices on the day-ahead and intraday market incentivize demand response of industrial processes. In prior work (Schäfer et al. AIChE J. 2020;66:1-14), we studied the demand response potential with a generalized process model, but neglected the intraday market. Extending our prior investigation, we account for uncertain intraday prices in a mixed-integer linear stochastic programming-based scheduling, that is, we minimize expected cost and conditional value-atrisk in a bi-objective optimization. We find that for very broad variations of the generalized process parameters, the conditional value-at-risk can be reduced significantly without drastically increasing the expected cost. Furthermore, simultaneously improving multiple process parameter leads to synergetic benefits. Moreover, the savings of three electrolysis processes can be more than doubled by marketing flexibility on the intraday market in addition to the day-ahead market. Overall, our model allows for a rapid early assessment of the demand response potential considering the two markets.

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A scenario-based robust optimization model for the sustainable distributed permutation flow-shop scheduling problem

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Simultaneously optimizing bidding strategy in pay-as-bid-markets and production scheduling

Cited by 13 publications

References 51 publications

Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X

Review of Energy Portfolio Optimization in Energy Markets Considering Flexibility of Power-to-X

Demand response potential of industrial processes considering uncertain short‐term electricity prices

A scenario-based robust optimization model for the sustainable distributed permutation flow-shop scheduling problem

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