 Open Access
 Total Downloads : 11
 Authors : Ashwani Tapde , Rahul Lodha , Amita Mahor
 Paper ID : IJERTV7IS060151
 Volume & Issue : Volume 07, Issue 06 (June 2018)
 Published (First Online): 20062018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Short Term Optimal Generation Scheduling of NCHES: A Review
Ashwani Tapde1*, Rahul Lodha2, Amita Mahor3
1*P.hd. scholar in Mewar University, NH – 79 Gangrar, Chittorgarh, Rajasthan312 901 2Training Dept., Mewar University, NH – 79 Gangrar, Chittorgarh, Rajasthan312 901 3Dean VIT Bhopal, Kithrikalan , Sehore Madhya Pradesh466114
Abstract: – In cascaded hydroelectric systems, the water deliverance from upstream power plants increases the tail race stratum of the same plant and reservoir stratum of its imminent downstream plant, as well with a delay of water traveling time. This detract the effective head of the upstream power project which results in sparing power generation. Generally in the cascaded systems, the available water at any time in the reservoir is determined with the water continuity equation. This equation provides the available water volume in any reservoir at time t+1 considering available water in that reservoir at time t, natural inflows with proper time delay and outflows. In the available literature, the traveling time between consecutive reservoirs has been considered as constant, however, this time varies as per the discharge of water. The optimal generation scheduling of NHECS considering varying water traveling time in water continuity equation of consecutive reservoir is addressed in three phases namely structuring of problem, problem decision and implementation or computational phase.
Keywords: Narmada Cascade Hydro Electric System (NCHES), Particle Swarm Optimization (PSO), Economical Load Dispatch (ELD), Problem Formulation.
INTRODUCTION
Diminution of fossil fuels, unremitting increase of power and environmental concerns have forced the power system engineers to adopt alternative sources of energy, energy conservation and use of efficient technologies. In India, the percentage of power generation through thermal power plants is higher than the hydro power plants generation and it is not surpassing seeing the global warming issues and shortage of energy sources. In such circumstance, it is a must that hydro potential should be properly harnessed and optimally utilized as they are the spotless sources of power generation.
In current scenario, most of the electrical power is generated by the fossil fuels i.e. coal, oil, gas, nuclear etc. which influence the atmosphere directly or indirectly. So it is must to use spotless sources of energy i.e. hydro and renewable sources. But contradictory to that seeing to the generation profile it is thermal dominated .The optimal generation scheduling of NHECS considering varying water traveling time in water continuity equation of consecutive reservoir is addressed in three phases namely structuring of problem, problem decision and implementation or computational phase. Initially, Economical Dispatch (ED) problem was formulated as economic cost dispatch (ECD), but further due to the amendment of clean air act in 1990s, existence of emission dispatch (EMD) leads to the formulation of combined emission economic dispatch (CEED) (89) and emission
controlled economic dispatch (ECED) problem formulation, as individual optimization of these two contradictory objectives will not serve the purpose. Various conventional methods like nonlinear optimization (31,45,48,58), Metaheuristic approaches (2), Lagrange relaxation (22,78), Bundle method (71), TVAC_PSO(33,41,82), Two Phase Neural Network(77), Dynamic Programming(76), Optimal based algorithm(79), Linear programming(86), Firefly Algorithm(18), Particle Swarm Optimization (PSO) (10,13,25,27,38,54,60,75), HPSO (1,49,56), IPSO (3,6,23,28,34,44,65,73), CPSO(4,5,63), APSO
(57), MPSO (35), CFPSO (11,12,42), DCPSO (7), IAGA (14),
NEIW_PSO (17,30) reported in the literature are used to solve such problems. Each method has its own advantages and disadvantages; however PSO has gained popularity as the best suitable solution algorithm for such problems. This paper is a response note on previous work of application of population based PSO algorithm to solve the various ED problems.
PROBLEM FORMULATION
Optimal generation scheduling of cascaded hydroelectric system requires the judicious optimization problem formulation which includes the objective function and various constraints as well. This problem is nonlinear and un predicting in nature due to the relation of power generation with effective head and Discharge from individual hydro power plants and unexpected load profile. It is having dynamic behavior due to the water continuity equation which considered as one of the important equality constraints in this scheduling problem. Economic dispatch requires a judicious formulation of practical ED problem. It can be formulated as single objective and multi objective problem which are nonlinear and non convex in nature. The primary of any ED problem is to reduce the operational cost of the system fulfilling the load demand within the limit of constraints. In present work problem will be formulated as multi objective function which needs the judicious mathematical modeling of all hydro power plants in cascade fashion involved in the test system.
OBJECTIVE FUNCTION
An objective function expresses the main aim of the model which is either to be minimized or maximized. In a cascade system satisfying all physical and operational constrains. These are in form of demand supply balance, flow balance or continuity equation, bounds on reservoir storage, bound on water release or discharge, limit on spillage and coupling constraints that put a boundary condition on the initial and
final reservoir level. The objective is to minimize the summation of the production cost of energy import or export over the scheduling period. The objective of this research is to increase the operational efficiency and output of this Narmada Cascade Hydro Electric System (NCHES), by economical and technical parameters optimization. In cascaded hydroelectric
system the water release from upstream power plants increases the tail race level of the same plant and reservoir level of its
EQUALITY CONSTRAINTS
j j j up up j j j j
This equation relates the previous interval water storage in reservoirs with current storage including delay in water transportation between reservoirs and expressed as: X t1 X t Yt Ut St Ut St IRt ELt
5
Where X t :reservoir storage of the jth plant at time t, Yt
j j
immediate downstream plant as well with a delay of water traveling time.
D j
T n
E Min[(1/ 2) *( p t pt )2 ] 1
:Natural inflow in the jth plant at time t, U t : Discharge through jth plant at time t, St : spill from the jth plant at time,
j
j
j
: time delay for water flow from upstream to downstream
t 1
j 1
plant. X t+1= Reservoir storage of the jth plant at t+1.
D
Where Pt
and Pt represented load demand power generated
WATER BALANCE EQUATION
j
through jth hydro power plant of hydroelectric system at time t respectively. The various kinds of objective function formulation are given below.

