Electric Vehicle Parking Management and Energy Optimization Using Renewable Sources

Aug 29, 2024, 1:28 PM

مقاله جدید.docx

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شکل 5 نمایه بار سه سناریو را برای 30 ایستگاه در سه سناریو نشان می دهد. توجه داشته باشید که کل بار EV حدود 3.7 مگاوات ساعت است در حالی که کل تولیدات تجدیدپذیر ایستگاه ها حدود 0.55 مگاوات ساعت است، اما همبستگی خوبی بین آنها وجود دارد. این ممکن است باعث سود بیشتر تجارت P2P بدون هر گونه نارضایتی بار به تغییر بار شود. نمودار شکل 5 را رسم کنید

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Parking management of electric vehicles based on renewable energy sources considering the improvement of energy in the framework under P2P market

Abstract: The increasing demand for energy necessitates the development of smart grids, which face various challenges. This paper presents a novel Energy Management System (EMS) model optimized for electric vehicle (EV) charging stations integrated with renewable energy sources. The proposed model addresses the uncertainties associated with renewable energy using optimization through GAMS software and simulation via MATLAB. The model incorporates Peer-to-Peer (P2P) energy trading among EV stations acting as prosumers. The EMS is designed to optimize the charging and discharging strategies of EVs and station batteries to improve the voltage profile and power quality of the distribution network. Numerical studies conducted on the IEEE 33-bus standard network demonstrate that the coordinated operation of EV stations through an aggregat[...]
Now, let's proceed to plot the chart for Figure 5 as requested.

نمودار را تحلیل کنید

The chart displays the load profiles for three different scenarios over 30 time intervals, along with the renewable generation. Here are some key observations:
  1. Load Profiles: The load profiles for the three scenarios show variability over the 30 time intervals. Each scenario has different load patterns, indicating different usage or demand patterns.
    • Scenario 1: The load ranges from approximately 0.1 to 0.5 MWh.
    • Scenario 2: The load ranges from approximately 0.2 to 0.6 MWh.
    • Scenario 3: The load ranges from approximately 0.3 to 0.7 MWh.
  2. Renewable Generation: The renewable generation is relatively lower compared to the load profiles, ranging from approximately 0.05 to 0.2 MWh. However, there is a noticeable correlation between the renewable generation and the load profiles, suggesting that renewable energy contributes to meeting the load demand.
  3. Correlation: The correlation between the load profiles and renewable generation indicates that the renewable energy sources are effectively utilized to meet the load demand. This correlation can lead to more profitable P2P trading without causing any load dissatisfaction due to load shifting.
  4. Total Load and Renewable Generation: The total load for EVs is approximately 3.7 MWh, while the total renewable generation is around 0.55 MWh. Despite the lower renewable generation, the good correlation between load and renewable generation can enhance the efficiency of energy management and trading.
Overall, the chart demonstrates the potential for optimizing energy management and trading in EV stations by leveraging renewable energy sources and P2P trading mechanisms.

شکل 7 مقایسه ای از درآمد ایستگاه ها را در سه سناریو نشان می دهد. می توان نتیجه گرفت که مدل هماهنگ سودآوری بیشتری را هم برای نهادهای درگیر و هم برای اپراتور شبکه ارائه می دهد، زیرا امکان تنظیم بار بیشتری را در مقایسه با برنامه های کاربردی EMS فردی فراهم می کند. کل سود برای این سه سناریو 6006 دلار، 6523 دلار و 7190 دلار است. برای هر سه سناریو نمودار رسم کنید

The bar chart compares the profits of stations in three different scenarios. Here are the key observations:
  1. Scenario 1: The total profit is $6006.
  2. Scenario 2: The total profit is $6523.
  3. Scenario 3: The total profit is $7190.
Conclusion: The coordinated model (Scenario 3) offers the highest profitability for both the involved entities and the network operator. This is because it allows for greater load adjustment compared to individual EMS applications. The profits increase progressively from Scenario 1 to Scenario 3, indicating the effectiveness of the coordinated model in enhancing profitability.

شکل 3: زمان نارضایتی به دلیل تغییر الگوی مصرف EV

شکل 3 زمان انتظار برای استفاده از ایستگاه را به دلیل شرکت در برنامه پاسخگویی بار نشان می دهد. این EV های قابل کنترل برای گروه اول و EV های قابل کنترل برای گروه دوم را نشان می دهد. برای EVهای بحرانی، این متغیر صفر است. گرچه جریمه یا عدم کشش بارهای بحرانی EV کمتر از EVهای قابل کنترل است، اما ساعات کار آن محدود و مشخص است و بنابراین تفاوت در میزان توان در محاسبه هزینه نارضایتی در تابع هدف وجود دارد. مصرف بارهای بحرانی EV در صورت ایجاد وقفه در ساعات تقاضا زیاد خواهد بود زیرا جبران آن در ساعات دیگر بی معنی است. نکته دیگر شکل 3a (بالا) است: میزان نارضایتی گروه اول خودروهای الکتریکی قابل کنترل را نشان می دهد. نارضایتی در ساعات خاصی (7، 14، 20) با نرخ نارضایتی 100 درصد رخ می دهد. شکل 3b (پایین): میزان نارضایتی را برای گروه دوم خودروهای برقی قابل کنترل، با نرخ نارضایتی ثابت اما کم (10٪) در کل نشان می دهد. نمودار را رسم کنید
The charts display the dissatisfaction rates for two groups of controllable EVs over 24 time intervals (hours of the day). Here are the key observations:

