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The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.

An electric power system is defined in the I.E.E. Regulation as a complex interconnection of simple electric devices (represented by active and passive elements) in which there is at least one closed path for the flow of current [

Traditionally, economic dispatch (ED) plays a significant role in order to allocate a combination of generation levels to the generating units so that the demand system could be comprehensive and most economic [

The objective of the Economic Dispatch Problems (EDPs) of electric power generation is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all the units and system equality and inequality constraints [

The economic dispatch problems have shown that they solve the problem by utilizing different types of optimization techniques. They have optimized the generator output power in a fixed range and so the basic procedures of optimization method are not scalable for variations of generator power. So the predicted generator output power will be inaccurate. The output of the optimizing techniques depends on the number of generation units. If the numbers of generation units are increased, satisfactory output may not be obtained and the fitness of the output power also gets affected. The affected output leads to power quality issues in the generated power. The power quality problem affects the real power value that leads to changes in the power factor. All these factors increase the power generating cost. Another most important parameter is emission cost. If the emission cost is high, it is considered that the system affects the environment more. In some of the optimization technique settings the initial value is difficult because the initial value has been chosen at random. So, the iterative processes become complex and the final solution requires to be approximated. In literature, though very few works have attempted to solve the economic dispatch problem by considering the generating cost and the emission cost using hybrid optimization techniques. From this, it has been observed that there exists a need for evolving simple and effective methods for obtaining an optimal solution for Economic Emission Dispatch Problem. Hence in this paper, an attempt has been made to hybrid PSO with ANN for obtaining optimum solution for the Economic Emission Dispatch Problem.

In a generation system, economic and emission dispatch problems are the two primary but different problems to be essentially considered. The economic dispatch problem targets to minimize the operating cost or total fuel cost of the system, which may violate the emission limits. The emission dispatched problem targets simply to minimize the emission from the system, which may violate the economic limits. These evaluations are used to determine the optimum combinations i.e., unit commitment of the generating units, which is subjected to minimize the total fuel cost. Therefore, it is necessary to determine the cost factors of both problems, which is analysed in the next section

Economic dispatch is the method to find the optimum output of the number of generating units, which may meet the system demand at the possible lowest cost. The main objective of the economic dispatch problem is to minimize the operating cost or fuel cost. The fuel cost of all the generating units is determined using the equation (1). The entire production cost of the system is the combination of each generating unit fuel cost. The expression of the total production cost is given as,

where,

where,

The power generation of the units must meet the total demand and losses of the system. The equality constraint is given by.

where,

With

The Price Penalty Factor consists of both the economic and emission dispatched problems, which is shown in the equation (8). Depending on this factor the rate of the penalty price is fixed. It is described as follows,

where, F is the price penalty factor, i is the highest fuel cost unit, j is the highest pollutant emission unit. In this proposed work, the combined objective function is described by

where,

where,

The Proposed Hybrid Technique is the combination of PSO and NN methods. It is used to reduce the evaluation time and the resultant is very accurate due to the tuning process. The PSO Technique is used to generate the random number of the combinations i.e., the best unit commitment for each demand, which is used to train the neural network. The neural network consists of two inputs i.e., current demand of the system and the previous demand of the system, that has the N number of outputs. The PSO technique is analyzed in the next section.

PSO is a multi-agent search technique that traces its evolution to the emergent motion of a flock of birds searching for food. It has quick convergence speed and optimal searching ability for solving large-scale optimization problems. It was developed by James Kennedy and Russel Eberhart in 1995. In a PSO system, particles fly around in a multidimensional search space. During flight, each particle adjusts its position according to its own experience, and the experience of neighbouring particles, making use of the best position encountered by itself and its neighbours [

Step 1: Initializes all the generation unit values.

Step 2: Randomly generates the combination of the generating units.

Step3: Evaluates the combination of the objective function, i.e., the combination of the economic and emission dispatch fuel costs. The generated values must satisfy the condition given below.

Step 4: Sets the iteration count

Step 5: Finds the initial velocity of each particle between the intervals

Step 6: Selects the new particle to update the velocity of each particle.

Step 7: Evaluates the new particles and select the

The process continues until the optimum combination of the generating unit is achieved. When the procedure is completed, the optimum combinations of the generating units are determined. Once the procedure is completed, the unit commitment for every demand is used for the training of the NN.

NN is an excellent technique to develop the mathematical structures with the ability to learn. It has the capacity to extract the meaning from the complicated data. It has many advantages, such as nonlinearity, mapping input signals to desired response, adaptivity and evidential response. In the proposed technique, the PSO optimum combination results are used to train the neural network, which gives optimum results in very less time. NN has two stages i.e., training and testing stage.

