arXiv subjects

Abheejeet Mohapatra

Publications and source records attributed to Abheejeet Mohapatra.

3 recordsLinked to original sources

arXiv2023

Robust Short-term Operation of AC Power Network with Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) for conventional non-linear ACOPF results in computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this paper proposes a single-stage non-integer non-recursive RC of ACOPF, using a dual transformation, for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of defined injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled without using any affine policy. The proposed approach also includes the budget of uncertainty constraints for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the obtained setpoints on NESTA and case9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low operational cost and computation time.

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arXiv2021

Robust AC Transmission Expansion Plans from A Novel Dual Based Bi-level Approach

The rapid integration of Renewable Energy Sources (RESs) strengthens the need for a power network that can robustly handle the system's uncertain scenarios. Thus, this paper proposes the first nonlinear novel dual based bi-level approach for robust AC Transmission Expansion Planning (TEP) plans with uncertainties in RES generations and loads. It utilizes a convex relaxation and is solved by Benders Decomposition (BD), where the master determines the robust AC TEP plan. The novel dual slave model for the second level of BD circumvents the issues in using the conventional conic dual theory and aids in the worst-case realization of uncertainties using additional novel constraints. The novel dual slave is solved using Interior Point Method (IPM), as it is not a mixed-integer problem. The proposed work also includes additional linear constraints to reduce the BD's slow convergence and direct the master towards optimality. The robustness of the AC TEP plans is verified by Monte-Carlo Simulation (MCS) of the actual nonlinear and non-convex AC Optimal Power Flow (OPF). The effect of the budget of uncertainty on the AC TEP plans is also investigated. A comparison with the results of a previous work reveals the superiority of the proposed work.

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arXiv2019

An Efficient Approach for obtaining Feasible solutions from SOCP formulation of ACOPF

Exact Second Order Conic Programming (SOCP) formulation of AC Optimal Power Flow (ACOPF) consists of non-convex arctangent constraints. Generally, these constraints have been ignored or approximated (at the expense of increased computational time) so as to solve the relaxed and convex SOCP formulation of ACOPF. As a consequence, retrieving unique and feasible bus voltage phasors for ACOPF of meshed networks is not always possible. In this letter, this issue has been addressed. The arctangent constraints have been represented by alternate linear constraints in the relaxed SOCP formulation of ACOPF, by exploiting the properties of the meshed power network. Numerical tests show that the proposed formulation gives an unique and feasible ACOPF solution, which is practically realizable from system operation perspective and with global optimality feature, as compared to other works reported in the literature. Moreover, the proposed formulation is extremely efficient as only one execution of formulation provides a feasible solution.

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