Deformation of an Open Sandwich Cylindrical Shell with CNT Reinforced Faces Using HDQ Method

Power system optimization academics have long been drawn to the idea of scheduling distributed energy resources (DERs) optimally in order to decrease the generating cost of a low voltage (LV) microgrid (MG) system. The present work implements two different incentive based demand response (IBDR) policies for load curtailment. The first one involves a price elasticity matrix to emphasis on paying incentives to the customers for curtailing load during peak hours only. The second IBDR policy is an optimization based approach which involves customer willingness to deliver economic benefit both to themselves as well as the DISCOM. The final restructured load demand is the base load demand minus the load curtailed by both the IBDR policies. Henceforth, generation cost minimization is percolated on the MG system for all the three load models. Three case studies are performed for an exhaustive techno-economic analysis of the subject MG system. The study uses the recently created quick and easy circle search algorithm (CSA) as its optimization tool. The generation cost was decreased from $25463 to $24969 and $24899 using IBDR1 and IBDR2 policies of load curtailment respectively. During IBDR1 80kW load was curtailed and the customers gained an incentive of $277 whereas using IBDR2 policy, 105kW of load was curtailed and the DISCOM benefitted $211. The consumers also benefitted $500 in the process. Numerical results also show that CSA outperformed various optimization algorithms from the literature and ample algorithms implemented in the work. Central tendency measurements further support the reliability and effectiveness of CSA.

Modelling demand response in smart microgrid with techno and economic objective functions and improvement of network efficiency

Wang,

Zhang,

Zhou

et al. 2024

Modelling smart energy consumption with hybrid demand management in off-grid electrical system considering techno-economic indices

Askar,

Pallathadka,

Sapaev

et al. 2024

The smart grid is an advanced network that integrates various technologies to enhance the efficiency, reliability, and sustainability of electricity distribution. This intelligent grid utilizes digital communication and automation to monitor and control the flow of electricity, enabling better management of power generation, and consumption. This paper aims to explore the techno-economic multi-objectives of a smart off-grid electrical system, with a focus on consumers' participation and day-ahead scheduling. The primary objectives of this study are twofold: firstly, to reduce the operation energy cost, and secondly, to enhance the voltage profile. Two strategies within the demand side are being proposed: demand shifting and strategic conversion of demand side management (DSM). These strategies aim to facilitate consumers' participation in optimal energy dispatch. The enhanced epsilon-constraint technique is used to extract the non-dominated solutions from objective functions by solving them using the General Algebraic Modeling System (GAMS) optimization software. In addition, a combination of fuzzy and weight sum procedures is employed to determine the optimal solution in decision-making. The effectiveness of the proposed method is ultimately validated by conducting numerical simulations on two case studies involving the IEEE 33-bus system. The results clearly demonstrate the significant efficiency of demand-side participation in optimizing energy dispatch and enhancing objectives. Regarding the obtained results, operation energy cost and voltage profile are improved by 21.58% and 13.36% due to non-participation of the demand side.

Optimal energy generation of hybrid energy systems considering economic and environmental multi-objective functions

Raman,

Sayed,

Alkhayyat

et al. 2024

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of both technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promoting environmental sustainability. Ultimately, the findings of this research could play a crucial role in shaping the future of energy sourcing in residential communities, paving the way for more resilient and sustainable energy systems