Minimization of Nitrogen Oxides from Fossil Fuel Power Plants using Swarm Intelligence Technique

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Minimization of Nitrogen Oxides from Fossil Fuel Power Plants using Swarm Intelligence Technique

R. Anandhakumar

Assistant Professor,

Department of Electrical and Electronics Engineering, Government College of Engineering, Sengipatti, Thanjavur, Tamil Nadu, India.

Abstract:- The pollutant from the fossil fuel plant threatening the entire world and ensure that the amount of emission such as sulfur dioxide (SO2) and nitrogen oxides (NOx) must be reduced. Hence it is necessary that the emission constraint must include in the economic dispatch problem and its objective is to minimize production cost with lowest emission. In this paper swarm intelligence technique has been proposed to solve an emission constrained economic dispatch problem. The performance of the proposed algorithm is tested on six unit test systems with various load demand and emission coefficients. The comparison of the simulation results prove that the proposed algorithm have a better performance than

in handling large and complex search spaces. In this paper, an ABC algorithm is proposed to determine the optimal solution for environmental economic dispatch problem.

2. PROBLEM FORMULATION

The reduction emission from fossil fuel fired power plants is essential for power industries due to clean Air Act Amendments of 1990 and the problem can be formulated as

The total emission of generation Ei can be

E P 2 P

existing algorithms.

Keywords: Sulfur Minimization, Nitrogen Minimization,

i i i

(2.1)

i i i

Emission Dispatch, Environmental dispatch, Swarm Intelligence.

  1. INTRODUCTION

Due to the strict environment act the power industries must reduce the emissions from the fossil fuel power plants. The pollutant from the fossil fuel plant threatening the entire world and ensure that the amount of emission such as sulfur dioxide (SO2) and nitrogen oxides (NOx) must be reduced. Hence it is necessary that the emission constraint must combine with economic dispatch problem and its objective is to minimize production cost with lowest emission [1-4]. The mathematical approaches like Interactive Search (IS) approach, Newton Raphson (NR) method, Non Linear Programming (NLP), and Quadratic Programming (QP) have been applied to solve economic emission dispatch [5-9]. The classical methods may have difficulties in finding an optimal solution due to the longest execution time and presence of non linear & discontinuity in the problem. The variety of artificial intelligence techniques and their hybrid versions has been applied to solve environmental emission dispatch problems [10-18]. Based on the shallow water theory named water evaporation optimization algorithm [19] have been applied to solve environmental economic dispatch problems.

Recently, inspired by the foraging behavior of honeybees, researchers have developed Artificial Bee Colony (ABC) algorithm for solving various optimization problems [20]. ABC is a relatively new population-based bio-inspired approach with the desirable characteristics such as robust and easy to implement. Further, ABC does not use any gradient based information and it incorporates a flexible and well balanced mechanism to adapt to the global and local exploration abilities within a short computation time. This makes the algorithm efficient

Ei is the function of emissions in (Kg/h) and i, i and i are the co-efficient of emission characteristics specific to each production unit.

  1. SWARM INTELLIGENCE TECHNIQUE

    The foraging bees are classified into three categories; employed bees, onlookers and scout bees. All bees that are currently exploiting a food source are known as employed. The employed bees exploit the food source and they carry the information about food source back to the hive and share this information with onlooker bees. Onlookers bees are waiting in the hive for the information to be shared by the employed bees about their discovered food sources and scouts bees will always be searching for new food sources near the hive. Employed bees share information about food sources by dancing in the designated dance area inside the hive. The nature of dance is proportional to the nectar content of food source just exploited by the dancing bee. Onlooker bees watch the dance and choose a food source according to the probability proportional to the quality of that food source. Therefore, good food sources attract more onlooker bees compared to bad ones. Whenever a food source is exploited fully, all the employed bees associated with it abandon the food source, and become scout. Scout bees can be visualized as performing the job of exploration, whereas employed and onlooker bees can be visualized as performing the job of exploitation.

    In the SI algorithm, each food source is a possible solution for the problem under consideration and the nectar amount of a food source represents the quality of the solution represented by the fitness value. The number of food sources is same as the number of employed bees and there is exactly one employed bee for every food source. This algorithm starts by associating all employed bees with

    randomly generated food sources (solution). In each iteration, every employed bee determines a food source in the neighbor- hood of its current food source and evaluates its nectar amount (fitness). The ith food source position is represented as Xi where i=1, 2, , N is a D-dimensional vector. The nectar amount of the food source located at Xi is calculated by using the Eq. (7). After watching the dancing of employed bees, an onlooker bee goes to the region of food source at Xi by the probability pi defined in Eq. (8).

