Department of Electrical Engineering

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    Enhancement of Operational Efficiency for PV System in the Application of Solar Energy
    (Department of Electrical Engineering, NITS, 2022-11-04) Debnath, Dipanwita
    In the modern decade, the intensive developments of technology and changing lifestyle of the current generation of people have made energy an essential and inseparable commodity, but at the same time growing shortage of finite natural resources and degradation of the environmental condition has focused attention on adding the renewable development principle with future technologies. Among all the possible green renewable resources, energy derived from solar power getting continuous significant importance due to countless clear reasons. It is fully clean and inexhaustible in nature and holds out the promise of providing the desired amount of electrical power in need. However solar energy has some drawbacks like lower operational efficiency and high investment cost which present room to expand the research problems. For a reliable solar unit, solar cell accurate modeling is necessary before actual implementation to improve the system performance. The modeling of PV cells involves the extraction of optimized parameters of a selected PV electrical diode model. Since the current vs. voltage (I-V) relationship of PV cell is nonlinear, accordingly model parameter estimation process is viewed as a complicated nonlinear optimization problem, and to denominate this problem numerous dynamic research is going on. So, this dissertation starts with the modeling of different photovoltaic solar cell models based on single diode model (SDM), double diode model (DDM), and a PV module to extract the most optimized parameters of selected models. Further, to maximize the energy of solar PV units, a maximum power point tracker with a potential tracking algorithm is needed for trapping the highest power continuously from a varying source. Under the situation of varying environmental conditions, the output power of the PV module fluctuates. The classical methods (like P&O and incremental conductance method) are unable to give an optimal response in that situation. Many MPPT techniques have been carried out by different researchers in the past to increase the operational efficiency of a PV system, but a lot of research probability still lies in developing an efficient MPPT algorithm. Adding more, to increase solar-based PV yields energy it is important to remember that the temperature of PV cells should not exit its expected module rated value. With increasing surface operating temperature, the operational efficiency of PV units decreases and for that reason to keep the surface temperature close to the surrounding ambient temperature a suitable smart cooling system is necessary. The parameter estimation represents a procedure where measured current-voltage data are used to calculate the parameters of a solar PV cell. Here a very modern and potential optimization technique, named sine-cosine algorithm (SCA) is utilized to accurately estimate the parameters of PV cells/module using experimentally measured I-V data for single and double diode models along with KC200GT PV module. Being SCA technique is a global optimization technique, it does not suffer from singularity problem and provide considerably the most optimized PV panel parameters with better system depiction. The comparison response of SCA outperforms the several published works in terms of root mean square of error (RMSE) and other statistical analysis means. After having success in estimating accurate parameters of solar cells, this thesis subsequently proceeds for developing a novel hybrid global MPPT algorithm to gain maximum power from PV module under the situation of partial shading conditions. Here, improved grey wolf optimization (IGWO) technique, a fusion of parallel grey wolf optimization (PGWO), and opposition-based learning is employed to design the MPPT control structure; for this study, the PV system along with the suggested MPPT algorithm with its components and subsystems is developed through simulation and experimental studies. Firstly, the potentiality of the recommended algorithm to handle the situations: (i) partial shading conditions (ii) gradual solar irradiance and level of temperature changes (iii) rapid irradiance and level of temperature changes have been tested through MATLAB/SIMULINK and subsequently, a prototype setup using ESP32 microcontroller is developed for its practical implementation under partial shading situation. The performance of IGWO has been benchmarked with two classical MPPT methods PSO and P&O. The simulated result of IGWO based MPPT shown here is more effective concerning tracking efficiency as compared to widely used PSO and P&O methods for gradual and rapid change in solar irradiance and temperature levels. The experimental outcomes also show an acceptable performance for the proposed technique. The proposed controller also offers stable sets of responses means there exists no oscillatory behavior around optimal value. The IGWO controller has also succeeded in tracking global maxima efficiently under partial shading. The third work recited in this thesis involves an experimental work equipped with Internet of Things (IoT) platform for enhancing the PV panel performance using the front panel water cooling method for the climate of Bhubaneswar, India. To achieve higher operational efficiency from PV panels, various cooling techniques have been proposed and implemented, but all those methods need continuous observation. A solar panel with no cooling arrangement is used as a base model and compares the test results for both with and without cooling conditions to get the consolidated outcomes. The main objective of using IoT is to give the cooling system a smart touch which ensured the underlying approach successfully records the real-time data for PV system. The result section of this part shows acceptable performance after considering the cooling circuit tooled in IoT system as compared to the non-cooled one.
