“This research proposal for me signifies diversity and unity. Three people from different backgrounds, collaborated to not just attempted to add to the wall of knowledge but over all to find solution to the ever progressing effects of climate change”
REDUCING THE EFFECTS OF CLIMATE CHANGE USING SMART GRID WITH AES
INTRODUCTION (Nandita, Namrata, Ralph)
Climate change is accelerating, the change dangerously close to being irreversible. [1] and [2] Climate change phenomenon mostly cause by human activities, such as carbon dioxide emissions. [1] [2] The effect of climate change is the increase of the Earth’s temperature mainly caused by the increasing carbon dioxide level (fig 1). According to [3], main contributors to CO2 emissions is the energy sector. It contributes to 25% of the total CO2 emissions.[4] reported the temperature anomaly of 0.98o C or 1.76o F last year 2019 relative to the average temperature of the previous years. This means that as CO2 increases in the atmosphere, temperature increases as well. It is crucial to adapt to sustainable methods of energy production and consumption. One such method is smart grid. A smart grid system intelligently manages the power supply distributions to its clients in an area considering the power demand of that area collected by an IOT system. The author of [7] mentioned also that producing and distributing enough electricity according to the demands will improve the efficiency. A technique called Reliability Centered Maintenance (RCM) was implemented has been widely used in various industries to identify the actual safety and reliability level of their equipment [6] to manage their preventive maintenance planning and predicting equipment failures. This result to up to 50 percent reduction in greenhouse gases and carbon dioxide per household. Our goal is to be able to ensure efficient and reliable operation of smart grids with a special feature introduced in it called the AES encryption standard. Smart grids are not secure enough for a proper transmission, and hence we would like to propose an existing encryption method called the AES standard method which plays a powerful role in encrypting and decrypting data.
AES is a block cipher that operates on discrete blocks of data using a fixed key and a formula as supposed to the previously used RC4 algorithm which is a stream cipher that does not have a discrete block size. Instead, it uses a keystream of pseudorandom bits that is combined to the data using an exclusive OR (XOR) operation. Having to implement smart grid and IOT technology with a good security feature for it, efficiency will be guaranteed, and as stated in the contents, [23] CO2 emissions of power plants will be decrease. Hence, the improved AES encryption algorithm is a hybrid encryption algorithm combining packet cipher and sequence cipher, which is more secure than the algorithm using packet cipher or sequence cipher algorithm.
LITERATURE REVIEW
- Smart Grid and its co-relation with IoT. (Nandita)
Smart grid network implies the convergence of operations technology (OT) – the grid’s physical infrastructure assets and applications and IT, the human interface that enables rapid and informed decision making [8]. The smart grid is a more complicated network complex where it generates, distributes, uses, controls and then stores. It generally is proactive real time protection and islanding. Using technologies like energy storage or PV systems, a household is able to temporarily go off-grid. The role of IoT in smart grid is crucial. The IoT applications in the smart grid could be used for monitoring the power transmission line, the substation, managing electric vehicle charging/discharging, user information collection etc.
[8] Internet of Things is in large part the enabler of smart grid as its technological and infrastructural components are mostly IoT-based. Sensor data is the core of the smart grid operation. Processing, sorting, cleaning, Analysis and visualization of real time IoT data provide visibility in the supply chain from the moment the energy is produced to the point it’s consumed by an end-user [9]. The smart grid requires the IoT applications to have high bandwidth, low latency and location-awareness. The use of advanced algorithms to analyse IoT data created in the smart grid supply chain is another way to make it more efficient. The IoT application solutions are power transmission line monitoring, smart patrol, smart home and electric vehicle management. The smart grid utilizes many Internet of Things (IoT) applications to support its intelligent grid monitoring and control [9]. The Internet of Things will provide actionable climate data, it will cut waste by improving the flow of people, energy, goods, and information.
- Smart Grid and its impact on CO2 emission (Namrata Rao)
Throughout it’s in the development stages, smart grid subject matter has been extended to many disciplines of engineering and applies science. Thus, smart grids have reached a richer framework that also mentioned by way of its economic, sociological and environmental impact. Main advantages of smart can be summarized as: (i) limit transmission losses that are induced through central power distribution and enhance the energy efficiency, (ii) strength prices are predicted to reduce because of huge utilization of free renewable power sources e.g. photo voltaic and wind energy [10]. A smart electrical power grid could decrease annual electric energy use and utility sector carbon emissions at least 12 percent by 2030, according to a new report from the Department of Energy’s Pacific Northwest National Laboratory [11].