Simplified economic cost function: – The Let Fi mean the
cost, expressed for example in dollars per hour, of producing energy in the generator unit i. The total controllable system production cost such an approach will not be workable for nonlinear functions in practical systems. Therefore will be
N
Generation associated discharge various with time and there environmental and weather factor which also contribute directly to change in water level. This water cotinuity equation relates the previous interval water storage in reservoirs with current storage including delay in water transportation between successive reservoirs water continuity has been expressed as,
X t1 X t Ut St Ut St
j j up up j j 6
ACTIVE POWER BALANCE EQUATION:
For power balance equation, equality constraints should be
FT Fi (Pi )
i1
2 satisfied. The total generated power should be the same as total load demand plus the total line loss,
The fuel inputpower output cost function of the ith unit is given as
N
Pi PD Ploss
F (P ) a b P c P2 3
i1 7
i i i i i i i
Where FT : total generating cost; Fi cost function of ith
Where PD is the total system demand and
Ploss is the total
generating unit; a b c ; cost coefficients of generator; P
line loss. Pi is power generation.
i, i, i
i Minimum and maximum power limits: Generator output of
power of generator I,n; number of generator.

Water dynamic balance equation with travel time: – The initial water level trajectory of each hydropower station is determined. It can be the dead water level or the normal water level. The respective equation in below,
each generator should be laid between maximum and minimum limits. The corresponding inequality constraints for each generator are
i i i
Pmin P Pmax 8
Vi1 Vi qi Qxi qi 4
Where
min
P
i
and
max
P
i
are the minimum and maximum
Where Vi,Vi1 represent separately the amount of water in the reservoir at the starting point and ending point of period i, qi represent the average amount of natural water flowing into the
output of generator i.
DESCRIPTION OF NCHES:
Narmada is the fifth largest river in India and largest west flowing river of Indian peninsula originating from Maikala
reservoir in period i.
Qxi
represent the amount of water
ranges at Amarkantak (in Madhya Pradesh) at an elevation of
flowing downward of the station and qi represents the loss of water.
System Constrains: – The optimal value of the objective
function as shown in equation is computed subjected to constraints of two kinds of equality constraints and inequality constraints or simple variable bounds as given below. The decision is discretized into one hour periods.
900 m. It flows westwards over a length of 1,312 Km before draining into the Gulf of Cambay, 50 Km west of Bharuch city. The basin lies between the East longitude 72 32' and 81 45', and the North latitudes 21 20' and 23 45'. The Vindhya range in the North, the Satpura range in the South, the Maikala range in the East and the Arabian Sea in the West forms the boundaries of the basin. In the first 1,077 Km reach, the river flows in Madhya Pradesh. The next 35 Km stretch of the river forms the boundary between the States of Madhya Pradesh and
Maharashtra. Again for the next 39 Km, it forms the boundary between Maharashtra and Gujarat. The last stretch of 161 Km lies in Gujarat. The annual utilizable quantity of water of Narmada at Navagam in Gujarat has been assessed as 34,537 Million Cubic Metre (MCM) i.e. 28 Million Acre Feet (MAF) at 75% dependability by NWDT. On full development, the Narmada has a potential of irrigating over 6 million ha (15 Million Acres) of land along with a capacity to generate about 3,460 Mega Watt hydro electric power. Out of the total catchment area of 98,796 sq. km., 85,115 sq. km. (86.15%) lies in Madhya Pradesh, 744 sq. km. (0.75%) in Chhatishgarh, 1,538 sq. km. (1.56%) in Maharashtra and 11, 399 sq. km. (11.54%) in Gujarat. It is also called the life line of Madhya Pradesh and Gujarat. The share of states in power generation and water are shown in table. In Madhya Pradesh share are very large in the other because of the area of the river is large.
Table: Share of States in Power Generation & Water
Party States 
Share of Party States 

Power from SSP(%age) 
Narmada Water at SSP(MAF) 