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    STRATEGIC MANAGEMENT OF VPP FOR DEREGULATED ENERGY MARKETS IN SMART GRID ENVIRONMENTS
    (Department of Electrical Engineering, NITS, 2025) Nadeem, Furquan
    Virtual Power Plants (VPPs) represent a paradigm shift in modern power systems, serving as a virtual aggregation of decentralized energy resources including renewable generators, energy storage units, and flexible demand-side assets. Unlike traditional centralized power stations, VPPs coordinate a multitude of geographically dispersed energy sources through advanced digital platforms. Their primary objective is to optimize energy production, reduce environmental impacts, and enhance grid stability. VPPs facilitate the seamless integration of Distributed Energy Resources (DERs) such as solar PV, wind turbines, battery storage systems, and demand response programs into the grid, thereby supporting the transition to a more decentralized, sustainable, and resilient energy system. This research offers an extensive study of the strategic management of VPPs within deregulated market structures, focusing on integrating Distributed Energy Resources (DERs) into energy markets in a secure, scalable, and economically feasible manner. The thesis begins by formulating a dual-layer energy scheduling framework for VPPs that coordinates resource participation in both Day-Ahead and Real-Time Markets. Employing Mixed Integer Linear Programming (MILP), the system dynamically allocates resources across time intervals to maximize profit and efficiency, while accounting for forecast uncertainties in both generation and load. The optimization framework also incorporates the operational characteristics of controllable and non controllable DERs, ensuring a realistic and robust market participation strategy. A major emphasis of this work lies in improving the economic operation of VPPs within competitive electricity markets. The economic viability of VPPs hinges on their ability to optimally schedule resources not only for profit maximization but also for market reliability. By actively participating in both energy and ancillary service markets, VPPs offer capacity, frequency regulation, and spinning reserve services. The proposed MILP-based framework evaluates real-time market conditions, forecasted prices, and DER constraints to make economically sound dispatch decisions. In this regard, the VPP acts as a price-responsive entity, adjusting its behavior dynamically to extract value from volatile market signals while ensuring that network constraints are not violated. In addition, the thesis introduces multi-objective cost-based optimization, taking into account startup costs, minimum up/down times of generators, and storage degradation costs, which are often neglected in simplistic VPP models. The integration of these economic parameters enhances the realism and financial sustainability of the operation. The objective functions are balanced to prioritize both short-term profits and long-term asset performance, thus ensuring operational resilience across diverse market scenarios. To further strengthen the economic framework, the concept of Levelized Cost of Energy (LCOE) is employed to benchmark the cost-effectiveness of various DERs within the VPP. LCOE analysis enables the operator to compare heterogeneous DERs on a common financial scale, supporting informed decisions on dispatch, investment, and bidding strategies. This model evaluates lifecycle costs and expected returns, offering a clear economic perspective that is vital in deregulated environments where marginal profitability and cost optimization are key to market survival. At the distribution level, the thesis addresses the growing complexity of DER coordination due to network constraints and load diversity. By implementing a Decentralized Locational Marginal Pricing (DLMP) scheme, the model allocates price signals based on local grid conditions, promoting economically efficient dispatch and reducing congestion. This economic signal-based control mechanism ensures that DERs are not only optimally located but also optimally utilized based on real-time voltage, congestion, and loss considerations. The incorporation of DLMP transforms traditional centralized pricing models into more responsive and location-sensitive market frameworks, better suited for modern distribution grids. Another central component of this research is the integration of communication and control strategies using modern smart grid technologies. Given the need for real-time coordination of numerous DERs across diverse regions, the thesis presents a hybrid communication architecture that fuses IEC 61850 (a power industry standard for data modeling and communication) with XMPP (Extensible Messaging and Presence Protocol). This combination allows the system to handle high-frequency data exchange securely, reduce latency, and enhance compatibility across multi-vendor infrastructures. The model not only ensures secure and scalable communication between VPP control units and DERs but also enhances situational awareness and responsiveness under dynamic operating conditions. The communication model is validated through simulations that demonstrate improved interoperability, latency management, and cybersecurity resilience. Results show the capability of the proposed architecture to facilitate robust communication between VPP control centers and field-level DER devices. Moreover, the proposed hybrid model supports scalability across larger grid infrastructures, making it suitable for wide-area implementations. This level of reliability