The training of the neural network and the weight adjustments of the neuron is achieved by using the back propagation algorithm.

Step1: Initializes the weights of all the neurons,

Step2: Training datasets are applied to the NN so as to determine the BP error.

Step3: The network output can be calculated.

Step4: Adjusts the weights of all the neurons as

Step 5: Repeats the process from step 2, until the back propagation error to a least value. Once the process gets completed, the network is ready to provide unit commitment output. The results minimize the objective function. The fine tuning process is illustrated in the following Section 3.2.

The fine tuning process is performed after the output of the NN, which is used to reorganize the generation units minimizes the objective function. Then the fitness evaluations are determined and analyses the objective function.

The Proposed Hybrid Technique is implemented in the MATLAB platform, which is a combination of the PSO Method and the NN Method. In PSO technique random number of generating units are selected depending on the demand, which finds the best combination among the given combinations. The optimum combination of the generating units are determined by the minimized objective function. Then the second step the NN is used to produce better unit commitment results for the every demand. Already the NN trained by the power demand with the corresponding unit commitment. The results are accurate and efficient in the proposed method. Here the performance of the Proposed Hybrid Technique is compared with the existing GA method. The performance of the Proposed Hybrid Method is analyzed with every demand for IEEE 30 bus system.

For every demand, the total cost of the system is identified, which is determined by both the techniques of hybrid method and GA. The results of the both hybrid method and the GA method is given in

Sl. No | Power Demand | Fuel Cost | Emission Cost | Total Cost Before Tuning | Total Cost After Tuning |
---|---|---|---|---|---|

MW | $/hr | $/hr | $/hr | $/hr | |

1 | 150 | 383.646 | 171.065 | 554.5943 | 459.7613 |

2 | 180 | 461.7312 | 198.496 | 553.7204 | 548.0276 |

3 | 200 | 429.7505 | 190.1615 | 386.5604 | 377.6988 |

4 | 230 | 632.8212 | 315.6549 | 703.6968 | 703.5854 |

5 | 250 | 714.6709 | 349.5866 | 744.2272 | 685.5941 |

6 | 260 | 723.8457 | 371.5646 | 761.4799 | 736.6453 |

7 | 300 | 893.6805 | 514.8531 | 865.5629 | 851.3701 |

8 | 340 | 939.4938 | 496.2872 | 939.5361 | 909.1563 |

9 | 350 | 1148.3271 | 701.2115 | 1078.706 | 1051.790 |

10 | 400 | 1057.8796 | 671.5002 | 1078.8 | 1077.110 |

SI.No | Power demand | Total cost using GA method | Total cost using hybrid method |
---|---|---|---|

MW | $/hr | $/hr | |

1 | 150 | 513.9195 | 459.7613 |

2 | 180 | 801.8116 | 548.0276 |

3 | 200 | 771.6955 | 377.6988 |

4 | 230 | 711.1035 | 703.5854 |

5 | 250 | 755.7312 | 685.5941 |

6 | 260 | 801.1265 | 736.6453 |

7 | 300 | 858.1526 | 851.3701 |

8 | 340 | 981.0335 | 909.1563 |

9 | 350 | 1298.1529 | 1051.7908 |

10 | 400 | 1132.032 | 1077.1109 |

Method | Total Cost $/hr |
---|---|

Classical Technique [ | 1247.5 |

Quadratic Programming [ | 1252.9 |

Evolutionary Programming (EPCEED) [ | 1246.7 |

Genetic Algorithm | 1132.032 |

Proposed PSO ANN Hybrid Approach | 1077.1109 |

follows 513.9195, 801.8116, 771.6955, 711.1035, 755.7312, 801.1265, 858.1526, 981.0335, 1298.1529 and 1132.032 (all in $/hr) respectively. The comparison between the two methods proven that the Proposed Hybrid Method is efficient method as it contains minimized fitness function and it reduces the total cost of the power system.

In this paper we have proposed the hybrid method for optimizing the Economic and Emission dispatch problem in the power system. The proposed hybrid method is the combination of the PSO and NN methods. The problem has been treated as a multi-objective model and the hybrid technique intends to solve the problem. PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. The condition for choosing random generating power value is to satisfy the load demand of the distribution system. By using PSO algorithm, the generating power optimized for the given is load demand and at generating cost. The proposed method effectiveness is tested by comparing it with the existing techniques. The comparison results prove the superiority of the proposed hybrid PSO-NN method.

R. Leena Rose,B. Dora Arul Selvi,R. Lal Raja Singh, (2016) Development of Hybrid Algorithm Based on PSO and NN to Solve Economic Emission Dispatch Problem. Circuits and Systems,07,2323-2331. doi: 10.4236/cs.2016.79202