Application of Multi-objective Hybrid DE-PSO Optimization Technique for Network Congestion Management Through Distributed Energy Storage System

Transmission congestion is one of the prevailing limitation present in the current scenario employing the restructured power system. It is the scarcity of transmission capacity to deliver the power to all the prioritized load transactions at transmission and distribution end. Several other issues rather than obstruction to continuous power flow have risen due to the congested network such as market inefficiency, market power and security. Transmission line parameters will be exceeded beyond its stability limits effecting voltage and thermal limits of the entire transmission system. Congestion management is a methodology that helps us to resolve the issues prevailing in the network owing to restriction in path of power flow through transmission lines having certain power flow limits. Management of power flow congestion in huge electric power system remains a challenging and tough task which can be achieved by introducing Distributed Energy Storage System (DESS) in the congested line. Optimized definite location and size of DESSs are necessary before placing it for maximizing the social welfare function which is nothing but profit to both the producers and consumer of electricity. Consequently, in this work, congestion management for ‘24 h’ time frame has been proposed. It manages the network congestion by selecting preeminent site and size of DESS with in ‘24 h’ time frame. Transmission Congestion Rent (TCR) is used to find the preeminent site for Distributed Generator (DG) whereas hybrid combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO) technique for optimal DG size. In this work, Solar PV along with Energy Storage System (ESS) is utilized as Distributed Energy Storage System (DESS). ESS along with DG is used to store surplus energy during off-load period which will be utilized at peak load thereby enhancing the overall efficiency of the system. Real 24 h’ solar irradiance and temperature data of Delhi are taken to mathematically model the power generated from Solar PV. The suggested methodology is verified on standard IEEE 30 bus system with certain modifications. At the meantime, 24 h’ demand data is created by considering 25% load of the original IEEE 30 bus demand data. The efficacy of proposed methodology is assessed by matching the proposed system results with PSO technique based system results as available in the literature. After analysis it is observed that hybrid optimization technique outperforms the PSO optimization technique in managing resources and congestion problem in the network for the ‘24 h’ time frame.