Robust Dynamic TEP With an Security Criterion: A Computationally Efficient Model

Mahroo, Reza; Kargarian, Amin; Mehrtash, Mahdi; Conejo, Antonio J.

doi:10.1109/tpwrs.2022.3163961

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…The conditions under which Algorithm 1 is guaranteed to converge to a stationary point of the augmented Lagrangian L ρ (7) are [17], [43], [44]:…”

Section: ) Convergence Of Mixed-integer Admmmentioning

confidence: 99%

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Mahroo,

Kargarian

2023

IEEE Trans. Quantum Eng.

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The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantumclassical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantum-classical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.

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“…The conditions under which Algorithm 1 is guaranteed to converge to a stationary point of the augmented Lagrangian L ρ (7) are [17], [43], [44]:…”

Section: ) Convergence Of Mixed-integer Admmmentioning

confidence: 99%

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Mahroo,

Kargarian

2023

IEEE Trans. Quantum Eng.

Self Cite

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show abstract

“…The conditions under which Algorithm 1 is guaranteed to converge to a stationary point of the augmented Lagrangian L ρ are [17], [42], [43]:…”

Section: ) Convergence Of Mixed-integer Admmmentioning

confidence: 99%

Hybrid Quantum-Classical Unit Commitment

Mahroo

Kargarian

2022

2022 IEEE Texas Power and Energy Conference (TPEC)

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The advent of quantum computing can potentially revolutionize how complex problems are solved. This paper proposes a two-loop quantum-classical solution algorithm for generation scheduling by infusing quantum computing, machine learning, and distributed optimization. The aim is to facilitate employing noisy near-term quantum machines with a limited number of qubits to solve practical power system optimization problems such as generation scheduling. The outer loop is a 3-block quantum alternative direction method of multipliers (QADMM) algorithm that decomposes the generation scheduling problem into three subproblems, including one quadratically unconstrained binary optimization (QUBO) and two non-QUBOs. The inner loop is a trainable quantum approximate optimization algorithm (T-QAOA) for solving QUBO on a quantum computer. The proposed T-QAOA translates interactions of quantum-classical machines as sequential information and uses a recurrent neural network to estimate variational parameters of the quantum circuit with a proper sampling technique. T-QAOA determines the QUBO solution in a few quantum-learner iterations instead of hundreds of iterations needed for a quantumclassical solver. The outer 3-block ADMM coordinates QUBO and non-QUBO solutions to obtain the solution to the original problem. The conditions under which the proposed QADMM is guaranteed to converge are discussed. Two mathematical and three generation scheduling cases are studied. Analyses performed on quantum simulators and classical computers show the effectiveness of the proposed algorithm. The advantages of T-QAOA are discussed and numerically compared with QAOA which uses a stochastic gradient descent-based optimizer.

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Transmission expansion planning: A deep learning approach

Dong,

Cao,

et al. 2025

Sustainable Energy, Grids and Networks

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Robust Dynamic TEP With an Security Criterion: A Computationally Efficient Model

Cited by 7 publications

References 32 publications

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Learning Infused Quantum-Classical Distributed Optimization Technique for Power Generation Scheduling

Hybrid Quantum-Classical Unit Commitment

Transmission expansion planning: A deep learning approach

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