Lagrangian relaxation method applied to realistic and practical unit commitment problems. Unit commitment by separable augmented lagrangian relaxation. The proposed method is designed to have a separable structure by introducing the quadratic terms with additional auxiliary terms in the augmented lagrangian function. Lagrangian framework is a successful method as discussed by a. Numerical tests suggestthat the proposed method is a reliable, ef. The greatest advantage of applying the lagrange relaxation method for the unit commitment problem is that it can relax. This paper focuses on the economical aspect of uc problem, while the previous hour scheduling as a very important issue is studied.
The lagrangian relaxation procedure for the ucp is discussed in section 3. Unit commitment scheduling by lagrange relaxation method taking. Using lagrangian relaxation, the original primal problem may be written in a dual formulation. A quick method for judging the feasibility of securityconstrained unit commitment problems within lagrangian relaxation framework sangang guo.
A new lagrangian relaxation method considering previous hour. Pdf unit commitment using lagrangian relaxation and. Solving the unit commitment problem by a unit decommitment method. Bundle relaxation and primal recovery in unit commitment. The approach to solving the problem is based on lagrangian relaxation and dynamic programming. Lagrangian relaxationis to try to use the underlyingnetwork structureof these problemsin order to use these ef. An improved lagrangian relaxation lr solution to the thermal unit commitment problem ucp is proposed in this paper. The lagrangian relaxation lr method provides a fast solution but. A new lagrangian multiplier update approach for lagrangian. Pdf this paper shows how the lagrange relaxation dual optimization algorithm is incorporated in solving a thermal unit commitment. Each value returned by p \displaystyle p is a candidate upper bound to the problem, the smallest of which is kept as the best upper bound. Evaluation of two lagrangian dual optimization algorithms for largescale unit commitment problems wen fan, yuan liao, jongbeom lee and yongkab kim abstract lagrangian relaxation is the most widely adopted method for solving unit commitment uc problems. We compare empirically and theoretically two methods designed to cope with the nonseparability of the lagrangian function. An improved flexible lagrangian relaxation technique in the lagrangian relaxation approach, the system operating cost function of 1 of the unit commitment problem is related to the power balance and the spinning reserve constraints via two sets of lagrangian multipliers to form a lagrangian dual function.
Pdf analysis of unit commitment problem through lagrange. Solving environmental economic dispatch problem with. Keywords security constrained unit commitment scuc, lagrangian relaxation lr, unit commitment problem ucp. Unit commitment, lagrange relaxation, priority list. The method guarantees an optimal solution for the longterm uc problem. The unit commitment uc problem is to optimize hour. Aug 24, 2009 generation scheduling is a crucial challenge in power systems especially under new environment of liberalization of electricity industry. Unit commitment by lagrangian relaxation and genetic algorithms chuanping cheng, chihwen liu, and chunchang liu abstract this paper presents an application of a combined the genetic algorithms gas and lagrangian relaxation lr method for the unit commitment problem. Using lagrangian relaxation in optimisation of unit commitment and.
A transmissionconstrained unit commitment method in power. All these methods have some weakness, a comprehensive algorithm that combines the strength of all the methods and overcome each other. A solution to the relaxed problem is an approximate solution to the original problem, and provides useful information. In this paper, various proposed methods for unit commitment have been described. Section 4 presents a detailed description of the proposed local search methods. This paper presents the solving unit commitment uc problem using modified subgradient method msg method combined with simulated annealing sa algorithm.
Lagrangian relaxationbased unit commitment considering. The main disadvantage of this group of methods is the difference. The lagrangian relaxation lr based methods are commonly used to solve the. The proposed paper provides a bibliographical survey, mathematical. D tdimensional vector of the load demands in each period t in the scheduling horizon. It is observed that favorable reserve and unit mw schedules are obtained by the proposed method while the system security is maintained.
This lagrangian relaxation method is dependent on the initial status of the lagrangian multipliers and the method used to update multipliers. This paper proposes elrpso, an algorithm to solve the uc problem using lagrangian relaxation lr and particle swarm optimization pso. A lagrangian decomposition model for unit commitment problem. A lagrangian relaxation algorithm thus proceeds to explore the range of feasible values while seeking to minimize the result returned by the inner problem.
Enk5ct200000094 project cofunded by the european community under the 5th framework programme 19982002 contract no. We show that the proposed method may be viewed as an approximate implementation of the lagrangian relaxation approach and that the number of iterations is bounded by the number of units. Pdf unit commitment using lagrangian relaxation and particle. The commitment decision determines which generating units are to be operated during each time period of scheduling horizon. Several lr based approach for solving unit commitment problem in deregulated industry is proposed in the literature 11 15. This solution is a matrix with the same dimensions of matrix u, whose elements ui,t. The constraints that the schedules must meet include system demand. By introducing lagrangian multipliers to relax global constraints, a largescale coupled problem could be transformed into a. Solving unit commitment problem using modified subgradient.
Ceubs12001 project cofunded by termoelektrarna toplarna ljubljana, d. Temporal decomposition for improved unit commitment in power. A new lagrangian relaxation method for unit commitment uc has been presented for solving generation scheduling problem. A hybrid solution technique involving priority list method and particle swarm optimization with time varying acceleration coefficients has been envisaged to solve unit commitment problem for a generation system involving wind generation and solar generation in conjunction with conventional thermal power plants, for given load pattern. Lagrangian relaxation method is more advantageous due to its. Unit commitment uc is a nphard nonlinear mixedinteger optimization problem.