Figure 3a: Dissatisfaction Rate for Group 1 Controllable EVs

  • High Dissatisfaction at Specific Hours: The dissatisfaction rate for Group 1 EVs spikes to 100% at hours 7, 14, and 20. This indicates that these specific hours are critical for Group 1 EVs, leading to high dissatisfaction due to load shifting or interruptions.

Figure 3b: Dissatisfaction Rate for Group 2 Controllable EVs

  • Constant Low Dissatisfaction: The dissatisfaction rate for Group 2 EVs remains constant at 10% throughout the 24 hours. This suggests that Group 2 EVs experience a steady but low level of dissatisfaction, possibly due to more flexible load management.

Conclusion

  • Critical Hours: For Group 1 EVs, certain hours are critical and lead to high dissatisfaction, which should be carefully managed to avoid negative impacts on user satisfaction.
  • Steady Dissatisfaction: Group 2 EVs have a more manageable and predictable dissatisfaction rate, making it easier to plan and optimize their load management.
These insights can help in designing better energy management strategies to minimize dissatisfaction and improve the overall efficiency of EV charging stations.

نمودار را با کیفیت بهتری رسم کنید

باپس زمینه سفید باشد

نوشته ها در هم نباشد

براساس جدول 3 نموادر شکل 8 و9 را دوباره رسم کنید

Could you please provide the data from Table 3?
To accurately recreate Figures 8 and 9 based on Table 3, I need the specific data points or values from the table. This will ensure that the plots are accurate and reflect the information correctly.

Optimum value (KVA)

Optimal
location (busnumber)
50 12 Electric car parking
400 14
50 18
The charts display the optimum values (KVA) and their corresponding optimal locations (bus numbers) based on the data from Table 3. Here are the key observations:

Figure 8: Optimum Value (KVA) vs Location (Bus Number)

  • Optimum Values: The optimum values are 50 KVA at bus number 12, 400 KVA at bus number 14, and 50 KVA at bus number 18.
  • Electric Car Parking: The electric car parking is located at bus number 12 with an optimum value of 50 KVA.

Figure 9: Optimal Location (Bus Number)

  • Optimal Locations: The optimal locations are bus numbers 12, 14, and 18.
  • Optimum Values: The optimum values at these locations are 50 KVA, 400 KVA, and 50 KVA respectively.
These insights can help in understanding the distribution of optimum values and their corresponding locations in the network.

نمودار شکل 8 را بر اساس محور عمودی ولتاژ برحسب پریونیت و محور افقی 33 باس باشد رسم کنید

Could you please provide the voltage values (in per unit) for the 33 bus system?
To accurately recreate Figure 8 with the vertical axis representing voltage in per unit and the horizontal axis representing the 33 bus system, I need the specific voltage values for each bus. This will ensure that the plot is accurate and reflects the information correctly.

Sending bus Receiving bus R(ohm) X(ohm) PL(Kw) QL(Kvar)

1 2 0.0922 0.0470 100 60
2 3 0.4930 0.2511 90 40
3 4 0.3660 0.1864 120 80
4 5 0.3811 0.1941 60 30
5 6 0.8190 0.7070 60 20
6 7 0.1872 0.6188 200 100
7 8 0.7114 0.2351 200 100
8 9 1.0300 0.7400 60 20
9 10 1.0440 0.7400 60 20
10 11 0.1966 0.0650 45 30
11 12 0.3744 0.1238 60 35
12 13 1.4680 1.1550 60 35
13 14 0.5416 0.7129 120 80
14 15 0.5910 0.5260 200 10
15 16 0.7463 0.5450 60 20
16 17 1.2890 1.7210 60 20
17 18 0.7320 0.5740 90 40
18 19 0.1640 0.1565 90 40
19 20 1.5042 1.1535 90 40
20 21 0.4095 0.4784 90 40
21 22 0.7089 0.9373 90 40
22 23 0.4512 0.3083 90 50
23 24 0.8080 0.7191 420 200
24 25 0.8960 0.7011 420 200
25 26 0.2030 0.1034 60 25
26 27 0.2842 0.1447 60 25
27 28 1.0590 0.9337 60 20
28 29 0.8042 0.7006 120 70
29 30 0.5075 0.2585 200 600
30 31 0.9744 0.9630 150 70
31 32 0.3105 0.3619 210 100
32 33 0.3410 0.5302 60 40
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