  2. SWARM INTELLIGENCE TECHNIQUE FOR ENVIRONMENT ECONOMIC DISPATCH

    The proposed algorithm for solving EED problem is summarized as follows.

    Step 1: Read the system data.

    Step 2: Initialize the control parameters of the algorithm. Step 3: An initial population of N solution is generated for each solution Xi (i=1, 2 N) is represented by a D- dimensional vector.

    Step 4: Evaluate the fitness value of each individual in the

    fiti

    pi

    1

    1 fi

    fiti

    N

    (7)

    colony.

    Step 5: Produce neighbor solutions for the employed bees and evaluate them.

    Step 6: Apply the selection process.

    Step 7: If all onlooker bees are distributed, go to step 10. Otherwise, go to the next step.

    fitn

    n1

    (8)

    Step 8: Calculate the probability values pi for the solutions

    Xi.

    Step 9: Produce neighbor solutions for the selected

    The onlooker finds a neighborhood food source in the vicinity of Xi by using the Eq. (9)

    onlooker bee, depending on the pi value and evaluate them.

    vij

    xij

    ij

    (xij

    • xkj )

    (9)

    Step 10: Determine the abandoned solution for the scout

    bees, if it exists and replace it with a completely new randomly generated solution and evaluate

    Where k 1,2,……Nand j 1,2,……D

    are randomly chosen indexes. Although k is determined

    them.

    Step 11: Memorize the best solution attained so far.

    Step 12: Stop the process if the termination criteria is

    randomly, it has to be different from i.

    ij

    is a random

    satisfied. Otherwise, go to step 3.

    number between [-1, 1]. If its new fitness value is better than the best fitness value achieved so far, then the bee moves to this new food source abandoning the old one, otherwise it remains in its old food source. When all eployed bees have finished this process, they share the fitness information with the onlookers, each of which selects a food source according to probability given in Eq. (8). With this scheme, good food sources will get more onlookers than the bad ones. Each bee will search for better food source around neighborhood patch for a certain number of cycles (limit), and if the fitness value will not improve then that bee becomes scout bee.

    It is clear from the above explanation that there are three control parameters used in the basic SI: The number of the food sources which is equal to the number of employed or onlooker bees (N), the value of limit and the maximum cycle number (MCN). Parameter-tuning, in meta-heuristic optimization algorithms influences the performance of the algorithm significantly. Divergence, becoming trapped in local extrema and time-consumption are such consequences of setting the parameters improperly. The SI, algorithm, as an advantage has few controlled parameters. Since initializing a population randomly with a feasible region is sometimes cumbersome, the SI algorithm does not depend on the initial population to be in a feasible region. Instead, its performance directs the population to the feasible region sufficiently

  3. SIMULATION RESULTS AND DISCUSSION Software package implementing the new proposed

    technique is developed using Intel(R) Core(TM)2 Duo CPU, 2.10 GHz processor. To illustrate the validity and effectiveness of the proposed technique, the 6 generating units test system given in [19] is studied and solved. The control parameters of SI algorithm are chosen as colony size 100, maximum cycle/generation number (MCN) 100, and limit value 30.

    In order to show the effectiveness of the proposed ABC algorithm it has been tested on six generating unit system for the load demand of 700 MW, 800 MW, 900 MW, 1000 MW. The system particulars are available in the literature [19]. The simulation results obtained by the proposed as well as existing algorithms are presented in Table 5.1 & 5.2.The results shows that the proposed ABC algorithm achieves the minimized emission of NOx for all load demands then existing algorithms. For the load demand of 700MW the proposed algorithm reaches the minimized emission value of 434.09 Kg/h, for 800MW the emission value is 548.54 Kg/h, for the load demand of 900MW it attain the value of 682.45 Kg/h and for the final load demand of 1000MW it obtain the better value of

      1. Kg/h.

        In all cases the proposed ABC algorithm achieves the competitive results with fully satisfies the system and problem constraints. The total production cost obtained by the proposed algorithm is also compared with existing techniques is also presented in Table 5.1. The comparison also shows that feasibility of the proposed algorithm reach better results in terms of least production cost. The

        proposed algorithm have capability of online implementation for reduction of emission and production cost. From the comparison it is clear that ABC algorithm outperforms the existing algorithms.