Unit commitment uc is to determine the optimal unit status and generation level during each time interval of the scheduled period. Numerical results are presented and discussed in section 5. The constraints, which are difficult or impractical to be implemented in unit commitment algorithm, can be handled by this expert system. Lagrangian relaxation the heuristics by solving the dual problem by a bundle method, without an extra computational effort, a convexified solution of the original problem is also available. The problem of maximizing the lagrangian function of the dual variables the lagrangian multipliers. Implementation of a lagrangian relaxation based unit commitment. Lagrangian relaxation and its application to the unit. In particular, a lagrangian relaxation, similar to our. Index terms lagrangian relaxation, optimization methods, power generation dispatch, tabu search, unit commitment i. The unit commitment uc problem is to optimize hourly schedules of unit operation and minimize system operating costs for a given time interval. Among various algorithms, lagrangian relaxation lr based method is one the most promising approaches. Uc problem is one of the important power system engineering hardsolving problems.
Unit commitment by augmented lagrangian relaxation. The transmission constraints, as well as the demand and spinning reserve constraints, are relaxed by attaching lagrange multipliers. New local search methods for improving the lagrangian. Based on a dc power flow model, the transmission constraints are formulated as linear constraints. Pdf unit commitment uc is a nphard nonlinear mixedinteger optimization problem.
The lagrangian relaxation lr based methods are commonly used to solve the uc problem. Numerical tests suggest that the proposed method is a reliable, efficient, and robust approach for solving the unit commitment problem. Optimal unit commitment by considering high penetration. A case of lagrangian relaxation versus mixed integer programming article pdf available in ieee transactions on power systems 204. This paper presents a transmissionconstrained unit commitment method using a lagrangian relaxation approach. Pdf lagrangian relaxation neural network for unit commitment. Bundle relaxation and primal recovery in uc problem 23 dual problem. In the lagrangian relaxation method, the metric matrix an approximate hessian inverse may become too large. Unit commitment problem solution for renewableintegrated. This study is concerned with the optimal scheduling of an electricity power system consisting of both hydro and thermal units. Since the unit commitment problem primal problem is formulated as a largescale mixedinteger progtamming problem, the lagrangian relaxation methcx. This paper presents a separable augmented lagrangian relaxation method for solving the unit commitment problem. Evaluation of two lagrangian dual optimization algorithms for.
Since the unit commitment problem primal problem is formulated as a largescale mixedinteger programming problem, the lagrangian relaxation method is employed to solve the problem efficiently. The purpose of uc is to minimize the total generation cost while satisfying system demand, reserve requirements, and unit constraints. One of the main drawbacks of the augmented lagrangian relaxation method is that the quadratic term introduced by the augmented lagrangian is not separable. A quick method for judging the feasibility of security. The unit commitment problem is solved by using the genetic algorithm tuning of the lagrangian multiplier the main function of the unit commitment takes into consideration the spinning reserve, startup cost for each unit is dependent on the amount of time the unit has been shutdown prior to startup. We go back to primal considerations in section 5, where we use another decomposition scheme combined with an inexact augmented lagrangian method to. Lagrangian relaxation and tabu search approaches for the unit. This paper proposes elrpso, an algorithm to solve the uc problem using lagrangian relaxation lr and particle swarm.
Lagrangian relaxation method for longterm unit commitment. S2 of the problems are separated into two groups, namely theeasy constraintss1 and thehard constraintss2. Genetic algorithms gas are a general purpose optimization technique based on principle of natural selection and natural genetics. Using lagrangian relaxation in optimisation of unit.
In the field of mathematical optimization, lagrangian relaxation is a relaxation method which approximates a difficult problem of constrained optimization by a simpler problem. The lagrangian relaxation is a method ofdecomposition. This paper presents a novel method for unit commitment by synergistically combining lagrangian relaxation for constraint handling with hopfieldtype recurrent neural networks for fast convergence. Lagrangian relaxation method for pricebased unit commitment. Unit commitment using lagrangian relaxation and particle. It has the advantage of being easily modified to model characteristics of specific utilities. Introduction an securityconstrained unit commitment scuc is a crucial problem in the power system scheduling of generation. Large scale unit commitment problems are of combinatorial nature and are usually very hard to solve. Thermal unit commitment solution using an improved lagrangian. A method to solve the dual and primal problem must also be chosen. This paper proposes elrpso, an algorithm to solve the uc problem.
Lagrangian relaxation method lagrangian relaxation is one of the most effective techniques for handling optimization problems with coupled structures. The utilization of lagrangian relaxation in production unit commitment problems is much more recent than the dynamic programming methods. Shahidehpour, unit commitment using a hybrid model between lagrangian relaxation and genetic algorithm in competitive electricity markets, electric power systems research 682004, 8392. School of mathematics and computer science, shaanxi university of technology, hanzhong, china. Genetic lagrangian relaxation selection method for the. Elrpso employs a stateoftheart powerful pso variant called comprehensive learning pso to find a feasible nearoptimal uc schedule.1541 1033 1214 764 305 1372 630 1396 125 326 1001 330 861 107 315 309 1027 1488 1580 1538 33 1537 603 1072 534 1354 550 1168 50 574 847 978 329 1163 1048 987 920 357 1043 8