scholarly journals A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8014
Author(s):  
Aml Sayed ◽  
Mohamed Ebeed ◽  
Ziad M. Ali ◽  
Adel Bedair Abdel-Rahman ◽  
Mahrous Ahmed ◽  
...  
Keyword(s):  
Unit Commitment ◽  
Cost Savings ◽  
Test System ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Hybrid Technique ◽  
Unit Commitment Problem ◽  
Unit System ◽  
Load Demand ◽  
Commitment Problem

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

A Priority List-Based Binary Crow Search Algorithm for Unit Commitment Problem

2021 ◽  
Vol 57 ◽  
pp. 211-224
Author(s):  
Adel A. Abou El Ela ◽  
Ragab El-Sehiemy ◽  
Abdullah M. Shaheen ◽  
Ayman S. Shalaby
Keyword(s):  
Unit Commitment ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Test System ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Unit Commitment Problem ◽  
Priority List ◽  
Good Convergence ◽  
Commitment Problem

Abstract-This paper proposes a novel hybrid technique that combines the priority list (PL) with the binary crow search algorithm (BCSA) for solving the unit commitment problem (UCP). Firstly, the PL method aims to sort the generating units in ascending order according to their average full load costs which are the total costs that are computed at the maximum generation outputs. Secondly, the BCSA is developed and employed to search for the optimal schedule of the generating units to face the next hourly demand with minimum total operating costs that are related to the optimal power generation schedule at certain loading level. BCSA is a new meta-heuristic optimizer, which is featured of the crow's intelligence. It has only two adjustable parameters that make its implementation very simple and easy compared to other optimization techniques. Its effectiveness and feasibility were confirmed by 4, 10, and 26-unit systems and the results are compared with those obtained by GA, PSO, APSO, and BDE. The simulation results demonstrate the capability of the proposed PLBCSA in solving the UC problem with good convergence rate compared with the previous methods in the literature. Around of 2-4% reduction in the total costs is achieved using the proposed PLBCSA for the 26-unit test system compared with GA, PSO and the implemented PL-BPSO solutions.

scholarly journals An Improved DA-PSO Optimization Approach for Unit Commitment Problem

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2335 ◽  
Author(s):  
Sirote Khunkitti ◽  
Neville R. Watson ◽  
Rongrit Chatthaworn ◽  
Suttichai Premrudeepreechacharn ◽  
Apirat Siritaratiwat
Keyword(s):  
Optimization Problem ◽  
Unit Commitment ◽  
Economic Dispatch ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Sigmoid Function ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Metaheuristic Techniques

Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mixed-integer part of obtaining the Unit Commitment problem. To verify the effectiveness of the iDA-PSO approach, it was tested on four different-sized systems (5-unit, 6-unit, 10-unit, and 26-unit systems). The unit commitment, generation schedule, total generation cost, and time were compared with those obtained by other algorithms in the literature. The simulation results show iDA-PSO is a promising technique and is superior to many other algorithms in the literature.

scholarly journals Mathematical Formulation of Multi-Area Unit Commitment Problem

2014 ◽  
pp. 80-90
Author(s):  
Vikram Kumar Kamboj ◽  
S.K. Bath
Keyword(s):  
Unit Commitment ◽  
Mathematical Formulation ◽  
Cost Savings ◽  
Unit Commitment Problem ◽  
Interconnected Power System ◽  
Load Demand ◽  
Commitment Problem ◽  
Tie Lines ◽  
Area Unit ◽  
Tie Line

Multi-Area Unit Commitment Problem is to determine the optimal commitment strategy for generating units located in multiple areas that are interconnected via tie–lines and joint operation of generation resources can result in significant operational cost savings. This research paper presents the mathematical formulation for Multi-Area Unit Commitment Problem along with tie-line concept of interconnected power system. The objective of this paper is to describe the multi-area unit commitment problem, mathematical formulation and tie line concept along with transmission interconnections constrains. Also, standard IEEE data for 26-Generating units of four area system along with 24-hours load demand is given as appendix, which can be used as quick reference by other researchers.

