Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimizati

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimization 1Dr.

K. Gnanambal 2A. Marimuthu 3K. Jeyanthi 1 Professor 2Associate Professor 3P.G Scholar 1,2,3 Department of Electrical and Electronics Engineering 1,2,3 K.L.N. College of Engineering, Pottapalayam, Sivagangai 630612, India Abstract This paper presents a practical approach to implement the economic power dispatch of the power system. The proposed economic dispatch method consists of two stages. The first stage involves the economic power dispatch with considering network loss using quasi-oppositional Based particle swarm optimization technique. The second stage involves economic dispatch considering network loss and security constraints, where two objectives are proposed for the second stage. One is loss minimization, and another is the minimum movement of generator output from the initial generation plan. For showing the effectiveness of the proposed two stage economic dispatch approach, the six unit system is used for testing. The test results show the two stage dispatch method can not only reduce the system losses and system fuel consumption.

Keyword- Power systems, economic dispatch, Loss minimization, Losses, smart grid, Quasi oppositional based particle swarm Optimization __________________________________________________________________________________________________

I. INTRODUCTION Security, economy and reliability are always the major aim of power systems operation, although the electric power industry is undergoing massive changes around the world. Despite the changes with different structures, market rules, and uncertainties, a power system dispatch control center must always be in place to maintain the security, economy, reliability, and quality of electric service [1]. A number of methods have been proposed to solve secure and economic operation of power systems such as optimal power flow (OPF) or simplified OPF (i.e. economic power dispatch) [1, 2].

This paper presents a two-stage economic dispatch approach according to the operation situation of power systems. The first stage involves the economic power dispatch with considering network loss using quasi-oppositional based particle swarm optimization [3]. The second stage involves economic dispatch considering network loss [2]. Since network losses are generally high in most of power systems.

The loss reduction is a major concern in power system economic operation. Therefore, two objectives are proposed for the second stage. One is loss minimization, and another is the minimum movement of generator output from the initial generation plan.

The paper presents the implementation details of the two stage economic power dispatch approach. Section 2 describes the problem of adding distributed generation (PV power generation &battery storage) operation during seven period time interval. Section 3 describes the calculation of the problem of economic dispatch considering network losses using quasioppositional based particle swarm optimization technique. Section 4 evaluates the saving of system total fuel including the portion of loss reduction in stage two operation. Section 5 analyzes the simulation results of two stage economic power dispatch approach using quasi-oppositional based particle swarm optimization technique.

II. DISTRIBUTED GENERATION Generally, distributed generation is connected to grid through the distribution system. This is called a grid connected distributed generation system, which can make the whole grid more secure because there’s less reliance on any particular source of power in the system. With several smaller distributed generation sources, if something goes wrong, it’s easier for another source of power to step in and fill the gap. This is essential for many renewable technologies like solar and wind, which produce intermittent power and for other technologies that may need to be shut down for periodic maintenance. Distributed generation encompasses a wide range of technologies including solar power, wind turbines, fuel cells, micro turbines, reciprocating engines, load reduction technologies, and battery storage systems. This paper only focuses on wind power generation and the energy storage.

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Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimization (GRDJE / CONFERENCE / ICIET - 2016 / 036)

A. Smart Grid Economic Dispatch with Single PV Generation & Battery Storage The economic dispatch (ED) problem is one of the fundamental problems in the power system. The objective of ED is to reduce the total power generation cost, subject to system security constraints. Owing to the addition of uncertain wind power and chargeable and dischargeable storage in the smart grid, economic dispatch problem in the smart grid environment is more complicated. This section describes a simple smart grid economic dispatch (SGED) approach with considering the distributed generation and battery storage. The simplest SGED problem is a single generator single load with one battery storage device [10]. As we mentioned before, generator cost function is quadratic and can be simply expressed as follows.

