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

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 security is realized to be one of the challenge that impact the efficiency of accurate calculation and distribution of network pricing among consumers. To address the aforementioned issue, this research analysed the network security on the basis of Long run incremental cost (LRIC) pricing to balance and reduce the network pricing for the DNOs in UK. However, this study presented an approach of Deep reinforcement learning (DRL) also called deep reinforcement learning algorithm (DQN) to optimize the reactive power values in the network to balance and reduce the network pricing while keeping the network security. The method considers IEEE14 bus as its mathematical model and practically simulates the method in MATLAB using DQN algorithm pseudo codes. The network security has been analysed with and without security factor before and after the nodal injection into the network.