is critical for ensuring that VPPs remain functional and secure under high-stress grid scenarios such as frequency deviations, demand surges, and cyber intrusions. To further ensure practical applicability, the thesis includes case studies that test the performance of the proposed frameworks under various market and operational scenarios. These case studies are implemented in the GAMS environment and supported by prototyped communication setups. The analysis highlights cost savings, improved energy dispatch, and reduced dependency on conventional generation sources. Comparative studies against existing centralized and decentralized strategies confirm the superiority of the proposed approaches in enhancing DER utilization, operational efficiency, and economic returns. Sensitivity analyses further illustrate how pricing mechanisms and scheduling strategies adapt under different levels of DER penetration and market volatility. The integration of optimization algorithms, economic modeling, and secure communication standards makes this thesis a holistic blueprint for VPP deployment in smart grids. It bridges theoretical development and practical realization, offering insights into real-world applications of future-ready energy systems. Each model and methodology introduced has been extensively validated through numerical experiments, providing robust evidence for its viability in enhancing DER participation, economic efficiency, and overall grid security. In conclusion, this thesis underscores the transformative role of VPPs in reshaping energy distribution and market participation in deregulated environments. It brings together novel scheduling strategies, economically informed pricing techniques, and state-of-the-art communication protocols. By doing so, it addresses several pain points faced by system operators and market participants, such as uncertainty in renewables, cybersecurity vulnerabilities, and lack of DER incentives. This research contributes significantly to the advancement of smart grid technologies and lays a strong foundation for the widespread adoption of VPPs as the backbone of sustainable, intelligent, and resilient energy infrastructures.
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    Adaptive Synchronization based Control Strategies for Harmonic Mitigation in Photovoltaic-Integrated Single-Phase Systems under Weak Grid Conditions
    (Department of Electrical Engineering, NITS, 2025) Hassan, Faridul
    Environmental degradation and the rapid depletion of fossil fuel reserves have emerged as pressing global challenges. The combustion of fossil fuels remains a major contribu tor to greenhouse gas emissions, particularly carbon dioxide (CO2), which significantly accelerates climate change. In response, there is a worldwide shift toward the adop tion and advancement of sustainable green energy technologies. This transition has prompted a large-scale deployment of renewable energy (RE) sources particularly pho tovoltaic (PV) system and their increasing integration into the power grid. The modern power system has evolved into a more complex and dynamic grid to accommodate these changes. To improve energy reliability and sustainability, PV systems are often cou pled with energy storage units and connected to the utility network. However, if not appropriately managed, the integration of such systems can pose risks to grid stability and may even lead to operational failures. Moreover, this paradigm shift has resulted in the widespread utilization of power electronic converters. Furthermore, the inherent intermittency of PV sources, combined with the extensive use of nonlinear power elec tronic converters/devices and loads, has introduced several power quality issues. These include the presence of harmonics, interharmonics, DC offsets, grid voltage sags/swells, and frequency fluctuations, all of which can deteriorate voltage and current waveforms across the grid. In single-phase grid-connected photovoltaic (GCPV) systems, it is essential to compen sate for the harmonics while simultaneously injecting high-quality of PV power into the grid, thereby improving the overall power quality. Numerous control strategies have been explored in the literature to ensure reliable and efficient operation of single-phase grid-connected PV systems. Among these strategies, the synchronization unit plays a critical role in enabling stable grid interfacing for all grid-connected power converters. Among various synchronization techniques phase-locked loops (PLLs) and frequency locked loops (FLLs) being the most widely adopted. The implementation of these methods requires two orthogonal signals, an in-phase component and a quadrature phase component, both separated by 90◦. While in three-phase systems these signals can be easily generated using Clarke’s transformation, single-phase systems face chal lenges in generating the required quadrature component. Traditionally, transport delay methods have been used to obtain the quadrature signal; however, PLLs based on such techniques often perform poorly under distorted grid conditions, DC offsets, and vari ations in grid voltage parameters. Quadrature signal generation can also be achieved using derivative-based or integral based techniques, each offering distinct advantages and limitations. Derivative-based methods exhibit strong capability in rejecting DC-offsets and faster dynamic response. However, they tend to amplify higher-order harmonics when the input grid voltage is distorted, which can adversely affect system performance, therefore it required pre filtering stage. Conversely, integral