| 1. |
- Cantillo-Luna, Sergio, et al.
(author)
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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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In: Energies. - : MDPI AG. - 1996-1073. ; 15:7
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Journal article (peer-reviewed)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.
(author)
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Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets
- 2022
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In: Energies. - : MDPI AG. - 1996-1073. ; 15:1
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Journal article (peer-reviewed)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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| 4. |
- Restrepo-Trujillo, Juliana, et al.
(author)
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Scenario Analysis of an Electric Power System in Colombia Considering the El Niño Phenomenon and the Inclusion of Renewable Energies
- 2022
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In: Energies. - : MDPI AG. - 1996-1073. ; 15:18
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Journal article (peer-reviewed)abstract
- This paper develops and analyzes four energy scenarios for Colombia that consider the El Niño phenomenon and the inclusion of renewable energies in the energy generation matrix for the period 2020–2035. A comparative analysis is presented between the results of the different scenarios proposed. The most relevant finding is the use of the reserve margin as an indicator of system reliability. A scenario which included 7214 MW of large-scale non-conventional renewable energy, 10,000 MW of distributed generation, and 12,240 MW of hydroelectric power was assumed, with a reserve margin of over 50%. Additionally, it was found that for the scenarios in which a generation capacity with non-conventional renewable energies of less than 10,000 MW in 2034 was assumed, the reserve margin of the system in the seasons of the El Niño phenomenon will be less than historical records of the system. Alternatively, it was found that the scenarios in which the inclusion of at least 9600 MW of the electric power generation capacity of non-conventional renewable energies proposed by 2034 offer benefits in the reduction in greenhouse gas (GHG) emissions, which contributes to the achievement of the emission reduction objectives of the Paris Agreement.
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| 5. |
- Zuluaga, Jorge, et al.
(author)
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Day-Ahead Unit Commitment for Hydro-Thermal Coordination with High Participation of Wind Power
- 2022
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In: IET Energy Systems Integration. - : Institution of Engineering and Technology (IET). - 2516-8401. ; n/a
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Journal article (peer-reviewed)abstract
- The variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day-ahead multi-period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in order to ensure an optimal power flow minimising the generation cost. A progressive hedging approach has been proposed and developed to solve efficiently the unit commitment problem as a two-stage stochastic programming problem to update each stage in parallel. The performance of progressive hedging is compared with a standard mixed-integer linear programming problem. The results indicate that the computation time is 50 times faster than standard mixed-integer linear programming. The test case system is based on a reduced version of the interconnected Colombian system. The comparative results indicate an important reduction in computational time.
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