TY - GEN
T1 - Efficient Congestion Management
T2 - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024
AU - Mehmood, Khawaja Khalid
AU - Arruda Moura, Ranier Alexsander
AU - Van Der Molen, Anne
AU - Tonkoski, Reinaldo
AU - Van Der Wielen, Peter
AU - Nguyen, Phuong Hong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The surge in renewable integration and escalating loads has posed significant challenges for distribution system operators (DSOs), resulting in increased congestion within distribution networks (DNs). This paper aims to analyze the impact of the new legal procedure to manage congestion in the Netherlands and optimize grid utilization for connecting new customers. Based on the comprehensive analysis, two optimization problems are formulated to address these challenges. Firstly, consumers share their demand forecasts with DSOs, who analyze the data to identify congestion points. In response to congestion, the DSOs employ the first optimization problem to optimize non-firm capacity limits, minimizing the cost of load curtailment. Subsequently, a second optimization problem is formulated to maximize grid utilization for connecting new customers. This problem identifies optimal loads that can be connected to each node in the DN. To solve these optimization problems, a hybrid genetic and pattern search algorithm is employed. The effectiveness of the proposed framework is evaluated using the IEEE 33-node test feeder. The results demonstrate a reduction in congestion and maximized grid utilization achieved by connecting new loads at each node of the DN.
AB - The surge in renewable integration and escalating loads has posed significant challenges for distribution system operators (DSOs), resulting in increased congestion within distribution networks (DNs). This paper aims to analyze the impact of the new legal procedure to manage congestion in the Netherlands and optimize grid utilization for connecting new customers. Based on the comprehensive analysis, two optimization problems are formulated to address these challenges. Firstly, consumers share their demand forecasts with DSOs, who analyze the data to identify congestion points. In response to congestion, the DSOs employ the first optimization problem to optimize non-firm capacity limits, minimizing the cost of load curtailment. Subsequently, a second optimization problem is formulated to maximize grid utilization for connecting new customers. This problem identifies optimal loads that can be connected to each node in the DN. To solve these optimization problems, a hybrid genetic and pattern search algorithm is employed. The effectiveness of the proposed framework is evaluated using the IEEE 33-node test feeder. The results demonstrate a reduction in congestion and maximized grid utilization achieved by connecting new loads at each node of the DN.
KW - Congestion management
KW - distribution system operations
KW - distribution systems
KW - firm capacity
KW - non-firm capacity
UR - http://www.scopus.com/inward/record.url?scp=85211922723&partnerID=8YFLogxK
U2 - 10.1109/EEEIC/ICPSEurope61470.2024.10751051
DO - 10.1109/EEEIC/ICPSEurope61470.2024.10751051
M3 - Conference contribution
AN - SCOPUS:85211922723
T3 - Proceedings - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024
BT - Proceedings - 24th EEEIC International Conference on Environment and Electrical Engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024
A2 - Leonowicz, Zbigniew
A2 - Stracqualursi, Erika
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 June 2024 through 20 June 2024
ER -
