An MILP Formulation for the Thermal Unit Commitment Problem Considering Start-Up and Shut-Down Power Trajectories

2014 ◽  
Vol 672-674 ◽  
pp. 493-498 ◽  
Author(s):  
Jun Deng ◽  
Hua Wei
Keyword(s):  
Unit Commitment ◽  
Mixed Integer ◽  
Test Case ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
Binary Variables ◽  
Computational Performance ◽  
Start Up ◽  
Commitment Problem ◽  
Mixed Integer Linear Formulation

This paper presents a mixed-integer linear formulation for the thermal unit commitment problem considering the start-up and shut-down power trajectories. A realistic and accurate modeling of the unit’s operating phase is given, which includes the phases of start-up, dispatchable and shut-down. The start-up type is decided by the unit’s prior off-line time. The start-up costs and power trajectories depend on the type of start-up. A new set of binary variables is introduced to represent the dispatchable status, which can decrease the binary variables and constraints significantly. Finally, a test case study is analyzed to verify the correctness and show the computational performance of the proposed formulation.

scholarly journals Evaluati on of long-term start up costs impact on short-term price based operational optimization of a CCGT using MILP

2019 ◽  
Vol 137 ◽  
pp. 01012
Author(s):  
Sylwia Gotzman ◽  
Paweł Ziόłkowski ◽  
Janusz Badur
Keyword(s):  
Power Plants ◽  
Unit Commitment ◽  
Real Life ◽  
Mixed Integer ◽  
Unit Commitment Problem ◽  
Operational Optimization ◽  
Start Up ◽  
Commitment Problem ◽  
Electricity Spot Market

An increasing share of the weather-dependent RES generation in the power system leads to the growing importance of flexibility of conventional power plants. They were usually designed for base load operation and it is a challenge to determine the actual long-term cycling costs, which account for an increase in maintenance and overhaul expenditures, increased forced outage rates and shortened life expectancy of the plant and components. In this paper, the overall impact of start up costs is evaluated by formulating and solving price based unit commitment problem (PBUC). The electricity spot market is considered as a measure for remunerating flexibility. This approach is applied to a real-life case study based on the 70 MWe PGE Gorzόw CCGT power plant. Different operation modes are calculated and results are used to derive a mixed integer linear programming (MILP) model to optimize the operation of the plant. The developed mathematical model is implemented in Python within the frame of the PuLP library and solved using GUROBI. Results of the application of the method to a numerical example are presented.

scholarly journals Stochastic Unit Commitment Problem, Incorporating Wind Power and an Energy Storage System

2020 ◽  
Vol 12 (23) ◽  
pp. 10100
Author(s):  
Khalid Alqunun ◽  
Tawfik Guesmi ◽  
Abdullah F. Albaker ◽  
Mansoor T. Alturki
Keyword(s):  
Energy Storage ◽  
Wind Power ◽  
Unit Commitment ◽  
Mixed Integer ◽  
Master Problem ◽  
Thermal Unit ◽  
Unit Commitment Problem ◽  
Battery Energy Storage ◽  
Commitment Problem ◽  
Battery Energy

This paper presents a modified formulation for the wind-battery-thermal unit commitment problem that combines battery energy storage systems with thermal units to compensate for the power dispatch gap caused by the intermittency of wind power generation. The uncertainty of wind power is described by a chance constraint to escape the probabilistic infeasibility generated by classical approximations of wind power. Furthermore, a mixed-integer linear programming algorithm was applied to solve the unit commitment problem. The uncertainty of wind power was classified as a sub-problem and separately computed from the master problem of the mixed-integer linear programming. The master problem tracked and minimized the overall operation cost of the entire model. To ensure a feasible and efficient solution, the formulation of the wind-battery-thermal unit commitment problem was designed to gather all system operating constraints. The solution to the optimization problem was procured on a personal computer using a general algebraic modeling system. To assess the performance of the proposed model, a simulation study based on the ten-unit power system test was applied. The effects of battery energy storage and wind power were deeply explored and investigated throughout various case studies.

scholarly journals A Mixed-Integer and Asynchronous Level Decomposition with Application to the Stochastic Hydrothermal Unit-Commitment Problem

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 235 ◽  
Author(s):  
Bruno Colonetti ◽  
Erlon Cristian Finardi ◽  
Welington de Oliveira
Keyword(s):  
Unit Commitment ◽  
Computing Time ◽  
Decomposition Methods ◽  
Mixed Integer ◽  
Unit Commitment Problem ◽  
Integer Variables ◽  
Computational Performance ◽  
Current State ◽  
Commitment Problem ◽  
Time Savings

Independent System Operators (ISOs) worldwide face the ever-increasing challenge of coping with uncertainties, which requires sophisticated algorithms for solving unit-commitment (UC) problems of increasing complexity in less-and-less time. Hence, decomposition methods are appealing options to produce easier-to-handle problems that can hopefully return good solutions at reasonable times. When applied to two-stage stochastic models, decomposition often yields subproblems that are embarrassingly parallel. Synchronous parallel-computing techniques are applied to the decomposable subproblem and frequently result in considerable time savings. However, due to the inherent run-time differences amongst the subproblem’s optimization models, unequal equipment, and communication overheads, synchronous approaches may underuse the computing resources. Consequently, asynchronous computing constitutes a natural enhancement to existing methods. In this work, we propose a novel extension of the asynchronous level decomposition to solve stochastic hydrothermal UC problems with mixed-integer variables in the first stage. In addition, we combine this novel method with an efficient task allocation to yield an innovative algorithm that far outperforms the current state-of-the-art. We provide convergence analysis of our proposal and assess its computational performance on a testbed consisting of 54 problems from a 46-bus system. Results show that our asynchronous algorithm outperforms its synchronous counterpart in terms of wall-clock computing time in 40% of the problems, providing time savings averaging about 45%, while also reducing the standard deviation of running times over the testbed in the order of 25%.

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