        Table 5.1 Optimal dispatches of proposed ABC and existing algorithms

        Power Demand MW

        Techniques

        P1 (MW)

        P2 (MW)

        P3 (MW)

        P4 (MW)

        P5(MW)

        P6 (MW)

        Pl (MW)

        700

        FA

        80.1523

        82.4019

        113.9655

        113.4758

        163.4493

        163.0944

        16.53

        BA

        80.1431

        82.4033

        113.9684

        113.4763

        163.4530

        163.0950

        16.53

        HYB

        80.1506

        82.4054

        113.9570

        113.4851

        163.4436

        163.0975

        16.53

        WEO[19]

        80.1439

        82.4043

        113.9657

        113.4772

        163.4471

        163.0951

        16.53

        ABC

        80.1326

        82.2178

        113.8765

        114.2367

        164.2232

        163.06537

        17.75

        800

        FA

        100.5399

        103.7475

        127.0118

        126.3499

        182.1959

        181.7376

        21.58

        BA

        100.5295

        103.7579

        127.0076

        126.3466

        182.2088

        181.7321

        21.58

        HYB

        100.5207

        103.7662

        127.0024

        126.3547

        182.1999

        181.7385

        21.58

        WEO[19]

        100.5211

        103.7511

        127.0032

        126.3518

        182.2081

        181.7382

        21.57

        ABC

        100.3456

        103.4321

        127.0023

        127.2458

        183.1076

        181.6789

        22.81

        900

        FA

        120.9389

        125.3301

        140.1958

        139.3394

        201.0812

        200.4822

        27.36

        BA

        120.9330

        125.3313

        140.1994

        139.3392

        201.0855

        200.4791

        27.36

        HYB

        120.9357

        125.3202

        140.1992

        139.3479

        201.0706

        200.4940

        27.36

        WEO[19]

        120.9362

        125.3211

        140.1993

        139.3393

        201.0808

        200.4812

        27.36

        ABC

        120.7653

        125.2455

        141.1876

        139.2212

        202.0704

        200.3271

        28.81

        1000

        FA

        125.0000

        150.0000

        156.2191

        155.2644

        224.0618

        223.1839

        33.73

        BA

        125.0000

        150.0000

        156.2704

        155.1559

        224.0577

        223.2458

        33.73

        HYB

        125.0000

        150.0000

        156.0719

        155.2412

        224.2263

        223.1934

        33.73

        WEO[19]

        125.0000

        150.0000

        156.0792

        155.2183

        224.2173

        223.2163

        33.73

        ABC

        125.0000

        150.000

        157.05435

        155.1789

        224.1234

        224.1221

        35.47

        Table 5.1 Comparison Results of Cost & Emission

        Power Demand

        Techniques

        Cost ($/hr)

        Emission (Kg/h)

        700 MW

        FA

        38101.09

        434.13

        BA

        38100.95

        434.13

        HYB

        38101.13

        434.13

        WEO [19]

        38100.72

        434.12

        ABC

        38100.65

        434.09

        800 MW

        FA

        43719.20

        548.70

        BA

        43719.15

        548.70

        HYB

        43719.14

        548.70

        WEO [19]

        43718.39

        548.69

        ABC

        43718.21

        548.54

        900 MW

        FA

        49650.29

        682.62

        BA

        49650.14

        682.62

        HYB

        49649.97

        682.62

        WEO [19]

        49649.53

        682.61

        ABC

        49649.34

        682.45

        1000 MW

        FA

        55456.64

        837.77

        BA

        55456.49

        837.77

        HYB

        55456.24

        837.77

        WEO [19]

        55456.12

        837.76

        ABC

        55456.08

        837.45

  4. CONCLUSION

The emission constrained economic load dispatch (ECELD) problem is a sub problem of an optimal power dispatch. In this paper emission constrained economic load dispatch problem is solved by using swarm intelligence technique named artificial bee colony algorithm. The simulation result of the proposed algorithm is compared with existing techniques. From the comparison it is clear that the proposed algorithm obtain the better results than existing algorithms for the load demands of 700MW, 800MW, 900MW, 1000MW. In all cases the proposed

algorithm clearly satisfies the system and problem constraints. The simulation results shows that the proposed algorithm have the ability to online implementation.

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