Smart Grid Architecture for Comprehensive Dynamic Pricing for PHEVs

Transcription

1 Smart Grid Architecture for Comprehensive Dynamic Pricing for PHEVs K.Anuja 1, P.Usha 2 Student, Associate professor anujakakarla@gmail.com, usha.himaja76@gmail.com Abstract Plug-in Hybrid Electric Vehicles (PHEVs) are the vehicles that depend on energy from power grid. When such vehicles are deployed in large scale, there will be great demand for energy in peakhours. There will be different levels of usage based on time. Towards this end, optimizing the supplydemand and also pricing for PHEVs is very challenging problem to be addressed. Recently there was a solution named Distributed Dynamic Pricing (D2P). The solution was based on smart grid architecture in order to optimize the energy usage patterns exhibited by PHEVs. Foreign microgrid energy microgrid energy are two kinds of serviced rendered. The pricing is also different based on the vehicles are in the home place of roaming. The aim of the method was to have costeffective charging and discharging of power to PHEVs. In this paper we built a prototype application to demonstrate the proof of concept. The empirical results are encouraging. Index Terms Smart grid, electric-grid, PHEV, dynamic pricing 1. INTRODUCTION appropriately and cost-effectively. It is also important to monitor sudden bursts of energy usage and handle the same with fair means. Another important concern is pricing. Pricing when the vehicle is local and the vehicle is roaming outside the home. There needs to be a decision making approach that can consider all these aspects. The recent distributed dynamic pricing approach is effective in handling such aspects. We built a prototype application that demonstrates the proof of concept. The experiments reveal that the application is useful to optimize power charging and discharging for both local and foreign PHEV users by employing different strategies. The remainder of the paper is structured as follows. Section II provides review of prior works. Section III presents the proposed approach. Section IV provides the experimental results while section V concludes the paper. The traditional electronic grids are prone to failures. To overcome this drawback electronic and digitalized grids came into existence. Recently we could see smart grids that cater to flexible services. A smart grid is the right combination of electric grid and communication network. With global changes in the environment, there is ever increasing thought on the usage of Plug-in Hybrid Electric Vehicles (PHEVs) to ensure green environments. They are considered to be used in large scale for many reasons. However, the supply of energy to such devices is the cause of concern. PHEVs are manufactured in such a way that they can get recharged from electric grid. Such grid is known as grid to vehicle (G2V). For discharge we have got Vehicle to Grid (V2G). Their green environment and low cost of charging make them an attractive solution. Renewable energy sources and PHEVs can be integrated through smart for demand side management (DSM) and outage. Due to the intermittent problem of the renewable resources of energy, it is essential to have a mechanism which is intelligence enough to ensure that the PHEVs get charged or discharged 11

2 2.RELATED WORKS This section provides review of literature. Many schemes came into existence that were used to charge and discharge PHEVs [1], [2], [3], [4], [5]- [6]. Distributed charging methods were proposed in [1]. The approach was integrated with smart grid for real time service and pricing. The real time demand is understood by the mechanism and supply is given through renewable and nonrenewable resources. Web services technology is used in order to ensure distributed computing in heterogeneous environment [7]. Grid energy and gas energy are the two important energy sources that are used to serve PHEVs. Recently in [2] Home Gateway Controller (HGC) was introduced to have energy transfer where HGC played a vital role in communicating with PHEVs. There was both electric grid and communication network to achieve this. Substation Control Centre (SCC) is also used to make decisions on approving or rejecting the requests sent by PHEVs. It all depends on the dynamics of demand and supply. A centralized pricing is used in [8] where wireless communications are used to know dynamic pricing details. In [9] aggregated load pattern was studied pertaining to multiple PHEVs. The authors also provided smart charging profile that can be used for PHEVs to have optimal allocation of energy sources. Different pricing policies such as usagebased dynamic pricing (UDP) was proposed in [1]. Other existing approaches are Distributed Demand Response (D2R) and [1], Quadratic Cost Function (QCF) [11], [12] and Usage-Based Dynamic Pricing (UDP). Optimal charging and discharging concepts were proposed in [13]. Their first approach was based on charging-discharging while their second approach was based on distributed method to ensure that PHEV users could participate in the process. With respect to optimal discharging it is explored in [14] for Vehicle 2 Grid (V2G) based on the dynamic programming approach towards cost optimization. In this paper we implemented an optimal charging and discharging method based on the Distributed Dynamic Pricing paradigm. 3. PROPOED APPORCH AND PROTOTYPE Our methodology for implementing a prototype application for charging and discharging PHEVs is based on the work done in [15]. The proposed system has two things such as communication architecture and charging and discharging mechanisms. Figure 1 Shows communication architecture (left) and charging and discharging mechanism As shown in Figure 1, it is evident that the communication architecture has provision for different micro-grids and PHEVs to be part of the network. Besides it has many data aggregator units and a utility or control centre. The decision making mechanism for charging and discharging is automated so as to avoid manual decisions. 4. Prototype Implementation We built a prototype application using Microsoft.NET platform. The application has provision grid based energy and gas based energy besides supporting a client application through which the simulation of the PHEVs involving in the process of charging and discharging. Figure 2 Simulation of gas energy mechanism As can be seen in Figure 2, it is evident that the gas energy agencies are integrated with this application through which PHEVs can have the charging and discharging. The application also can show the 111

