Formulation Of Optimal Power Flow Problem Research Articles

Application of Covariance Matrix Adaptation‐Evolutionary Strategy for Modified Constrained Optimal Power Flow Problem Incorporating Valve Point and Emission Effect

A prevailing problem in power and energy subsystems is the smooth operation of electric energy systems. This work presents recent, efficient, and reliable evolutionary algorithm for solving the optimal power flow (OPF) analysis. All various practical complex equality and inequality constraints, namely, bus voltages, real powers of the generator buses, tap settings of the transformers and the reactive power generations, shunt compensation, and emission, are considered for the real‐world scenario. Primary feature in a gas power plant that raises a lot of computational shortcomings with nonlinear structure in fuel cost is valve point effect. The existing research works have not factored the valve point effect and lack the accuracy in the fuel cost minimization and do not reflect the various practical complexities such as valve point and emission effects in the OPF problem formulation. This paper, for the first time, introduces modified OPF problem formulation incorporating valve point effect and applies covariance matrix adaptation‐evolution strategy (CMA‐ES) for solving the modified OPF problem. The algorithm is scrutinised and tested on a modified IEEE‐30‐bus platform for various OPF objectives such as cost minimization, transmission loss, and total voltage deviation, subjected to practical constraints. Load flow analysis has been carried out using the Newton–Raphson method. This work aims to lay the foundation in such a way that it can be applicable in a real‐world scenario for any number of buses.

Optimal Power Flow in AC/DC Microgrids With Enhanced Interlinking Converter Modeling

In this article, we propose an optimal power flow (OPF) paradigm for hybrid ac/dc microgrids. A meticulous model of the interlinking converter (IC) is developed and integrated into the OPF problem formulation. The resulting formulation is capable of solving the OPF of ac and dc subgrids in their standalone operations. A computationally efficient parabolic relaxation method transforms the nonconvex OPF model into a convex quadratic-constrained quadratic programming form. A sequential penalization method is applied to the relaxed OPF to achieve feasible solutions for the original formulation. The developed OPF formulation is tested on multiple modified 5-, 14-, 30-, 24-, 118-bus test systems, and a significant reduction in computational time is achieved in comparison with semidefinite programming relaxation. For a sequential penalized ac/dc OPF, the optimal values of both relaxations have a maximum difference of 0.025% for a 5-bus system. Finally, the modified IEEE 14-bus system is emulated in a real-time controller/hardware-in-the-loop setup to validate the proposed framework with continuously varying loads and IC switching status.

Multi-period optimal power flow for identification of critical elements in a country scale high voltage power grid

The paper focuses on the identification and numerical ranking of critical elements in a power transmission network. This problem is closely linked to the identification of critical infrastructure in a power grid. A quantitative ranking of the power system elements is necessary for the prioritization of actions enhancing system resilience, as the most critical elements require higher attention ensuring protection, functionality, redundancy and reliability. The paper presents a methodological approach based on alternating current multi-period optimal power flow problem formulation and application on a reference network replicating a country scale system. The multi-period optimal power flow method was tested and applied to the reference system and a number of critical elements were identified. The paper further discusses possible protection and mitigation measures.

Optimal feeder flow control for grid connected microgrids

The optimal management of distributed energy resources is one of the existing challenges for the deployment of microgrids. When microgrids operate under feeder flow control mode, trying to maintain a constant self-consumption, generators adapt their output power to compensate load and non-dispatchable generation variations. So, due to the uncertainty, finding the optimal operation point is an important task that can become complex. This paper proposes an optimal power flow problem formulation for feeder flow controlled microgrids. It iated as a mixed integer second order cone program considering the optimal power flow equations in its relaxed form and uncertainty by means of stochastic formulation. In addition, an algorithm is developed in order to find a feasible optimum solution of the original non-relaxed problem. The proposed algorithm can also be used in other optimal power flow problems with the condition that they must use the same relaxation. The algorithm is validated through the IEEE 33-Bus distribution test system.

OPTIMAL POWER DISPATCH FOR LARGE SCALE POWER SYSTEM USING STOCHASTIC SEARCH ALGORITHMS

This paper presents solution of optimal power flow (OPF) problem of large distribution systems via two stochastic search algorithms. A simple genetic algorithm (GA) and evolutionary programming (EP) are used to minimize the fuel cost and keep the power outputs of generators, bus voltages, shunt capacitors/reactors and transformers tap-setting in their secure limits. CPU times can be reduced by decomposing the optimization to constraints and constraints. The active which affect directly the cost function are manipulated by GA or EP. The passive which affect indirectly this function are maintained in their soft limits using a conventional constraint load flow. If the objective used in the OPF problem formulation is the minimization of the total cost of real power then the active are the active power generations only and the passive are generator bus voltages, transformer taps and reactive power generations. The two stochastic search algorithms are applied to IEEE 30-bus model system (6 generators, 41 lines and 20 loads) and to IEEE 118-bus model system (54 generators, 186 (line + transformer) and 99 loads). The numerical results have demonstrated the effectiveness of the stochastic search algorithms because it can provide accurate dispatch solutions with reasonable time.