scholarly journals Evaluating the Performance of Various ACOPF Formulations Using Nonlinear Interior-Point Method

2021 ◽  
Author(s):  
Sayed Abdullah Sadat
Keyword(s):  
Nonlinear Optimization ◽  
Electricity Markets ◽  
Interior Point ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Wide Range ◽  
Decision Making Processes ◽  
Point Line

Alternating current optimal power flow (ACOPF) problem is a non-convex and a nonlinear optimization problem. Similar to most nonlinear optimization problems, ACOPF is an NP-hard problem. On the other hand, Utilities and independent service operators (ISO) require the problem to be solved in almost real-time. The real-world networks are often large in size and developing an efficient and tractable algorithm is critical to many decision-making processes in electricity markets. Interior-point methods (IPMs) for nonlinear programming are considered one of the most powerful algorithms for solving large-scale nonlinear optimization problems. However, the performance of these algorithms is significantly impacted by the optimization structure of the problem. Thus, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. Different ACOPF formulations are evaluated in this paper for computational viability and best performance using the interior-point line search (IPLS) algorithm. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of varying sparsity. The numerical experiments suggest that the least sparse ACOPF formulation with polar voltages yields the best computational results. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.

scholarly journals Evaluating the Performance of Various ACOPF Formulations Using Nonlinear Interior-Point Method

2021 ◽  
Author(s):  
Sayed Abdullah Sadat
Keyword(s):  
Nonlinear Optimization ◽  
Electricity Markets ◽  
Interior Point ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Wide Range ◽  
Decision Making Processes ◽  
Point Line

Alternating current optimal power flow (ACOPF) problem is a non-convex and a nonlinear optimization problem. Similar to most nonlinear optimization problems, ACOPF is an NP-hard problem. On the other hand, Utilities and independent service operators (ISO) require the problem to be solved in almost real-time. The real-world networks are often large in size and developing an efficient and tractable algorithm is critical to many decision-making processes in electricity markets. Interior-point methods (IPMs) for nonlinear programming are considered one of the most powerful algorithms for solving large-scale nonlinear optimization problems. However, the performance of these algorithms is significantly impacted by the optimization structure of the problem. Thus, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. Different ACOPF formulations are evaluated in this paper for computational viability and best performance using the interior-point line search (IPLS) algorithm. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of varying sparsity. The numerical experiments suggest that the least sparse ACOPF formulation with polar voltages yields the best computational results. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.

scholarly journals Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
Kibaek Kim
Keyword(s):  
Nonlinear Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Alternating Current ◽  
Box Constraints ◽  
Optimal Power ◽  
Wide Range ◽  
The Impact

<div>Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be solved in almost real time. Interior point methods (IPMs) are one of the powerful methods for solving large-scale nonlinear optimization problems and are a suitable approach for solving ACOPF with large-scale real-world transmission networks. Moreover, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. In this paper, different ACOPF formulations with various linear solvers and the impact of employing box constraints are evaluated for computational viability and best performance when using IPMs. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of sparsity. The numerical experiments suggest that the least sparse ACOPF formulations with polar voltages yield the best computational results. Additionally, nodal injected models and current-based branch flow models are improved by enforcing box constraints. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.</div>

scholarly journals Numerical Performance of Different Formulations for Alternating Current Optimal Power Flow

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
Kibaek Kim
Keyword(s):  
Nonlinear Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Alternating Current ◽  
Box Constraints ◽  
Optimal Power ◽  
Wide Range ◽  
The Impact

<div>Alternating current optimal power flow (ACOPF) problems are nonconvex and nonlinear optimization problems. Utilities and independent service operators (ISO) require ACOPF to be solved in almost real time. Interior point methods (IPMs) are one of the powerful methods for solving large-scale nonlinear optimization problems and are a suitable approach for solving ACOPF with large-scale real-world transmission networks. Moreover, the choice of the formulation is as important as choosing the algorithm for solving an ACOPF problem. In this paper, different ACOPF formulations with various linear solvers and the impact of employing box constraints are evaluated for computational viability and best performance when using IPMs. Different optimization structures are used in these formulations to model the ACOPF problem representing a range of sparsity. The numerical experiments suggest that the least sparse ACOPF formulations with polar voltages yield the best computational results. Additionally, nodal injected models and current-based branch flow models are improved by enforcing box constraints. A wide range of test cases, ranging from 500-bus systems to 9591-bus systems, are used to verify the test results.</div>

scholarly journals Customized Sequential Quadratic Programming for Solving Large-Scale AC Optimal Power Flow

