Modern mixed-integer quadratic solvers generally handle binary variables more efficiently than nonlinear mixed-integer solvers. This is relevant to the power system operation models as the unitcommitment formulations...
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Modern mixed-integer quadratic solvers generally handle binary variables more efficiently than nonlinear mixed-integer solvers. This is relevant to the power system operation models as the unitcommitment formulations typically contain a large number of binary variables. This paper investigates how to achieve the accuracy level close to the one of the exact nonlinear models, but by utilising convex models and solvers. The presented unitcommitment model is based on a Taylor-series expansion where both the voltage magnitude and angle are quadratically constrained. To achieve high accuracy, the model takes advantage of the meshed transmission network structure that enables replacement of the quadratic inequality constraints that cause constraint relaxation errors with the linear equality constraints. Quadratic constraints to be replaced as well as the operating point parameters are determined based on the presolve. The first presented case study validates the model's accuracy and the convergence of the iterative algorithm, while the second is a non-iterative full unitcommitment problem. unitcommitment results show superior accuracy and similar computation times to the existing quadratic formulations on one hand and faster computation times than the exact nonlinear polar formulation on the other.
The integration of large-scale stochastic renewable energy, the aging of transmission facilities, and the growth of load demand all contribute to the increasing congestion levels of transmission systems. Such factors ...
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The integration of large-scale stochastic renewable energy, the aging of transmission facilities, and the growth of load demand all contribute to the increasing congestion levels of transmission systems. Such factors pose considerable stress on the economical and secure operation of power systems and the accommodation of large-scale renewable energies. However, under the smart grid circumstance, some cutting-edge transmission technologies can bring potential cost-effective solutions to leverage the potential capacity of existing transmission infrastructures. Such technologies can help the utilities to deal with the rapid change of operating conditions of the power system in a more flexible manner. For example, the network topology optimization (NTO) technology can change the transmission topology based on the operating conditions, which increases the flexibility of the transmission system. Dynamic thermal rating (DTR) can evaluate the maximum transmission capacity of transmission lines dynamically according to the weather condition parameters around the conductor. These two cost-effective technologies are promising in improving the congestion mitigation performance and can contribute to the efficient utilization of transmission network-so they will bring potential economic and reliability benefits. This paper incorporates NTO and DTR in the network-constrained unit commitment (NCUC) framework to study their synergistic effect on the power system day-ahead schedule. Case studies are performed on a modified RTS-79 system. The numerical results verify that the coordination of NTO and DTR may help decrease the generation cost and wind power curtailment.
This paper presents a tight model of ideal and generic storage systems in the network-constrained unit commitment (NCUC) problem. Specifically, compared with the basic storage model, the minimum charging and dischargi...
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ISBN:
(纸本)9781728119816
This paper presents a tight model of ideal and generic storage systems in the network-constrained unit commitment (NCUC) problem. Specifically, compared with the basic storage model, the minimum charging and discharging time requirement as well as the cycle limit of energy storage assets are introduced. Moreover, we introduce a new class of inequality constraints to provide a tighter description of feasible operation strategies. Numerical experiments on a modified IEEE 118-bus system with energy storage systems demonstrate the significant reduction in switching times and computational efforts to reach optimal solutions.
This paper presents an optimization method by generating multiple strong Benders cuts for accelerating the convergence of Benders Decomposition (BD) when solving the network-constrained generation unitcommitment (NCU...
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This paper presents an optimization method by generating multiple strong Benders cuts for accelerating the convergence of Benders Decomposition (BD) when solving the network-constrained generation unitcommitment (NCUC) problem. In NCUC, dc transmission network evaluation subproblems are highly degenerate, which would lead to many dual optimal solutions. Furthermore, the classical BD cuts are often low-density which involve only a limited number of decision variables in the master problem. Therefore, the dual optimal solutions and the corresponding Benders cuts are of crucial importance for improving the efficiency of the BD algorithm. The proposed method would generate multiple strong Benders cuts, which are pareto optimal, among candidates from multiple dual optimal solutions. Such cuts would be high-density in comparison with low-density cuts produced by the classical BD. The proposed multiple strong Benders cuts are efficient in terms of reducing the total iteration number and the overall computing time. The high-density cuts may restrict the feasible region of the master unitcommitment (UC) problem in each iteration as they incorporate more decision variables in each Benders cut. The multiple strong Benders cuts would accordingly reduce the iteration number and overall computing time. Numerical tests demonstrate the efficiency of the proposed multiple strong Benders cuts method in comparison with the classical BD algorithm and the linear sensitivity factors (LSF) method. The proposed method can be extended to other applications of BD for solving the large-scale optimization problems in power systems operation, maintenance, and planning.
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