Unit Commitment Using Meta - Heuristic Search Algorithm - IJIR

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in

Unit Commitment Using Meta - Heuristic Search Algorithm 1

Sundaram A, 2Ashnafi Paulo’s Forsido & 3 Kibru Mesfin Germamo, 1,2,3

Wolkite University, Department of Electrical and Computer Engineering, Wolkite, Ethiopia.

Abstract -The main aim of this paper is to obtain the best optimal solution to solve the Unit Commitment problem with minimal requirement of fuel cost. To achieve the optimal solution for a unit commitment problem, the proposed method is engrossed with a Meta - Heuristic search algorithm called BAT algorithm. BAT has a flexible and well-balanced mechanism to enhance exploration and exploitation abilities. So far the BAT algorithm has not been applied to solve any other power system optimization problem. In this proposed method BAT algorithm has been implemented successfully to solve the Unit Commitment problem in the power system. Also the best optimal solution is found by taking several constraints into account. To observe the performance of the proposed system, the fuel cost is also compared with rest of the existing algorithm and found to be the best. The iteration time is also reduced with accurate results. Keywords: Unit commitment, Meta - Heuristic Search Algorithm, Swarm Optimization, Ant Colony Algorithm 1. Introduction In an interconnected grid system the important operation is the optimal power flow (OPF) with respect to transmission loss and other operational constraints of the power system. The new emerging industries use latest technology equipment’s with new methods of operation and control strategies there by increasing the power demand over the estimated real time demand. Thus creating the Stress on the power system and disturb the normal flow of power which leads to power congestion in the system. The problem of power congestion has to be solved to ensure smooth and quality flow of power. This can be achieved by rescheduling the generation level of generators to run at minimum cost and with minimum loss, subjected to load balance. It was observed that the performance of many conventional optimization techniques were not satisfied to solve this problem because of certain drawbacks such as long time consumption for computation of new generation level, nonlinear,

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discontinuous, non-differentiable characteristics of objective function and inferior quality of solutions. Soft Computing Techniques were developed to over comes this problem. It is under stood that the electric power system works in cycle. The power demand during the day is higher and late evening and early morning is lower. This 24 hours cyclical demand forecasted for power generation on hourly basis. The Unit commitment method is used to schedule the generating units to commit for power generation according to the load demand with economic operation including all constrains with on and off state to save a considerable amount of fuel cost. Bat algorithm generates a sequence of parameter to be tested using the system under consideration, constraints and the objective function to which it should be maximized or minimized. A novel Meta-heuristic search algorithm, called BAT algorithm motivated by micro-bats has been introduced very recently (Yang 2010). It is characterized as a simple concept that is easy to implement. BAT has a flexible and well-balanced mechanism to enhance exploration and exploitation abilities. In this paper, BAT algorithm is used which is more effective and capable of solving nonlinear optimization problems faster and with better accuracy in detecting the global best solution. To investigate the efficiency of the proposed method sample cases with and without ramp rate constraints are considered. It is observed that the total generation cost can be remarkably reduced while considering various constraints reflecting the practical system. 2. Optimization Techniques 2. 1. Particle Swarm Optimization This is a population-based evolutionary technique that has many advantages over other optimization techniques. Swarm optimization is a derivative-free algorithm unlike many conventional techniques. It has the flexibility to be integrated with other optimization techniques to form hybrid tools. It is less sensitive to the nature of the objective function, i.e., convexity or continuity. It Page 5

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in has less parameters to adjust unlike many other competing evolutionary techniques. It has the ability to escape local minima. It is easy to implement and program with basic mathematical and logic operations. It can handle objective functions with stochastic nature, like in the case of representing one of the optimization variables as random. It does not require a good initial solution to start its iteration process. However, such competing techniques tend to have major drawbacks such as the following: More parameter tuning is required, require extensive computational time, heavily involved programming skills are required to develop and modify competing algorithm to suit different classes of optimization problems. Also it require binary conversion instead of working with direct real valued variables.

