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Optimal planning of electric power generation in thermal power system
Abstract
The task of optimal planning of electric power generation (OPEPG) for optimal operation and expansion planning of all-thermal power system is considered in this paper. The OPEPG problem is looked as a two-stage problem. During the first stage, the loading of working units is optimized. In the second stage the optimal combination of units considering optimal loading of units during the first stage is determined. The deterministic and min-max models of OPEPG will be presented. For the solving of min-max optimization tasks their deterministic equivalents are calculated.
... A common trend in previous treatment of the unit commitment problem (UCP) is utilizing fixed values of load demand and strict level of spinning reserve requirements123456789101112131415161718. This may result in an overestimated solution and consequently higher operating costs. ...
... In the UCP under consideration, one is interested in a solution that minimizes the total operating cost of the generating units during the scheduling time horizon while several constraints are satisfied [1,891011. ...
This paper presents a new fuzzy unit commitment problem (UCP) model. The model treats the uncertainties in the load demand and the spinning reserve constraints in a fuzzy logic (FL) frame. The simulated annealing (SA) method is used to solve the combinatorial part of the unit commitment problem, while the nonlinear part of the problem is solved via a quadratic programming routine. A polynomial time cooling schedule is used in the implementation of the SA algorithm. Numerical results show the superiority of the solutions obtained compared to methods with traditional UCP models.
... Optimization tasks are also used for planning economically feasible operating modes of thermal power plants [9], determining the optimal load of turbine generators and boiler units [10] - [11] in normal and complex asymmetric modes [12]. An important task is also to calculate the composition of the fuel mixture, which ensures fuel economy and reduces the cost of fresh steam required for generating heat and electricity, as well as taking into account the production selection [13]. ...
... There are several possibilities to optimize the processes and systems under uncertainty conditions [3] [10]: 1) Laplace criterion 2) Min-max cost criterion 3) Min-max risk or min-max regret criterion. The best criterion for optimization under uncertain information is the min-max risk criterion [6]-[9]. ...
The principles of optimal load dispatch in condensing thermal power plant under uncertain information are presented in this paper. The uncertainty factors are the input-output characteristics of power units and the load demand of power plant. For optimal load dispatch under uncertainty conditions the criterion of min-max risk is recommended. The min-max optimal load dispatch problems are suitable to solve by the planned characteristic method. The method consists of two stages: 1) computation of planned characteristics of units; 2) solving the deterministic equivalent of min-max optimization problem.
This paper presents a fuzzy model for the unit commitment problem
(UCP). The model takes the uncertainties in the forecasted load demand
and the spinning reserve constraints in a fuzzy frame. The genetic
algorithm (GA) approach is then used to solve the proposed fuzzy UCP
model. In the implementation for the GA, coding of the UCP solutions is
based on mixing binary and decimal representations. A fitness function
is constructed from the total operating cost of the generating units
plus a penalty term determined due to the fuzzy load and spinning
reserve membership functions. Numerical results showed an improvement in
the solutions costs compared to the results reported in the literature
and the GA with crisp UCP model
This paper presents a new algorithm based on integrating simulated
annealing and fuzzy logic methods to solve the unit commitment problem.
The uncertainties in the load demand and the spinning reserve
constraints are formulated in a fuzzy logic frame. The simulated
annealing is used to solve the combinatorial part of the unit commitment
problem, while the nonlinear part of the problem is solved via a
quadratic programming routine. A simple cooling schedule has been
implemented to apply the simulated annealing test in the algorithm.
