The development of an optimization technique for transmission expansion planning with renewable energy integration

Abstract

The demand for electrical energy is rapidly increasing due to various socioeconomic factors, including industrialization, population growth, urbanization, and the advancement of modern technologies within the context of the Fourth Industrial Revolution. The rapid increase in energy demand poses a significant challenge to the power system. The desire for sustainability is driving significant changes in the global energy sector. Keeping the global average temperature within bounds is a critical concern, prompting countries to take tangible steps to reduce energy system dependence on fossil fuels. Recently, transmission network expansion planning (TNEP) was studied. Power network planning requires TNEP to determine the locations, timing, and quantity of additional transmission lines while ensuring grid stability to guarantee all equipment performs within limitations. Renewable energy (RE) is used in grid connectivity, small businesses, and photovoltaic (PV) solar systems in homes. This research proposes adding RE resources to transmission line expansion to enhance capacity. Studies have examined RE inclusion into power grid expansion plans. South Africa's electricity system uses solar and wind. However, the integration of RE in power systems can cause instability as most of the renewable energy has an intermittent nature. Therefore, this problem is discussed to provide an effective solution for generation and TEP. The first part of this study is the comprehensive analysis of Approaches for TNEP, which includes various approaches, methodologies, and technologies utilized in the expansion process, highlighting their advantages, limitations, potential implications, and reliability in transmission expansion planning (TEP), distributed generation, electrical markets, insecurity, line congestion, and reactive power planning (RPP). It also analyzes innovative transmission expansion planning models that integrate renewable energy sources (RES) utilizing improved optimization methods. The second case study shows the importance of TEP and is divided into sub-sections. The first, Tie Open Point Optimisation (TOPO) techniques in conjunction with Genetic Algorithm (GA), denote switchable connections among network segments, allowing system operators to reorganize a network for improved reliability, efficiency, minimal losses, and cost-effectiveness. After that, hosting capacity development and RE integration evaluate and upgrade the power grid's capacity to accommodate distributed energy resources (DERs) such as solar and wind while maintaining reliability to add more RE without strengthening the network. Reliability assessment with contingency is proposed, which examines the network response to any possible faults. The short and long-term TEP with load and generator forecasts predicts gridbehavior in different seasons over a year and over many years, when load growth increases with RE uncertainty, such as wind farms and solar PV plants. To assess network performance over time, quasi-dynamic simulation uses load flow computations at specified times. Finally, probabilistic analysis in conjunction with Quasi-Monte Carlo simulation (QMCS) is suggested to evaluate system performance under uncertainty, determine the best location for additional lines to maintain grid stability, and analyze system behavior, demand growth, generation availability, and network restrictions. It helps decision-makers evaluate expansion possibilities and mitigate power system development risks throughout time. A power park analysis tool evaluates wind farm profitability, losses, and energy. Basic energy analysis and probabilistic analysis using the QMCS are employed. The network with hybrid renewable plant integration was successfully constructed through both short-term and long-term transmission planning amidst various uncertainties. The optimal techniques employed for transmission expansion planning effectively maintained system stability over 15 years, as shown in Figure 6.38 and Table 6.10 / different conditions, as shown in Figure 6.23 and Table 6.9 with minimal losses under reliability assessment.