  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.
