A sustainable microgrid based on renewable energy (RWE) sources is a useful solution to reduce the environmental impact and meet customer demand as it offers advantages on the economic, environmental, and social objectives. Several previous studies have applied peer-to-peer energy trading (P2P-ET) as a feasible solution to reduce the power loss during power supply over long distances and to enhance the operating efficiency of microgrids. Due to the intermittent nature of RWE sources and demand load, it is challenging to control and manage the operation and distribution of microgrids in P2P-ET. However, the availability and the number of other local microgrids needed to connect to microgrids through electric cables in P2P-ET as well as the mechanisms to manage and balance the power loss between supply and demand have not been investigated in the current sustainable microgrid design literature. Therefore, this paper examines the sustainable microgrid design problem with P2P-ET under blockchain application, time value of money, elasticity coefficient of demand, and uncertainties to maximize overall earnings and minimize the collective environmental expenses while meeting customer demand. The two-phase stochastic programming is integrated with fuzzy probabilistic programming to determine the number, location, type of RWE, and capacity of renewable distributed generation units, the number of other local microgrids and electric cables required to connect with microgrids in the P2P-ET, the electricity flows, and price for selling electricity to demand areas and to P2P-ET. The results from the numerical experiments, conducted to assess the performance of the proposed model and the fuzzy solution algorithm, demonstrate that the use of the blockchain application in the proposed model increases the total profit by an average of 14.75% and reduces the total environmental cost by an average of 13.25% when compared to the case without the blockchain application.

[1] Debouza M, Al-Durra A, EL-Fouly THM, and Zeineldin HH. Survey on microgrids with
flexible boundaries: Strategies, applications, and future trends. Electric Power Systems Research, 205:107765, 2022.
[2] Milis K, Peremans H, and Van Passel S. The impact of policy on microgrid economics: A review. Renewable and Sustainable Energy Reviews, 81:3111–3119, 2018.
[3] Han D and Lee JH. Two-stage stochastic programming formulation for optimal design
and operation of multi-microgrid system using data-based modeling of renewable energy
sources. Applied Energy, 291:116830, 2021.
[4] Yu VF, Le THA, and Gupta JND. Sustainable microgrid design with multiple demand areas and peer-to-peer energy trading involving seasonal factors and uncertainties. Renewable and Sustainable Energy Reviews, 161:112342, 2022.
[5] Che L and Shahidehpour M. Dc microgrids: Economic operation and enhancement of
resilience by hierarchical control. IEEE Transactions on Smart Grid, 5(5):2517–2526,
2015.
[6] Rajamand S. Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand. Energy, 116917, 2020.
[7] Parag Y and Ainspan M. Sustainable microgrids: Economic, environmental and social
costs and benefits of microgrid deployment. Energy for Sustainable Development, 52:
72–81, 2019.
[8] Tsao Y-C, Thanh V-V, and Wu Q. Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs. International Journal of Electrical Power & Energy Systems, 125:106418, 2021.
[9] Chang C, Wu J, Long C, and Cheng M. Review of existing peer-to-peer trading projects. Energy Procedia, 105:2563–2568, 2017.
[10] Tsao Y-C and Thanh V-V. Toward sustainable microgrids with blockchain technologybased peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach. Renewable and Sustainable Energy Reviews, 136:110452, 2021.
[11] Andoni M, Robu V, Flynn D, Abram S, Geach D, Jenkins D, McCallum P, and Peacock
A. Blockchain technology in the energy sector: a systematic review of challenges and
opportunities. Renewable and Sustainable Energy Reviews, 100:143–174, 2019.
[12] Govindan G, Gosar V, and Singh R. Blockchain for power utilities: A view on capabilities and adoption. Digital System and Technology, 2018.
[13] Yahaya AS, Javaid N, Alzahrani FA, Rehman A, Ullah I, Shahid A, and Shafiq M.
Blockchain based sustainable local energy trading considering home energy management
and demurrage mechanism. Sustainability, 12:3385, 2020.
[14] Chen W, Zehui S, Karzan W, Nahla A, and Sarminah S. An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a modified krill herd algorithm. Journal of Cleaner Production, 272:122364, 2020.
[15] Mohammad H, Behnam MI, and Mehdi A. Day-ahead profit-based reconfigurable microgrid scheduling considering uncertain renewable generation and load demand in the presence of energy storage. Journal of Energy Storage, 28:101161, 2020.
[16] Ahmad N, Abolfazl N, Miadreza SK, and Joao PSC. Day-ahead optimal bidding of
microgrids considering uncertainties of price and renewable energy resources. Energy,
227:20476, 2021.
[17] Seyed SF, Mostafa S and Mohammad EK. Day-ahead energy management and feeder
reconfiguration for microgrids with CCHP and energy storage systems. Journal of Energy Storage, 29:101301, 2020.
[18] Kharrich M, Mohammed OH, Alshammari N and Akherraz M. Multi-objective optimization and the effect of the economic factors on the design of the microgrid hybrid system. Sustainable Cities and Society, 65:102646, 2021.
[19] Adefarati T and Bansal RC. Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources. Applied energy, 236:1089–1114, 2019.
[20] Gildenhuys T, Zhang L, Ye X, and Xia X. Optimization of the operational cost and environmental impact of a multi-microgrid system. Energy Procedia, 158:3827–3832, 2019.
[21] Barua A, Jain AK, Mishra PK, and Singh D. Design of grid connected microgrid with solar photovoltaic module. Materials Today: Proceedings, 2214–7853, 2021.
[22] Akhtar I, Kirmani S, and Jamil M. Analysis and design of a sustainable microgrid primarily powered by renewable energy sources with dynamic performance improvement. IET Renewable Power Generation, 13(7):1024–1036, 2019.
[23] Villanueva-Rosario JA, Santos-García F, Aybar-Mejía ME, Mendoza-Araya P, MolinaGarcía A. Coordinated ancillary services, market participation and communication of multi-microgrids: A review. Applied Energy, 308:118332, 2022.
[24] Narayan A and Ponnambalam K. Risk-averse stochastic programming approach for microgrid planning under uncertainty. Renewable Energy, 101:399–408, 2017.
[25] Tabar VS, Jirdehi MA, and Hemmati R. Sustainable planning of hybrid microgrid towards minimizing environmental pollution, operational cost and frequency fluctuations. Journal of cleaner production, 203:1187–1200, 2018.
[26] Tsao Y-C, Thanh V-V, Lu J-C, and Yu V. Designing sustainable supply chain networks under uncertain environments: Fuzzy multi-objective programming. Journal of Cleaner
Production, 174:1550–1565, 2018.
[27] Keshtkar A and Arzanpour S. An adaptive fuzzy logic system for residential energy management in smart grid environments. Applied Energy, 186:68–81, 2017.
[28] Ervural BC, Evren R, and Delen D. A multi-objective decision-making approach for sustainable energy investment planning. Renewable energy, 126:387–402, 2018.
[29] Zhang X, Huang G, Zhu H, and Li Y. A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties. Energy, 123:664–676, 2017.
[30] Tsao YC, Thanh VV, and Lu JC. Multiobjective robust fuzzy stochastic approach for sustainable smart grid design. Energy, 176:929–939, 2019.
[31] Alam MR, St-Hilaire M, and Kunz T. Peer-to-peer energy trading among smart homes. Applied energy, 238:1434–1443, 2019.
[32] Long C, Zhou Y, and Wu J. A game theoretic approach for peer to peer energy trading. Energy Procedia, 159:454–459, 2019.