Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game
Abstract
:1. Introduction
- Development of a peer-to-peer energy trading platform to facilitate the secure exchange of energy amongst prosumers.
- Formulation of energy trading market models as a Stackelberg game involving prosumers, and explanation of the existence of equilibrium in the suggested Stackelberg game.
- Introduction of a novel method for winner determination of the game using a modified Cuckoo search algorithm and the architecture of blockchain technology.
- Implementation of the proposed trading mechanism on the energy trading platform and a numerical study of the winner determination methods.
Organization of the Paper
2. Peer-to-Peer Energy Trading Platform
2.1. Considerations for the Design of the Platform
- Privacy—The specifics of an agent’s energy use, production, and exchange should not be disclosed to other agents. If other agents have access to this information, they can utilize it to build and design strategies.
- No single point of failure—Agents are connected to the platform, and it is not governed by a central authority. If a system agent or node fails, other agents should not be affected.
- Integrity—Malicious agents/nodes should not be able to profit from making faulty bids through deceptive conduct.
- Authentication—The devices and agents connecting to the system must be correctly identified and authenticated.
2.2. Overview of the Platform
3. Architecture of the Market
3.1. Design Considerations of the Market
- Decentralized communication—There are two mechanisms that can be used.
- −
- Direct bargaining—Prosumers are capable of interacting directly with one another and reaching an agreement. In this mechanism, prosumers continually seek to maximize their welfare, regardless of the grid’s overall performance. Additionally, participants must divulge sensitive information to others, jeopardizing the agents’ confidentiality.
- −
- Smart contract—A smart contract can be implemented in the blockchain layer with the primary goal of maximizing the overall profit of the smart grid while boosting participant payoffs. This approach alleviates the need for prosumers to disclose sensitive information. Additionally, the aggregate profit is increased rather than the profit of individual agents, guaranteeing that no agent is mistreated.
Hence, a governing smart contract is used that will ensure that the overall profit of the market is optimized. - Delivery period—This element defines the market’s time scale. It can range from day-ahead to real-time scheduling, depending on the agents’ requirements.
- Winner determination/price clearing method—The mechanism by which the quantity of energy traded between prosumers and the price at which they agree on the transaction are determined.
3.2. Market Design
3.2.1. Initialization
3.2.2. Affiliation
- AffiliationID—The ID of the agent given by the ETSC after affiliation.
- ActorType—The indicator of whether the agent will act as a buyer or a seller
- Timestamp—The timestamp at which the bid was sent by the user. This is used to prevent double spending of the tokens [15].
- AcceptablePrice—If the actor type is a buyer, the highest price the buyer can afford is specified; if the actor type is a seller, the lowest price acceptable is specified.
- Quantity—The maximum quantity that is provided/required by the prosumer.
4. Energy Trading
4.1. Utility Function
5. Stackelberg Game
5.1. Existence of Stackelberg Equilibrium
5.2. Solutions to the Stackelberg Game
6. Swarm Intelligent Solution
6.1. Social Welfare Smart Contract (SWSC)
6.2. Penalty Functions
7. Illustrative Results
7.1. Implementation of the Platform
7.2. Experimental Setup
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Zhang, C.; Wu, J.; Long, C.; Cheng, M. Review of existing peer-to-peer energy trading projects. Energy Procedia 2017, 105, 2563–2568. [Google Scholar] [CrossRef]
- Yatchew, A.; Baziliauskas, A. Ontario feed-in-tariff programs. Energy Policy 2011, 39, 3885–3893. [Google Scholar] [CrossRef]
- Colak, I.; Fulli, G.; Sagiroglu, S.; Yesilbudak, M.; Covrig, C.F. Smart grid projects in Europe: Current status, maturity and future scenarios. Appl. Energy 2015, 152, 58–70. [Google Scholar] [CrossRef]
- Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system. Decentralized Bus. Rev. 2008, 21260. [Google Scholar]
- Soto, E.A.; Bosman, L.B.; Wollega, E.; Leon-Salas, W.D. Peer-to-peer energy trading: A review of the literature. Appl. Energy 2021, 283, 116268. [Google Scholar] [CrossRef]
- Edussuriya, C.; Vithanage, K.; Bandara, N.; Alawatugoda, J.; Sandirigama, M.; Jayasinghe, U.; Shone, N.; Lee, G.M. BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform. Electronics 2020, 9, 1525. [Google Scholar] [CrossRef]
- Hamouda, M.R.; Nassar, M.E.; Salama, M. A Novel Energy Trading Framework Using Adapted Blockchain Technology. IEEE Trans. Smart Grid 2020, 12, 2165–2175. [Google Scholar] [CrossRef]
- Pop, C.; Cioara, T.; Antal, M.; Anghel, I.; Salomie, I.; Bertoncini, M. Blockchain based decentralized management of demand response programs in smart energy grids. Sensors 2018, 18, 162. [Google Scholar] [CrossRef] [Green Version]
- Wang, S.; Taha, A.F.; Wang, J.; Kvaternik, K.; Hahn, A. Energy crowdsourcing and peer-to-peer energy trading in blockchain-enabled smart grids. IEEE Trans. Syst. Man Cybern. Syst. 2019, 49, 1612–1623. [Google Scholar] [CrossRef] [Green Version]
- Thakur, S.; Breslin, J.G. Peer to peer energy trade among microgrids using blockchain based distributed coalition formation method. Technol. Econ. Smart Grids Sustain. Energy 2018, 3, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Jamil, F.; Iqbal, N.; Ahmad, S.; Kim, D. Peer-to-peer energy trading mechanism based on blockchain and machine learning for sustainable electrical power supply in smart grid. IEEE Access 2021, 9, 39193–39217. [Google Scholar] [CrossRef]
- Son, Y.B.; Im, J.H.; Kwon, H.Y.; Jeon, S.Y.; Lee, M.K. Privacy-preserving peer-to-peer energy trading in blockchain-enabled smart grids using functional encryption. Energies 2020, 13, 1321. [Google Scholar] [CrossRef] [Green Version]
- Tushar, W.; Zhang, J.A.; Smith, D.B.; Poor, H.V.; Thiébaux, S. Prioritizing consumers in smart grid: A game theoretic approach. IEEE Trans. Smart Grid 2014, 5, 1429–1438. [Google Scholar] [CrossRef] [Green Version]
- Liu, N.; Yu, X.; Fan, W.; Hu, C.; Rui, T.; Chen, Q.; Zhang, J. Online energy sharing for nanogrid clusters: A Lyapunov optimization approach. IEEE Trans. Smart Grid 2017, 9, 4624–4636. [Google Scholar] [CrossRef]
- Chen, S.; Shroff, N.B.; Sinha, P. Energy trading in the smart grid: From end-user’s perspective. In Proceedings of the 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 3–6 November 2013; pp. 327–331. [Google Scholar]
- Doan, H.T.; Cho, J.; Kim, D. Peer-to-Peer Energy Trading in Smart Grid Through Blockchain: A Double Auction-Based Game Theoretic Approach. IEEE Access 2021, 9, 49206–49218. [Google Scholar] [CrossRef]
- Guerrero, J.; Chapman, A.C.; Verbič, G. Decentralized P2P energy trading under network constraints in a low-voltage network. IEEE Trans. Smart Grid 2018, 10, 5163–5173. [Google Scholar] [CrossRef] [Green Version]
- Abdalzaher, M.S.; Seddik, K.; Muta, O. An effective Stackelberg game for high-assurance of data trustworthiness in WSNs. In Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece, 3–6 July 2017; pp. 1257–1262. [Google Scholar]
- Abdalzaher, M.S.; Seddik, K.; Muta, O. Using Stackelberg game to enhance cognitive radio sensor networks security. IET Commun. 2017, 11, 1503–1511. [Google Scholar] [CrossRef]
- Abdalzaher, M.S.; Muta, O. Employing game theory and TDMA protocol to enhance security and manage power consumption in WSNs-based cognitive radio. IEEE Access 2019, 7, 132923–132936. [Google Scholar] [CrossRef]
- Abdalzaher, M.S.; Muta, O. A game-theoretic approach for enhancing security and data trustworthiness in IoT applications. IEEE Internet Things J. 2020, 7, 11250–11261. [Google Scholar] [CrossRef]
- Chen, Y.; Wei, W.; Wang, H.; Zhou, Q.; Catalão, J.P. An energy sharing mechanism achieving the same flexibility as centralized dispatch. IEEE Trans. Smart Grid 2021, 12, 3379–3389. [Google Scholar] [CrossRef]