- Maintenance techniques used previously in smart grid. (Ralph)
As we know, electrical energy is an indispensable resource for our world to function, expectations for higher efficiency and more reliable energy supply are increasing. The earlier methods used were Time-Based Maintenance (TBM) and Condition-Based Maintenance (CBM) which were eventually replaced by Reliability Centred Maintenance [5]. Condition-Based Maintenance from the name itself is a maintenance technique that uses the actual state or condition of an equipment to identify the faults, its location, and severity [12] triggered by the predetermined level of condition. On the other hand, Time-Based Maintenance is a maintenance technique that are done periodically according to predetermined schedules or in other words time is the trigger of this actions [13].
- AES as a Better Encryption Method Than Previous Encryptions
(Nanditha, Namrata)
Spritz encryption which is based on RS4 algorithm was found to be an efficient way of providing smart meter users with privacy. Due to its speed, its application not resulted in poor system performance due to excessive delays [18]. The response time to an attack is twice slower than the original RS4, it would take approximately 3 times longer to break by brute force. Since RC4 has been deployed in operational smart meters (although found to be weak) [19]. This affect the performance of smart grids, and in our research paper we are specifically addressing the response time of attacks by using AES algorithm.
AES is a block cipher that operates on discrete blocks of data using a fixed key and a formula while RC4, RC5 are stream ciphers that does not have a discrete block size. Instead, it uses a keystream of pseudorandom bits that is combined to the data using an exclusive OR (XOR) operation.
RC4, RC5 encryption methods are trademarked since it was initially a trade secret, which led to some people coming up of inventive ways to call the leaked description. [22] On the other hand, AES is publicly available and can be freely used without hitting any legal problem.
METHODOLOGY
- Smart grid communication network architecture. (Namrata)
Smart Grid Interoperability Reference Model (SGIRM) is used for End-to-End (E2E) communication which is based on IEEE 2030 standard (Fig.2). This model provides communication between Smart Grid (SG) generation, transmission and distribution domains [15]. SGs consists of three layers: Electric Power system Layer, Communication layer and Application Layer. The Electric layer has four domains which are generation domain, transmission domain and customer domain. Smart grid layer is the management layer of the grid and the Reliability centered Maintenance (RCM) algorithm runs on this layer. SG collects a large amount of raw data from the end users [14]. This may lead to security threat on users personal or business data. This in turn jeopardizes the entire SG system if not carefully handled. For e.g.: Inefficient user authentication results in manipulation of the data. This defeats the purpose of energy management and also a threat to the users.
In our paper we are proposing an end to end AES encryption to be implemented in the communication layer which forms the IoT cloud of the system as shown in Fig 3. This layer is susceptible to cyber-attacks. The communication layer is the backbone of the system and communicates between every other layer in the smart grid.
- EQUIPMENT MAINTENANCE USING RCM (Ralph)
The authors of [5] mentioned that the use of RCM is popular on planning and execution of preventive maintenance systems to achieve an efficient level of reliability. At the same time, the authors [16] and [17] shares the same idea and mentioned that this tool is important to understand the potential failures and be able to prevent it. For its implementation, we will select the appropriate maintenance task by assessing each equipment based on its fault and the sequences of the fault. The results will be analyzed using the RCM flow chart to select the appropriate maintenance task to the equipment.
For the reliability calculation, we will calculate for the system reliability using the System Average Interruption Frequency Index (SAIFI) by dividing the interruption duration (hours) over the total operating time (hours). This will confirm the reliability of the system after the implementing RCM.
- Enhanced security features – (NANDITHA)
In implementing the RCM system with IOT technology, security is another aspect to be added in the system. Looking at the limitations of wired transmission in the smart grid, low data transmission efficiency and poor security, we are trying to propose a smart grid with proper security system for a better change in the climate. To ensure the normal operation of the grid equipment, the establishment of a safe and reliable high-efficiency smart grid is the development trend of the existing grid system. Here we are trying to introduce the wireless local area network technology on the basis of the original power grid detection system and then improve its security by adding the AES encryption algorithm to it. [24] This AES encryption method is applied to the smart grid so that it is more secure and helps to reduce the emissions of CO2 in a better way. Generally, for securing data, there are methods such as RC4, RC5 etc., but we are proposing a more advanced and a secured method called AES. The AES also known as Rijndael encryption in cryptography, is a block encryption standard. While we try to encrypt the data here, all the data will be in bytes.