Madhya Pradesh 
57 
18.25 
Gujarat 
16 
9.00 
Maharashtra 
27 
0.25 
Rajasthan 
– 
0.50 
Total 
100 
28.00 
REVIEWS FOR PSO IN ECONOMIC DISPATCH PROBLEM:
Prabakaran et al. [1] proposed a new Hybrid Particle Hybrid Optimization (HPSO) approach to solve the problem of the Economic Dispatch (ED) considering the forbidden operating zone, the increase rate limits, the capacity limits and the power balance restrictions. This method integrates evolutionary programming (EP) techniques and optimization of wake particles (PSO) to exploit the qualities of the optimization method and determine the quality solution. The result shows that the previous method is able to produce the best solutions to reduce the fuel cost of generating units with faster convergence characteristics.
Hidalgo et al. [2] formulated the shortterm generation scheduling problem of the two cascaded Brazilian hydroelectric plants, which are JupiÂ´a and Porto Primavera plants, that belong to the national interconnected system. This problem has been solved using two metaheuristic approaches, that is the hybridization of the genetic algorithm with the Pareto evolutionary force algorithm and the optimization of the ant colony.
Khan et al. [3] introduced to the optimization of the swarm of inertial particles of natural exponent weight to solve the problem of the expenditure of economic load. The inertial weight strategy optimizes the swarm of particles to achieve optimal energy expenditure with satisfaction of restrictions and minimization of operating costs. The results are compared between the classical methods of optimization of the Swarm particle (CPSO), e1PSO, e2PSO.
Mistry et al. [4] adopted the particle swarm optimization (PSO) algorithm to solve the economic dispatch (ED) problem. Here the objective is to minimize the total operating cost of the committed generating units considering various
equality and inequality constraints. For the results opting compare the conventional method and PSO method for minimize the fuel cost.
Jadoun et al.[5] optimized a Non convex Economic Dispatch Using Particle Swarm Optimization with Time Varying Operators. Particle swarm optimization (PSO) is used to solve difficult combinatorial optimization problems, such as the problem of nonconvex and discontinuous economic dispatching (ED) of large thermoelectric power plants.
Yadav et al. [6] presented a improved particle swarm optimization (IPSO) algorithm for solving the system short term hydrothermal scheduling (STHTS) problem considering valve point loading for fixed head hydrothermal system. short term hydrothermal scheduling (STHTS) problem which is a complicated nonlinear dynamic constrained optimization problem.
Jaodun et al. [7] developed a dynamically controlled swarm particle optimization method (DCPSO) to solve the shortterm energy system (EESTHS) problem with a variety of operational and network limits. This method is studied in two different test systems that have different operational and network constraints.
Awasthi et al. [8] presented a concept to regulate the release of water in order to obtain optimal performance of the units in a hydroelectric plant while satisfying the load demand. This concept is used in hydraulic and electrical losses in a hydroelectric plant and satisfies the load demand with a minimum amount of water.
Ramya et al. [9] proposed a stochastic economic dispatching model (SED) that incorporates wind energy storage generators as well as thermal generators. Compare the optimalresult for the model mentioned above with the two adoptive methods, namely the modified PSO algorithm (MPSO).
Ghani et al. [10] illustrated the application of PSO in ED problems, which is the most complex optimization problems. The practical problems of economic transmission (ED) are an objective function of the nonlinear, nonconvex type, and have restrictions of intense equality and inequality.
Salma et al. [11] solved the short term fixed head hydrothermal scheduling problem with transmission line losses. Genetic algorithm (GA) and constriction factor based particle swarm optimization (CFPSO) technique are improved the performance efficiency of the hydrothermal test system comprising of three thermal units and one hydro power plant. SALAMA et al. [12] solved short term multi chain hydrothermal scheduling problem with non smooth fuel cost objective functions. This algorithm is demonstrated on hydrothermal test system comprising of three thermal units and four hydro power plants are in cascade system.
Tiwari et al. [13] presented an overview of the basic PSO to provide a complete survey on the problem of economic freight transport as an optimization problem. Dispatching the economic load is a nonlinear optimization problem that is of great importance in power systems. Optimization of the swarm of classical particles (CPSO) as optimization to solve the quadratic cost function based on constraints with generator constraints and power loss.
Zheng et al. [14] improved Adaptive Genetic Algorithm (AGA) and its application in the optimal functioning of the shortterm set of Qing. Hydroelectric stations of the river
cascade. a new selection operator is adopted to maintain the diversity of the population in the selection process by performing a nonline conversion to the fitness function.
Sharma et al. [15] reviewed that Economic Load Dispatch (ELD) for valve point loading. Particle swarm optimization (PSO) with chaotic sequence can be applied. Particle swarm optimization is an effective & reliable evolutionary based approach.
RAHMANI et al. [16] proposed a developmentally modified PSO (Particle Swarm Optimization) is used to find fast and efficient solutions for different energy systems with different numbers of generating units. This algorithm minimizes the total cost function of the generating units.
Kishore et al. [17] presented an Optimum Swarm Optimization approach to the weight of natural exponential inertia (NEIW_PSO) to determine the optimal generation program of hydropower plants in the cascade hydroelectric test system.
Yang et al. [18] presented a new approach to determine the optimal and feasible solution of erectile dysfunction problems using the recently developed Firefly (FA) algorithm. This method can find cheaper rates than those determined by other methods.
Li et al. [19] proposed optimal programming of the cascade hydroelectric system using the grouping differential evolution algorithm. The algorithm is applied to a medium and long term cascade hydroelectric system case. Its algorithm is a complex problem of the optimal programming of the cascade hydroelectric system, a new algorithm of differential development of the grouping (GDE).
Luo et al. [20] presented Longterm optimal scheduling of cascade hydropower stations using fuzzy multiobjective dynamic programming approach. A multiobjective fuzzy dynamic programming model for used on three Gorges cascaded hydropower plant.
XIE et al. [21] analyzed optimal scheduling processes for short term power generation rules for cascade hydropower stations based on hybrid algorithm. A mathematical model was established based on the principle of reservoir operation.
Rodrigues et al.[22] implemented problem of shortterm programming of hydrothermal energy systems through the relaxation of Lagrange and the increase of Lagrangiana, which translates into a problem of nonlinear programming of large scale mixed whole. A Lagrangian relaxation (LR) is designed according to the variable division in which the resulting two fold problem is solved by a Bundle method.
Mandal et al. [23] presented a new technique of improved particle swarm optimization called selforganized hierarchical swarm optimization technique with variable acceleration coefficients over time (SOHPSO_TVAC) to solve the short term economic generation program of hydrothermal systems to avoid premature convergence.
Mahor et al. [24] suggested. Practical economic dispatch (ED) problems have nonlinear, nonconvex type objective function with intense equality and inequality constraints. Metaheuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems.
Rugthaicharoencheep et al. [25] developed based particle swarm optimization (PSO) algorithm, is applied to search for the optimal schedule of all generations units that can supply
the required load demand at minimum fuel cost while satisfying all unit and system operational constraints.
PENG et al. [26]proposed to improved artificial fish swarm algorithm (IAFSA) is used to solve the problem of optimal operation of cascade reservoirs. Artificial fish swarm algorithm (AFSA) is a novel method for searching the global optimum.
Sreenivasan et al. [27] presented Shortterm hydrothermal programming based on OSPs with prohibited discharge areas. A new approach is to determine the optimal time schedule for generating energy in a hydrothermal energy system using the PSO technique.
Dieu et al. [28] proposed a newly improved particle swarm optimization (NIPSO) is based on the particle swarm optimization with timevarying acceleration coefficients (PSO TVAC) with more improvements including the use of sigmoid function with random variation for inertia weight factor, pseudogradient for guidance of particles, and quadratic programming for obtaining initial condition.
Saber et al. [29] proposed economic load dispatch using particle swarm differential evolution optimization (PSDEO) algorithm. A new hybrid algorithm combining the conventional Particle Swarm Optimization (PSO) algorithm with Differential Evolution (DE) has been suggested wherein PSO is used for exploitation, DE is used for exploration and the hybrid PSDEO has a good balance between local and global search abilities for ELD.
Kishore et al. [30] presented a natural approach to optimize the exponential inertial particle swarms (NEIW_PSO) to determine the optimal generation program of hydroelectric power plants in the cascade hydroelectric test system.
DÃaz et al. [31] proposed a nonlinear programming model based on a programming that determines both the optimal engagement of the unit and the sending of the generation of the units involved. The power generated by each hydraulic unit is considered a nonlinear function of the water discharge and the volume of the associated tank.
Tu Vu et al. [32] adopted the optimization problems of optimal power flow (OPF) and the economic load dispatch (ELD) with valvepoint effects in power systems are recently solved by some types of artificial intelligent (AI) algorithms. In this paper, based on improving the function of weight parameters. Mahor et al. [33] adopted a Hydrologically efficient operation of power plants in such cascaded system requires that water resources should be managed efficiently, so that it can dispatched to predicted demand considering all physical and operational constraints. Time Varying Acceleration coefficients PSO (TVAC_PSO) has been used to determine the optimal generation schedule of real operated cascaded hydroelectric system located at Narmada River in state Madhya Pradesh, India.
Park et al. [34] presented an improved particle swarm optimization (IPSO) efficient approach for solving economic dispatch (ED) problems with nonconvex cost functions. It is applied to large scale power system of Korea.
Bhattacharya et al. [35] presented optimization of swarms of modified particles (MPSO) to solve the nonconvex economic dispatch. A new swarm particle optimizer combined with the roulette selection operator to solve the problem of sending
economic load (ELD) of the thermal generators of a power system. Compared to the MPSO and PSO method.