3 tatus of the energy being used by the vehicles. 5 4 Price(Cents) Time(sec) D Figure 5 Pricing comparison between D2P and UDP Figure 3 - Grid Energy Mechanism As can be seen in Figure 2, it is evident that the grid energy agencies are integrated with this application through which PHEVs can have the charging and discharging. The application also can show the status of the energy being used by the vehicles. As shown in Figure 5, the pricing of D2P is better. The horizontal axis represents time while the vertical axis represents the price in cents. The results reveal that the D2P is able to optimize pricing when compared with that of UDP Total($) Number of PHEVS D 2 P Figure 4 Shows client UI As shown in Figure 4 it is evident that the client user interface helps the PHEVs to get fuel changed and discharged besides finding the status of the fuel. This application can help PHEVs to connect either locally or remotely. Figure 6 Total cost comparison between D2P and others As shown in Figure 6, the total cost of D2P is better. The horizontal axis represents time while the vertical axis represents the total cost in dollars. The results reveal that the D2P is able to optimize the total cost when compared with that of other approaches. 5.EXPERIMENTAL RESULTS Experiments are made with prototype application through simulations. The proposed approach is based on D2P and it is compared with other approaches in terms of pricing, total utility and number of PHEVs. 112

4 Price(cents) Figure 7 Pricing comparison between D2P and others As shown in Figure 6, the pricing of D2P is better. The horizontal axis represents time while the vertical axis represents the price in cents. The results reveal that the D2P is able to optimize the pricing when compared with that of other approaches. Total Ulitity()$) Time (sec) Number of PHEVS WithRoming -price Figure 8 Total utility comparison between local and roaming As shown in Figure 6, the total utility of PHEVs without roaming is better. The horizontal axis represents number of PHEVs while the vertical axis represents the total utility in dollars. The results reveal that the utility of PHEVs without roaming is better than those who are in real roaming. 6. CONCLUSIONS AND FUTURE WORK PHEVs became very important vehicles that promote green environment and help in various real world applications. However, they consume electricity in such mode. Supplying energy to such vehicle as and when required is a challenging problem to be addressed. Moreover, the vehicles might run the local place or foreign place. Supplying energy or charging PHEVs plays crucial role and the same is with discharging when required. Many approaches came into existence to serve this purpose. Recently Distributed Dynamic Pricing was proposed that could provide optimal pricing to PHEVs besides providing them with energy in local and remote mote. In this paper, we implemented the Distributed Dynamic Pricing approach to enable PHEVs to have optimal charging and discharging facilities as and when required. The empirical study is encouraging. This research can be extended further with a comprehensive approach that can serve PHEVs better than ever. REFERENCES [1] Z. Fan, A Distributed Demand Response Algorithm and Its Application to PHEV Charging in Smart Grids, IEEE Trans. on Smart Grid, vol. 3, no. 3, pp , Sept [2] M. Erol-Kantarci and H. Mouftah, Management of PHEV batteries in the smart grid: Towards a cyber-physical power infrastructure, in Proc. of IWCMC, Istanbul, July 211, pp [3] C. Wei, Z. Fadlullah, N. Kato, and A. Takeuchi, GT-CFS: A Game Theoretic Coalition Formulation Strategy for Reducing Power Loss in Micro Grids, IEEE Trans. on Parallel and Distributed Systems, vol. PP, no. 99, pp , July 213. [4] M. Erol-Kantarci, J. Sarker, and H. Mouftah, Communication-based Plug-In Hybrid Electrical Vehicle load management in the smart grid, in Proc. of IEEE ISCC, Kerkyra, June 211, pp [5] S. Sojoudi and S. Low, Optimal charging of plug-in hybrid electric vehicles in smart grids, in Proc. of IEEE PES General Meeting, San Diego, July 211, pp

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