Distributed Energy and Smart Grids: Sustainable Energy for the Future

A key component of the Sustainable Development Goals is the switch to renewable energy sources in order to attain a net-zero global energy system. RESs, energy efficiency, and electrification are three important areas that need to be addressed right away. There are other routes as well, all of which call for a major rise in regulatory frameworks, policies, and investments in research. In this sense, attaining a net-zero global energy system remains greatly aided by solar energy. The most plentiful and accessible resource is solar power, and its price has dropped dramatically in recent years. To increase the effectiveness and economy of PV systems as well as their integration with other energy systems, there is an urgent need for ongoing technological advancements and innovations. Additionally, integrating various energy systems can lead to emissions reductions and energy savings. This chapter will examine the various energy system types that can be combined with PV systems in urban settings, as well as the energy balance calculation of each system. It will also examine the technologies and techniques employed, the difficulties encountered, and the solutions and approaches taken to overcome these difficulties. Furthermore, we will talk about smart energy management solutions for demand-side management, energy storage, and distributed generation that integrate various technologies more effectively and sustainably.

The integration of renewable energy sources into smart grids is essential for achieving sustainable, efficient, and resilient energy systems. With increasing global demand for cleaner energy and the rapid development of renewable technologies, smart grids offer a flexible infrastructure that can accommodate the unique characteristics of renewables, such as variability and decentralization. This chapter reviews key challenges and solutions for integrating solar, wind, and other renewable sources into smart grids. Key topics include demand response, energy storage, grid stability, and real-time energy management, along with the role of Internet of Things (IoT) and artificial intelligence (AI) in enabling adaptive and predictive grid operations. Furthermore, the chapter explores case studies that demonstrate the successful implementation of renewable-smart grid integration and highlights future directions for research. The findings underscore the critical importance of advanced control systems, dynamic pricing, and regulatory support in maximizing the reliability and efficiency of renewable energy within smart grids, contributing to a sustainable energy future. 

This chapter examines technologies and strategies to improve the efficiency, reliability, and adaptability of the modern power grid. As global energy demand increases, grid automation has become essential for integrating renewable energy sources, effectively managing electricity loads, and quickly resolving system issues. By implementing advanced technologies and automated control mechanisms, grid automation enables a resilient and intelligent infrastructure that can easily cope with day-to-day operations and unforeseen challenges. Such adaptation is critical in supporting a changing energy landscape where traditional approaches are no longer sufficient. This chapter focuses on these advances and how automated power grids can contribute to a stable, flexible, and sustainable electricity supply. 

By combining advanced metering, data analysis, and machine learning technology, smart grids have revolutionized energy management systems. This chapter explores the key components of energy management and optimization in smart grids, with an emphasis on anomaly detection, secure communications, and the utilization of renewable energy sources. The first stage is investigating methods for gathering and processing data using smart meters in order to examine variables including power, voltage, current, and power factor. Machine learning models are used to identify abnormalities, including contextual and point anomalies, as well as cybersecurity risks, to preserve grid stability and dependability. The chapter also covers optimization techniques that improve resource use and lower energy expenses, enabling a dynamic balance between supply and demand. In order to give consumers relevant information and suggestions, secure communication protocols are emphasized, underscoring the significance of cybersecurity in contemporary power grids. In the end, integrating demand-responsive renewable energy sources is suggested as a workable way to address future energy requirements. This thorough analysis shows how intelligent energy management systems might revolutionize the development of an energy landscape that is robust, sustainable, and efficient.

In areas of surplus wind availability, wind becomes an imperative source, and wind power generation at all possible conditions of wind velocity is a confronting task. This study emphasizes the evaluation of various control methods for the Rotor Side Converter (RSC) in a standalone Doubly Fed Induction Generator (DFIG) based Wind Energy System (WES). The stator voltage and frequency are regulated by controlling the RSC. Voltage control, Cascaded Voltage-Current Control, and Sequence-based Voltage-Current Control are deployed for RSC, and the performance is compared. The primary merit of this work, along with its comparative analysis, lies in ensuring efficient power transmission within the system across all operational modes, regardless of fluctuations in wind velocity. The comparative analysis is conducted using the Simpower Systems toolbox of MATLAB, and the validation, along with effective power transfer in various modes, is performed in real time using the Software-in-Loop (SIL) OPAL-RT (OP4510) real-time simulator. The findings demonstrate the effective performance of the DFIG-based WECS within a standalone DC microgrid across various operational modes, employing different control strategies. 

