Solving the problem of combination of Multi-fuel electric generator units using lagrange multiplier theory

This paper presents an approach to solve the unit commitment problem with multifuel options in the thermal power plants. Traditionally, each generator unit is used to each fuel option with the segmented piecewise quadratic functions, so that it is not difficult to solve them. However, it is more realistic to represent the fuel cost function for each fossil fired plant as the segmented piecewise quadratic functions. Those units are faced with the difficulty of determining which fuel is the most economical to burn. Therefore, this paper presents an approach to solve the unit commitment problem with multi-fuel options. An advantage of the method is to formulate Lagrange mathematical function easily based on the Lagrange multiplier theory. The simulation result for 10 generator systems are compared with others methods to show that the approach is a new method and an effective method to solve the minimizing of electricity production cost of generator units with multi-fuel option.

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Impact of demand response programs in unit-commitment problems in energy optimization

10.33774/miir-2021-bh7q2 ◽

2021 ◽

Author(s):

Caio Costa de Barros Pimentel Luke ◽

Danielle de Freitas ◽

Felipe Atenas Maldonado ◽

Luigi Viola ◽

Tiago Lino Bello ◽

...

Keyword(s):

Demand Response ◽

Power Plants ◽

Unit Commitment ◽

Thermal Power ◽

Unit Commitment Problem ◽

Commitment Problem ◽

System Operator ◽

Pros And Cons ◽

Demand Response Programs ◽

Commitment Problems

Demand response is currently being tested by the Brazilian independent system operator, ONS, and by the trading chamber, CCEE. The program considers the reduction of consumption of some registered clients, as an alternative to dispatching thermal power plants according to merit order. The DESSEM computational tool, developed by CEPEL, is currently run by the ONS to define the next-day dispatch for the whole country. The results obtained using an academic version of DESSEM are used to benchmark and compare DESSEM's performance to relocate the load of demand offered by the operator to different clients under different configurations of the power system. Pros and cons are analyzed for different mathematical formulations, particularly regarding their impact on prices, operating costs, and computational times. Special attention is paid to determining the robustness of the considered models for a variety of optimality requirements for solving the unit-commitment problem.

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Application of BARON Solver for Solution of Cost Based Unit Commitment Problem

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2020.12.4.7 ◽

2020 ◽

Vol 12 (4) ◽

pp. 807-827

Author(s):

Vineet Kumar ◽

◽

R Naresh ◽

Keyword(s):

Power Plants ◽

Unit Commitment ◽

Thermal Power ◽

Operating Cost ◽

Optimal Solution ◽

Ramp Rate ◽

Unit Commitment Problem ◽

Commitment Problem ◽

Ramp Rate Limits ◽

Algebraic Modelling

This paper presents the solution to cost-based unit commitment (CBUC) problem with and without ramp rate limits of thermal power plants using general algebraic modelling system (GAMS) with BARON solver. The BARON solver in GAMS environment takes care of different units and system constraints to find an optimal solution. To validate the effectiveness of the proposed GAMS solution, simulations have been performed on six different systems consisting of 10-units, 20-units, 40-units, 60-units, 80-units and 100-units, respectively. The analysis also includes the valve-point loading along with the ramp rate limits of thermal units. Results obtained with BARON solver in GAMS have been compared with other approaches available in literature. Comparative analysis shows that the performance of GAMS is better as compared to other existing techniques in terms of operating cost obtained and satisfaction level of constraints.

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Security Constrained Unit Commitment Problem Employing Artificial Computational Intelligence for Wind-Thermal Power System

Advances in Intelligent Systems and Computing - Computational Intelligence, Cyber Security and Computational Models ◽

10.1007/978-981-10-0251-9_26 ◽

2015 ◽

pp. 261-275

Author(s):

K. Banumalar ◽

B. V. Manikandan ◽

K. Chandrasekaran

Keyword(s):

Power System ◽

Computational Intelligence ◽

Unit Commitment ◽

Thermal Power ◽

Unit Commitment Problem ◽

Thermal Power System ◽

Commitment Problem

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

E3S Web of Conferences ◽

10.1051/e3sconf/201913701012 ◽

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.

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Unit commitment based reliability analysis with contingency constraint

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i1.pp74-81 ◽

2019 ◽

Vol 16 (1) ◽

pp. 74

Author(s):

Raheema Syed ◽

P. Srinivasa Varma ◽

R. B. R Prakash ◽

Ch. Rami Reddy

Keyword(s):