based approaches offers effective filtering charac teristics. Yet, these methods are susceptible to issues such as accumulation and over lapping errors over time. The second-order generalized integrator (SOGI) has emerged as a widely accepted solution for QSG and also used for implementing synchroniza tion schemes such as PLLs and FLLs, and for control strategies in single-phase grid connected photovoltaic (GCPV) systems. In the present work, both derivative-based and generalized integrator-based quadrature signal generation (QSG) together with synchronization techniques are investigated and modified for improved performance. First, a comprehensive advancement in derivative based QSG and synchronization techniques for single-phase grid-connected systems is presented. An improved derivative-based PLL (IDPLL) is developed, incorporating two improved derivative-based QSG (IDQSG) blocks to eliminate gain tuning require ments and inter-loop dependencies, thereby simplifying the design and significantly enhancing dynamic performance. Furthermore, a frequency-adaptive enhanced deQSG (AEdeQSG) is proposed by introducing a tuneable feedback gain (γ) for bandwidth control and employing a mapping-based fractional-order method to achieve frequency adaptability. The proposed methods effectively address challenges such as amplitude amplification, harmonic distortion, and unequal quadrature signal amplitudes under varying grid frequency conditions. Following this, the generalized integrator-based QSG approach is further explored in this work. An improved SOGI-QSG, referred to as iSOGI-FLL, is proposed. This enhanced design integrates an Enhanced Dynamic and Harmonic/DC Rejection (ED HDCR) loop to improve the performance of the conventional SOGI under conditions involving low-order harmonics and DC offset in the grid voltage. The modification en sures better dynamic response and superior harmonic attenuation. A control strategy built upon the iSOGI-FLL is developed, aiming to deliver more accurate quadrature signals, current references, and synchronization signals under various non-ideal grid conditions, enabling seamless operation of grid-connected photovoltaic (GCPV) sys tems. Additionally, the modern complex grid exhibits weak grid characteristics—being heav ily distorted with lower-order harmonics, interharmonics/subharmonics, and DC offset, and is highly prone to disturbances. In such scenarios, the standard second-order gen eralized integrator (SOGI) fails to effectively attenuate these harmonics. Therefore, to address weak grid conditions and to extract more accurate fundamental components with improved dynamic performance, two solutions based on the generalized integrator complex filter (GICF), also known as the complex coefficient filter (CCF), have been proposed. First, an enhanced generalized integrator-based complex filter (eGICF)-FLL is introduced. This structure enables precise extraction of the fundamental voltage and current components, resulting in accurate synchronization and reference signal genera tion, particularly under weak grid conditions. Second, a higher-order complex filtering method, termed the enhanced second-order complex coefficient filter (eSO-CCF), is developed for fast and accurate extraction of fundamental components of quadrature signals in single-phase system applications. The eSO-CCF significantly improves fre quency selectivity, enhances the accuracy of fundamental component extraction, and exhibits robust DC offset rejection. In contrast to traditional second-order and fourth order GI filters, the eSO-CCF effectively isolates the fundamental frequency from low order harmonics, interharmonics, and DC components. To evaluate its performance, the proposed filter is rigorously benchmarked against widely used methods such as SOGI, FOGI, CCF, and SO-CCF in both simulation and experimental settings. A comparative analysis based on the frequency spectrum of the generated quadrature signal (QSG) is also presented. Building upon this foundation, a single-phase enhanced second-order complex co efficient filter-based frequency-locked loop (1Φ-eSO-CCF-FLL) is designed and im plemented. This advanced synchronization strategy is utilized for the control of a single-stage grid-connected PV system integrated with a battery energy storage sys tem (BESS). The proposed control scheme ensures accurate frequency tracking while significantly improving power quality, even under weak grid conditions characterized by severe distortion, including lower-order harmonics, interharmonics, and DC offsets. To assess the effectiveness of the proposed approach, encompassing fundamental com ponent extraction, quadrature signal generation, synchronization signals estimation and frequency-adaptive control : a 5 kW experimental hardware prototype of a single-phase grid-connected photovoltaic (PV) system integrated with a battery energy storage sys tem (BESS) was developed. At the core of the setup is a Semikron-based converter stack configured as a voltage source converter (VSC), which utilizes an IGBT switch (SKM100GB125DN) and SKYPER-32 Pro gate drivers. The AC side of the system in cludes essential interfacing and protection components such as fuses, a grid interfacing inductor, a ripple filter, and other auxiliary circuitry. Grid connection is established through an NHR 9410 grid simulator. On the DC side, a PV emulator (AMETEK TerraSAS) is interfaced via a DC-DC boost converter. The VSC performs multiple functions, as it converts the DC output from the PV emulator into AC for injection into the grid or for supplying local AC loads connected