Frequency Support of Fast-Multi-Energy Storage Systems in Low Rotational Inertia Scenarios

Ersdal, Anne Mai; González-Longatt, Francisco; Acosta, Martha N.; Rueda, José L.; Pálenský, Peter

doi:10.1109/isgt-europe47291.2020.9248843

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Currently, there is a wide variety of ESS technologies, but among all the ESS, it is worth highlighting batteries (BESS), supercapacitors (UCESS) and flywheels (FESS) [3]. Authors in [4] proposed the use of a multi-electrical energy storage system (MEESS) to provide fast frequency response service, precisely the Enhanced Frequency Response (EFR) [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Configuration of the Actor and Critic Network of the Deep Reinforcement Learning controller for Multi-Energy Storage System

Paramo-Balsa

González-Longatt

Acosta

et al. 2022

2022 4th Global Power, Energy and Communication Conference (GPECOM)

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The computational burden and the time required to train a deep reinforcement learning (DRL) can be appreciable, especially for the particular case of a DRL control used for frequency control of multi-electrical energy storage (MEESS). This paper presents an assessment of four training configurations of the actor and critic network to determine the configuration training that produces the lower computational time, considering the specific case of frequency control of MEESS. The training configuration cases are defined considering two processing units: CPU and GPU and are evaluated considering serial and parallel computing using MATLAB ® 2020b Parallel Computing Toolbox. The agent used for this assessment is the Deep Deterministic Policy Gradient (DDPG) agent. The environment represents the dynamic model to provide enhanced frequency response to the power system by controlling the state of charge of energy storage systems. Simulation results demonstrated that the best configuration to reduce the computational time is training both actor and critic network on CPU using parallel computing.

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Section: Introductionmentioning

confidence: 99%

Configuration of the Actor and Critic Network of the Deep Reinforcement Learning controller for Multi-Energy Storage System

Paramo-Balsa

González-Longatt

Acosta

et al. 2022

2022 4th Global Power, Energy and Communication Conference (GPECOM)

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“…Therefore, the power system is facing several challenges to adapt to being a modern power system [3,4]. However, this inevitable transition also is bringing many opportunities, and it opens the door to taking advantage of the controllability and other features provided by the power electronic converter (PECs) based technologies [5,6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Daily Cost of Reactive Power Procurement by Smart Inverters

et al. 2021

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The reactive power control mechanisms at the smart inverters will affect the voltage profile, active power losses and the cost of reactive power procurement in a different way. Therefore, this paper presents an assessment of the cost–benefit relationship obtained by enabling nine different reactive power control mechanisms at the smart inverters. The first eight reactive power control mechanisms are available in the literature and include the IEEE 1547−2018 standard requirements. The ninth control mechanism is an optimum reactive power control proposed in this paper. It is formulated to minimise the active power losses of the network and ensure the bus voltages and the reactive power of the smart inverter are within their allowable limits. The Vestfold and Telemark distribution network was implemented in DIgSILENT PowerFactory and used to evaluate the reactive power control mechanisms. The reactive power prices were taken from the default payment rate document of the National Grid. Simulation results demonstrate that the optimal reactive power control mechanism provides the best cost–benefit for the daily steady-state operation of the network.

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“…Furthermore, the energy storage device is considered an effective way to enhance the security and stability of the power grid. While compensating for imbalanced power in the island grid, the energy storage device can efficiently suppress low-frequency oscillation due to its flexible control modes and rapid response (Ersdal et al, 2020). However, the high cost of the energy storage device configuration will lower the operational economy of the island grid.…”

Section: Introductionmentioning

confidence: 99%

Rapid Power Compensation-Based VSC-HVDC Control Strategy for Low-Frequency Oscillation Suppression of the Island Power System

Abuduwayiti

Meng

et al. 2021

Front. Energy Res.

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Owing to the increased proportion of new energy power generation, such as wind power and photovoltaics, connected to the island grid, the system powered by the voltage source converter-based HVDC (VSC-HVDC) is prone to oscillate or even lose stability when disrupted. In this study, considering the rapid power compensation (RPC), an active power control strategy has been suggested for the receiving-end converter station of VSC-HVDC that could efficiently suppress the low-frequency oscillation of the island power system. First, the mechanism of VSC-HVDC inhibiting low-frequency oscillation of the island grid is analyzed in this study, and then, it theoretically determines that the damping capacity and inertia level of the rapid power compensation control strategy are stronger than those of conventional droop control and inertia control. Second, the receiving-end converter station switches from the RPC mode to droop control in order to allow the system to have a smooth recovery from the steady-state operation in the later stage of oscillation suppression. Moreover, detailed control logic and state-switching strategies have been designed. Finally, the simulation reflects that the proposed control strategy has a stronger oscillation suppression ability, allowing it to obtain rapid suppression of low-frequency oscillation.

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Frequency Support of Fast-Multi-Energy Storage Systems in Low Rotational Inertia Scenarios

Cited by 4 publications

References 12 publications

Configuration of the Actor and Critic Network of the Deep Reinforcement Learning controller for Multi-Energy Storage System

Configuration of the Actor and Critic Network of the Deep Reinforcement Learning controller for Multi-Energy Storage System

Assessment of Daily Cost of Reactive Power Procurement by Smart Inverters

Rapid Power Compensation-Based VSC-HVDC Control Strategy for Low-Frequency Oscillation Suppression of the Island Power System

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