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- Cantillo-Luna, Sergio, et al.
(författare)
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A Type-2 Fuzzy Controller to Enable the EFR Service from a Battery Energy Storage System
- 2022
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- The increased use of distributed energy resources, especially electrical energy storage systems (EESS), has led to greater flexibility and complexity in power grids, which has led to new challenges in the operation of these systems, with particular emphasis on frequency regulation. To this end, the transmission system operator in Great Britain has designed a control scheme known as Enhanced Frequency Response (EFR) that is especially attractive for its implementation in EESS. This paper proposes a Type-2 fuzzy control system that enables the provision of EFR service from a battery energy storage system in order to improve the state-of-charge (SoC) management while providing EFR service from operating scenarios during working and off-duty days. The performance of the proposed controller is compared with a conventional FLC and PID controllers with similar features. The results showed that in all scenarios, but especially under large frequency deviations, the proposed controller presents a better SoC management in comparison without neglecting the EFR service provision.
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| 2. |
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| 3. |
- Cantillo-Luna, Sergio, et al.
(författare)
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Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets
- 2022
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Ingår i: Energies. - : MDPI AG. - 1996-1073. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.
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