Numerical results show the superiority of the solutions obtained
compared to the classical methods and the simulated annealing method as
individual
We propose a new hybrid algorithm for solving the unit commitment
problem (UCP). The algorithm integrates the use of simulated annealing
(SA) and tabu search (TS) for solving the UCP. The algorithm includes a
step to find an initial control parameter, at which virtually all trial
solutions are accepted. It uses a polynomial-time cooling schedule that
is advocated in the SA literature. Furthermore, the short-term memory
procedures of the TS method are embedded in the SA test to prevent
cycling of accepted solutions. Numerical examples from the literature
are solved. Results showed an improvement in the solutions costs
compared to previously obtained results
An application of the tabu search (TS) method to solve the unit
commitment problem (UCP) is presented. The TS seeks to counter the
danger of entrapment at a local optimum by incorporating a memory
structure that forbids or penalises certain moves that would return to
recently visited solutions. New rules for randomly generating feasible
solutions for the UCP are introduced. The problem is divided into two
subproblems: a combinatorial optimisation problem and a nonlinear
programming problem. The former is solved using the tabu search
algorithm (TSA) while the latter problem is solved via a quadratic
programming routine. Numerical results show an improvement in the
solution cost compared to previously obtained results
This paper presents a simulated annealing algorithm (SAA) to solve
the unit commitment problem (UCP). New rules for randomly generating
feasible solutions are introduced. The problem has two subproblems: a
combinatorial optimization problem; and a nonlinear programming problem.
The former is solved using the SAA while the latter problem is solved
via a quadratic programming routine. Numerical results showed an
improvement in the solutions costs compared to previously obtained
results
It is shown how a standard nonlinear programming approach can be
applied to solve a sophisticated version of the static optimal mix
problem in generation planning. The model presented includes technical
minima of thermal capacity, detailed operating models of storage-hydro
and pumped-hydro units, a realistic model of capital costs for hydro
plants, operating reserve and minimum demand constraints, and capacity
already in existence. The model formulation is in a format that can be
directly handled by the well-known MINOS code and can be efficiently
solved. The use of a general-purpose nonlinear optimization program
results in a great flexibility, making it possible to modify the model
formulation easily or to adapt it to the characteristics of a particular
electric system. A realistic application to the Spanish generation
system is presented
As the demand for energy continues to grow, optimization has risen to the forefront of power engineering research and development. Continuing in the bestselling tradition of the first edition, Electric Power System Applications of Optimization, Second Edition presents the theoretical background of optimization from a practical power system point of view, exploring advanced techniques, new directions, and continuous application problems. The book provides both the analytical formulation of optimization and various algorithmic issues that arise in the application of various methods in power system planning and operation. The second edition adds new functions involving market programs, pricing, reliability, and advances in intelligent systems with implemented algorithms and illustrative examples. It describes recent developments in the field of Adaptive Critics Design and practical applications of approximate dynamic programming. To round out the coverage, the final chapter combines fundamental theories and theorems from functional optimization, optimal control, and dynamic programming to explain new Adaptive Dynamic Programming concepts and variants. With its one-of-a-kind integration of cornerstone optimization principles with application examples, this second edition propels power engineers to new discoveries in providing optimal supplies of energy.
The task of optimal long-term planning of electricity production capacity considering uncertainty intervals of the base, peak and intermediate loads, and multi- stage nature of the planning process will be tackled. Theoretical min-max approach to the problem will be given. Model uses dynamic programming and takes into account also constraints on structure of generating capacity and security of supply.
v.1. Matrix games, programming, and mathematical economics. -- v.2. The theory of infinite games Incluye bibliografía e índice
A power system operation can be optimized on the deterministic, probability, interval uncertainty and fuzzy level. A new approach to the optimization of power system operation on the interval uncertainty level has been proposed. The characteristics of power generation units, power demand, deviations of active power of power plants and water discharges of hydro power plants are considered as interval uncertainty factors. The objective of the optimization task is to minimize the maximum value of the risk caused by the uncertainty of information. The mathematical models for minimax optimization of short-term hydro-thermal scheduling problem and a new solution method called "method of planned characteristics" are presented in this paper. The scheduling problem can be solved using the planned characteristic's method in two stages: calculation of planned characteristics for the uncertainty factors, solving of the deterministic equivalent of the min-max problem. In addition the problem of unit commitment under uncertainty conditions and some problems of computation are discussed.
Economy loading of power plants and electric systems
- M J Steinberg
- T. H Smith
Economical load distribution between in parallel operating power plants
- V M Gornstein
One-stage tasks of power system operation optimization under incomplete information
- M Valdma