International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016

ISSN (Online): 2347 - 4718

SMART GRID OVERVIEW USING MODELLING AND SIMULATION WITH OPTIMIZATION CONTROL 1,2

Hiren M.Patel1, Fagun N.Upadhyay2 Lecturer, VPMP Polytechnic, Gandhinagar, Gujarat, India

Abstract: With urging problem of energy and pollution, smart grid is becoming very important. By gradually changing the actual power grid system, smart grid may evolve into different systems by means of size, elements and strategies. The Smart Grid is still in the process of becoming standardized for widespread use. This paper provides an overview of smart grids and recent advances in distributed sensing, modelling, and control, particularly at both the high-voltage power grid and at consumer level. Such advances may contribute toward the development of an effective, intelligent, distributed control of power system networks with a focus on addressing distributed sensing, computation, estimation, controls and dynamical systems challenges and opportunities ahead. I. INTRODUCTION The Smart Grid is a pervasive new concept intended to provide sophisticated features to the electrical grid, including energy resource sharing, distribution, and load balancing [4]. Energy technologies are vital for social and economical development of our society. Electrical grids providing electricity to households, businesses and industries, our society has become dependent on electricity at every level. The genesis of early power systems and electric power grids during the past 130 years was enabled by automation and control of electromechanical machinery and power delivery networks. Today’s end-to-end power and energy systems (from fuel source to end use) fundamentally depend on embedded and often overlaid systems of sensors, computation, communication, control and optimization. There are even more opportunities and challenges in today’s devices and systems, as well as in the emerging modern power systems – ranging from dollars, watts, emissions, standards, and more – at nearly every scale of sensing and control. Recent policies combined with potential for technological innovations and business opportunities, have attracted a high level of interest in smart grids. The potential for a highly distributed system with a high penetration of intermittent sources poses opportunities and challenges. Any complex dynamic infrastructure network typically has many layers, decision-making units and is vulnerable to various types of disturbances. Effective, intelligent, distributed control is required that would enable parts of the networks to remain operational and even automatically reconfigure in the event of local failures or threats of failure [1]. A wide variety of research has been conducted to determine what technological aspects and risks should be considered in the creation of the Smart Grid, such as smart metering technology [5], information system development [6], future

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standards, The concept of an efficient power grid has taken a global concern and the expression “Smart Grid” has expended into different dimensions: -some see it as a numerical solution for downstream counter and mostly residential customers, while others believe that it is a global system vision that transcends the current structure of the energy market to generate economical, environ- mental and social benefits for everyone. However, a view accepted by the most is that smart grid is to improve the current power grid and to achieve goals of green energy and reducing greenhouse pollution [5]. In this paper, we illustrate our approach through the modelling of smart grid in terms of optimization. Contribution of our approach consists in treating the smart grid as a complex system, locating the problems at local as well as global levels, and solving them with coordinated methods. In other words, through studying and analyzing smart grid, we isolate homogeneous parts with similar behaviours or objectives, and apply classical optimization algorithms at different levels with coordination. Thanks to combining those interdependent methods, our approach guarantees the flexibility in terms of system size. Be- sides, generality of our approach allows its applicability in different scenarios and models, as well. In this paper, we propose a novel framework to harness the power of simulation in the verification and validation processes for Smart Grid environments. Our framework leverages use case repositories to change its form to identifiable simulation entities and performs automatic validation tasks with corresponding assessment library [1].

Table 1 Domains in the smart grid conceptual model [1] II. SMART GRID The term smart grid is coined by Amin in 2005 [5]. Smart grid is a type of electrical grid which attempts to predict and intelligently respond to the behaviour and actions of all electric power users connected to it suppliers, consumers and those that do both in order to efficiently deliver reliable,

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International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016 economic, and sustainable electricity services [6]. Smart grid is defined as an intelligent grid which provides bi-directional flow of electricity and information, with improving the power grid reliability, security, and efficiency of electric system from generation to transmission and distribution. It is driven by the need to provide a more robust, flexible and efficient electric system to overcome the increasing demand of electricity, uprising treat from green house gases emission, depletion of energy resources and other rising issues in traditional grid [1]. Smart Grid has three economic goals: to enhance the reliability, to reduce peak demand and to reduce total energy consumption. To achieve these goals, various technologies have been developed and integrated in the electrical network. It is not intended to replace the current power grid system but only to improve it. smart grid enables the (i) integration of renewable energy resources (such as PV, wind turbine and etc.) at distribution network, (ii) supervisory control and real-time status monitoring on the power network, (iii) self-monitoring and (iv) self-healing feature, adaptive response to fault and etc.

ISSN (Online): 2347 - 4718

illustrated in Figure 1. It contains four subsections which are generation, transmission, distribution and control network [1]. Each network interconnected from various locations, information exchange and communicates through smart communication subsystem such as an access point with wired or wireless communication infrastructure. Raw information on the network healthiness or performance is obtained from smart information subsystem such as a smart meter, sensor and phasor measurement unit (PMU). Real time network monitoring, management and control are performed at the control network such as the electric utility control center. Besides that, a distribution network can be an individual when dispersed generation (DG) (renewable energy resources) is embedded, that allowing electricity supply from both DG and utility.