2. 2. Ant Colony Algorithm The ant colony optimization algorithm (ACO) is a probabilistic technique is graphical method for solving computational problems. This algorithm is a member of ant colony algorithms family which was initially proposed by Marco Dorigo in 1992. The first algorithm was aiming to search for an optimal path in a graph, based on the behaviour of ants seeking a path between their colony and a source of food [7-8]. As a result, several problems have emerged considering various aspects of the behaviour of ants and on observing the exploitation of food resources among ants, to find the shortest path between a food source and the nest.

Fig. 1. The above sketch shows how real ants find a shortest path. The first ant finds the food source (F), via any way (a), then returns to the nest (N), leaving behind a trail pheromone (b) as shown in Fig. 1(a). Then the ants indiscriminately follow four possible ways as shown in Fig. 1(b), but the strengthening of the runway makes it more attractive as the shortest route. Finally ants take the shortest route, long portions of other ways lose their trail pheromone as shown in Fig. 1(c). The use of this ant algorithm in unit commitment have two main drawbacks such as the size of the system increases the available solution decreases and only ON state of the system is considered for evaluation. 2. 3. Genetic Algorithm Genetic algorithms (GAs) are parallel and global search techniques that offer a new and powerful approach to the optimization problems with high performance computers at relatively low


costs. This replaces the closed-form optimization technique and has recently found extensive applications in solving global optimization searching problems and capable of evaluating many parameter simultaneously. Optimal Power Flow (GAOPF) problem is solved based on the use of a genetic algorithm load flow, and it propose the use of gradient information by the use of the steepest decent method. The method is not sensitive at the starting points and not capable of determining the global optimum solution to the OPF for the different range of constraints and objective functions. But GAOPF needs two load flow to be performed per individual, per iteration because all the controllable variables are included in the process. The main drawbacks of this algorithm are no assurance for finding optimum results and the application is limited because of random solutions. Page 6

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in broadly analysed in power system operation and planning. Many techniques like linear programming (LP), nonlinear programming (NLP), and quadratic programming (QP) have been applied for solving the OPF problem. For implementation of these methods, the problem has to be simplified to ensure the convexity. The classical optimization methods are highly sensitive to the starting points and frequently converge to local optimum solution or diverge altogether. Met heuristic search techniques such as genetic algorithm (GA), gradient projection method, and evolutionary programming (EP) have been implemented for solving the OPF problem. Hence, it is important to develop new and more general and reliable algorithms for dealing with the nonlinear OPF problem. The use of GA has identified some deficiencies in the performance. Recently, a new efficient technique called artificial bee colony (ABC), has been proposed and introduced by Karaboga. The ABC algorithm is developed based on the behaviours of real bees that share the real information of food sources to the bees in their hive. The main advantages of the ABC algorithm over other optimization methods for solving optimization are simplicity, high flexibility, strong robustness, few control parameters, ease of combination with other methods, ability to handle the objective with stochastic nature, fast convergence, and both exploration and exploitation. The fuel cost of BAT algorithm is compared with other algorithms and found to be the efficient one, with lower fuel cost value.

2. 4. Firefly Algorithm This is inspired by the flashing behaviour of fireflies and is a met heuristic algorithm. The main purpose of a firefly's flash is to act as a signal system to attract other fireflies. Xin-She Yang formulated this firefly algorithm by assuming all fireflies are unisexual, so that one firefly will be attracted to all other fireflies, attractiveness is proportional to their brightness, and for any two fireflies, the less bright one will be attracted by (and thus move to) the brighter one; however, the brightness can decrease as their distance increases and if there are no fireflies brighter than a given firefly, it will move randomly. The brightness should be associated with the objective function. It has many applications but the computational time required to solve the problem is more. 2. 5. Artificial Bee Colony The ABC algorithm is more suitable for solving both continuous and discrete control variables associated with OPF problem. The continuous controllable system quantities are the generators that are used for controlling the voltage and magnitude. The discrete control variables are controlled using switchable shunt devices and transformer tap settings. The main objective is to minimize the fuel cost of thermal generators by optimizing the control variables within their limits so that they cause no violation on other quantities (e.g., transmission-circuit loading, load bus voltage magnitude, and generator MVAR) occurring in either the normal or outage case of system operating conditions. The OPF problem has been

Table. 1. Algorithms and their fuel costs GA PSO IPSO FA ABC 565275 565302 565103 564772 564285

Algorithms Fuel Cost

BAT 564274

3. Proposed System The Start-up time, shut-down time and power output of the power generating units at each hour in the scheduled time can be determined using the unit commitment problem. It is also used to minimise the total production costs of the system and within the constraints.

