Optimal Power Flow Thesis

Thus, in this thesis, we also develop a simple online algorithm where the knowledge of the future demand information is not necessary.Leveraging the zero duality gap property in the OPF problem, we first characterize the optimal solution of the OPF problem and decomposed problems which are decoupled over time.Energy management is of prime importance for power system operators to enhance the use of the existing and new facilities, while maintaining a high level of reliability.

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Therefore, in this thesis, we study the optimal power flow problem with energy storage dynamics in purely resistive power networks.

We first formulate it as a dynamic nonconvex OPF problem.

First, we study the optimal power flow (OPF) in ac-dc grids, which is a non-convex optimization problem.

We use convex relaxation techniques and transform the problem into a semidefinite program (SDP).

Then we propose an efficient second order cone programming (SOCP) relaxation for this nonconvex problem.

Next, by exploiting a recently-discovered zero duality gap property in the OPF problem, we apply the optimization decomposition techniques to the problem and propose efficient algorithms to obtain the global optimal solution using distributed message passing algorithms.We use ℓ₁-norm approximation and convex relaxation techniques to transform the problem into an SDP. We develop an algorithm to determine a near-optimal solution. Although these decomposition algorithms are very efficient, they are offline algorithms, where the future demand information is needed.However, this type of information is not available before the end of the whole period in reality.To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.A novel approach to multiobjective particle swarm optimization (MOPSO) technique for solving optimal power flow (OPF) problem is proposed in this chapter.We use cookies to make interactions with our website easy and meaningful, to better understand the use of our services, and to tailor advertising.For further information, including about cookie settings, please read our Cookie Policy . Make your own flashcards that can be shared with others.Learn with extra-efficient algorithm, developed by our team, to save your time.