Khamsawang et al. [36] proposed to solve the economic dispatch problem using Novel Particle Swarm Optimization (NPSO). An improved approach based on Conventional Particle Swarm Optimization (CPSO) for solving an economic dispatch(ED) problem with considering the generator constraints.
Stojanovic et al. [37] introduced a short term and long term management for hydropower plant in a cascade system. This modes is management used in a Vlasinske HPPs that is a cascade that consists of 4 hydropower plants. It solves the water level in the storage the discharge through the power house and the power and electricity generation realized by hydropower plant.
Chen et al. [38] present a new methodology based on a PSO for solving the hydro units scheduling problem in the daily hydrothermal coordination. New solution methods and results based on a particle swarm optimization (PSO) for solving the hydro generation scheduling problem.
Catalao et al. [39] proposed a Novel MixedInteger Quadratic Programming Approach (NMIQPA). It has short term hydro scheduling (STHS) in head dependant cascade hydro system. This method approach allows an efficient consideration of the nonlinear dependence power generation, water discharge and head.
Wang et al. [40] investigated multi area environmental/economic dispatch (MAEED) is addressing the environmental issue during the economic dispatch (ED). An improved multi objective particle swarm optimization (MOPSO) algorithm is developed to derive a set of Pareto optimal solutions.
Khokhar et al. [41] proposed a efficient particle swarm optimization (EPSO) approach with time varying acceleration coefficients (TVAC_PSO) for an extensive study of the economic dispatch problem with valve point loading (EDVPL).It is minimize transmission losses and valvepoint loading (VPL) have been considered.
Salama et al. [42] proposed optimal generation scheduling of cascade hydrothermal system using genetic algorithm (GA) and particle swarm optimization with constriction factor (CFPSO).
Abido et al. [43] proposed a new technique of optimization of multiobjective particle swarms (MOPSO) for the problem of environmental / economic dispatching (EED). The MOPSO technique develops a multiobjective version of PSO proposing the redefinition of the best and best local people in the field of multiobjective optimization.
Hota et al. [44] presented a new approach to the optimal energy generation solution for the problem of short term hydrothermal programming, using the improved technique of particle sweep optimization (IPSO). , the delay in water transport and the connection of programming times that make it difficult to find the global optimum using standard optimization methods.
Catalao et al. [45] proposed nonlinear optimization approach for short term hydro scheduling considering head dependent reservoirs under competitive environment. This method used in hydroelectric power generation as well as water discharge and of the head, but also that the maximum water discharge,
giving the maximum power generation, is a function of the head.
Vlachogiannis et al. [46] introduced An improved algorithm for particlebased swarm optimization based on aggregation (ICAPSO) serves to solve the optimal problem of dispatching the economic load (ELD) in energy systems. The ICAPSO algorithm is tested in a series of power systems.
Moradi et al. [47] introduced to evaluate the Particle Swarm Optimization (PSO) algorithms for solving complex problems of water resources management. The standard particle swarm optimization algorithm and the modified method named ElitistMutation Particle Swarm Optimization (EMPSO) are used to determine optimal operating of a single reservoir system.
Catalao et al. [48] proposed a new nonlinear approach to solve the problem of shortterm hydraulic planning in deregulation, considering the dependence on the head. The hydraulic coupling of hydroelectric systems in cascade and the complexity associated with the generation of headsensitive hydroelectric energy still represent a real challenge for modelers.
Jiekang et al. [49] proposed a model for shortterm scheduling optimization of cascaded hydro plants, which includes uncertainties, spatialtemporal constraints among cascaded reservoirs. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is to use for the solution of global optimization problems.
Sugsakarn et al. [50] presented an effective method for solving economic dispatch problem (EDP) with non smooth cost function using a hybrid method that integrates particle swarm optimization (PSO) with sequential quadratic programming (SQP).A hybrid PSOSQP method is applied to solve EDP of a test system with ten generator units.
Sriyanyong et al. [51] proposed a method of combining the conventional PSO algorithm with Gaussian mutation (GM) operator to enhance the global search capability and investigate the performance of the proposed hybrid PSOGM algorithm, while solving the Economic Dispatch (ED) problem considering nonsmooth cost functions.
Diniz et al. [52] suggested the selfscheduling of a hydro plant a precise integer modeling of the hydro power production function (HPF) is convenient, in the security constrained short term hydrothermal dispatch problem for largescale systems a strategy which best balances an accurate representation with an acceptable computational burden is required.
Chaturvedi et al. [53] applied a new optimization of wake of selforganized hierarchical particles (SOH_PSO) for the non convex economic expedition (NCED). Performance is further improved when variable acceleration coefficients are included over time.
Samudi et al. [54] presented a new approach of particle swarm optimization (PSO) algorithm for short term Hydro Thermal Scheduling (HTS) problems.
Zhu et al. [55] presented the particle swarm optimizer (PSO) is a stochastic, population based optimization technique that can be applied to a wide range of applications.
Sriyanyong et al. [56] presented the application of Optimization Sweat Optimization (EPSO) combined with the Gauss Mutation (GM) to solve the problem of the Dynamic Economic Dispatch (DED) taking into account the operating
limits of the generators. EPSO consists of a standard PSO and a modified heuristic search approach.
Panigrahi et al. [57] presented adaptive particle swarm optimization (APSO) approach for static and dynamic economic load dispatch problem a novel heuristic optimization approach to constrained economic load dispatch (ELD) problems using the adaptive variable population PSO technique.
CatalÃ£o et al. [58] proposed a new nonlinear optimization method to consider the generation of hydroelectric energy as a function of water discharge and also of the head. Head dependence is considered in shortterm hydropower programming to achieve more realistic and feasible results.
Yuan et al. [59] proposed an optimized algorithm for particle swarm optimization (EPSO) to solve the optimal daily problem of hydropower generation programming. Improvements mainly include three aspects. Firstly, the concept of repellent that acts in a complementary way to the concept of attractor is introduced in PSO, in second place, chaotic sequences based on the logistic map are adopted in place of random sequences in PSO; thirdly, a selection comparison technique based on feasibility and a heuristic rule is designed to effectively manage the constraints in PSO. Mandal et al. [60] presented Particle swarm optimization is applied to determine the optimal time schedule of power generation in a hydrothermal energy system. It is considered a cascade hydroelectric system of multiple deposits with a non linea relationship between water discharge, net head and energy generation.
Ling et al. [61] suggested a new hybrid particle swarm optimization (HPSO) that incorporates a wavelet theory based mutation operation for solving economic load dispatch. Economic load dispatch, that optimizes the operation cost with respect to the load demands of customers, is one of the most important problems in power systems.
Coelho et al. [62] suggested solve the problem of the shipment of the economic load in the power system through the chaotic and Gaussian particle swarm optimization (GPSO). The objective of the Economic Dispatch Problems (EDP) problems of the generation of electricity is to program the results of the production units engaged in order to satisfy the requested load request at a minimum operating cost while satisfying all the units and the equality of the system and inequality constraints of the system.
Jiejin et al. [63] proposed a Chaotic particle swarm optimization (CPSO) methods are optimization approaches based on particle swarm optimization (PSO) with adaptive inertia weight factor (AIWF) and chaotic local search (CLS). Two CPSO methods based on the logistic equation.
Yu et al. [64] introduced the approaches based on different particle swarm optimization (PSO) techniques are applied to solve the shortterm hydrothermal scheduling problem. This algorithm is demonstrated through an example system.
Titus et al. [65] presented a solution procedure using particle swarm optimization to solve the hydrothermal coordination problem for power generation considering prohibited operating zones (POZ). Prohibited operating zones (POZ) induce nonlinear characteristics into the problem. A PSO based algorithm to solve the HTC problem.
Selvakumar et al. [66] proposed a new version of the classical particle swarm optimization (CPSO), new PSO (NPSO), to solve non convex economic dispatch problems. In the classical PSO, the movement of a particle is governed by three behaviors, inertial, cognitive, and social. A simple local random search (LRS) procedure is integrated with NPSO. Selvakumar et al [67] proposed a new particle swarm optimization (PSO) antipredatory particle swarm optimization (APSO) to solve non convex economic dispatch problems. The antipredatory activity is modeled and embedded in the classical PSO to form APSO.
Marques et al. [68] concerned to estimate the benefits of coordination in the operation of hydroelectric power system by optimization model representing the detail operating characteristics of hydro plants. The benefits of coordination have been estimated through the application of the optimization model in two different approaches.
Glanzmann et al. [69] presented a supervisory controller for cascade river power plants, which is based on model predictive control. In the power plant cascade is derived from the Saint Venant equation by the requirement of linear discrete time model.
EusÃ©bio et al. [70] proposed a practical deterministic approach for computing the shortterm economic value of the water stored in a power system reservoir, emphasizing the need to considerer water stored as a scarce resource with a shortterm economic value. Mezger et al. [71] presented a dual approach to solve the short term hydrothermal scheduling (STHS) problem for systems under poolbilateral markets. The sub problems are solved analytically or by a primaldual interior point method, whereas the dual problem is solved via bundle method.
Chuanwen et al. [72] proposed a novel selfadaptive chaotic particle swarm optimization (SAC_PSO) algorithm to solve the short term generation scheduling of a hydrosystem better in a deregulated environment. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the approach introduces chaos mapping and an adaptive scaling term into the particle swarm optimization algorithm, which increases its convergence rate and resulting precision.