at the point of common coupling (PCC). A custom-fabricated rectifier is also included on the load side for testing pur poses. Additionally, it supports harmonic compensation and reactive power injection to improve power quality. For BESS integration, a dual active half-bridge (DAHB) converter is connected at the DC side of the VSC. A high-frequency transformer is coupled to the AC side of the DAHB through an external inductor, both of which are designed and built in-house. To mitigate high-frequency switching noise, a film capaci tor is placed at the DAHB output. An ITech battery emulator (IT6012B-500-80) is used to emulate battery behavior and support charging operations. The control strategy is implemented on a field-programmable gate array (FPGA)-based controller (WAVECT WCU300), built on the Xilinx Zynq SoC XC7Z020 platform. A HIOKI power quality analyzer is used to record power quality data. Resulting waveforms and performance behavior are recorded using a mixed-signal oscilloscope (MSO58, 350 MHz, 6.25 GS/s). Additionally, the entire system—including the PV interface, VSC control, and BESS integration—is modeled and simulated in the MATLAB/Simulink environment and val idated through hardware experimentation. The proposed approaches, such as IDPLL, AEdeQSG, iSOGI, eGICF, and eSO-CCF-based fundamental component extraction, synchronization and for grid-connected PV integrated with BESS—have all been vali dated on a developed hardware test setup.
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    SMART CHARGING OF PLUG-IN ELECTRIC VEHICLES IN RENEWABLE INTEGRATED DISTRIBUTION SYSTEMS
    (Department of Electrical Engineering, NITS, 2026) Das, Bijaya Kumar
    The rapid adoption of plug-in electric vehicles (PEVs) coupled with the increasing penetration of renewable energy sources (RESs) in distribution networks offer significant opportunities for reducing greenhouse gas (GHG) emissions and fossil fuel dependence. While these trends support global decarbonization goals, they also introduce new complexities in grid management due to the intermittent nature of RESs and the stochastic, high-power demand from uncoordinated PEV charging. This PhD research presents an integrated approach combining multi-objective optimization and machine learning to address these emerging challenges and enable intelligent, environmentally conscious, and technically robust smart charging strategies in modern distribution networks. The thesis begins by exploring smart charging of PEVs in distribution systems, highlighting the implications of uncoordinated charging on PEV charging cost, load variance, and power losses. A methodology is developed to model the stochastic charging demand of PEVs using probability distribution functions (PDFs) for charging start time, initial state of charge (SOC), and daily driving distance. A centralized multi-objective smart charging strategy is formulated under time-of-use (TOU) pricing. The optimization problem addressed using Ant Lion Optimization (ALO). To ensure robustness, the results are validated against Grey Wolf Optimization (GWO) and Non-dominated Sorting Genetic Algorithm II ((NSGA-II). Simulation studies are conducted on IEEE 33 and 69 bus distribution networks to demonstrate the proposed strategy. A central contribution lies in the development of a smart grid framework that coordinates RESs and PEVs to simultaneously minimise both fuel cost and emission. RESs particularly wind and solar can significantly reduce emissions from electricity generation while PEVs with vehicle-grid (V2G) capability can mitigate emissions from the transportation sector. The study highlights the proposed smart grid model enabling maximum utilization of renewable resources for cost and emission reduction. Particle Swarm Optimization (PSO) and Ant Lion Optimization (ALO) are employed to solve the optimization problem and generate pareto fronts. To validate the proposed methodology, three case studies are analysed on a hybrid system comprising a ten-unit generation system and a 33-bus distribution network To further improve system intelligence and responsiveness, the thesis integrates machine learning (ML) models for wind energy forecasting and SOC estimation. An adaptive coulomb counting method combined with SOC-OCV look up table incorporating temperature effects is presented for more accurate SOC estimation. Additionally, given the intermittent nature of wind, an empirical mode decomposition (EMD) machine learning based is proposed for wind energy forecasting. The developed model reduces the prediction errors and improves reliability in renewable based smart grids. The work also addresses congestion and emission management in power systems. A congestion management framework is developed for distribution system with high PEV penetration, leveraging machine learning methods (Adaboost and XGBoost) to forecast SOC for optimal selection of participating vehicles. The coordination problem is solved using particle swarm optimization (PSO) and tested on a 38-bus radial distribution system integrated with a solar powered charging and parking lot (SPCPL). The proposed work demonstrates effective congestion management and CO2 emission reduction. The proposed approaches collectively demonstrate that smart charging strategies coupled with RESs integration, optimization techniques, and ML methods can significantly improve the technical, economic, and environmental performance of distribution systems. The findings of this research contribute toward realizing the vision of sustainable, resilient, and intelligent power systems enabled by PEVs and RESs.