- Esmat, A.; de Vos, M.; Ghiassi-Farrokhfal, Y.; Palensky, P.; Epema, D. A novel decentralized platform for peer-to-peer energy trading market with blockchain technology. Appl. Energy 2021, 282, 116123. [Google Scholar] [CrossRef]
- Hart, D.G. Using AMI to realize the Smart Grid. In Proceedings of the 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008; Volume 10. [Google Scholar]
- Reddy, P.P.; Veloso, M.M. Strategy learning for autonomous agents in smart grid markets. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain, 16–22 July 2011. [Google Scholar]
- Anoh, K.; Bajovic, D.; Vukobratovic, D.; Adebisi, B.; Jakovetic, D.; Cosovic, M. Distributed energy trading with communication constraints. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 21–25 October 2018; pp. 1–6. [Google Scholar]
- Tushar, W.; Saha, T.K.; Yuen, C.; Morstyn, T.; Poor, H.V.; Bean, R. Grid influenced peer-to-peer energy trading. IEEE Trans. Smart Grid 2019, 11, 1407–1418. [Google Scholar] [CrossRef] [Green Version]
- Yan, M.; Shahidehpour, M.; Paaso, A.; Zhang, L.; Alabdulwahab, A.; Abusorrah, A. Distribution network-constrained optimization of peer-to-peer transactive energy trading among multi-microgrids. IEEE Trans. Smart Grid 2020, 12, 1033–1047. [Google Scholar] [CrossRef]
- Anoh, K.; Maharjan, S.; Ikpehai, A.; Zhang, Y.; Adebisi, B. Energy peer-to-peer trading in virtual microgrids in smart grids: A game-theoretic approach. IEEE Trans. Smart Grid 2019, 11, 1264–1275. [Google Scholar] [CrossRef]
- Carøe, C.C.; Schultz, R. Dual decomposition in stochastic integer programming. Oper. Res. Lett. 1999, 24, 37–45. [Google Scholar] [CrossRef]
- Gandomi, A.H.; Yang, X.S.; Alavi, A.H. Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Eng. Comput. 2013, 29, 17–35. [Google Scholar] [CrossRef]
- Wang, Z.; Qin, C.; Wan, B.; Song, W.W. A Comparative Study of Common Nature-Inspired Algorithms for Continuous Function Optimization. Entropy 2021, 23, 874. [Google Scholar] [CrossRef]
- Parsopoulos, K.E.; Vrahatis, M.N. Particle swarm optimization method for constrained optimization problems. Intell. Technol. Theory Appl. New Trends Intell. Technol. 2002, 76, 214–220. [Google Scholar]
- Hyperledger. Open Source Blockchain Technologies. Available online: https://www.hyperledger.org/ (accessed on 20 October 2022).
- Ongaro, D.; Ousterhout, J. In search of an understandable consensus algorithm. In Proceedings of the 2014 USENIX Annual Technical Conference (Usenix ATC 14), Philadelphia, PA, USA, 19–20 June 2014; pp. 305–319. [Google Scholar]
- Androulaki, E.; Barger, A.; Bortnikov, V.; Cachin, C.; Christidis, K.; De Caro, A.; Enyeart, D.; Ferris, C.; Laventman, G.; Manevich, Y.; et al. Hyperledger fabric: A distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference, Porto, Portugal, 23–26 April 2018; pp. 1–15. [Google Scholar]
- U.S. Energy Information Administration (EIA). The United States Energy Consumption by Sources. Available online: https://www.eia.gov/ (accessed on 10 October 2022).
Time Slot | Total Energy | CUO | CSA-SWSC | AVG |
---|---|---|---|---|
1 | 5.0077 | 0.9399 | 0.565 | 0.8346 |
2 | 9.598 | 2.4317 | 1.2947 | 1.2519 |
3 | 12.5192 | 5.0027 | 3.5231 | 3.7557 |
4 | 14.1884 | 7.6362 | 5.0909 | 5.425 |
5 | 13.3538 | 4.6005 | 3.1536 | 3.3384 |
6 | 17.5268 | 3.8667 | 2.8322 | 6.2596 |
7 | 15.4403 | 9.0495 | 7.744 | 8.3461 |
8 | 15.8576 | 3.4755 | 2.7033 | 2.5038 |
9 | 16.6922 | 6.6624 | 3.4755 | 8.3461 |
10 | 16.6922 | 5.5974 | 3.5784 | 4.1731 |
11 | 14.6057 | 3.298 | 2.2661 | 2.5038 |
12 | 4.1731 | 1.0609 | 0 | 0.4173 |
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Edussuriya, C.; Marikkar, U.; Wickramasinghe, S.; Jayasinghe, U.; Alawatugoda, J. Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game. Energies 2023, 16, 2434. https://doi.org/10.3390/en16052434
Edussuriya C, Marikkar U, Wickramasinghe S, Jayasinghe U, Alawatugoda J. Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game. Energies. 2023; 16(5):2434. https://doi.org/10.3390/en16052434
Chicago/Turabian StyleEdussuriya, Chathurangi, Umar Marikkar, Subash Wickramasinghe, Upul Jayasinghe, and Janaka Alawatugoda. 2023. "Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game" Energies 16, no. 5: 2434. https://doi.org/10.3390/en16052434