Enhancing Power System Efficiency and Profitability through Nodal Pricing and Arithmetic Optimization

Electricity trading has become more competitive due to recent sector reforms. Technology, environmental concerns, and small-scale power production have driven distributed generation (DG) sources. Distribution networks and local customers link smaller, more diversified DG sources. They decrease power losses, system costs, voltage, power quality, and infrastructure investments. DG reduces greenhouse gas emissions and improves power system integration, reliability, and efficiency. DG integration into the existing electricity system has presented management and planning issues. DG reduces losses and improves system performance, but poor installation may increase losses, voltage concerns, and costs. Optimizing distribution network usage maximizes DG. Power market deregulation increased competitiveness and efficiency. Transmission line congestion persists. Generation rescheduling, load shedding, and FACTS devices may reduce congestion and assure power supply. Power generation and distribution are private in a deregulated power market, but transmission is monopolized. Transmission Open Access (TOA) lets non-owners utilize the system. Nodal pricing was used to determine the appropriate distributed generation (DG) distribution for profit maximization, loss minimization, and voltage improvement to address voltage rise. The work improved arithmetic optimization for DG allocation. The thesis demonstrated how nodal pricing might boost power system economic efficiency and operator profit. DG location optimizes system performance and losses. This thesis indicates that nodal pricing-based DG allocation and increased arithmetic optimization are beneficial. Profits, system losses, and voltage stability increase. The results assist power system designers, operators, and regulators arrange DGs in their networks. The algorithm needs further testing and improvement under real-world conditions. To complete and apply the concept, further study should incorporate renewable energy integration, system reliability, and environmental impact.