Do investments in flexibility enhance sustainability? A simulative study considering the German electricity sector

Schäfer, Pascal; Daun, Torben M.; Mitsos, Alexander

doi:10.1002/aic.17010

Cited by 19 publications

(37 citation statements)

References 67 publications

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“…The higher flexibility of the low time constant β = 0.36 h allows to consume more cooling in hour 16 and less in hour 17 compared to the case where β = 0.72 h. Still, the larger time constant can capture the main trend of the electricity price profile, which has a peak in the morning and another one in the afternoon. Such a price profile is typical for the German market where the main price periodicities are 24 and 12 hours (Schäfer et al, 2020). Even if β is increased by a factor of 10, the scheduling can still capture the main trend of the electricity price profile (Figure 14).…”

Section: Resultsmentioning

confidence: 95%

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Baader,

Bardow,

Dahmen

2021

Preprint

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Increasingly volatile electricity prices make simultaneous scheduling optimization for production processes and their energy supply systems desirable. Simultaneous scheduling needs to account for both process dynamics and binary on/off-decisions in the energy system and thus leads to challenging mixed-integer dynamic optimization problems. In this contribution, we propose an efficient scheduling formulation that consists of three parts: a linear scale-bridging model for the closed-loop process output dynamics, a data-driven model for the process energy demand, and a mixed-integer linear model for the energy system. Process dynamics are discretized by collocation yielding a mixed-integer linear programming (MILP) formulation. We apply the scheduling method to a single-product reactor, with 5.6 % economic improvement compared to steady-state operation, and a multi-product reactor, with 5.2 % improvement compared to sequential scheduling. While capturing 85 % and 96 % of the improvement realized by a nonlinear optimization, the MILP formulation achieves optimization runtimes sufficiently fast for real-time scheduling.

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Section: Resultsmentioning

confidence: 95%

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Baader,

Bardow,

Dahmen

2021

Preprint

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“…Based on this trajectory, the real process inputs are determined by the underlying control (Baldea and Harjunkoski, 2014). Accordingly, for negligible process dynamics, no process model is needed but only the process energy demands must be described as piece-wise affine function of the production rate (Schäfer et al, 2020;Brée et al, 2019).…”

Section: Electricity Pricementioning

confidence: 99%

“…For product storage (P1e), we assume a buffer storage with filling level S. As all decision variables, the filling level S is subject to upper and lower bounds. Moreover, the final storage filling level S(t f ) might be constrained to be greater than or equal to the initial filling level S(t 0 ) to avoid depletion of the storage (Schäfer et al, 2020). The storage unit is filled by the production rate ρ of the process and emptied with a constant nominal product demand rate ρ nom (Caspari et al, 2019;Schäfer et al, 2020;Pattison et al, 2016).…”

Section: Nonlinear Mixed-integer Dynamic Schedulingmentioning

confidence: 99%

“…Moreover, the final storage filling level S(t f ) might be constrained to be greater than or equal to the initial filling level S(t 0 ) to avoid depletion of the storage (Schäfer et al, 2020). The storage unit is filled by the production rate ρ of the process and emptied with a constant nominal product demand rate ρ nom (Caspari et al, 2019;Schäfer et al, 2020;Pattison et al, 2016). The instantaneous energy costs (P1f) are the sum over specific price times consumption for all energy forms in E given by the input power of energy system components Q in i , the set of energy system components that consume energy e, C cons e , the energy prices p e , and the power exchanged with the grid ∆P e .…”

Section: Nonlinear Mixed-integer Dynamic Schedulingmentioning

confidence: 99%

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Dynamic Ramping for Demand Response of Processes and Energy Systems based on Exact Linearization

Baader¹,

Althaus²,

Bardow³

et al. 2021

Preprint

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The increasing share of volatile renewable electricity production motivates demand response. Substantial potential for demand response is offered by flexible processes and their local multi-energy supply systems. Simultaneous optimization of their schedules can exploit the demand response potential, but leads to numerically challenging problems for nonlinear dynamic processes. In this paper, we propose to capture process dynamics using dynamic ramping constraints. In contrast to traditional static ramping constraints, dynamic ramping constraints are a function of the process state and can capture high-order dynamics. We derive dynamic ramping constraints rigorously for the case of single-input single-output processes that are exactly input-state linearizable. The resulting scheduling problem can be efficiently solved as a mixed-integer linear program. In a case study, we study two flexible reactors and a multi-energy system. The proper representation of process dynamics by dynamic ramping allows for faster transitions compared to static ramping constraints and thus higher economic benefits of demand response. The proposed dynamic ramping approach is sufficiently fast for application in online optimization.

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“…Furthermore, DSM approaches were developed for steel production, 23 combined heat and power plants, 24 metal casting, 25 residential HVAC systems, 26 electrolysis to produce aluminum 27,28,29 and chlorine, 30,31 and seawater desalination, 32 to name a few examples. There is also literature that considers generic processes with a more abstract focus 33,34 . For an overview of the field, we refer to recent review articles 5,3,35 .…”

Section: Literature Reviewmentioning

confidence: 99%

A decoupling strategy for protecting sensitive process information in cooperative optimization of power flow

et al. 2021

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Optimal power flow (OPF) with close cooperation between the power grid and flexible electricity‐intensive chemical processes can reduce costs for the grid and for electricity users. However, this would require sharing detailed chemical process models, which may reveal confidential information and potentially jeopardize competitive advantages for a chemical process operator. We propose an algorithm that enables economically advantageous cooperation without the need to exchange sensitive information. Low‐order linear models are used to represent the dynamic behavior of electricity‐intensive processes. We integrate these models into the OPF problem and solve the problem using a decoupling strategy based on Benders‐type cuts. The cuts introduce limited communication between the chemical processes and the grid without exchanging explicit information pertaining to the process dynamics and performance. Our results reproduce solutions obtained by sharing detailed process models up to a user‐defined optimality gap for several test cases that reflect both normal and congested grid states. We also investigate the trade‐off between the value of the optimality gap and computational effort. Finally, we study the scaling behavior of the iterative procedure with respect to flexible loads at multiple grid locations.

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Do investments in flexibility enhance sustainability? A simulative study considering the German electricity sector

Cited by 19 publications

References 67 publications

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Dynamic Ramping for Demand Response of Processes and Energy Systems based on Exact Linearization

A decoupling strategy for protecting sensitive process information in cooperative optimization of power flow

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