He et al. [73] improved particle swam optimization (PSO) to solve optimal power flow (OPF) problems. The standard PSO algorithm is extended by incorporating a biology concept passive congregation to prevent premature convergence and refine the convergence performance.
Basu et al. [74] presented a method for achieving objectives based on simulated annealing for the shipment of the economic emissions load of the fixedhead hydrothermal energy system. A new multiobjective optimization method for sending economic emission costs of fixedhead hydroelectric plants and thermal plants with nonuniform fuel cost and emissions functions.
Gaing et al. [75] proposed a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. The PSO algorithm has been demonstrated to have superior features including high quality solution, stable convergence characteristic and good computation efficiency.
Arce et al. [76] adopted a dynamic programming model has been developed to optimize the number of generating units in
operation at each hour of the day in order to attain the total generation scheduling of the plant in the most economic way. The optimal dispatch of generating units of Itaipu, the worlds largest hydroelectric plant is in operation.
Naresh et al. [77] presented a solution technique based on two phase neural network for short term scheduling of hydropower generation. It is based in solution of a set of differential equation realized from transformation of an augmented Lagrangian energy function.
CONCLUSIONS
Due to deregulation of the power industry and drastic environment regulations, highly nonlinear constraints, multi objective problem with inconsistent objective functions are besmeared in ELD problem. In practical ELD problem it is essential to ponder valve point loading, prohibited zones with ramp rate limit as well as transmission network losses and multifuels with valve point effects. The insolubility further increases when dimensionality of power system increases. The basic PSO is insufficient to address the problem of practical ELD. The universal optimal solution and universal search ability, premature convergence and convergence speed, and stuck in local optima are certain major issues for PSO. In this review, it is shown that many new algorithms are developed to resolve this problem of PSO. If PSO is applied with another evolutionary programming technique it can provide much better results.
REFERENCES.

