2. Implementation of Vehicle to Grid using Fuzzy Logic Controller
of load profile at node6.3is shown in Figure 2.30 . It can be seen that, as a result of this V2G operation, there is a reduction in the peak demand deficit and a rise in the off peak load. In the figure, load leveling has been shown for the peak and off-peak hours, only for the simulated time. However, this operation can be extended for 24 hrs if the EV penetration in that area is large. The charging and discharging of EVs for such a scenario can be done in slots at the charging station.
12 15 18 21 24 3 6 9 12
Power (kW)
Time(Hours) Peak Hours
Peak hours due to EVs (104 kW)
Off−peak loading due to EVs (170 kW)
Figure 2.30: Flattening of load profile at node 6.3 during discharging and charging of EVs (Scenario I)
2.8 Summary
easily controlled using an FLC. Power leveling and peak saving can be achieved by charging of EVs during off peak hours and discharging the EVs energy during peak hours.
In the next chapter, coordination of EVs at the charging station to achieve the desired power flow is presented. The coordination of individual EVs are achieved by designing a suitable aggregators at the CS level and at the node level.
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Multi Charging Station for dynamic load management
Contents
3.1 Introduction . . . 54 3.2 Modelling of the system . . . 56 3.3 Design of MCS for dynamic load management . . . 60 3.4 Results and discussions . . . 68 3.5 Summary . . . 83
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3.1 Introduction
In the previous chapter, concept of CS has been introduced to achieve voltage support as well as peak shaving and valley filling using Fuzzy controllers. Aggregated EVs energy is assumed at the CS to show the control aspects of power flow between the CS and the grid.
Two FLCs have been developed to control the flow of power
• Between the node and CS.
• Between the CS and EVs’ batteries.
However, the control strategy of charging/discharging of individual battery present in the CS has not been considered in the previous chapter. This chapter is devoted to the discussion of coordination among each EV present at the CS to achieve the desired power flow between the CS and the grid. Furthermore, the coordination of CSs connected at a single distribution node is also achieved to support the grid in terms of valley filling and peak shaving.
In this chapter, three CSs are modelled and connected to a test node of a radial subfeeder of a substation. A group of CSs connected to a single distribution node is termed as Multi Charging Station (MCS). Each CS consists of charging bays to accommodate the EVs to charge their batteries. All the EVs batteries will charge in accordance to grid norms. Here grid norms implies node voltage and it should be within specified limits (0.95 p.u to 1.05 p.u) as per the Central Electricity Authority(CEA). The purpose of designing the MCS is due to the mobility of most of the EVs at different places such as residential area, office area and market area. If all these areas share a common distribution node, the grid can be supported throughout a day.
Moreover, in a realistic situation EVs may traverse between the CSs or can be in transit/idle.
The EVs not present in these areas is understood to be in transit or in idle.
The reason of having a four area based system is to provide continuous support to the test node using the EVs. Since, the EVs would traverse within these four areas, the number of EVs at any instant of time will nearly be the same. All the CSs may have unequal numbers of EVs in different time slots but the total number of EVs at the MCS will always be equal in any time slot. Thus having MCS at one node covering these regions would ensure grid support in
3.1 Introduction
terms of voltage, peak shaving and valley filling at any instant of time. By considering a real situation, one can model the MCS to support the grid throughout a day.
In this chapter, the concept of a multi charging station (MCS) together with its control ar- chitecture has been presented. A simulation based analysis of the proposed concept is done to test the CSs capability to handle different scenarios. Different scenarios include sudden arrival and departure of EVs at the CSs. Novelty of the proposed system is that, while maintaining the voltage profile of the node, it takes into account an individual battery’s preferred charg- ing/discharging rate (Crate) as set by the EV owner. Moreover, the system does not discharge the batteries beyond the specified SOC limit set by the owner. The block diagram of MCS connected to the distribution grid is shown in Figure 3.1.
station 1 Charging
Charging station n Distribution
Node (PCC)
MCS
Power signal Control signal PCC : Point of common coupling
Node Voltage (p.u)
Total Energy of MCS
Aggregator ControllerFuzzy Logic
Duration (Hours)
Figure 3.1: MCS connected to the distribution grid.
At the heart of the architecture, a fuzzy controller is located at the distribution node. This controller decides the direction and the magnitude of power transfer between the grid and the MCS using three inputs. The first input is the available energy of the EVs’ batteries. The second input is the duration for grid support and the third input is the voltage of the distribution node at which the CSs are connected. The output of the FLC is the power required from the CSs which may be negative or positive. Negative power implies that energy is transferred to the grid from the batteries while positive power implies that energy is drawn from the grid to charge TH-1125_09610202
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the batteries. The entire model of the MCS is implemented on a typical distribution system of Guwahati city (the state capital of Assam, India).
• MCS has been designed at system level.
• Converters connected to charge and discharge the EVs have not been modelled since, the aim of this work is to show the V2G implementation at system level.
The chapter is organised as follows: Section 3.2 discusses the modelling of the MCS, distribution grid and the battery. Section 3.3 presents the design methodology adopted for the MCS, algorithm and development of possible scenarios. The results and their analyses are presented in Section 3.4. Finally, conclusions are presented in Section 3.5.