Article
Automation & Control Systems
Julie Mulvaney-Kemp, Salar Fattahi, Javad Lavaei
Summary: This article analyzes solution trajectories for optimal power flow (OPF) with time-varying load. The empirical study on California data shows that local search methods can solve OPF to global optimality with enough variation in the data. Introducing a backward mapping that relates time-varying OPF's global solution to desirable initial points can explain this phenomenon.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Francois Pacaud, Daniel Adrian Maldonado, Sungho Shin, Michel Schanen, Mihai Anitescu
Summary: This paper proposes a novel feasible-path algorithm to solve the real-time optimal power flow problem. The algorithm uses second-order derivatives and operates in the reduced space induced by the power flow equations. Feasibility is maintained at each iteration, and operational constraints are softly enforced through augmented Lagrangian penalty terms.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Mathematics
Oscar Danilo Montoya, Farhad Zishan, Diego Armando Giral-Ramirez
Summary: This paper presents a new optimal power flow formulation using a recursive convex representation for monopolar DC networks. The method represents the relation between voltages and power as a linear constraint and proposes a recursive evaluation model to solve the OPF problem. Numerical results demonstrate that the proposed method can efficiently solve the power flow problem in monopolar DC networks and reduce convergence error.
Article
Computer Science, Artificial Intelligence
Dharmbir Prasad, Aparajita Mukherjee, Vivekananda Mukherjee
Summary: This paper introduces a novel chaotic whale optimization algorithm for solving temperature dependent optimal power flow problems in power systems. Through evaluation on three test systems, the algorithm demonstrates superior performance in comparison to other evolutionary optimization techniques in recent literature.
Article
Computer Science, Information Systems
Amro M. Farid
Summary: ACOPF, a crucial optimization problem in electric power systems formulated in 1962, remains unsolved due to its non-convex nature. Literature offers various relaxations and approximations to tackle the issue, but they may lead to suboptimal solutions and decreased reliability. Addressing this challenge is essential for the sustainable energy transition.
Article
Automation & Control Systems
Fengyu Zhou, James Anderson, Steven H. Low
Summary: This article examines the optimal power flow problem as an operator and provides a characterization of the problem under restricted parameter sets. The results give a clear physical interpretation of the mathematical properties.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Amir Lotfi, Mehrdad Pirnia
Summary: Due to the nonlinear and non-convex attributes of power system optimization problems, traditional iterative algorithms are time-consuming. In this paper, a Deep Neural Network-based Optimal Power Flow (DNN-OPF) algorithm is proposed to solve these problems using machine learning, improving the accuracy and efficiency of the algorithm.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Energy & Fuels
Constance Crozier, Kyri Baker, Bridget Toomey
Summary: In this paper, an optimal transmission switching (OTS) heuristic based on DC optimal power flow (OPF) is developed, and its efficacy in AC OPF is assessed. The heuristic algorithm identifies and ranks the constraints that limit the DC OPF feasible region, allowing for substantial cost reduction without solving any mixed integer programs.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2022)
Article
Computer Science, Information Systems
Elnaz Davoodi, Ebrahim Babaei, Behnam Mohammadi-Ivatloo, Miadreza Shafie-Khah, Joao P. S. Catalao
Summary: The article proposes a new SDP-based multiobjective OPF model, which overcomes the difficulty of dealing with multiple objective functions by incorporating a parameterization strategy, generating the Pareto front, and producing globally nondominated Pareto optimal solutions. The numerical results show that the model is more effective than commonly used heuristic methods on different test systems.
IEEE SYSTEMS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Carleton Coffrin, Bernard Knueven, Jesse Holzer, Marc Vuffray
Summary: Research has shown that nonlinear optimization methods are highly sensitive to the mathematical formulation of piecewise linear functions, with a poor choice of formulation potentially slowing down algorithm performance by a factor of ten.
ELECTRIC POWER SYSTEMS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Enes Kaymaz, Serhat Duman, Ugur Guvenc
Summary: Optimal power flow (OPF) is a fundamental optimization problem in modern power systems, especially with the integration of renewable energy sources like wind power. This paper introduces an improved method based on the Levy Coyote optimization algorithm (LCOA) for solving the OPF problem with stochastic wind power, showing that LCOA is more effective than other optimization methods in reaching optimal solutions.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Fatih Cengil, Harsha Nagarajan, Russell Bent, Sandra Eksioglu, Burak Eksioglu
Summary: Machine learning-based methods are proposed to accelerate convergence to global solutions for the AC Optimal Power Flow problem. By leveraging historical data, a subset of variables can be selected to tighten bounds and find near-global optimal solutions at faster run-times.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Zhexin Fan, Zhifang Yang, Juan Yu, Kaigui Xie, Gaofeng Yang
Summary: Linear power flow models are widely used in power system analysis for computational benefits, and improving linearization accuracy is crucial for power system operation. This paper presents a model to minimize linearization error by optimizing variable space selection, with effectiveness verified in IEEE and Polish test systems through power flow and OPF calculations.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
Article
Computer Science, Information Systems
Abdel Rahman Aldik, Bala Venkatesh
Summary: This paper investigates the relationship between the recently introduced McCormick-based Quadratic Convex (QC) relaxation of Optimal Power Flow (OPF) and other available convex relaxations. It also extends the convex envelope of the tangent function for test cases with different voltage angle difference ranges. A computational study is conducted comparing QC-LW OPF and QC-BI OPF formulations using different operational test cases. The results show that the QC-LW relaxation is neither dominated by nor dominates the QC-BI relaxation in terms of solution quality, and it reduces the number of relaxed trigonometric functions and McCormick envelopes compared to QC-BI OPF, resulting in faster solution time for most test cases.
Article
Engineering, Electrical & Electronic
Mohammad Rasoul Narimani, Daniel K. Molzahn, Mariesa L. Crow
Summary: This paper investigates two improvements to convex relaxation methods for optimal power flow problems, one using polar representation of branch admittances and the other based on a coordinate transformation via complex per unit base power normalization. These improvements make the QC envelopes tighter, enhancing the accuracy of the convex relaxation approach.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2021)