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    BIDDING STRATEGY OF VIRTUAL POWER PLANT IN ELECTRICITY AND CARBON EMISSIONS TRADING MARKETS
    (Department of Electrical Engineering, NITS, 2026) Singh, Kumari Nutan
    The growing adoption of renewable energy and the shift toward decentralized power generation have driven the development of Virtual Power Plants (VPPs), which serve as an effective approach to combine various Distributed Energy Resources (DERs) into a single, adaptable, and market-oriented system. This thesis presents a comprehensive study aimed at enhancing the operational efficiency, economic viability, environmental sustainability, and cyber security of VPPs through five interrelated objectives. First, a forecasting model for renewable energy resources is designed to precisely estimate the generation from variable sources like solar and wind. Leveraging advanced machine learning methods, this model enhances the reliability and scheduling efficiency of VPPs, enabling more stable grid integration and improved participation in energy markets. Second, recognizing the volatile nature of carbon trading markets, a Carbon Price Forecasting Framework is proposed. This model incorporates market uncertainties and utilizes hybrid forecasting techniques, including deep learning, time series decomposition, and feature selection methods, to provide precise carbon price predictions. Accurate forecasting supports VPPs in making informed trading and operational decisions while complying with carbon regulations. Third, social welfare oriented bidding strategy is developed to guide VPPs in participating in both energy and carbon markets, aiming to balance profit maximization with broader social and environmental obligations. The forecasted results for solar PV and wind power from chapter 2 have been taken for participating in VPP trading. Various carbon emission reduction methods are presented namely: carbon tax, cap-and-trade scheme and uplift payment scheme. The carbon credit price is calculated using carbon tax and uplift payment scheme and then participated in EM. For a day ahead bidding strategy considers carbon trading constraints, emission penalties, and renewable integration while aiming to enhance overall welfare, particularly in systems with high renewable penetration. iii After considering bidding strategy, the thesis conducts a thorough analysis on Unit Commitment (UC) for VPP systems. Therefore, fourth objective this thesis, bi-level UC model for VPP has been presented. For UC model the VPP is again integrated with carbon trading market (CTM) and also with green certificate trading market (GCTM). The proposed bi-level unit commitment model aims to maximize the total profit of the overall VPP systems by minimizing the total cost. By integrating EM with CTM and GCTM, the model optimizes energy dispatch, minimizes emissions, and supports regulatory compliance. The analysis also evaluates the economic implications of carbon prices on dispatch decisions and generation cost. Since, the concepts of VPP is ICT based structure and therefore the maintaining its security is foremost challenge for a power engineer. Finally, fifth objective of the thesis explores the Cyber security aspects of VPPs, recognizing the critical importance of secure data communication and control in a digitally connected energy ecosystem. This also presents the possible cyber-attacks in in VPP systems and its impacts. Potential threats and vulnerabilities are identified, and a cyber-security framework is proposed, including anomaly detection, secure protocols, and resilience strategies to safeguard VPP operations against cyber-attacks. The proposed algorithm Autoencoder utilized for anomaly detection for 9-bus and IEEE-39 bus modified VPP systems created in MATLAB Simulink. The outcomes of this research offer meaningful insights and practical approaches to enhance the technical efficiency, economic viability, and environmental sustainability of VPPs. By addressing forecasting accuracy, carbon market integration, optimal bidding, emission reduction, and cyber security, the thesis contributes to the advancement of future-ready, low-carbon, and resilient power systems.