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
mostafa Sahraei-Ardakani
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Test Cases ◽  
Time Limits ◽  
Optimal Power ◽  
Wide Range

After decades of research, efficient computation of AC Optimal Power Flow (ACOPF) still remains a challenge. ACOPF is a nonlinear nonconvex problem, and operators would need to solve ACOPF for large networks in almost real-time. Sequential Quadratic Programming (SQP) is one of the powerful second-order methods for solving large-scale nonlinear optimization problems and is a suitable approach for solving ACOPF with large-scale real-world transmission networks. However, SQP, in its general form, is still unable to solve large-scale problems within industry time limits. This paper presents a customized Sequential Quadratic Programming (CSQP) algorithm, taking advantage of physical properties of the ACOPF problem and the choice of the best performing ACOPF formulation. The numerical experiments suggest that CSQP outperforms commercial and noncommercial nonlinear solvers and solves test cases within the industry time limits. A wide range of test cases, ranging from 500-bus systems to 30,000-bus systems, are used to verify the test results.

scholarly journals Customized Sequential Quadratic Programming for Solving Large-Scale AC Optimal Power Flow

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
mostafa Sahraei-Ardakani
Keyword(s):  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Test Cases ◽  
Time Limits ◽  
Optimal Power ◽  
Wide Range

After decades of research, efficient computation of AC Optimal Power Flow (ACOPF) still remains a challenge. ACOPF is a nonlinear nonconvex problem, and operators would need to solve ACOPF for large networks in almost real-time. Sequential Quadratic Programming (SQP) is one of the powerful second-order methods for solving large-scale nonlinear optimization problems and is a suitable approach for solving ACOPF with large-scale real-world transmission networks. However, SQP, in its general form, is still unable to solve large-scale problems within industry time limits. This paper presents a customized Sequential Quadratic Programming (CSQP) algorithm, taking advantage of physical properties of the ACOPF problem and the choice of the best performing ACOPF formulation. The numerical experiments suggest that CSQP outperforms commercial and noncommercial nonlinear solvers and solves test cases within the industry time limits. A wide range of test cases, ranging from 500-bus systems to 30,000-bus systems, are used to verify the test results.

scholarly journals Computational Impacts of SVCs on Optimal Power Flow using Approximated Active-Set Interior Point Algorithm

2021 ◽  
Author(s):  
Sayed Abdullah Sadat ◽  
Xinyang Rui ◽  
mostafa Sahraei-Ardakani
Keyword(s):  
Interior Point ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Optimization Problems ◽  
Interior Point Algorithm ◽  
Active Set ◽  
Computational Performance ◽  
Optimal Power ◽  
Power Dispatch

Interior point methods (IPMs) are popular and powerful methods for solving large-scale nonlinear and nonconvex optimization problems, such as AC optimal power flow (ACOPF). There are various ways to model ACOPF, depending on the objective and the physical components that need to be optimized. This paper models shunt flexible AC transmission systems (FACTS). Shunt FACTS devices such as static VAR compensators (SVCs) are sources for reactive power compensations and addressing voltage stability issues. The co-optimization of SVCs with power dispatch can impact the computational performance of ACOPF. In this paper, we evaluate the performance of different ACOPF formulations with approximated active-set interior point (AASIP) algorithm and co-optimization of SVC set points alongside other decision variables. Our numerical results suggest that both AASIP and SVCs alone improves the computation performance of almost all formulations. The gain in performance, however, depends on the sparsity of the formulation. The most spares formulation, such as branch power flow rectangular voltages (BPFRV), shows the highest gain in performance. In the event of co-optimizing SVCs with power dispatch using AASIP, the performance gain is minimal. Finally, the results are verified using various test cases ranging from 500-bus systems to 9591-bus systems.

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