scholarly journals OPTIMUM UNIT COMMITMENT SOLUTION OF A POWER SYSTEM BASED ON SALP SWARM ALGORITHM

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-95-1-107
Author(s):  
Marwan A. Mahmood ◽  
◽  
Kassim A. Al-Anbarri ◽  
Keyword(s):  
Power System ◽  
Unit Commitment ◽  
Power Balance ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Salp Swarm Algorithm ◽  
Demand Pattern ◽  
Start Up ◽  
Commitment Problem ◽  
Swarm Algorithm

This paper presents an algorithm to solve the unit commitment problem in a power system. The proposed algorithm employs the Salp swarm algorithm technique to search the optimum unit schedule for a particular daily demand pattern and specific time horizon. Different constraints are taken into consideration, transition cost (start-up and shut down )cost, mean-up time, mean-down time, spinning reserve, and power balance. The proposed algorithm is applied to 10-units and 26-unit. The obtained results are compared with other methods. It reveals the robustness of the proposed algorithm in terms of minimizing overall running costs.

scholarly journals A meliorated Harris Hawks optimizer for combinatorial unit commitment problem with photovoltaic applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Power Generation ◽  
Power System ◽  
Electric Power ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Electric Power System ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Commitment Problem ◽  
Sine Cosine Algorithm

AbstractConventional unit commitment problem (UCP) consists of thermal generating units and its participation schedule, which is a stimulating and significant responsibility of assigning produced electricity among the committed generating units matter to frequent limitations over a scheduled period view to achieve the least price of power generation. However, modern power system consists of various integrated power generating units including nuclear, thermal, hydro, solar and wind. The scheduling of these generating units in optimal condition is a tedious task and involves lot of uncertainty constraints due to time carrying weather conditions. This difficulties come to be too difficult by growing the scope of electrical power sector day by day, so that UCP has connection with problem in the field of optimization, it has both continuous and binary variables which is the furthermost exciting problem that needs to be solved. In the proposed research, a newly created optimizer, i.e., Harris Hawks optimizer (HHO), has been hybridized with sine–cosine algorithm (SCA) using memetic algorithm approach and named as meliorated Harris Hawks optimizer and it is applied to solve the photovoltaic constrained UCP of electric power system. In this research paper, sine–cosine Algorithm is used for provision of power generation (generating units which contribute in electric power generation for upload) and economic load dispatch (ELD) is completed by Harris Hawks optimizer. The feasibility and efficacy of operation of the hybrid algorithm are verified for small, medium power systems and large system considering renewable energy sources in summer and winter, and the percentage of cost saving for power generation is found. The results for 4 generating units, 5 generating units, 6 generating units, 7 generating units, 10 generating units, 19 generating units, 20 generating units, 40 generating units and 60 generating units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The efficacy of the offered optimizer has been verified for several standard benchmark problem including unit commitment problem, and it has been observed that the suggested optimizer is too effective to solve continuous, discrete and nonlinear optimization problems.

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.

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 Eagle Strategy based Crow Search Algorithm for solving Unit Commitment Problem

2018 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Rachid Habachi ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Optimization Strategy ◽  
Unit Commitment Problem ◽  
Spinning Reserve ◽  
Two Stage ◽  
Commitment Problem ◽  
Local Optimizer ◽  
Eagle Strategy

<p>Eagle strategy is a two-stage optimization strategy, which is inspired by the observation of the hunting behavior of eagles in nature. In this two-stage strategy, the first stage explores the search space globally by using a Levy flight; if it finds a promising solution, then an intensive local search is employed using a more efficient local optimizer, such as hillclimbing and the downhill simplex method. Then, the two-stage process starts again with new global exploration, followed by a local search in a new region. One of the remarkable advantages of such a combina-tion is to use a balanced tradeoff between global search (which is generally slow) and a rapid local search. The crow search algorithm (CSA) is a recently developed metaheuristic search algorithm inspired by the intelligent behavior of crows.This research article integrates the crow search algorithm as a local optimizer of Eagle strategy to solve unit commitment (UC) problem. The Unit commitment problem (UCP) is mainly finding the minimum cost schedule to a set of generators by turning each one either on or off over a given time horizon to meet the demand load and satisfy different operational constraints. There are many constraints in unit commitment problem such as spinning reserve, minimum up/down, crew, must run and fuel constraints. The proposed strategy ES-CSA is tested on 10 to 100 unit systems with a 24-h scheduling horizon. The effectiveness of the proposed strategy is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by reported numerical results, it has been found that proposed strategy yields global results for the solution of the unit commitment problem.</p><p> </p>

scholarly journals A Cross-Entropy-Based Hybrid Membrane Computing Method for Power System Unit Commitment Problems

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 486 ◽  
Author(s):  
Min Xie ◽  
Yuxin Du ◽  
Peijun Cheng ◽  
Wei Wei ◽  
Mingbo Liu
Keyword(s):  
Power System ◽  
Membrane Computing ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
Hybrid Membrane ◽  
Cross Entropy ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Dynamic Economic Dispatch ◽  
Computing Method

The cross-entropy based hybrid membrane computing method is proposed in this paper to solve the power system unit commitment problem. The traditional unit commitment problem can be usually decomposed into a bi-level optimization problem including unit start-stop scheduling problem and dynamic economic dispatch problem. In this paper, the genetic algorithm-based P system is proposed to schedule the unit start-stop plan, and the biomimetic membrane computing method combined with the cross-entropy is proposed to solve the dynamic economic dispatch problem with a unit start-stop plan given. The simulation results of 10–100 unit systems for 24 h day-ahead dispatching show that the unit commitment problem can be solved effectively by the proposed cross-entropy based hybrid membrane computing method and obtain a good and stable solution.

Extreme Ray Feasibility Cuts for Unit Commitment with Uncertainty

2020 ◽  
Author(s):  
Chao Li ◽  
Muhong Zhang ◽  
Kory Hedman
Keyword(s):  
Power System ◽  
Transmission Lines ◽  
Unit Commitment ◽  
Deterministic Model ◽  
Dual Cone ◽  
Scheduling Problems ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Industry Practice ◽  
Extreme Rays

The unit commitment problem with uncertainty is considered one of the most challenging power system scheduling problems. Different stochastic models have been proposed to solve the problem, but such approaches have yet to be applied in industry practice because of computational challenges. In practice, the problem is formulated as a deterministic model with reserve requirements to hedge against uncertainty. However, simply requiring a certain level of reserves cannot ensure power system reliability as the procured reserves may be nondispatchable because of transmission limitations. In this paper, we derive a set of feasibility cuts (constraints) for managing the unit commitment problem with uncertainty. These cuts eliminate unreliable scheduling solutions and reallocate reserves in the power system; they are induced by the extreme rays of a polyhedral dual cone. This paper shows that, with the proposed reformulation, the extreme rays of the dual cone can be characterized by combinatorial selections of transmission lines (arcs) and buses (nodes) of the power system. As a result, the cuts can then be characterized using engineering insights. The unit commitment problem with uncertainty is formulated as a deterministic model with the identified extreme ray feasibility cuts. Test results show that, with the proposed extreme ray feasibility cuts, the problem can be solved more efficiently, and the resulting scheduling decision is also more reliable.

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