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Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation

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TLDR
The augmented Lagrangian relaxation method enhanced by the decomposition and coordination techniques avoids oscillations associated with piece-wise linear cost functions and is fast and efficient in dealing with numerous power system constraints.
Abstract
This paper proposes a new approach based on augmented Lagrangian relaxation for short term generation scheduling problems with transmission and environmental constraints. In this method, the power system constraints, e.g. load demand, spinning reserve, transmission capacity and environmental constraints, are relaxed by using Lagrangian multipliers, and quadratic penalty terms associated with power system load demand balance are added to the Lagrangian objective function. Then, the decomposition and coordination technique is used, and nonseparable quadratic penalty terms are replaced by linearization around the solution obtained from the previous iteration. In order to improve the convergence property, the exactly convex quadratic terms of decision variables are added to the objective function as strongly convex, differentiable and separable auxiliary functions. The overall problem is decomposed into N subproblems, multipliers and penalty coefficients are updated in the dual problem and power system constraints are satisfied iteratively. The corresponding unit commitment subproblems are solved by dynamic programming, and the economic dispatch with transmission and environmental constraints is solved by an efficient network flow programming algorithm. The augmented Lagrangian relaxation method enhanced by the decomposition and coordination techniques avoids oscillations associated with piece-wise linear cost functions. Numerical results indicate that the proposed approach is fast and efficient in dealing with numerous power system constraints. >

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Journal ArticleDOI

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

TL;DR: In this paper, a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty is proposed, which only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data.
Journal ArticleDOI

Factoring the elasticity of demand in electricity prices

TL;DR: In this paper, the effect of the market structure on the elasticity of the demand for electricity is analyzed and the consumers' behavior can be modeled using a matrix of self-and cross-elasticities.
Journal ArticleDOI

A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output

TL;DR: In this article, a combined sample average approximation (SAA) algorithm is developed to solve the unit commitment problem with uncertain wind power output, and the convergence property and the solution validation process of the proposed combined SAA algorithm is discussed and presented in the paper.
Journal ArticleDOI

Multi-Stage Robust Unit Commitment Considering Wind and Demand Response Uncertainties

TL;DR: In this article, a robust optimization approach was developed to derive an optimal unit commitment decision for the reliability unit commitment runs by ISOs/RTOs, with the objective of maximizing total social welfare under the joint worst-case wind power output and demand response scenario.
Journal ArticleDOI

Security-constrained unit commitment with AC constraints

TL;DR: An efficient SCUC approach with ac constraints that obtains the minimum system operating cost while maintaining the security of power systems is introduced.
References
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Journal ArticleDOI

IEEE Reliability Test System

TL;DR: In this article, a load model, generation system, and transmission network which can be used to test or compare methods for reliability analysis of power systems is described. But the authors focus on the reliability of the power system and do not consider the transmission system.
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Spot Pricing of Electricity

TL;DR: In this paper, the authors present a method to find the most relevant information from Bibliogr. : p. 255-266. Index Reference Record created on 2004-09-07, modified on 2016-08-08
Journal ArticleDOI

Validation of subgradient optimization

TL;DR: It is concluded that the “relaxation” procedure for approximately solving a large linear programming problem related to the traveling-salesman problem shows promise for large-scale linear programming.
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Optimal Power Flow By Newton Approach

TL;DR: In this paper, a direct simultaneous solution for all of the unknowns in the Lagrangian function on each iteration is proposed, where each iteration minimizes a quadratic approximation of the Lagrangeian.
Journal ArticleDOI

Further developments in LP-based optimal power flow

TL;DR: In this article, the authors describe developments that have transformed the LP (linear programming) approach into a truly general-purpose OPF (optimal power flow) solver, with computational and other advantages over even recent nonlinear programming (NLP) methods.
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