Systems for underwater propulsion are essential to military applications, research, and marine exploration. In order to improve overall system performance and power quality, this study suggests an underwater propulsion system driven by solar photovoltaics with an incorporated Shunt Active Power Filter. Through the use of a photovoltaic array, the device effectively collects solar energy and stores it in a battery bank, guaranteeing uninterrupted function even when submerged. By successfully reducing power quality problems, including transient disturbances, harmonics, and voltage sags, an SAPF ensures steady and effective propulsion system operation. The Maximum Power Point Tracking approach is used to maximize the solar PV system's energy harvesting. The purpose of the Shunt Active Power Filter is to improve the system’s efficiency by reducing harmonics produced by nonlinear loads. It functions using a hysteresis controller and the instantaneous reactive power theory. The system can maintain voltage stability and produce minimum Total Harmonic Distortion under a variety of operating situations, according to simulation findings. The suggested method offers a dependable and environmentally responsible substitute for traditional propulsion methods, opening the door for cutting-edge and sustainable undersea technology.

The increasing energy demand in the transport sector is stimulating a transition towards Electric Vehicles (EVs). For the propulsion of EV, an advanced Brushless Direct Current (BLDC) motor is integrated. To power the BLDC motor, instead of using the conventional energy that emits greenhouse gases and affects human health, sustainable energy is essential. Henceforth, renewable energy, such as Photovoltaic (PV) based Double Quadratic Boost Converter (DQBC), is proposed. DQBC efficiently boosts the voltage of low output power from the PV system, achieving higher efficiency and voltage gain. The Chicken Swarm Optimization (CSO) algorithm is integrated to tune the parameters of the Proportional-Integral (PI) controller to maximize energy flow and ensure system stability under dynamic conditions. The Bidirectional converter promotes efficient energy transmission between the battery and the EV motor. Moreover, a 3-phase Voltage Source Inverter (3Φ VSI) converts the Direct Current (DC) voltage source to Alternating Current (AC) for regulating the BLDC motor. The proposed system highlights the advancements in optimization techniques like CSO and integrating DQBC to enhance the energy efficiency, reduce losses, and improve the overall system performance for EV energy systems. The experimental verification of the presented model is implemented in MATLAB/Simulink, which shows a higher efficiency of 96.01%. Overall, the proposed model significantly boosts the system's reliability, making it suitable for EV applications.

Traditional centralized power stations face challenges such as high greenhouse gas emissions, reliance on non-renewable resources, and inefficiencies in meeting peak power demand. Additionally, Renewable Energy Sources (RESs) like solar and wind complicate efforts to maintain a consistent power supply and grid stability. This study proposes a Hybrid Renewable Energy System (HRES) that integrates a Photovoltaic (PV) system with a Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS) to address these challenges. The PV system incorporates a High Gain Cubic Boost Converter (HG-CBC), regulated by a Raccoon Optimized Algorithm (ROA) based Proportional Integral (PI) controller, to enhance voltage levels. The DFIG based WECS stabilizes power flow through a Pulse Width Modulation (PWM) rectifier and a PI-controlled PWM generator. Moreover, a 3-phase Voltage Source Inverter (3Φ VSI) converts DC power into AC for grid integration. Experimental verification using MATLAB/Simulink demonstrates a high efficiency of 98%, showcasing the model's ability to significantly boost system reliability and suitability for HRES-powered grids.

