TY - JOUR
T1 - Chance constrained load frequency control of power systems with wind resources
AU - Ma, Tong
AU - Barajas-Solano, David Alonso
AU - Tartakovsky, Alexandre M.
N1 - This work was partially supported by the U.S. Department of Energy (DOE) Office of Science, Office of Advanced Scientific Computing Research (ASCR) as part of the Multifaceted Mathematics for Rare, Extreme Events in Complex Energy and Environment Systems (MACSER) project under Contract DE-AC05-76RL01830.
This work was partially supported by the U.S. Department of Energy (DOE) Office of Science, Office of Advanced Scientific Computing Research (ASCR) as part of the Multifaceted Mathematics for Rare, Extreme Events in Complex Energy and Environment Systems (MACSER) project. Pacific Northwest National Laboratory is operated by Battelle for the DOE under Contract DE-AC05-76RL01830 .
PY - 2025/1
Y1 - 2025/1
N2 - We propose a semidefinite programming (SDP) framework for load frequency control (LFC) of a power system with significant wind power generation. The presence of stochastic wind and load disturbances causes frequency deviations which may lead to power grid instability, it is reasonable to formulate a stochastic model predictive control (SMPC) framework to suppress the load frequency deviation and minimize the mechanical power cost. To reduce the computational burden, we reformulate the quadratic cost function and chance constraints as linear ones with linear matrix inequalities, which yields a tractable SDP framework. The SDP framework is more computationally efficient than the scenario-based MPC, it also guarantees convergence and recursive feasibility which is lacking in scenario-based MPC. The SDP framework with time-varying feedback control gains achieves 95% reduction in frequency deviation, which outperforms the one that uses constant feedback control gains.
AB - We propose a semidefinite programming (SDP) framework for load frequency control (LFC) of a power system with significant wind power generation. The presence of stochastic wind and load disturbances causes frequency deviations which may lead to power grid instability, it is reasonable to formulate a stochastic model predictive control (SMPC) framework to suppress the load frequency deviation and minimize the mechanical power cost. To reduce the computational burden, we reformulate the quadratic cost function and chance constraints as linear ones with linear matrix inequalities, which yields a tractable SDP framework. The SDP framework is more computationally efficient than the scenario-based MPC, it also guarantees convergence and recursive feasibility which is lacking in scenario-based MPC. The SDP framework with time-varying feedback control gains achieves 95% reduction in frequency deviation, which outperforms the one that uses constant feedback control gains.
KW - Chance constraints
KW - Load frequency control (LFC)
KW - Power systems
KW - Semidefinite programming (SDP)
KW - Stochastic model predictive control (SMPC)
KW - Wind resources
UR - https://www.scopus.com/pages/publications/85213294764
UR - https://www.scopus.com/pages/publications/85213294764#tab=citedBy
U2 - 10.1016/j.jfranklin.2024.107478
DO - 10.1016/j.jfranklin.2024.107478
M3 - Article
AN - SCOPUS:85213294764
SN - 0016-0032
VL - 362
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
IS - 2
M1 - 107478
ER -