III. SMART GRID STRUCTURE

Figure 1. Typical smart grid structures [7] A Smart Grid integrates advanced sensing technologies, control methods and integrated communications into current electricity grid both in transmission and distribution levels [7]. The Smart Grid should have following key characteristics: 1) self-healing; 2) consumers motivation and participation; 3) attack resistance; 4) Higher quality power; 5) different generation and storage options; 6) flourished markets; 7) efficiency and 8) high- er penetration of intermittent power generation sources. Smart grids are composed of an enormous number of devices of various types, from smart meters and solar inverters to electrical substation equipment and sensors on power lines. Electricity can be produced by multiple processes: from the stable production of a nuclear plant, to the storage via electric vehicles, and integration of renewable energy which production may depend on environmental factors. A huge distribution and energy trans- port network has been created over the years but it is neither mastered nor optimized. The goal is to make power grids more efficient by integrating renewable energies and taking advantages of information and communication technologies. A typical smart grid structure is

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Figure-2 Smart Grid Components [8] The smart grid can be conceptualized as an extensive cyberphysical system that supports and significantly enhances Controllability and responsiveness of highly distributed resources and assets within electric power systems. Renewable generation will make an increasingly important contribution to electric energy production into the future. IV. MODELLING AND SIMULATION OF SMART GRID A. ACTIVE POWER ANALYSIS METHOD:Active power is the real power which flows in electrical network viz. transmission and distribution networks. Depending upon the load angle gradient the flow of active power takes place from source to load or from one area to another area.

Figure-3-Single line diagram of the power source connected to the load via a transmission line [2]

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International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016 From Fig.3 the active power flowing from sending end to the receiving end. For small “δ”, active power can be controlled by changing the angle “δ” between the sending ends and receiving end voltages, VS and VR respectively. Moreover reactive power can be controlled by controlling the difference between voltage magnitudes of VS and VR. For a two area system, during normal operation the real power transferred over the tie line is given by

ISSN (Online): 2347 - 4718

with the ac power network.

....................... (1)

Figure-5 Simulation model of smart grid

Figure-4 Tie Line Power Representation [2] In an interconnected power system, different areas are connected with each other via tie-lines. When the frequencies in two areas are different, a power exchange occurs through the tie-line that connected the two areas. In case of Wind power plant DFIG is used. Doubly-fed electric machines are basically electric machines that are fed ac currents into both the stator and the rotor windings. Doubly-fed induction generators when used in wind turbines is that they allow the amplitude and frequency of their output voltages to be maintained at a constant value, no matter the speed of the wind blowing on the wind turbine rotor. Because of this, doubly-fed induction generators can be directly connected to the ac power network and remain synchronized at all times

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V. RESULT AND DISCUSSION Initial load values are taken from problem of and simulation has been done. Some of the results are discussed here with their graph data are as:

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CASE-1 [2]

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International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016 The graph of Active power obtained from this SIMULINK model at this load values has shown below:-

ISSN (Online): 2347 - 4718 CASE-3 [2]

The graph of Active power obtained from this SIMULINK model at this load values has shown below:-

Figure-6-Graph of Active power for case-I Hence this is observed from above graph that the active power values are:.B1- 620.4 MW , B2- 458.7 MW , B3748.3 MW, B4- 674.2 MW , B5- 673.5MW. CASE-2 [2]

Figure-8 Graph of Active power for case-III Hence this is observed from above graph that the active power values are: B1- 489.7 MW, B2- 213.2 MW, B3- 887.9 MW, B4- 527.1 MW, B5- 568.1 MW. CASE-4 [2]

The graph of Active power obtained from this SIMULINK model at this load values has shown below:-

The graph of Active power obtained from this SIMULINK model at this load values has shown below:-

Figure-7 Graph of Active power for case-II Hence this is observed from above graph that the active power values are:. B1- 639.2 MW, B2- 473.2MW, B3- 976.9 MW ,B4- 654.8 MW ,B5- 684.3MW.

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Figure-9 Graph of Active power for case-IV

