COMPREHENSIVE TECHNO-ECONOMIC IMPROVEMENT DIRECTIVES FOR SMART DISTRIBUTION SYSTEM

Abstract

The ongoing transformation of electric power distribution systems involves the gradual adoption of cutting-edge advancements in communication, control, measurement, and metering technologies. This evolution is geared towards realizing the concept of a smart distribution system, characterized by increased flexibility, sustainability, reliability, and efficiency. The future distribution systems are anticipated to engage actively and involve customers while incorporating various distributed energy resources (DERs) like renewable energy sources (RESs), battery energy storage (ESs), and electric vehicles (EVs). This shift is expected to usher in a new era contributing to environmental and economic well-being. However, a comprehensive planning and operational strategy with strategic goals is imperative to reap the benefits of smart distribution systems fully. This thesis delves into the exploration of methodologies for optimal planning and operation within smart distribution systems, aiming to achieve comprehensive techno-economic advantages. To address existing gaps in current studies, a novel index method, based on Shannon’s Entropy (SE-IM), is introduced. This method identifies the optimal location for capacitor bank placement, incorporating multiple criteria such as voltage stability, loss reduction, and system load-ability. Utilizing particle swarm optimization, the thesis determines the optimal size for the capacitor bank placement. The application of this method to three distribution systems—IEEE 12-bus, 34-bus, and a practical 108-bus radial distribution network from an Indian utility—is discussed, demonstrating superior results compared to existing techniques. Furthermore, the analysis of optimal capacitor bank placement in the presence of distributed generation (DG) and load uncertainty highlights the approach’s capacity to enhance network quantities while maintaining security and reliability constraints. Given the increasing significance of planning in smart distribution systems, particularly in analyzing various techno-economic v measures, a method is proposed to assess the weights associated with objectives in a weighted multi-objective optimal DG placement problem. This method employs Shannon’s Entropy formula to evaluate the relative importance of each objective function, subsequently determining unbiased weights. The efficacy of this approach is validated through numerical simulations on a 38-bus test system, showcasing its effectiveness across various scenarios. This work introduces a practical method for optimizing the day-ahead schedule of electric vehicle charging stations (EVCSs) in a smart distribution system. The primary goal is to minimize real power loss payments, accounting for various operational constraints, voltage limits, and the intricate dispatching and storage constraints of EVCSs. The formulation includes sophisticated demand response modeling and addresses uncertainties in electricity prices and load forecasting. The method leverages mixed-integer nonlinear programming, coupled with the strategic use of single-agent and multi-agent control strategies to provide the optimal solution. The real-world applicability and effectiveness of this approach are demonstrated through simulations conducted on a modified 12-bus distribution network, highlighting its tangible benefits. In addition, this thesis unveils a framework designed to proficiently manage a diverse array of DERs within the smart distribution system. The emphasis is on optimizing the operations of local energy communities and elevating grid services at the utility level. For this, an aggregation modeling of DERs is proposed, assessing cumulative flexibility based on individual preferences, spatial considerations, and temporal behavior. A sophisticated hierarchical control framework is presented to facilitate coordinated dispatch among key entities, namely the electric utility (EU) operator, community aggregators (CAs), and individual DERs. CAs are tasked with minimizing operational costs within their respective energy communities, while the EU operator focuses on enhancing grid services, all within the confines of distribution network constraints. Simulations performed on a modified IEEE-123 bus radial distribution network affirm the efficacy of this proposed approach.