Effective Transmission Congestion Management via Optimal DG Capacity Using Hybrid Swarm Optimization for Contemporary Power System Operations

Abstract

Managing transmission congestion had been a major problem with growing competition in the power networks. Competitiveness emerges through the network's reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. The present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network's technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. The performance of the proposed methodology of HSO and PSO has been tested successfully on standard IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

“…This big battery is highly stochastic in terms of the EV number and SOC. [50][51][52][53] Furthermore, the number itself depends on the incoming and exiting cars. For this reason, the parking lot battery is simplified by a probability density function (PDF) as will be demonstrated in the next section.…”

“…Based on the SOC, the parking lot is a large battery, whose capacity is defined by the size of the individual parking vehicle batteries' that are shared in the DR program. This big battery is highly stochastic in terms of the EV number and SOC 50–53 . Furthermore, the number itself depends on the incoming and exiting cars.…”

How Much Do Swarm Intelligence and Evolutionary Algorithms Improve Over a Classical Heuristic From 1960?

“…They have also been widely used in various fields of engineering, including software faults prediction [18], smart grid development and control [19], system fault diagnosis [20], transmission congestion in power networks [21], development of charging stations for electrical vehicles [22], or planning of the renewable energy sources [23] [24]. Different kinds of EAs are applied to different kinds of problemscontinuous, discrete or combinatorial, single-objective or multi-objective, static or dynamic, constrained or unconstrained [25].…”

Power Loss Minimization in Smart Transformer Enabled Low Voltage Islanded Meshed Hybrid Microgrid

“…In [24], a new topology of distribution network is proposed in presence of renewable energy resources for minimizing the total cost of planning while considering the power loss cost. In [25], the optimal sizing of the DG capacity is performed for effective congestion management while considering the power loss reduction. In [26], the unified power quality conditioner is used to minimize power losses in distribution system.…”

“…with optimal integration of intermittent RES to improve topological flexibility. [25] Congestion management is done with Hybrid distribution system is not considered. Performance is tested optimal DG capacity.…”