B. Cost Function of Generator The optimum load dispatch problem involves the solution of two different problems. The first of these is the unit commitment or pre dispatch problem wherein it is required to select optimally out of the available generating sources to meet the expected load and provide a specified margin of operating reserve over a specified period time .The second aspect of economic dispatch is the on-line economic dispatch wherein it is required to distribute the load among the generating units paralleled with the system in such manner so as to minimize the total cost of operation.

(1) C. Cost Function of Battery Storage Distributed energy management technologies include energy storage devices and various methods for reducing overall electrical load. Energy storage technologies are essential for meeting the levels of power quality and reliability required by high-tech industries. Energy storage is important for other distributed energy devices by giving them more load-following capability, and also supporting renewable technologies such as wind and solar electricity by making them dispatch able. In the smart grid, reducing electrical load can be accomplished by improving the efficiency of end-use equipment and devices, or by switching an electrical load to an alternative energy source—heating water or building interiors with heat from the earth or sun

(2)

III. SMART GRID ECONOMIC DISPATCH-STAGE ONE Economic dispatch is the short-term determination of the optimal output of a number of electricity generation facilities, to meet the system load, at the lowest possible cost, subject to transmission and operational constraints. The Economic Dispatch Problem is solved by specialized computer software which should honor the operational and system constraints of the available resources and corresponding transmission capabilities. A smart grid is an electrical grid which includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources, and energy efficiency resources.

Given the input-output characteristics of NG generating units are đ??šđ??ş1 (đ?‘ƒđ??ş1 ), đ??šđ??ş2 (đ?‘ƒđ??ş2 ), ‌ , đ??šđ??şđ?‘› (đ?‘ƒđ??şđ?‘› ) respectively. There are NR renewable resources such as solar power (đ?‘ƒ1 , đ?‘ƒ2 , ‌ , đ?‘ƒđ?‘› ), NE storage devices (đ?‘ƒđ??¸1 , đ?‘ƒđ??¸2 , ‌ , đ?‘ƒđ??¸đ?‘› ), and ND loads (đ?‘ƒđ??ˇ1 , đ?‘ƒđ??ˇ2 , ‌ . , đ?‘ƒđ??ˇđ?‘› ).

The NE and ND are fixed during this stage. Especially, the solar power generation is estimated since it has a big forecasted error at this stage. The problem is to minimize the total operation cost subject to the components and network security constraints for a time period (for example, 24 hours). That is

(3)

(4)

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Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimization (GRDJE / CONFERENCE / ICIET - 2016 / 036)

where

The first term in equation (3) corresponds to the overall costs of electricity, and the generation cost function is generally quadratic. The second term in equation (3) represents penalties for all storage devices when they are passing their optimal operation limits.

IV. SMART GRID ECONOMIC DISPATCH-STAGE TWO The second stage of economic power dispatch includes loss correction and security constraints. One Hand, the system power losses are major concern of the economic power dispatch in the power systems. On the other hand, the operators expect the optimal dispatch points close to the economic operation points of first stage. Thus, the following two objectives are adopted in the second stage of economic dispatch.

A. Minimization the Adjustment of Generator Output (Stage Two)

(8) B. Fuel Consumption of the Power Losses (Stage Two) In the practical system operation, the system total fuel consumption is mainly concerned. Generally, the system total fuel consumption includes two parts: The total fuel consumption of the generators. The equivalent fuel consumption of the system power losses. Generally, the system total fuel consumption obtained in stage one is taken as reference point. It is expected that the system total fuel consumption obtained in stage two is less than that in stage one.

(9)

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Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimization (GRDJE / CONFERENCE / ICIET - 2016 / 036)

V. TEST EXAMPLES The proposed two stage economic power dispatch method is implemented and used in the power system In smart Grid. Here we only show simple results of the six unit system. The fuel consumption functions of the generators are quadratic curves and are shown in Table I. The two stage economic dispatch results are shown in Table II and III. The fuel consumption functions of the generators are quadratic curves and are shown in Table I. The two stage economic dispatch results are shown in Table II and III. Table II shows the generation plans for two stages respectively.

Tables III shows system total losses and fuel consumption for two stages respectively. It can be observed from Table III that the system losses and fuel consumption of the second stage are lower that those from the first stage.