S. Prabakaran, V. Senthilkumar and G. Baskar, Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints,J Electr Eng Technol.2015; 10(4) pp: 1441 1452

Ieda G. Hidalgo, Regiane S. de Barros, JÃ©ssica P. T. Fernandes, Joao Paulo F. Estrocio, and Paulo B. Correia, Metaheuristic Approaches for Hydropower System Scheduling, Hindawi Publishing Corporation, Journal of Applied Mathematics Volume 2015, pp16

Mohd Javed Khan, Hemant Mahala, Particle Swarm Optimization by Natural Exponent Inertia Weight for Economic load Dispatch ,IJAREEIE, vol. 3, issue 12,
December 2014, pp. 1365713662

Payal Mistry, Sanjay Vyas, A STUDY ON : Optimisation of Economic Load Dispatch Problem by PSO, indian journal of applied research, vol. : 4, issue : 6, june 2014, pp171173

Vinay Kumar Jadoun, Nikhil Gupta, K. R. Niazi, and Anil Swarnkar, Nonconvex Economic Dispatch Using Particle Swarm Optimization with Time Varying Operators, Hindawi Publishing Corporation Advances in Electrical Engineering Volume 2014, pp1 13.

Deepika Yadav, R. Naresh and V. Sharma, Improved Particle Swarm Optimization Algorithm for Hydrothermal Generation Scheduling, HYDRO NEPAL issue no. 15,
JULY, 2014, pp. 6572

Vinay K. Jadoun, Nikhil Gupta, K.R. Niazi and Anil Swarnkar, Economic Emission Shortterm Hydrothermal Scheduling using a Dynamically Controlled Particle Swarm Optimization, Research Journal of Applied Sciences,
Engineering and Technology 8(13)2014, pp. 15441557 
Shambhu Ratan Awasthi, Vishnu Prasad, Saroj Rangnekar, Demand Based Optimal Performance Of A Hydroelectric
Power Plant, IJARET, vol. 4, issue 7, Nov. Dec. 2013, pp. 109119

N. M. Ramya, M. Ramesh Babu, T.D. Sudhakar , Solution of Stochastic Economic Dispatch Problem using Modified PSO Algorithm, International Journal of Computer Applications ICIIIOSP2013.pp1723.

Mohd Ruddin Ab Ghani, Saif Tahseen Hussein, M.T. Mohamad, Z. Jano, An Examination of Economic Dispatch Using Particle Swarm Optimization MAGNT Research Report, vol.3 (8). pp. 193209.

M.M. Salama, M.M. Elgazar, S.M. Abdelmaksoud, H.A. Henry, Short Term Optimal Generation Scheduling of Fixed HeadHydrothermal System UsingGenetic Algorithm andConstriction Factor Based Particle Swarm Optimization Technique, International Journal of Scientific and Research Publications, vol. 3, issue 5, May 2013,pp. 19.

M.M. Salama, M.M. Elgazar, S.M. Abdelmaksoud, H.A. Henry, Short Term Optimal Generation Scheduling of MultiChain Hydrothermal System Using Constriction Factor Based Particle Swarm Optimization Technique (CFPSO), International Journal of Scientific and Research Publications, Vol. 3, Issue 4, April 2013.pp19.

Shubham Tiwari, Ankit Kumar, G.S Chaurasia, G.S Sirohi, Economic Load Dispatch Using Particle Swarm Optimization, IJAIEM, vol. 2, issue 4, April 2013, pp.476 485.

Jiao Zheng, Kan Yang, Ran Zhou, Yonghuai Hao, Guoshuai Liu, Improved Adaptive Genetic Algorithm and Its Application in ShortTerm Optimal Operation of Cascade Hydropower Stations , Communications in Information Science and Management Engineering Mar. 2013, vol. 3 iss. 3, pp. 167174.

Jaya Sharma, Amita Mahor, Particle Swarm Optimization Approach For Economic Load Dispatch: A Review,
IJERA, vol. 3, issue 1, JanuaryFebruary 2013, pp.013022

Rasoul Rahmani, Mohd Fauzi Othman, Rubiyah Yusof, Marzuki Khalid, Solving Economic Dispatch Problem Using Particle Swarm Optimization By An Evolutionary Technique For Initializing Particles, Journal of Theoretical and Applied Information Technology, December 2012. vol. 46 No.2 pp. 526536.

Nand Kishore, Amita Mahor ,Optimal Generation Scheduling of Cascaded Hydroelectric System using Natural Exponential Inertia Weight PSO, International Journal of Scientific Engineering and Technology, vol. no.1, 01 April 2012, issue no.2 pp:2733.

XinShe Yang, Seyyed Soheil Sadat Hossein, Amir Hossein Gandomic, Firefly Algorithm for solving nonconvex economic dispatch problems with valve loading effect,
Applied Soft Computing 12 (2012) pp 11801186

Yinghai Li, Jian Zuo, Optimal Scheduling of Cascade Hydropower System Using Grouping Differential Evolution Algorithm, International Conference on Computer Science and Electronics Engineering, 2012, pp 625629

Peng Luo, Jianzhong Zhou, Hui Qin, Youlin Lu, Long term optimal scheduling of cascade hydropower stations using fuzzy multiobjective dynamic programming approach, Fourth International Conference on Intelligent Computation Technology and Automation,2011, pp.174 176

Wei XIE, Changming JI, Zijun YANG, Xiaoxing ZHANG, Shortterm power generation scheduling rules for cascade hydropower stations based on hybrid algorithm, Water Science and Engineering, 2012,:pp 46 58.

761
[22] 
Rafael N. Rodrigues, Edson L. da Silva, Erlon C. Finardi, 
Short and LongTerm Management, Journal of the Serbian 

and Fabricio Y. K. Takigawa, Solving the ShortTerm 
Society for Computational Mechanics / vol. 3 / no. 1, 2009 / 

Scheduling Problem of Hydrothermal Systems via 
pp. 210227 

Lagrangian Relaxation and Augmented Lagrangian, 
[38] 
PoHung Chen, LiMing Chen, An Liu, HungCheng Chen, 

Hindawi Publishing Corporation Mathematical Problems in 
Application of Particle Swarm Optimization to Hydro 

Engineering Volume 2012, , pp118. 
Generation Scheduling, International Conference on 

[23] 
Kamal K. Mandal, Niladri Chakraborty, Optimal 
Energy and Environment Technology, IEEE, 97807695 

Scheduling of Cascaded Hydrothermal Systems Using a 
38198/09,pp541544. 

