TY - GEN
T1 - Synergistic Integration of Renewable Energy and HVDC Technology for Enhanced Multi-objective Economic Emission Dispatch Using the Salp Swarm Algorithm
AU - Gbadega, Peter Anuoluwapo
AU - Sun, Yanxia
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This paper proposes a Salp Swarm Algorithm (SSA), a unique optimization technique for the synergistic integration of renewable energy and High Voltage Direct Current (HVDC) technology to enhance the performance of multi-objective economic emission dispatch (MODED). The primary aim is to optimize both the economic and environmental aspects of power systems. A mathematical model for MODED based on Wind-Solar-Thermal integrated energy has been carefully constructed, considering variables like the valve point effect, equality constraints, and inequality constraints. The study determines optimal generation levels and associated costs for six thermal generating units under various power demands, exploring diverse scenarios such as Economic Dispatch for High Voltage Alternating Current (HVAC) with Losses, Economic Dispatch for HVDC with Losses, Economic Dispatch for HVDC addressing challenges related to voltage instability, protection difficulties and losses in DC systems, Economic Dispatch HVAC & HVDC with Losses and Economic Dispatch for HVAC & HVDC with Renewable Energy (RE). To validate the model, tests have been conducted on the IEEE 30 Bus System with a substantial presence of renewable energy.
AB - This paper proposes a Salp Swarm Algorithm (SSA), a unique optimization technique for the synergistic integration of renewable energy and High Voltage Direct Current (HVDC) technology to enhance the performance of multi-objective economic emission dispatch (MODED). The primary aim is to optimize both the economic and environmental aspects of power systems. A mathematical model for MODED based on Wind-Solar-Thermal integrated energy has been carefully constructed, considering variables like the valve point effect, equality constraints, and inequality constraints. The study determines optimal generation levels and associated costs for six thermal generating units under various power demands, exploring diverse scenarios such as Economic Dispatch for High Voltage Alternating Current (HVAC) with Losses, Economic Dispatch for HVDC with Losses, Economic Dispatch for HVDC addressing challenges related to voltage instability, protection difficulties and losses in DC systems, Economic Dispatch HVAC & HVDC with Losses and Economic Dispatch for HVAC & HVDC with Renewable Energy (RE). To validate the model, tests have been conducted on the IEEE 30 Bus System with a substantial presence of renewable energy.
KW - HVAC
KW - HVDC technology
KW - Multi-objective dynamic economic dispatch
KW - Renewable energy
KW - Salp swarm algorithm
UR - http://www.scopus.com/inward/record.url?scp=85205512497&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7004-5_17
DO - 10.1007/978-981-97-7004-5_17
M3 - Conference contribution
AN - SCOPUS:85205512497
SN - 9789819770038
T3 - Communications in Computer and Information Science
SP - 232
EP - 249
BT - Neural Computing for Advanced Applications - 5th International Conference, NCAA 2024, Proceedings
A2 - Zhang, Haijun
A2 - Li, Xianxian
A2 - Hao, Tianyong
A2 - Meng, Weizhi
A2 - Wu, Zhou
A2 - He, Qian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Neural Computing for Advanced Applications, NCAA 2024
Y2 - 5 July 2024 through 7 July 2024
ER -