Abstract
Economic dispatch (ED) is an important class of optimization problem in Power System Operation. As both conventional and heuristic methods to solve EDP are centrally controlled, which may leads to some performance limitations, a Consensus based distributed algorithm is proposed in this paper to solve Economic Dispatch with inclusion of losses. Earlier, some papers dealt with the consensus based methods to solve Economic dispatch, but here in this paper the losses are included and the variation of losses at each iteration are also used to update the mismatch, which has some major prominence in the present day Power system environment. In this paper, the mismatch between load demand and total power generation is collectively learnt by the each generator, unlike the centralized approach, through the strongly connected communication network. MATLAB results in IEEE 6-bus system validate the potency and efficacy of the proposed technique
Highlights
Problem Formulation: The Economic dispatch problem is formulated as followsThe above problem, a Lagrangian unconstrained objective function is formulated and is minimized using gradient methods
Economic dispatch (ED) is an important class of optimization problem in Power System Operation
Let the graph be defined as G = (V, E, A) where vertex V is a set of power system nodes, E is a set of pairs of nodes called edges and A is a real matrix of dimension m×m and is called an Adjacency matrix; its ijth element represents distance between nodes i and j
Summary
The above problem, a Lagrangian unconstrained objective function is formulated and is minimized using gradient methods. The well – known solution of the above problem [1, 2, 3] is given below:. Where, (IC)i is the Incremental Cost at the ith generator. (IC)i = 2aiPGi + bi = λ (ii) If the losses are taken into account, λ = (IC)i. Λ is called the Lagrange Multiplier and indicates the cost of generation. (4) & (5) are solved by the well-known lambda iteration method The eqs. (4) & (5) are solved by the well-known lambda iteration method
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