MPNG: A MATPOWER-Based Tool for Optimal Power and Natural Gas Flow Analyses

Garcia-Marin, Sergio; González-Vanegas, Wilson; Murillo-Sanchez, C.

doi:10.1109/tpwrs.2022.3195684

Cited by 3 publications

(6 citation statements)

References 23 publications

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“…Now, in this multifaceted optimization environment, the integration of tools such as MATPOWER, GEKKO, and CVXPY significantly expands the available options. MATPOWER is essential for solving energy system issues and supports solvers like Gurobi, Xpress, and IPOPT for linear, mixed-integer, and nonlinear programming [52][53][54]. GEKKO specializes in dynamic systems and nonlinear models, offering a holistic and open-source Python platform [55,56].…”

Section: Related Workmentioning

confidence: 99%

“…We study a gas-powered system as a function of flow and pressure. For this purpose, a synthetic network of eight nodes is used, as detailed in [74] and illustrated in Figure 4. In particular, the NOPT problem is written as:…”

Section: Unsupervised Constrained Optimization: Gas-powered Systemmentioning

confidence: 99%

“…Next, the node pressure is stored in π ∈ R W . In turn, the q-th flow x q ∈ x is selected according to the network structure from B to fulfill the Weymouth equality with k q ∈ R and Q ≤ P [54].…”

Section: Unsupervised Constrained Optimization: Gas-powered Systemmentioning

confidence: 99%

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A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

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Nonlinear optimization (NOPT) is a meaningful tool for solving complex tasks in fields like engineering, economics, and operations research, among others. However, NOPT has problems when it comes to dealing with data variability and noisy input measurements that lead to incorrect solutions. Furthermore, nonlinear constraints may result in outcomes that are either infeasible or suboptimal, such as non-convex optimization. This paper introduces a novel regularized physics-informed neural network (RPINN) framework as a new NOPT tool for both supervised and unsupervised data-driven scenarios. By using custom activation functions and regularization penalties in an artificial neural network (ANN), RPINN can handle data variability and noisy inputs. Besides, it uses physics principles to build the network architecture, computing the optimization variables based on network weights and learned features. In addition, it uses automatic differentiation training to make the system scalable and cut down on computation time through batch-based back-propagation. The test results for both supervised and unsupervised NOPT tasks show that our RPINN can provide solutions that are competitive compared to state-of-the-art solvers. In turn, RPINN’s robustness against noisy input measurements makes it particularly valuable in environments with fluctuating information. Additionally, RPINN’s ANN-based foundation offers significant flexibility and scalability.

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Section: Related Workmentioning

confidence: 99%

Section: Unsupervised Constrained Optimization: Gas-powered Systemmentioning

confidence: 99%

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A Regularized Physics-Informed Neural Network to support Data-Driven Nonlinear Constrained Optimization

Perez-Rosero,

Álvarez-Meza,

Castellanos-Dominguez

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To validate the performance of the solution obtained by the proposed approach, it was compared with three approaches used in the state of the art to solve this same problem: replacing the equality constraint with a series of linear inequalities using the Taylor Series method [13], convexifying the problem using second-order cone programming (SOC) [14] and approximating the equation in a defined interval using a polynomial of degree five with odd coefficients only [15].…”

Section: Case Studymentioning

confidence: 99%

“…As an example of relaxation through convexification, the Second-order cone (SOC) programming introduces continuous and binary auxiliary variables and guarantees a global optimum on the approximation [14]. More recently, a polynomial regression holding odd coefficients approximates the Weymouth equation, its first and second derivative at the ends of a predefined operating interval [15]. Despite the reduced complexity and compatibility with conventional solvers, previous strategies result in Weymouth approximations that infringe on physical pipeline behavior, some of them to a great extent.…”

Section: Introductionmentioning

confidence: 99%