To generate a random sequence by using a pseudo-random algorithm likely from fig.4 a pseudo-random sequence can satisfy the non-speculative needs in cryptographic operations under certain conditions. The flow of generating pseudo-random numbers is shown in the figure above. This is a widely used pseudo-random number generation algorithm whose recursion formula is X(n+1) = (a X(n) + c) mod m, n = 1,2,3…n. The linear congruence algorithm has the advantages of fast generation speed and long sequence period of generation, making it an ideal pseudo-random number generation algorithm.
- Decryption and Encryption of Improved AES Encryption Algorithm-
(NANDITHA)
The specific process of encrypting by using a pseudo-random number is as follows: a random sequence is generated by a pseudo-random number generation algorithm, then the random sequence produces a key stream sequence, and the plaintext and the key stream are packet-encrypted according to a certain length. For the cryptanalyst, even if a sufficient number of plaintext and ciphertext pairs are intercepted, the entire key sequence cannot be inferred because the encryption keys of each packet are not correlated.
The traditional AES encryption algorithm obtains the same ciphertext by independently encrypting the same plaintext multiple times, and since the packet key is expanded by the original seed key, the correlation between the ciphertexts of each packet is stronger. [24] Here, the simulation is performed independently using the same plaintext. The decrypted result is the same as the plaintext, and the correctness of the improved AES encryption algorithm is verified. However, in these two simulations, the ciphertext is completely different. This is because the key in the improved AES algorithm is a random sequence obtained by the linear congruence algorithm using the stream cipher idea. Therefore, the keys used for each encryption are different. Even if the same plaintext is independently encrypted multiple times, the keys are different each time, and the ciphertext obtained each time changes like shown below.
The improved AES encryption algorithm is a hybrid encryption algorithm combining packet cipher and sequence cipher, which is more secure than the algorithm using packet cipher or sequence cipher alone.
Distribution grid management combines sensor technologies and automation to continuously maintain voltage levels, locate faults, reconfigure feeders, and control distributed generation so that equipment performs optimally, and outages are minimized.
CONCLUSION
Global climate change has already had observable effects on the environment. Scientists have high confidence that global temperatures will continue to rise for decades to come, largely due to greenhouse gases mainly by combustion of fossil fuels [20]. Hence it is important to look for sustainable energy alternatives and managing current energy resources [21]. Smart grids have proven to be efficient in energy management. AES algorithm is one of the most powerful encryptions, it is very efficient when combined with smart grid system running on RCM. This encryption technique provides a faster encryption and decryption as compared to previous techniques i.e. RC4. Another important feature of AES algorithm is security between client and server in the IoT cloud. The encryption and decryption are based on SPN (Substitution and permutation network). SPN defines the number of mathematical operations that are carried out in the block cipher algorithms [22] The decryption processes the exact inverse of the encryption process, i.e. they share the same key. Advanced AES encryption ensures reliability, authenticity and increased productivity. Till date there has been no record of hackers able to intrude an AES encrypted system, as the possibility of cracking the code is 228 = 268,435,456 possibilities for one cipher text. Although to high computational efficiency, the cost of implementation of AES is also high.
References
[1] E. Maibach, T. Myers and A. Leiserowitz, “Climate scientists need to set the record straight: There is a scientific consensus that human‐caused climate change is happening,” Earth’s Future, vol. 2, no. 5, pp. 295-298, 2014.
[2] “The Causes of Climate Change,” NASA, 14-Apr-2020. [Online]. Available: https://climate.nasa.gov/causes/. [Accessed: 24-Apr-2020].
[3] J. Hansen, M. Sato, P. Kharecha, G. Russell, D. W. Lea and M. Siddall, “Climate Change and Trace Gases,” Philosophical Transactions The Royal Society, vol. 365, no. 1856, pp. 1925-1954, 2007.
[4] “Global Surface Temperature,” NASA, 23-Jan-2020. [Online]. Available: https://climate.nasa.gov/vital-signs/global-temperature/. [Accessed: 24-Apr-2020].
[5] M. Rafiei, M. Khooban, M. A. Igder and J. Boudjadar, “A Novel Approach to Overcome the Limitations of Reliability Centered Maintenance Implementation on the Smart Grid Distance Protection System,” in IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 2, pp. 320-324, Feb. 2020.
[6] J. Moubray, “Reliability-Centered Maintenance,” Technometrics, vol. 38, no. 1, p. 87, 1996.
[7] J. A. Cardenas, L. Gemoets, J. H. A. Rosas and R. Sarfi, “A literature survey on Smart Grid distribution: an analytical approach,” Journal of Cleaner Production, vol. 65, pp. 202-216, 2014.