scholarly journals A Comparative Study of Three Different Mathematical Methods for Solving the Unit Commitment Problem

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Mehmet Kurban ◽  
Ümmühan Başaran Filik
Keyword(s):  
Penalty Function ◽  
Unit Commitment ◽  
Test System ◽  
Optimization Techniques ◽  
Duality Gap ◽  
Mixed Integer ◽  
Computational Time ◽  
Unit Commitment Problem ◽  
Duration Time ◽  
Number Of Iterations

The unit commitment (UC) problem which is an important subject in power system engineering is solved by using Lagragian relaxation (LR), penalty function (PF), and augmented Lagrangian penalty function (ALPF) methods due to their higher solution quality and faster computational time than metaheuristic approaches. This problem is considered to be a nonlinear programming-(NP-) hard problem because it is nonlinear, mixed-integer, and nonconvex. These three methods used for solving the problem are based on dual optimization techniques. ALPF method which combines the algorithmic aspects of both LR and PF methods is firstly used for solving the UC problem. These methods are compared to each other based on feasible schedule for each stage, feasible cost, dual cost, duality gap, duration time, and number of iterations. The numerical results show that the ALPF method gives the best duality gap, feasible and dual cost instead of worse duration time and the number of iterations. The four-unit Tuncbilek thermal plant which is located in Kutahya region in Turkey is chosen as a test system in this study. The programs used for all the analyses are coded and implemented using general algebraic modeling system (GAMS).

scholarly journals A review of optimization techniques applied to solve unit commitment problem in microgrid

2019 ◽  
Vol 15 (3) ◽  
pp. 1161
Author(s):  
Karthik N ◽  
A K Parvathy ◽  
Arul Rajagopalan ◽  
S Baskar
Keyword(s):  
Literature Review ◽  
Optimization Problem ◽  
Time Perspective ◽  
Unit Commitment ◽  
Optimization Techniques ◽  
Energy Sources ◽  
Power Demand ◽  
Unit Commitment Problem ◽  
Ecological Constraints ◽  
Commitment Problem

<p>Unit Commitment (UC) is an optimization problem used to find out the least cost dispatch of obtainable generation resources to meet up an expected electric power demand over a certain time perspective under generational, technical and ecological constraints. In the midst of the momentous increase of non-conventional energy sources incorporation into the power system networks, effects caused by these system alterations to the UC are dynamically being studied and examined by worldwide researchers. This paper presents a literature review of application of several optimization algorithms to elucidate the UC problem in microgrids. Lastly a few basic challenges arising from the new optimization approaches in microgrids are addressed.</p>

scholarly journals A New Solution to Profit Based Unit Commitment Problem Considering PEVs/BEVs and Renewable Energy Sources

2020 ◽  
Vol 184 ◽  
pp. 01070
Author(s):  
Ayani Nandi ◽  
Vikram Kumar Kamboj
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Production Cost ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Unit Commitment Problem ◽  
Load Demand ◽  
Commitment Problem ◽  
The Impact

Daily load demand for industrial, residential and commercial sectors are changing day by day. Also, inclusion of e-mobility has totally effected the operations of realistic power sector. Hence, to meet this time varying load demand with minimum production cost is very challenging. The proposed research work focuses on the mathematical formulation of profit based unit commitment problem of realistic power system considering the impact of battery electric vehicles, hybrid electric vehicles and plug in electric vehicles and its solution using Intensify Harris Hawks Optimizer (IHHO). The coordination of plants with each other is named as Unit commitment of plants in which the most economical patterns of the generating station is taken so as to gain low production cost with higher reliability. But with the increase in industrialization has affected the environment badly so to maintain the balance between the generation and environment a new thinking of generating low cost power with high reliability by causing less harm to environment i.e. less emission of flue gases is adopted by considering renewable energy sources.

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.

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