M.R. Mohan

Optimal short-term hydrothermal scheduling using decomposition approach and linear programming method

Abstract The operational planning problem of hydrothermal scheduling for the next days demand in a power system is concerned with the determination of generation schedules for hydro and thermal plants to meet the daily system demand so that the total fuel cost of the thermal plants over the day is minimized subject to the operating constraints associated with the thermal and hydro plants as well as the network security constraints. This paper presents an effective algorithm which decomposes the problem into hydro and thermal subproblems and solves them alternatively. While the hydro subproblem is solved using a search procedure, the local variation method, the thermal subproblem is solved using the participation factors/Linear Programming method. The algorithm is very effective in enforcing security constraints and gives an optimal generation schedule which can be readily implemented for the next day. Results obtained from a 9-bus system and a 66-bus utility system demonstrate the effectiveness of the proposed algorithm.

Fuel restricted short term economic dispatch using evolutionary programming for utility system

The objective of the paper is to minimize the production cost of the thermal power generation. An elegant approach is presented in order to obtain the equivalent cost function of the participating non-fuel restricted units and the Economic Dispatch Calculations (EDC) are carried out along with fuel restricted units. The Evolutionary Programming (EP) technique is used for real power optimization with fuel restricted units. The optimal solution is obtained neglecting losses. The Fast Decoupled Load Flow (FDLF) analysis is conducted to find the losses by substituting the generation values. Then the loss is participated among all generating units using participation factor method. The load flow is conducted again and the voltage limit violation is checked. The Algorithm is tested on IEEE 6-bus system IEEE 30-bus system and a 66-bus utility system. The results obtained by this new approach are compared with those obtained using classical method. It is observed that the proposed method is more reliable and efficient.

Improved genetic algorithm solution to unit commitment problem

As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement. Previously utilized to schedule generation units in a manner that minimizes costs while meeting all demand, the Unit Commitment (UC) algorithm must be updated. This work deals with the Improved Genetic Algorithm (IGA) Solution to UC problem. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A simple GA implementation using the standard reproduction, cross over and mutation operators has been utilized to get optimal solution. In this paper, we have obtained the satisfactory solutions for the UC problem using the varying quality function technique and by adding problem specific operators. The idea has been implemented using technical simulation package MATLAB and the results for the same were obtained. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach. Numerical results are shown to compare the superiority of the cost solutions obtained using the conventional methods.

Refined simulated annealing method for solving unit commitment problem

The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints with temperature and demand as control parameter. Neyveli Thermal Power Station - II in India, demonstrates the effectiveness of the proposed approach.

Short-term hydrothermal scheduling of power systems with a pumped hydro plant using the local variation approach

Abstract In light-load seasons, the operational planning problem of hydrothermal scheduling with a pumped hydro plant for the next days demand in a power system is concerned with the determination of generation schedules for the hydro, thermal and pumped hydro plants to meet the daily system demand so that the total fuel cost of the thermal plants over the day is minimized subject to the operating constraints associated with the thermal, hydro and pumped hydro plants as well as the security constraints. The security constraints include network security constraints and the generator outage induced security constraint. This paper presents a new problem formulation by including the generator outage induced security constraint on water storage level to operate the pumped hydro plant as a spinning reserve unit and a method for the selection of the initial trajectory for the pumped hydro plant. The effective algorithm used comprises a judicious combination of the participation factor method and the linear programming method. The algorithm is very effective in enforcing security constraints and gives an optimal generation/pumping schedule which can be readily implemented for the next day. A comparison of results obtained with and without the outage induced security constraint is made on a nine-bus system.

Neural-based Tabu Search method for solving unit commitment problem for utility system

This paper presents a new approach to solving short-term Unit Commitment Problem (UCP) using Neural-Based Tabu Search (NBTS) for utility system. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimised, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for next H hours. A 66-bus utility power system with 12 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consisting of 24, 57 and 175 buses. Numerical results are shown to compare the superiority of the cost solutions obtained using the Tabu Search method, Dynamic Programming and Lagrangian Relaxation methods in reaching the proper unit commitment.

A FAST ALGORITHM FOR OPTIMAL HYDROTHERMAL SCHEDULING WITH CASCADED AND INDEPENDENT HYDRO PLANTS

ABSTRACT This paper presents a fast algorithm for solving the short-term hydrothermal scheduling problem in a power system consisting of cascaded plants with time delay and independent hydro plants. The operational planning of such problem is concerned with the determination of scheduling for hydro as well as thermal plants to meet the daily system demand with the objective of minimizing the total fuel cost of the thermal plants over the day subject to the relevant operating constraints associated with the thermal and hydro plants. The algorithm employs a fast and simple alternating solution approach for hydrothermal scheduling in which the hydro subproblem is solved using the method of local variation while the associated thermal subproblem is solved through a judicious combination of Successive Linear Programming (SLP) method and Participation Factor method. Many computational features are incorporated in the solution algorithm exploiting the inherent characteristic of the complex hydrothermal schedulin...

A simple and effective local variation algorithm for long-range hydrothermal scheduling

Abstract A simple and effective sequential method is presented for the long-range (one-year) hydrothermal scheduling problem. The dynamic optimization problem is decomposed into a sequence of static optimization problems by discretizing time span into a number of equal intervals. Methods based on the discrete maximum principle, dynamic programming and non-linear programming are available for solving this problem. In this paper, at each time interval, the hydro subproblem is solved using the method of local variation, while the associated thermal subproblem is formulated as a non-linear programming problem and solved by the lambda iteration method and/or through the use of participation factors of the thermal units. The results obtained by solving two different systems reveal the effectiveness of the method for practical application.

Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch

Abstract This paper presents a new algorithm based on hybridizing Bat Algorithm (BA) and Artificial Bee Colony (ABC) with Chaotic based Self-Adaptive (CSA) search strategy (CSA-BA-ABC) to solve the large-scale, highly non-linear, non-convex, non-smooth, non-differential, non-continuous, multi-peak and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence rate than the standard BA and ABC. The proposed algorithm works based on the three mechanisms. The first one is a novel adaptive search mechanism, in which one of the three search phases (BA phase, directed onlooker bee phase and modified scout bee phase) is selected based on the aging level of the individual’s best solution (pbest). In this regard, ABC’s phases can assist BA phase to search based on deeper exploration /exploitation pattern as an alternative. In periodic intervals, the second mechanism called as CSA updates algorithm control parameters using chaotic system based on prevailing search efficiency in the swarm. Lastly, the third mechanism is enhancing the algorithm performance by incorporating individual’s directional information, habitat selection and self-adaptive compensation. The effectiveness and robustness of the proposed algorithm are tested on a set of 23 benchmark functions and three CHPED problems. The obtained results by the suggested algorithm in terms of quality solution, computational performance and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better cost- effective solution.