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� ; �� where FCi is the fuel cost of the ith unit which is taken as quadratic function; ng is the number of generating units; this the total number of hours; � , � , � are the fuel cost coefficients of the ith unit; Pgi is the power output of the ith generating unit at the tth hour; SUCi is the start-up cost of the ith unit; HSCi, CSCi are the hot start-up cost and cold start-up cost of the ith unit; Toffi is the continuous off time duration of the ith unit; Tdowni is

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� + � the minimum down time of the ith unit; Tcoldi is the cold start hours of the ith unit; Ui,t is the status of the ith generating unit at the tth hour; and SDCi is the shutdown cost of the ith generating unit.

3. 1. Constraints The UC problem is subject to equality and inequality constraints depending on the nature of the power system under operation. The equality Page 7

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in constraints is the load balancing constraints and the inequality constraints include the unit capacity constraints, the spinning reserve constraints, UP/DOWN time constraints and ramping constraints. However, the ramping constraints are not considered in the simulation results of this article. The mathematical formulation of the above mentioned constraints are described below. Equality constraint: For, each tth hour the sum of the output powers of the committed generators is equal to the forecasted power demand and is given by∑�= � �,� �,� = �� , where PDt represents the power demand at tth hour. Generatingunit’s constraint: Each committed unit must operate within its operating limits as shown below �� ,� �� , where �� , �� are the minimum and maximum operating limits of the ith generating unit. Spinning reserve constraints: ∑�= �� ,� �� + � , where SRt is the maximum reserve at the tth hour and PDt is the power demand at the tth hour. Minimum up time constraint: The unit once started up should not be shut-down before a minimum uptime period is met and it is mathematically expressed for ith generating unit as � �, � Where is the ON time duration of the ith � generating unit and � � is the minimum up time of the generating unit. Minimum down time constraint: The unit once started to shut down should not be shut down before a minimum down-time period is met and it is mathematically expressed for ith generating unit as , Where �� is the off time � duration of the ith generating unit and � � is the minimum down time of the ith generating unit. 3.2. BAT Algorithm BAT algorithms are the only mammals with wings and they also have a habit of echolocation. Among all the species, micro bats are famous example as micro bats use echolocation extensively to a certain degree, while mega bats do not. In the dark Micro bats use a type of sonar, called echolocation, to detect prey, avoid obstacles, and locate their roosting crevices. By identifying some of the echolocation characteristics of micro bats, we can develop various bat-inspired algorithms or bat algorithms. For simplicity, we now use the following approximate or idealized rules. All bats use echolocation to sense distance, and they also know the difference between food/prey and background barriers. Bats fly randomly with velocity vi at position xi with a fixed frequency fmin (or wavelength λ), varying wavelength λ (or frequency f) and loudness A0 to search for prey.


They can automatically adjust the wavelength (or frequency) of their emitted pulses and adjust the rate of pulse emission r € [0, 1], depending on the proximity of their targets. Although the loudness can vary in many ways, we assume that the loudness varies from a large (positive) A0 to a minimum value Amin. Another obvious simplification is that no ray tracing is used in estimating the time delay and three dimensional topography. In addition to these simplified assumptions, we also use the following approximations, for simplicity. In general the frequency f in a range [fmin, fmax] corresponds to a range of wavelengths [λmin, λmax]. For example, a frequency range of [20 kHz, 500 kHz] corresponds to a range of wavelengths from 0.7 mm to 17 mm. In simulations, we use virtual bats naturally. We have to define the rules how their positions xi and velocities vi in a d-dimensional search space are updated. Furthermore, as the iterations proceed, the loudness Ai and the rate ri of pulse emission have to be updated. Once the pulse emission decrease, the loudness decreases, to make sure that the bat has found its prey, while the rate of pulse emission increases, the loudness can be chosen as any value of convenience. Usually, A0 = 100 and Amin = 1. For simplicity, we can also use A0 = 1 and Amin = 0, assuming Amin = A0 means that a bat has just found the prey and temporarily stop emitting any sound.