Gen No

Ρ

Pimin ( MW ) Pi max ( MW )

ai ($ / MW 2 )

bi ($ / MW )

c i ($)

1

50

200

0.0053

11.669

213

-

2

20

80

0.0089

10.333

200

-

3

15

50

0.0074

10.833

240

-

4

10

35

0.0000

0.000

0.0

-

5

10

30

0.0000

0.000

0.0

-

6

12

40

0.0000

0.000

0.0

-

7

2

20

-

-

-

0.08

Table 1: the fuel consumption function of generators

The fuel consumption functions of generations are quadric curves are shown in table I. The two stage economic dispatch results are shown in table II and III. Table II shows the generation plans for two stages respectively. Tables III shows system total losses and fuel consumption for two stages respectively.

Generator no.

1 2 3 4 5 6 7(battery) Total power output(MW)

Stage one 64.7047 32.0009 14.9280 35.0022 39.6352 22.5692 11.0000

Stage two 63.7089 23.1120 12.9280 36.1110 39.1190 34.5265 4.0000

211.0898

210.9828

Ploss(MW) 1.0898 0.9828 Total generation cost($/MW) 2765.2 3063.2 Table 2: the results of generation scheduling (using QOPSO) stage Stage one Stage two Total system loss(MW)

1.0898

0.9828

Total system fuel consumption

2765.2

3063.2

Table 3: the results of system fuel consumption (using qopso)

The proposed two stage approach is applied in Power System. The testing results show that the system losses and fuel consumption of the second stage are lower than those from the first stage. Generally the loss reduction is about 0.5 ~ 1.0%.

VI. CONCLUSION A practical approach to implement the economic power dispatch of the power system is discussed in the paper. The proposed economic dispatch method consists of two stages. The first stage involves the classic economic power dispatch with considering loss of the network using quasi-oppositional based particle swarm optimization. The second stage involves economic dispatch considering network loss and security constraints. Two objectives are proposed for the second stage. One is loss minimization, and another is the minimum movement of generator output from the initial generation plan. The proposed two stage economic dispatch approach is tested six unit system. The test results show the two stage dispatch method can not only reduce the system losses and system fuel consumption, but also control the movement of the generator output, so that there is no generation output jump for dynamic economic dispatch.

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Two Stage Approach for Economic Dispatch in Using Quasi-Oppositional Based Particle Swarm Optimization (GRDJE / CONFERENCE / ICIET - 2016 / 036)

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[2] Two stage approach for economic power dispatch jizhong Zhu, Senior Member, IEEE, Xiaofu Xiong, Shan Lou,Mingzhong Liu, Zhiqiang Yin, Bin Sun, Cheng Lin,IEEE 2008.

[3] Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique Weifeng Gao , San-yang Liu, Ling-ling Huang Gharavi H, Ghafurian R. [4] .Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints Zwe-Lee Gaing [5] Smart grid: the electric energy system of the future. Proc. of IEEE 2011;99(6):917–921.

[6] Fang X, Misra S, Xue GL, Yang DJ. Smart grid—the new and improved power grid: a survey. IEEE Commun. Surv. Tutor.

2012;14(4):944–980.

[7] M.R. Irving, and M.J.H. Sterling, “Economic Dispatch of Active Power with Constraint Relaxation,” IEE Proc. C, Vol.130, No.4, 1983 [8] M.R. Irving, and M.J.H. Sterling, “Efficient Newton-Raphson Algorithm for Load Flow Calculation in Transmission and Distribution Networks,” IEE Proc. C, Vol.134, 1987 [9] J.Z. Zhu and G.Y. Xu, “A New Real Power Economic Dispatch Method with Security,” Electric Power Systems Research, Vol.25, No.1,1992, pp9-15 [10] D.C. Walters and Z.C. Sheble, “Genetic Algorithm Solution of Economic Dispatch with Valve Point Loading,” IEEE Trans., on Power Systems, Vol.8, No.3, 1993

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