The integration of Renewable Energy Sources (RES) into the power grid presents challenges related to their inherent variability and intermittency. To address these issues, this paper introduces an efficient energy storage and control system designed for seamless integration of RES into the grid. The Photovoltaic (PV) system utilizes a Triflex DC-DC boost converter, which effectively boosts the voltage output. The system's performance is further optimized through an Improved Kangaroooptimized Proportional Integral (PI) controller, which enhances the stability and efficiency of the energy conversion process by minimizing voltage deviation. In parallel, the wind energy system, based on a Doubly Fed Induction Generator (DFIG), is managed by a PWM rectifier and a PI-controlled PWM generator ensuring stable power flow integration with the grid. The MATLAB simulation tool is employed to model the proposed hybrid system, with detailed simulations conducted to optimize the control strategies and evaluate the system's performance. The results demonstrated that the proposed converter improves the overall system performance, achieving an energy conversion efficiency of 97%. This study highlights the potential of advanced control approaches and hybrid systems in enhancing the integration of renewable energy into modern grids, while addressing challenges such as variability, efficiency, and grid stability. 

Smart grids are regarded as an advanced solution for energy distribution due to their ability to optimize power flow and integrate renewable sources. Additionally, it achieves greater efficiency and sustainability when paired with Photovoltaic (PV) systems. However, the limited output from PV not always meets the full energy demands of the grid. Henceforth, this paper presents a PV-based smart grid, utilizing a SEPIC-Boost converter integrated with an improved Elephant Optimized PI controller. The proposed converter is designed to boost the voltage from the PV system while improving the efficiency and stability of the smart grid. The Elephant Optimized Proportional Integral (PI) controller tunes the control parameters, minimizing system deviations and improving power regulation. Internet of Things (IoT) based monitoring allows real-time data from PV and converter collection and remote management, ensuring better system performance, fault detection, and energy optimization. The outcomes of the proposed work are evaluated and simulated using MATLAB software, which shows the proposed converter achieving a higher efficiency of 93% and a voltage gain of 1:8.

Smart Grids (SG) have become a groundbreaking technology in the energy sector, enabling efficient power management. They combine various energy sources and rely on an extensive control and communication network through the Internet of Things (IoT), promoting a more reliable, intelligent, and carbon-free energy system. The digital technologies also expose SGs to various cybersecurity threats and attacks. Henceforth, this work proposed an Artificial Intelligence (AI) solution to address cyber threats in SG, utilizing an energy management system based on a routing algorithm. An SG system incorporating wind, Photovoltaic (PV), and a battery-based energy management system has been adopted. The Artificial Fish Swarm Optimization Algorithm (AFSOA) and the Artificial Neural Network (ANN) model are proposed for sending sensed data securely to the end-user via the shortest path. The performance of the microgrid has been tested and validated across various operational modes, including direct wind energy supply, energy feedback to the grid, battery-powered supply, power supply from the external grid, and load-limiting modes. The results demonstrate significant improvements in terms of network throughput, end-to-end delay, packet delivery ratio, energy consumption, and reduced encryption and decryption times, enhancing both performance and security. The system aims to provide real-time monitoring and analysis to end users, with effective control. This proposed work addresses the challenges of energy efficiency, security, and sustainability in modern energy systems. 

Generation Expansion Planning (GEP) plays a pivotal role in ensuring sustainable and cost-effective electricity generation to meet future energy demands. GEP involves selecting and integrating appropriate power generation technologies while considering economic, technical, and environmental constraints. This chapter provides a comprehensive overview of static GEP, focusing on its application to a hypothetical 3-bus test system. This study provides a comprehensive evaluation of renewable energy systems by assessing their economic feasibility, environmental impacts, and grid stability challenges. It focuses on solar, wind, and hydro energy technologies, employing advanced methodologies such as sensitivity analysis, payback period calculations, and return on investment (ROI) evaluations to determine financial performance. Emphasis is placed on renewable integration benefits, including emission reductions, socio-economic growth, and land-use optimization. Key challenges such as storage requirements, grid failures, and peak load management are analyzed using simulations and predictive models. Policy incentives and grid upgrades are investigated to enhance financial feasibility and system resilience. Scenarios for grid stability under varying renewable penetration rates (30%, 50%, 70%) are modeled, highlighting the importance of hybrid storage systems, dynamic energy dispatch, and smart grid technologies. Findings underscore the potential of renewable systems to deliver economic returns while reducing environmental impacts, provided strategic planning, policy support, and technological advancements are implemented. Recommendations focus on storage expansion, flexible energy dispatch, and integrated socio-economic planning to enable a transition toward a sustainable energy future.