This paper presents a method in expansion planning of transmission systems using the AC optimal power flow (AC-OPF). The AC-OPF provides a more accurate picture of power flow in the network compared to the DC optimal power flow (DC-OPF) that is usually considered in the literature for transmission expansion planning (TEP). While the AC-OPF-based TEP is a mixed-integer nonlinear programming prob...

This paper presents a novel method to approximate the nonlinear AC optimal power flow (OPF) into tractable linear/ quadratic programming (LP/QP) based OPF problems that can be used for power system planning and operation. We derive a linear power flow approximation and include a convex reformulation of the power losses in the form of absolute value functions. We show three ways how we can incor...

The deterministic methods generally used to solve DC optimal power flow (OPF) do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be wel...

This paper presents a scalable method for improving the solutions of AC Optimal Power Flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The focus of the paper is on providing solutions that are more robust to short-term deviations, and which optimize both the initial operating point and a parametrized response policy for c...

This paper proposes a component-based dual decomposition of the nonconvex AC optimal power flow (OPF) problem, where the modified dual function is solved in a distributed fashion. The main contribution of this work is that is demonstrates that a distributed method with carefully tuned parameters can converge to globally optimal solutions despite the inherent nonconvexity of the problem and the ...

Alternating current optimal power flow (AC OPF) is one of the most fundamental optimization problems in electrical power systems. It can be formulated as a semidefinite program (SDP) with rank constraints. Solving AC OPF, that is, obtaining near optimal primal solutions as well as high quality dual bounds for this non-convex program, presents a major computational challenge to today’s power ind...

In this paper, a novel multi-agent decision making architecture based on dual’s dual problem formulation is designed for economic operation. The benefits of such architecture include its limited information exchange among autonomous entities and the ease of implementation. Existing AC optimal power flow (OPF) software packages can be easily incorporated. The proposed decision making is based on...

A prominent challenge to the safe and optimal operation of modern power grid arises due growing uncertainties in loads renewables. Stochastic flow (SOPF) formulations provide a mechanism handle these by computing dispatch decisions control policies that maintain feasibility under uncertainty. Most SOPF consider simple such as affine are mathematically resemble many used current practice. Motiva...

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future maintain stable and economic system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed solve AC-OPF with high computational efficiency. Its unique design predicts voltages of all buses then uses them reconstruct remaining variables without so...

Electricity network operators use computationally demanding mathematical models to optimize AC power flow (AC-OPF). Recent work applies neural networks (NN) rather than optimization methods estimate locally optimal solutions. However, NN training data is costly and current cannot guarantee or feasible This study proposes a robust approach, which starts with small amount of seed uses iterative f...