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This preprint has been withdrawn at the request of the corresponding author due to internal coordination requirements and project data privacy considerations.

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The integration of solar photovoltaic (PV) systems and smart grids has enabled distributed energy trading, yet the development of regulatory frameworks for microgrid energy markets remains a challenge. Rising energy costs and greenhouse gas emissions necessitate innovative strategies to ensure affordable, sustainable, and reliable power for communities. This paper proposes a Community Energy Market (CEM) leveraging Linear Programming (LP) optimization to minimize energy costs and enhance renewable energy utilization. The results demonstrate that the CEM approach significantly increases energy self-sufficiency, reducing reliance on the grid. This method achieves Rs.38,830 cost saving. Furthermore, local energy trading within communities yields 68.75% % energy savings and reduces CO2 emissions by 88.01%. These findings highlight the effectiveness of the CEM model in fostering community collaboration, improving microgrid resilience, and promoting environmental sustainability. The proposed solution emphasizes the need for diversifying energy sources and adopting advanced energy market systems to deliver long-term, cost-effective, and eco-friendly energy solutions.

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This paper presents an integrated analytical approach to assess the reliability of power electronic converters in Permanent Magnet Synchronous Generator (PMSG)-based wind farms under variable wind conditions. The study focuses on analyzing the impact of wake effect turbulences and thermal management on power converter reliability, driven by the thermal stress induced by fluctuating wind speeds on power converters. Through extensive simulations using FLORIS and MATLAB, the thermal behavior of converters in wind farms affected by wake interactions was examined to identify potential reliability issues. The methodology involved modeling an 80-turbine wind farm in FLORIS to simulate wake effects, processing high-resolution wind speed data in MATLAB to refine wind speed profiles, and using Simulink to simulate the thermal profiles of power electronics. The results of FLORIS simulations highlighted the variations in turbulence intensity (TI) and power output, while the MATLAB and Simulink models quantified critical thermal stresses in power converters, correlating the locations of the turbine rows with temperature fluctuations and potential failures. Machine learning models, including Gradient Boosting and Random Forest Regressor, were utilized to refine and predict the multi-objective reliability function. The findings underscore the importance of understanding and managing thermal dynamics to improve the reliability and operational resilience of the power converter, supporting sustainable wind farm operations in dynamically changing wind conditions.

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Power quality (PQ) issues, including weak grids, voltage transients, harmonics, notches, current imbalance, and voltage sags, are critical challenges in the textile industry. Even a brief power interruption can halt industrial processes, leading to substantial financial losses. This paper proposes a Model Predictive Control (MPC)-based Unified Power Quality Conditioner (UPQC) as a robust solution to mitigate these PQ disturbances in textile industry-integrated distribution grids. The proposed UPQC is designed to enhance voltage stability, suppress harmonics, regulate reactive power, and correct current imbalance, ensuring uninterrupted industrial operation. A key contribution of this work is the realistic modeling of a textile industry’s electrical network, replicating actual industry ratings to evaluate system performance. The proposed MPC-based UPQC is assessed through five case studies, addressing weak vs. strong grids, voltage transients, current imbalance, and voltage sags—the most significant PQ challenges in textile applications. Simulation results demonstrate that the UPQC significantly improves voltage profiles, reduces harmonic distortion, and effectively compensates for current imbalance. Compared to conventional Proportional-Integral (PI) controllers, the MPC-based UPQC exhibits superior performance in dynamic PQ disturbance mitigation and grid stabilization. These findings underscore the proposed system’s suitability for large-scale industrial deployment, offering a cost-effective and robust solution to enhance operational efficiency and grid reliability in the textile sector.

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This paper addresses the challenges of integrating large shares of renewable energy sources into the power system, focusing on managing operational reserves in multi-area systems and their long-term adequacy. Unlike previous studies, this paper investigates the long-term impact of procurement and activation of operational reserve in adjacent areas, considering energy scheduling and interconnection line constraints. Three procurement schemes for multi-area energy and reserve exchanges are proposed and analyzed through Sequential Monte Carlo Simulation. These schemes vary in their approach to interconnection line capacity constraints and the simultaneous or phased procurement of energy and synchronized reserve. The mathematical operationalization of these schemes is achieved through simple linear programming models, facilitating the quantification of marginal prices for both products. The impact of these schemes on operational reserve adequacy, marginal prices, and interconnection line utilization is demonstrated using configurations of the IEEE RTS 96 system. This analysis incorporates long-term uncertainty and diverse operational conditions and provides valuable insights into the interplay between energy and reserve procurement strategies in multi-area systems.