Abstract
This research report investigates a novel optimization approach for the economic dispatch problem (EDP) based on the weighted sum of generators’ costs under supply-demand balance. Unlike conventional approaches, we present a distributed optimization approach that ensures optimality using weighted incremental cost (IC) consensus and sign-consensus error convergence. We can apply the optimization of a weighted sum of generators’ costs to address several constraints, such as capacity and environmental constraints, in addition to the supply-demand balance. The proposed distributed weighted incremental cost consensus approach has been applied to the IEEE-30 bus and IEEE-118 bus systems over a communication topology. The results indicate the efficacy of weights to address generation constraints and the convergence of weighted ICs under supply-demand balance.
Highlights
Technological progress and changes in ecological policies around the world are forcing the energy markets to follow distributed generation models (El-Baz et al, 2019; Flore et al, 2019) and (Schubert and Stadelmann, 2015)
The IEEE-118 bus system has been adapted to test the application of the proposed approach to a complex system and to investigate adjustment in the resultant method for the application to a large-scale system
We consider IEEE-30 bus system (He et al, 2019) for testing the proposed weighted economic dispatch problem (EDP) approach, which consists of six distributed generators
Summary
Technological progress and changes in ecological policies around the world are forcing the energy markets to follow distributed generation models (El-Baz et al, 2019; Flore et al, 2019) and (Schubert and Stadelmann, 2015). The solution to this problem is challenging for a distributed EDP environment, as the conventional optimality conditions and the conventional consensus methods cannot be applied in this situation Motivated by these concerns, the present work is a step toward formulating and establishing conditions for distributed EDP with weighted cost function under supply-demand constraints, which can consider various generation constraints as well. The optimization problem (Eq 3) becomes complex owing to different formats of objective function (weighted) and constraints (nonweighted) and due to consideration of distributed as well as dynamic optimization over a graph Similar to He et al (2019), the total power generation mismatch (ΔP) is given by ΔP PD − PGi. If we take the derivative of Eq 1 with respect to PGi, we obtain the IC of ith unit as η (t) dCi PGi β + 2γ PG
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