Long Run Incremental Cost (LRIC) Distribution Network Pricing in UK, advising China's Distribution Network
Asad Mujeeb, Wang Peng

TL;DR
This paper proposes a deep reinforcement learning approach to optimize Long Run Incremental Cost (LRIC) based network pricing in UK distribution networks, aiming to enhance security and reduce costs amid increasing distributed generation.
Contribution
It introduces a novel DQN-based method to optimize reactive power and improve network security and pricing in distribution systems with high DG penetration.
Findings
DQN effectively balances reactive power to reduce network costs.
Network security improves with the proposed DQN approach.
LRIC-based pricing adapts to high DG integration in distribution networks.
Abstract
Electricity distribution network system is considered one of the key component of the modern electrical power system. Due to increase in the energy demand, penetration of renewable energy resources into the power system has been extensively increasing in recent years. More and more distributed generations (DGs) are joining the distribution network to create balance in the power system and meet the supply and demand of consumers. Today, large amount of DGs inclusion in the distribution network system has completely modernized power system resulting in a decentralize electricity market. Hence, Government of UK is pressurizing 14 distribution network operators (DNOs) to include more DGs into their distribution network system. DGs inclusion in the network system might be helpful due to many factors, but it creates many challenges for distribution network system in the long term. The network…
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Taxonomy
TopicsSmart Grid Energy Management · Optimal Power Flow Distribution · Smart Grid Security and Resilience
MethodsQ-Learning · Convolution · Dense Connections · Deep Q-Network