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International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016 Hence this is observed from above graph that the active power values are: B1-588.4 MW, B2- 420.4 MW, B3- 576.1 MW, B4- 643.2 MW, and B5- 640.3 MW. In load analysis of above simulation model, Inductive and Active loads are varied in RL series load which is taken at both load buses i.e. B3 and B6. Whereas capacitive load kept as it is with the initial value taken in series load. The value of capacitive load at bus bar B3 is 200 MVAR where as 350 MVAR of capacitive value is taken at bus bar B6. Initially active and inductive load at B3 is taken as 1050MW and 130 MVAR respectively and at B6 the value of active load is 1850 MW and the value of inductive load is 140 MVAR. The maximum and minimum measured frequency values on these loads are 50.252 Hz and 49.90 Hz respectively [2]. The active power values at this load condition is measured by the obtained graph is B1- 620.4 MW , B2- 458.7 MW , B3- 748.3 MW, B4- 674.2 MW , B5- 673.5MW. Inductive and Active loads at both buses has increased simultaneously and this is found that when inductive and active load values at bus B3 is 180 MVAR and 1030MW and load value at bus B6 is 190 MVAR and 1950 MW then active power measured at all buses namely B1, B2, B3, B4 and B5 are not constant. Active power values at B1 has changed to 639.2 MW from 620.4 MW, at B2 it is found that active power values changed from 458.7 MW to 473.2 MW and likewise active power values changes at all buses like B3, B4 and B5. The maximum values of load has already taken to check the maximum limit of this smart grid network .To know these load ranges, value of inductive load on bus bar B3 is decreased to 60MVAR from their initial value which is 130 MVAR and value of active load is decreased to 700 MW from their initial value of 1050 MW where as at bus bar B6, inductive load is decreased from 140 MVAR to 50 MVAR and active load is decreased from 1850 MW to 1500 MW. At these load values, the values of maximum and minimum frequencies are measured to 50.80 Hz and 49.20 Hz. The active power values at this load condition are calculated from the simulation graph is B1588.4 MW, B2- 420.4 MW, B3- 576.1 MW, B4- 643.2 MW, and B5- 640.3 MW [2]. VI. SMART GRID OPTIMIZATION In the previous section we have discussed the smart grid and complex system, and optimization difficulties in complex systems. In this section, we will discuss the complex system approach on which we have embarked to provide a solution to optimization problems in smart grid [1]. The problems of electrical networks have been known for long, and research as well as industrial works has been carried out to find effective and competitive solutions. Nevertheless the efforts are often concentrated on specific cases, and solutions are, too, specific without any room for evolution [1]. Among the proposed solutions we can mention: Distributed generation/micro grids: since a centralized optimization is very costly in terms of time and memory, optimization should be done at all levels. The micro grids can change the centralized interface into a distributed interface; therefore optimization can be carried out in a distributed

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ISSN (Online): 2347 - 4718

manner. Consequently calculation benefits in terms of time and memory are significant, while ensuring optimal at different scales. Design of intelligent network (home automation): domestics or smart devices can better understand the real needs of consumers. While optimizing local consumption, they optimize overall consumption as a result [1]. Energy storage device: the energy storage coupled with energy optimization from beginning to end, regulates consumption and clears consumption peaks [1]. Reduction of Transmission and Distribution T&D network losses by automated distribution: One of the strong points of our model is the distribution optimization by local and global algorithms which reduce the loss of congestion or routing errors [1]. Intelligent control of price: when the network becomes intelligent, it is necessary that the consumer prices may also change in order to follow the new consumer behaviour [1].

VII. CONCLUSIONS In this paper, load analysis has been done on this smart grid to check the stability in terms of active power flow. Active power values at all buses has been changed with respect to changes in active and inductive load values at bus bar B3 and B6 keeping capacitive load constant. Frequency has been also measured and keeping values of both active power and frequency, magnitude of inductive and active load has been deduced while maintaining synchronism of the proposed smart grid model. REFERENCES [1] Murat Ahat1, 2, Soufian Ben Amor1, Marc Bui3, Alain Bui1, Guillaume Guérard1, Coralie Petermann1, “Smart Grid and Optimization”American Journal of Operations Research, 2013, 3, 196-206, Published Online January 2013. [2] Vikash Kumar, Prof. Pankaj Rai, “Active Power analysis of a Smart GridUsing MATLAB/SIMULINK Approach”. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Volume 1 Issue 8 (September 2014). [3] Lee-Cheun Hau, Jer-Vui Lee, Yea-Dat Chuah and An-Chow Lai, “Smart Grid – The Present and Future of Smart Physical Protection: A Review”. International Journal of Energy, Information and Communications Vol. 4, Issue 4, August, 2013. [4] C.Marinescu and S. I, “Analysis of frequency stability in a residential autonomous microgrid based on the wind turbine and microhydel power plant,” Optimization of electrical and electronic equipment, vol. 50, pp. 1186–1191, 2010 [5] P.Piagi, “Microgrid control,” Ph.D. dissertation, Electrical engineering department, University of Wisconsin -Madisson, August 2005.

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International Journal For Technological Research In Engineering Volume 3, Issue 5, January-2016

ISSN (Online): 2347 - 4718

[6] P.Piagi and R. Lasseter, “Autonomous control of micro grids,” 2006. [7] M. Amin and J. Stringer, “The Electric Power Grid: To- day and Tomorrow,” MRS Bulletin, Vol. 33, No. 4, 2008, pp. 399-407. doi:10.1557/mrs2008.80. [8] National Institute of Standards and Technology, “NIST Framework and Roadmap for Smart Grid Interoperability Standards,” National Institute of Standards and Technology, Gaithersburg, 2010. [9] X. Fang, S. Misra, G. Xue and D. Yang, “Smart Grid-the New and Improved Power Grid: A Survey,” IEEE Communications Surveys and Tutorials (COMST), Vol. 14, No. 4, 2012, pp. 944980. doi:10.1109/SURV.2011.101911.00087.

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