[8] A. Kadam, “Tech & Services: IoT: Paving the Way for Smart Grid Network,” Power Watch India, 2016. Available: https://search.proquest.com/docview/1803236037?accountid=14541.
[9] D. Team, “The Role of IoT in Smart Grid Technology and Applications,” Digiteum, 18-Dec-2019. [Online]. Available: https://www.digiteum.com/iot-smart-grid-technology. [Accessed: 29-Mar-2020].
[10] A. Kaygusuz, Ö. Tuttokmağı, C. Keleş and B. B. Alagöz, “Possible Contributions of Smart Grids to Regional Development of Countries,” 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 2018, pp. 1-4
[11] R. G. Pratt, P. J. Balducci, C. Gerkensmeyer, S. Katipamula, M. C. Kintner-Meyer, T. F. Sanquist, K. P. Schneider, and T. J. Secrest, “The Smart Grid: An Estimation of the Energy and CO2 Benefits,” 2010.
[12] Y. Chen, S. Li, L. Zhang, J. Han, X. Qiu and Z. Dai, “Condition-Based Maintenance of Protection Systems Based on Hierachical Fuzzy Evaluation,” in 2019 IEEE PES Innovative Smart Grid Technologies Asia, Asia, 2019.
[13] J. Kim, Y. Ahn and H. Yeo, “A comparative study of time-based maintenance and condition-based maintenance for optimal choice of maintenance policy,” Structure and Infrastructure Engineering, vol. 12, no. 12, p. 1525–1536, 2016.
[14] N. S. Nafi, K. Ahmed, M. A. Gregory, and M. Datta, “A survey of smart grid architectures, applications, benefits and standardization,” Journal of Network and Computer Applications, vol. 76, pp. 23–36, 2016.
[15] X. Fang, S. Misra, G. Xue, and D. Yang, “Smart Grid — The New and Improved Power Grid: A Survey,” IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 944–980, 2012.
[16] J. Sikorska, L. Hammond and P. Kelly, “Identifying Failure Modes Retrospectively using RCM Data,” ICOMS Asset management conference, 2007.
[17] B. Hoppenstedt et al, “Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems—A Bibliometric Analysis,” Applied Sciences, vol. 8, (6), 2018. Available: https://search-proquest com.mutex.gmu.edu/docview/2315523484?accountid=14541. DOI: http://dx.doi.org.mutex.gmu.edu/10.3390/app8060916.
[18] R. L. Rivest and J. C. N. Schuldt, Spritz—A Spongy RC4-like Stream Cipher and Hash Function, IACR Cryptology ePrint Archive, Santa Barbara, CA, USA, 2014. [2] Kiarie, Lincoln Kamau, et al. “Application of Spritz Encryption in Smart Meters to Protect Consumer Data.” Journal of Computer Networks and Communications, vol. 2019, 2019, pp. 1–10., doi:10.1155/2019/5910528.
[19] Kiarie, L., Langat, P. and Muriithi, C., 2019. Application of Spritz Encryption in Smart Meters to Protect Consumer Data. Journal of Computer Networks and Communications, 2019, pp.1-10.
[20] IPCC Fifth Assessment Report, 2014 United States Global Change Research Program, “Global Climate Change Impacts in the United States,” Cambridge University Press, 2009 Naomi Oreskes, “The Scientific Consensus on Climate Change,” Science 3 December 2004: Vol. 306 no. 5702 p. 1686 DOI: 10.1126/science.1103618
[21] Mike Lockwood, “Solar Change and Climate: an update in the light of the current exceptional solar minimum,” Proceedings of the Royal Society A, 2 December 2009, doi 10.1098/rspa.2009.0519;
Judith Lean, “Cycles and trends in solar irradiance and climate,” Wiley Interdisciplinary Reviews: Climate Change, vol. 1, January/February 2010, 111-122.
[22] “Data Encryption Standard (DES) and Advanced Encryption Standard (AES).” Springer Reference, doi:10.1007/springerreference_73130.
[23] Markovic, Dragan & Branović, Irina & Popovic, Ranko. (2015). Smart Grid and nanotechnologies: a solution for clean and sustainable energy. Energy and Emission Control Technologies. Volume 3. 1 – 13. 10.2147/EECT.S48124
[24] Yang, C., Wu, J., Wang, L., Zhang, X., Li, L., & Liu, S. (2020). Smart Grid Monitoring Systems based on Advanced Encryption Standard and Wireless Local Area Network. IOP Conference Series: Materials Science and Engineering, 719, 012056. doi:10.1088/1757-899x/719/1/012056