New Improved Particle Swarm Optimization Technique, 
[39] 
J. P. S. CatalÃ£o, H.M.I. Pousinho, V.M.F. Mendes, 

Smart Grid and Renewable Energy, 2011, 2, pp282292 
Scheduling of Headdependent Cascaded Hydro Systems: 

[24] 
Amita Mahor, Vishnu Prasad, Saroj Rangnekar, Economic 
mixedintrger quadratic programming approach,2009 

dispatch using particle swarm optimization: A review, 
Elsevier Ltd. In 17 march 2009, pp123. 

Renewable and Sustainable Energy (2009) pp21342141 
[40] 
Lingfeng Wang, Chanan Singh, Reserveconstrained 

[25] 
N. Rugthaicharoencheep, S. Thongkeaw, S. Auchariyamet, 
multiarea environmental/economic dispatch based on 

Economic Load Dispatch with Daily Load Patterns Using 
particle swarm optimization with local search, 

Particle Swarm Optimization, UPEC 2011 46th 
Engineering Applications of Artificial Intelligence 22 

International Universities' Power Engineering Conference 
(2009)pp 298307 

September 2011 Soest Germany. 
[41] 
Bhuvnesh Khokhar, K. P. Singh Parmar, Surender Dahiya, 

[26] 
Yong PENG, An improved artificial fish swarm algorithm 
An Efficient Particle Swarm Optimization with Time 

for optimal operation of cascade reservoirs, Journal Of 
Varying Acceleration Coefficients to Solve Economic 

Computers, Vol. 6, No. 4, April 2011pp19. 
Dispatch Problem with Valve Point Loading, Energy and 

[27] 
G.Sreenivasan, Dr. C.H.Saibabu, Dr.S.Sivanagaraju, 
Power 2012, 2(4): 7480. 

PSO Based ShortTerm Hydrothermal Scheduling with 
[42] 
M.M. Salama, M.M. Elgazar, S.M. Abdelmaksoud, H.A. 

Prohibited Discharge Zones, International Journal of 
Henry, Optimal Generation Scheduling of Cascaded 

Advanced Computer Science and Applications, vol. 2, no. 
Hydrothermal System Using Genetic Algorithm and 

9, 2011,pp 97105. 
Constriction Factor Based Particle Swarm Optimization 

[28] 
Vo Ngoc Dieu, Peter Schegner, and Weerakorn Ongsakul, 
Technique, International Journal of Scientific & 

A Newly Improved Particle Swarm Optimization for 
Engineering Research, vol. 4, issue 5, may 2013,pp750 

Economic Dispatch with Valve Point Loading Effects, 

IEEE , 9781457710025/11, pp. 18 
[43] 
M.A. Abido, Multiobjective particle swarm optimization 

[29] 
Ahmed Yousuf Saber, Senior Member, IEEE and Dewan 
for environmental /economic dispatch problem, Electric 

Md Fayzur Rahman, Student Member, IEEE, Economic 
Power Systems Research 79 (2009) pp.11051113 

Load Dispatch using Particle Swarm Differential Evolution 
[44] 
P.K. Hota, A.K. Barisal , R. Chakrabarti, An improved 

Optimization, IEEE, 9781457710025/11 
PSO technique for shortterm optimal hydrothermal 

[30] 
Nand Kishore, Amita Mahor, Optimal Generation 
scheduling Electric Power Systems Research (2009)pp17 

Scheduling of cascade Hydroelectric system using Natural 
[45] 
J.P.S. Catalao,H.M.I. Pousinho,and V.M.F.Mendes, 

Exponential Inertia Weight PSO, pp110. 
Nonlinear Optimization Approach for short termhydro 

[31] 
Juan I. PÃ©rezDÃaz, JosÃ© R. Wilhelmi, JosÃ© Ãngel SÃ¡nchez 
schesuling considering head dependency, international 

FernÃ¡ndez, Shortterm operation scheduling of a 
journal on power system optimization, vol. 1 no. 1,January 

hydropower plant in the dayahead electricity market, 
june 2009, pp16. 

Electric Power Systems Research (2010) pp15351542 
[46] 
John G. Vlachogiannis and Kwang Y. Lee, Economic 

[32] 
PhanTu Vu, DinhLuong Le, NgocDieu Vo and Josef 
Load DispatchA Comparative Study on Heuristic 

Tlusty, A Novel WeightImproved Particle Swarm 
Optimization Techniques With an Improved Coordinated 

Optimization Algorithm for Optimal Power Flow and 
AggregationBased PSO, ieee transactions on power 

Economic Load Dispatch Problems, IEEE, 97814244 
systems, vol. 24, no. 2, may 2009 pp9911001. 

65477/10. 
[47] 
A.M. Moradi, A.B. Dariane, Particle Swarm 

[33] 
Amita Mahor, Saroj Rangnekar, Short term generation 
Optimization:Application to Reservoir Operation 

scheduling of cascaded hydro electric system using time 
Problems, IEEE International Advance Computing 

varying acceleration coefficients PSO, International 
Conference,9781T424418886/08 pp10481051 

Journal Of Energy And Environment vol. 1, issue 5, 2010 
[48] 
J. P. S. CatalÃ£o, S. J. P. S. Mariano, V. M. F. Mendes, and 

pp.769782. 
L. A. F. M. Ferreira, Scheduling of HeadSensitive 

[34] 
JongBae Park YunWon Jeong, JoongRin Shin, and 
Cascaded Hydro Systems: A Nonlinear Approach, IEEE 

Kwang Y. Lee, An Improved Particle Swarm 
transactions on power systems, vol. 24, no. 1, february 

Optimization for Nonconvex Economic Dispatch 
2009 pp337346 

Problems, IEEE transactions on power systems, vol. 25, 
[49] 
Wu Jiekang, Zhu Jianquan, Chen Guotong, and Zhang 

no. 1, february 2010. 
Hongliang, A Hybrid Method for Optimal Scheduling of 

[35] 
Aniruddha Bhattacharya, Pranab Kumar Chattopadhyay, 
ShortTerm Electric Power Generation of Cascaded 

A Modified Particle Swarm Optimization for Solving the 
Hydroelectric Plants Based on Particle Swarm Optimization 

NonConvex Economic Dispatch, IEEE, 97814244 
and ChanceConstrained Programming, ieee transactions 

33889/09. 
on power systems, vol. 23, no. 4, november 2008 pp1570 

[36] 
Khamsawang and S. Jiriwibhakorn, Solving the Economic 
1579. 