Power Plants ◽

Benders Decomposition ◽

Unit Commitment ◽

Reliability Assessment ◽

Branch And Cut ◽

Mixed Integer ◽

Unit Commitment Problem ◽

Commitment Problem ◽

Model Reliability ◽

The Cost

<span lang="EN-IN">Unit commitment state’s the strategic choice to be prepared in order to define which of the accessible power plants should be taken into account to supply power. It permits utilities to reduce generation price of power. In this paper, the unit commitment problem is elucidated by taking N-1-1 contingency as a foremost constraint. The standard N-1-1 contingency takes the loss of sequential two components in the network having intervening interval for network modifications in the middle of two losses. The crucial objective to carry out contingency constrictions is to make certain that the operations of power system are adequately strong to unexpected losses of the components of the network. The optimal scheduling/allocation of the generating units is resolved by taking into account the N-1-1 criterion of contingency. By considering the N-1-1 criterion of contingency, the problem results to give an optimised model which is a linear model of mixed integer form. The linear program of mixed integer is a technique of an operational assessment in which restriction is imposed on few variables to be integers. Primarily benders decomposition was considered but for the improvement of results, the algorithm of branch and cut is presented. IEEE 30 bus system is taken into consideration and widespread analysis is accomplished to associate performance of the system under N-1-1 criterion contingency. The computational outcomes determine the value for taking into concern the intervening interval for the adjustments of the system with respect to the cost and robustness of the system. Later to the above model reliability assessment is proposed to calculate the Loss Of Load Expected (LOLE). This model is solved using MATLAB/MATPOWER software.</span>

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On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming

Sustainability ◽

10.3390/su13052446 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2446

Author(s):

Yinhe Bu ◽

Xingping Zhang

Keyword(s):

Unit Commitment ◽

Thermal Power ◽

Mixed Integer ◽

Ancillary Service ◽

Unit Commitment Problem ◽

Service Market ◽

Price Rule ◽

Commitment Problem ◽

The Government ◽

Price Differences

China has declared ambitious carbon emission reduction targets and will integrate increasing shares of variable renewables for the next decades. The implementation for flexibility modification of thermal power units and deep peak regulation ancillary service market alleviates the contradiction between rapid capacity growth and limited system flexibility. This paper establishes three flexibility modification schemes and two price rules for simulation and proposes an analysis framework for unit commitment problem based on mixed-integer linear programming to evaluate the policy mix effects. Results confirm the promoting effects of flexibility modification on integrating variable renewables and illustrate diverse scheme selections under different renewables curtailment. Particularly, there is no need for selecting expensive schemes which contain more modified units and more developed flexibility, unless the curtailment decrement is compulsorily stipulated or worth for added modification cost. Similarly, results also prove the revenue loss compensation effect of deep peak regulation ancillary service market and illustrate diverse price rule selections under different curtailment intervals. Price rule with more subdivided load intervals and bigger price differences among them is more effective, especially under the higher requirement for curtailment rate. Therefore, the government should further enlarge flexibility modification via but not limited to more targeted compensation price, while generators should further consider a demand-based investment.

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Analysis of infeasible unit-commitment solutions arising in energy optimization

10.33774/miir-2022-ztpbq ◽

2022 ◽

Author(s):

Leonardo Delarmelina Secchin ◽

Guilherme Matiussi Ramalho ◽

Claudia Sagastizábal ◽

Paulo Silva ◽

Kenny Vinente

Keyword(s):

Power Plants ◽

Unit Commitment ◽

Thermal Power ◽

Point Of View ◽

Mixed Integer ◽

Fixed Costs ◽

Unit Commitment Problem ◽

Optimal Dispatch ◽

Marginal Costs ◽

Integer Problem

The day-ahead problem of finding optimal dispatch and prices for the Brazilian power system is modeled as a mixed-integer problem, with nonconvexities related to fixed costs and minimal generation requirements for some thermal power plants. The computational tool DESSEM is currently run by the independent system operator, to define the dispatch for the next day in the whole country. DESSEM also computes marginal costs of operation that CCEE, the trading chamber, uses to determine the hourly prices for energy commercialization. The respective models sometimes produce an infeasible output. This work analyzes theoretically those infeasibilities, and proposes a prioritization to progressively resolve the constraint violation, in a manner that is sound from the practical point of view. Pros and cons of different mathematical formulations are analyzed. Special attention is put on robustness of the model, when the optimality requirements for the unit-commitment problem vary.

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Optimization of Unit Commitment Problem Using Genetic Algorithm

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2021070102 ◽

2021 ◽

Vol 10 (3) ◽

pp. 21-37

Author(s):

Aniket Agarwal ◽

Kirti Pal

Keyword(s):

Genetic Algorithm ◽

Unit Commitment ◽

Thermal Power ◽

Test System ◽

Optimal Location ◽

Variable Load ◽

Fuel Cost ◽

Unit Commitment Problem ◽

Solar Thermal Power ◽

Commitment Problem

The main objective of the paper is to minimize the use of conventional generators and optimize the fuel cost. To minimize the use of conventional generators, solar thermal power plant (STPP) is proposed in this paper. An approach for optimal location of STPP is also proposed in this paper. To minimize the fuel cost, firstly unit commitment (UC) is applied in conventional generators. Then genetic algorithm (GA) is used to optimize the fuel cost of committed generators. The suggested method is tested on an IEEE 14 bus test system for 24 hr. schedule with variable load. The effectiveness of the proposed methodology is illustrated in three cases. Case 1 is used to identify the STPP location to reduce the fuel cost of conventional generator. In Case 2, unit-commitment is applied to save considerable fuel input and cost. In order to optimize the committed fuel cost, a genetic algorithm is applied in Case 3.

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