Transfer-Reinforcement-Learning-Based rescheduling of differential power grids considering security constraints

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.

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Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning

Energies ◽

10.3390/en14071991 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1991

Author(s):

Kirstin Beyer ◽

Robert Beckmann ◽

Stefan Geißendörfer ◽

Karsten von Maydell ◽

Carsten Agert

Keyword(s):

Online Learning ◽

Reinforcement Learning ◽

Reactive Power ◽

Low Voltage ◽

Dynamic Environment ◽

Power Grids ◽

Grid Connection ◽

Learning Agent ◽

Communication Devices ◽

Smart Inverters

The increasing penetration of the power grid with renewable distributed generation causes significant voltage fluctuations. Providing reactive power helps balancing the voltage in the grid. This paper proposes a novel adaptive volt-var control algorithm on the basis of deep reinforcement learning. The learning agent is an online-learning deep deterministic policy gradient that is applicable under real-time conditions in smart inverters for reactive power management. The algorithm only uses input data from the grid connection point of the inverter itself; thus, no additional communication devices are needed and it can be applied individually to any inverter in the grid. The proposed volt-var control is successfully simulated at various grid connection points in a 21-bus low-voltage distribution test feeder. The resulting voltage behavior is analyzed and a systematic voltage reduction is observed both in a static grid environment and a dynamic environment. The proposed algorithm enables flexible adaption to changing environments through continuous exploration during the learning process and, thus, contributes to a decentralized, automated voltage control in future power grids.

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Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

Energies ◽

10.3390/en14248270 ◽

2021 ◽

Vol 14 (24) ◽

pp. 8270

Author(s):

Nikita Tomin ◽

Nikolai Voropai ◽

Victor Kurbatsky ◽

Christian Rehtanz

Keyword(s):

Reinforcement Learning ◽

Voltage Control ◽

Power Grids ◽

Droop Control ◽

Electrical Networks ◽

Heavy Load ◽

Market Interaction ◽

Secondary Voltage ◽

Multi Agent ◽

Nodal Voltage

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

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