Dispatch Problem using Novel Particle Swarm 
[50] 
Wilasinee Sugsakarn, Parnjit Damrongkulkamjorn, 

Optimization, International Journal of Electrical, 
Economic Dispatch with Nonsmooth Cost Function using 

Computer, Energetic, Electronic and Communication 
Hybrid Method, Proceedings of ECTICON 2008, pp 

Engineering vol:3, no:3, 2009,pp529534. 
889892. 

[37] 
Z. Stojanovi, D. Vukosavi, D. Diva, N. Milivojevi, D. 

Vukovi, Hydropower Plants Cascade Modeling of 

Pichet Sriyanyong, Solving Economic Dispatch Using Particle Swarm Optimization Combined with Gaussian Mutation, Proceedings of ECTICON 2008, pp885888.

Andre Luiz Diniz, and Maria Elvira PiÃ±eiro Maceira, A FourDimensional Model of Hydro Generation for the Shortterm hydrothermal dispatch problem considering head and spillage effects, ieee transactions on power systems, vol. 23, no. 3, august 2008,pp12981308

K. T. Chaturvedi, Manjaree Pandit, and Laxmi Srivastava, SelfOrganizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch, IEEE transactions on power systems, vol. 23, no. 3, august 2008, pp10791087

Chandrasekar Samudi, Gautham P. Das, Piyush C. Ojha, Sreeni.T.S,Sushil Cherian, Hydro Thermal Scheduling using Particle Swarm Optimization, IEEE, 97814244 19043/08/ PP15 .

Hui Zhu ,Syahrulanuar Ngah ,Ying Xu ,Yuji Tanabe and Takaaki Baba, A Random Timevarying Particle Swarm Optimization for Local Positioning Systems, IJCSNS International Journal of Computer Science and Network Security, vol.8 no.6, june 2008,pp4960.

Pichet Sriyanyong, A Hybrid Particle Swarm Optimization Solution to Ramping Rate Constrained Dynamic Economic Dispatch, World Academy of Science, Engineering and Technology 23 2008, pp374379

B.K. Panigrahi, V. Ravikumar Pandi, Sanjoy Das, Adaptive particle swarm optimization approach for static and dynamic economic load dispatch, Energy Conversion and Management 49 (2008) pp14071415

J. P. S. CatalÃ£o1, S. J. P. S. Mariano, V. M. F. Mendes and
L. A. F. M. Ferreira, Nonlinear optimization method for shortterm hydro scheduling considering head dependency, 2008 John Wiley & Sons, Ltd. pp117.

Xiaohui Yuan, Liang Wang, Yanbin Yuan, Application of enhanced PSO approach to optimal scheduling of hydro system, Energy Conversion and Management 49 (2008) pp29662972.

K.K. Mandal, M. Basu, N. Chakraborty, Particle swarm optimization technique based shortterm hydrothermal scheduling , Applied Soft Computing 8 (2008) pp 1392 1399

Sai H. Ling, Herbert H. C. Iu, Kit Y. Chan and Shu K. Ki, Economic Load Dispatch: A New Hybrid Partice Swarm Optimization Approach, Manuscript received September 11, 2007.pp18.

Leandro dos Santos Coelho, ChuSheng Lee, Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches, Electrical Power and Energy Systems 30 (2008) pp 297307

Cai Jiejin, Ma Xiaoqian, Li Lixiang, Peng Haipeng, Chaotic particle swarm optimization for economic dispatch considering the generator constraints, Energy Conversion and Management 48 (2007) pp645653

Binghui Yu, Xiaohui Yuan, Jinwen Wang, Shortterm hydrothermal scheduling using particle swarm optimization method, Energy Conversion and Management 48 (2007) pp 19021908.

S. Titus and A. Ebenezer Jeyakumar, Hydrothermal scheduling using an imporoved particle swarm optimization technique considering prohibited opersting zone International journal of soft computing 2 (2); 313 3119,2007.

A. Immanuel Selvakumar, and K. Thanushkodi, A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems, IEEE transactions on power systems, vol. 22, no. 1, february 2007 pp4251

A. Immanuel Selvakumar, K. Thanushkodi , Anti predatory particle swarm optimization: Solution to nonconvex economic dispatch problems , Electric Power Systems Research 78 (2008) pp 210

T. C. Marques, M. A. Cicogna and S. Soares, Benefits of Coordination in the Operation of Hydroelectric Power Systems: Brazilian Case, IEEE , 1424404932/06/pp18.

Gabriela Glanzmann, Martin Von Siebenthal, Tobias Geyer, George Papafotiou,Manfered Morari, Supervisory water level conrol for cascaded river power plants, pp 19.

Eduardo EusÃ©bio, Cristina Camus, Victor Mendes, Short term Value for the Water Stored in Head sensitivity Power System Reservoirs, 2011 8th International Conference on the European Energy Market (EEM) 2527 May 2011,pp 275280.

Alfredo J. Mezger, Katia C. de Almeida, Short term hydrothermal scheduling with bilateral transactions via bundle method, Electrical Power and Energy Systems 29 (2007) pp 387396.

Jiang Chuanwen, Etorre Bompard, A selfadaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment, Energy Conversion and Management 46 (2005) pp 26892696.

S.He, J.Y. wen, E. Prempain, Q. H. Wu, J. Finch, S. Mann, An Improved Particle Swarm Optimization for Optimal Power Flow, International Confarence on power system technology. Powercon 2004, pp16331637

M. Basu, A simulated annealingbased goalattainment method for economic emission load dispatch of fixed head hydrothermal power systems, Electrical Power and Energy Systems 27 (2005)pp 147153.

ZweLee Gaing, Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints, IEEE transactions on power systems, vol. 18, no. 3, august 2003, pp11871195.

A. Arce, T. Ohishi, and S. Soares, Optimal Dispatch of Generating Units of the ItaipÃº Hydroelectric Plant, IEEE transactions on power systems, vol. 17, no. 1, february 2002 pp154158

R. Naresh, J. Sharma, Short term hydro scheduling using two phase neural network, Electrical power and energy systems 24 (2002)pp 583590.