Market Response for Real-Time Energy Balancing with Fuzzy Logic

Röben, Felix; Meißner, Anna

doi:10.1109/eem49802.2020.9221945

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…A previous version of fuzzy logic was used to anticipate the behavior of BRPs in changing market environments and a 1 GW imbalance test case [25]. The new version introduced in the following was extended to represent BRPs market response according to a situation in which all market participants would join smart balancing.…”

Section: Fuzzy Behavior Of Smart Brpsmentioning

confidence: 99%

Smart Balancing of Electrical Power in Germany: Fuzzy Logic Model to Simulate Market Response

et al. 2021

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Recent EU legislation enforces the integration of European balancing markets, with harmonized products and international platforms for the procurement and activation of reserves; nonetheless, different power balancing strategies remain. The Netherlands and Belgium encourage market participants to support balancing the control block by publishing real-time information. This article refers to such concepts as smart balancing, and a market simulation tool was developed to assess the relevant market parameters for effective smart balancing. This shall contribute to the true integration of real-time balancing energy markets. The scope of the assessment of relevant market parameters was Germany, and the results showed that a pricing scheme had less impact on the results, as currently is understood by European TSOs and regulators. Moreover, the accuracy and frequency of real-time publication indicate the effectiveness of smart balancing and the associated reduction of the activation of balancing energy and associated costs. Consequently, this article proposed a road map for Germany to introduce an adapted smart balancing approach, starting with a simple traffic light.

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Section: Fuzzy Behavior Of Smart Brpsmentioning

confidence: 99%

Smart Balancing of Electrical Power in Germany: Fuzzy Logic Model to Simulate Market Response

et al. 2021

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“…System modeling can be highly complex and present non-linearities, making controller design difficult. Techniques such as fuzzy systems allow obtaining a very close approximation of their response without an established mathematical model [8], [9]. Since the mathematical model is a necessity for the design of control strategies, fuzzy logic presents multiple examples of its adaptation to avoid this requirement [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Embedded fuzzy controller for water level control

Baquero

Ortega

Moreno

2022

IJECE

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This article presents the design of a fuzzy controller embedded in a microcontroller aimed at implementing a low-cost, modular process control system. The fuzzy system's construction is based on a classical proportional and derivative controller, where inputs of error and its derivate depend on the difference between the desired setpoint and the actual level; the goal is to control the water level of coupled tanks. The process is oriented to control based on the knowledge that facilitates the adjustment of the output variable without complex mathematical modeling. In different response tests of the fuzzy controller, a maximum over-impulse greater than 8% or a steady-state error greater than 2.1% was not evidenced when varying the setpoint.

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Market Response for Real-Time Energy Balancing with Fuzzy Logic

Cited by 2 publications

References 2 publications

Smart Balancing of Electrical Power in Germany: Fuzzy Logic Model to Simulate Market Response

Smart Balancing of Electrical Power in Germany: Fuzzy Logic Model to Simulate Market Response

Embedded fuzzy controller for water level control

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