Fig. 2. BAT Algorithm Page 8

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in 4. Simulation Results BAT algorithm is applicable in few areas of power system and is comparatively a new optimization algorithm. In this paper the BAT algorithm has been implemented to solve UC problems considering a population size of 50 and the maximum number of generation (iterations) of

(a) Fuel cost vs. time in hours

1000 are taken. Software is developed in MATLAB 11 to solve UC problems and tested on a core i7 processor of 2.20 GHz with 8 GB RAM personal computer. The proposed method is employed to solve UC problem without considering shut down cost. The algorithm is tested without ramp rate constraints.

(b) Generating units vs. power in MW

Fig. 3. Simulation results of a proposed system 4. Conclusion This paper presents a BAT Algorithm based approach to developed, implemented and successfully solve various types of UC problems. From the simulation results it is observed that the fitness value obtained are better than the earlier


best reported results by the proposed BA method after satisfying all the constraints of all the systems. It is also found that the proposed method has superior features, including stable convergence characteristic and avoids premature convergence. The statistical results are obtained for all the test Page 9

Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in systems. It is observed that proposed approach appears to be a robust and reliable optimization algorithm for solving small and large-scale UC problems. The computational analysis shows that the proposed method is capable of reaching the optimum solution in less computational time than the time that is required for other algorithms. Therefore, it may finally be concluded that the proposed bat algorithm has great potential to generate better optional solution in less computational time than several other solution approaches presented in the literature. References Sisworahardjo N.S, El-Keib A.A. (2002). Unit commitment using the ant colony search algorithm “Proc. of Int. Conf. Power Engineering and Large Engineering Systems”, pp. 2-6. Mori H, Matsuzaki O. (1999). A parallel tabu search approach to unit commitment in power systems “Proc. of Int. Conf. Systems, Man, and Cybernetics”, Tokyo, pp. 509-514. Mori H. (2002). Applications of meta-heuristics to power systems in Japan “Proc. of Int. Conf. Transmission and Distribution”, Japan, pp. 661-663. Gwo-Ching Liao, Ta-Peng Tsao. (2004). A novel GA-based and meta-heuristics method for short-term unit commitment problem “Proc. of IEEE-Int. Conf. Power Engineering Society General Meeting”, Denver, pp. 1088 - 1093. El-Sharkh M.Y, Sisworahardjo N.S, Rahman A, Alam M.S. (2006). An Improved Ant Colony Search Algorithm for Unit Commitment Application “Proc. of IEEE-Int. Conf. Power Systems Conference and Exposition”, Atlanta, GA, pp. 1741 - 1746. Bang Ban Ha, Nghia Nguyen Duc. (2013). A metaheuristic algorithm combining between Tabu and Variable Neighborhood Search for the Minimum Latency Problem “Proc. of IEEEInt. Conf. Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF)”, Hanoi, pp. 192 - 197. Mittal S, Maskara S.L. (2014). A novel Bayesian Belief Network structure learning algorithm based on bio-inspired monkey search meta heuristic “Proc. of IEEE-7th Int. Conf. Contemporary Computing (IC3)”, Noida, pp. 141 - 147. Abu-Mouti F.S, El-Hawary M.E. (2011). Novel constrained search-tactic for optimal dynamic economic dispatch using modern metaheuristic optimization algorithms “Proc. of IEEE-Int. Conf. Electrical Power and Energy Conference (EPEC)”, Winnipeg, pp. 170-175.


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