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Development and Analysis of Optimization Algorithm for Demand-Side Management Considering Optimal Generation Scheduling and Power Flow in Grid-Connected AC/DC Microgrid
The world energy sector is experiencing many challenges, such as maintaining a demand–supply balance with continuous increases in demand, reliability issues, and environmental concerns. Distributed energy resources (DERs) that use renewable energy sources (RESs) have become more prevalent due to environmental challenges and the depletion of fossil fuel reserves. An increased penetration of RESs in a microgrid system facilitates the establishment of a local independent system. However, these systems, due to the uncertainties of RESs, still encounter major issues, like increased operating costs or operating constraint violations, optimal power management, etc. To solve these issues, this paper proposes a stochastic programming model to minimize the total operating cost and emissions and improve the operational reliability with the help of a generalized normal distribution optimization (GNDO). A day-ahead demand response is scheduled, aiming to shift loads to enhance RES utilization efficiency. Demand-side management (DSM) with RESs is utilized, and battery energy storage systems in low-voltage and medium-voltage microgrids are shown. Mathematical formulations of each element in the microgrids were performed. Optimal and consumer-friendly solutions were found for all the cases. Environmental concerns based on the amount of harmful emissions were also analyzed. The importance of demand response is demonstrated vividly. The aim is to optimize energy consumption and achieve optimum cost of operation via DSM, considering several security constraints. A comparative analysis of operating costs, emission values, and the voltage deviation was carried out to prove and justify their potential to solve the optimal scheduling and power flow problem in AC/DC microgrids.
Development and Analysis of Optimization Algorithm for Demand-Side Management Considering Optimal Generation Scheduling and Power Flow in Grid-Connected AC/DC Microgrid
The world energy sector is experiencing many challenges, such as maintaining a demand–supply balance with continuous increases in demand, reliability issues, and environmental concerns. Distributed energy resources (DERs) that use renewable energy sources (RESs) have become more prevalent due to environmental challenges and the depletion of fossil fuel reserves. An increased penetration of RESs in a microgrid system facilitates the establishment of a local independent system. However, these systems, due to the uncertainties of RESs, still encounter major issues, like increased operating costs or operating constraint violations, optimal power management, etc. To solve these issues, this paper proposes a stochastic programming model to minimize the total operating cost and emissions and improve the operational reliability with the help of a generalized normal distribution optimization (GNDO). A day-ahead demand response is scheduled, aiming to shift loads to enhance RES utilization efficiency. Demand-side management (DSM) with RESs is utilized, and battery energy storage systems in low-voltage and medium-voltage microgrids are shown. Mathematical formulations of each element in the microgrids were performed. Optimal and consumer-friendly solutions were found for all the cases. Environmental concerns based on the amount of harmful emissions were also analyzed. The importance of demand response is demonstrated vividly. The aim is to optimize energy consumption and achieve optimum cost of operation via DSM, considering several security constraints. A comparative analysis of operating costs, emission values, and the voltage deviation was carried out to prove and justify their potential to solve the optimal scheduling and power flow problem in AC/DC microgrids.
Development and Analysis of Optimization Algorithm for Demand-Side Management Considering Optimal Generation Scheduling and Power Flow in Grid-Connected AC/DC Microgrid
Abdulwasa Bakr Barnawi (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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