Solving realistic reactive power market clearing problem of wind-thermal power system with system security

Abstract

This paper proposes a security-constrained single and multi-objective optimization (MOO) based realistic security constrained-reactive power market clearing (SC-RPMC) mechanism in a hybrid power system by integrating the wind energy generators (WEGs) along with traditional thermal generating stations. Pre-contingency and post-contingency reactive power price clearing plans are developed. Different objective functions considered are the reactive power cost (RPC) minimization, voltage stability enhancement index (VSEI) minimization, system loss minimization (SLM), and the amount of load served maximization (LSM). These objectives of the SC-RPMC problem are solved in a single objective as well as multi-objective manner. The choice of objective functions for the MOO model depends on the load model and the operating condition of the system. For example, the SLM is an important objective function for the constant power load model, whereas the LSM is for the voltage-dependent/variable load model. The VSEI objective should be used only in near-critical loading conditions. The SLM/LSM objective is for all other operating conditions. The reason for using multiple objectives instead of a single objective and the rationale for the choice of the appropriate objectives for a given situation is explained. In this work, the teaching learning-based optimization (TLBO) algorithm is used for solving the proposed single objective-based SC-RPMC problem, and a non-dominated sorting-based TLBO technique is used for solving the multi-objective-based SC-RPMC problem. The fuzzy decision-making approach is applied for extracting the best-compromised solution. The validity and efficiency of the proposed market-clearing approach have been tested on IEEE 30 bus network.

A hybrid optimization approach for strategically placing electric vehicle charging stations in a radial distribution IEEE-33 bus system

“…The substation transformer at bus 1 has a capacity of 3 MW. The system is divided into five zones such as Zone 1 (2,(19)(20)(21)(22), Zone 2 (3,(23)(24)(25), Zone 3 (4-6, 26-29, 31, 32), Zone 4 (9-12), and Zone 5 (13)(14)(15)(16)(17)(18), respectively. Five distribution generations (DGs) are deployed for the five zones having nominal load 100 kW, 330 kW, 685 kW, 390 kW, and 360 kW respectively.…”

“…The IEEE 33 test system maximizes the accuracy of the positioning and sizing of energy-conserving batteries (EVCS) and renewable energy sources (RESs) by employing the GA-PSO method. As stated in the cited source [17], the objective is to diminish power loss, voltage fluctuations, and storage costs. An improvement in the Voltage Reliability Power index of the IEEE 33-bus system has been achieved through the utilization of the genetic algorithm (GA) technique [18].…”

Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources

“…This helps the distributed resources which are coming from disparate locations to respond quickly to the energy supply and demand Refs. [1][2][3][4]. The goal of the VPP is to handle the energy demand of consumers communally and to resolve the future failure of networks.…”