Optimization of DER Integrated Distribution System by Sequential Quadratic Programming (SQP)

Hasan, Md Shamim; Kamalasadan, Sukumar

doi:10.1109/globconpt57482.2022.9938177

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…The SLP is an iterative approach that linearizes the nonlinear constraints around the current feasible solution, i.e., using first-order Taylor series expansions, and solves the approximate linear program in each iteration. Sequential quadratic programming (SQP) is a successive approximation methodology similar to SLP and is also used to solve the power flow problem [25]- [27]. Although the SLP and SQP methods are effective for solving large nonlinear optimization problems, the performance of these methods in terms of solution quality and execution time depends on the initialization of the SLP [23] and SQP [25] algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Sequential quadratic programming (SQP) is a successive approximation methodology similar to SLP and is also used to solve the power flow problem [25]- [27]. Although the SLP and SQP methods are effective for solving large nonlinear optimization problems, the performance of these methods in terms of solution quality and execution time depends on the initialization of the SLP [23] and SQP [25] algorithms. To improve the computational performance of the SLP algorithm, various techniques are presented in [28], [29] to initialize the algorithm with a near-optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Successive Convexification Algorithms for Optimizing Power Systems With Energy Storage Models

Tziovani,

Hadjidemetriou,

Timotheou

2024

IEEE Trans. Smart Grid

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Energy storage systems (ESSs) are increasingly used in power system optimization. Different ESS mathematical models are developed that consider nonlinear functions for power losses. However, these models require non-convex constraints to represent the ESS losses, resulting in challenging optimization problems. To reduce the complexity, convex relaxation models are often derived but generate infeasible solutions when the relaxation exactness is violated. To deal with this issue, this work develops two successive convexification algorithms that generate fast and high-quality feasible solutions when the derived solution is not exact. The first algorithm handles general loss functions, while the second algorithm enhances performance when piecewise-linear loss functions are used. Specifically, the algorithms reduce the feasible region of the relaxed ESS models using a tightening box trust region around the current solution in successive iterations. The proposed algorithms are applied to the Unit Commitment and Peak Shaving and Energy Arbitrage problems to investigate their performance considering piecewiselinear and quadratic ESS loss functions. Simulation results demonstrate the impact of the ESSs relaxation violation on the actual system operation and validate the algorithms efficacy to generate high-quality feasible and even optimal solutions with significantly lower execution times compared to problems utilizing exact ESS models.

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