peer-to-peer energy sharing for enhancing networked microgrids resilience considering threats to data availability

This paper studies the simultaneous resilience enhancement of networked microgrids (nmgs) operation in a peer-to-peer way against extreme weather events and threats to data availability (da). applying the model predictive control (mpc) method and dynamic usage of energy storage helps microgrids (mgs) to mitigate the uncertainties of events impacts and increase their adaptation ability by rescheduling at each time step. however, despite the decentralized implementation, da threats, like a denial of service attack or mgs’ communication network damage due to the main event impact, cause communication network islanding and result in incorrect convergence of consensus values for energy sharing. hence, mgs share the prespecified preamble vectors along with shared energy values using the same communication protocol to overcome the above problems. furthermore, the impact of reducing the length of shared data by utilizing the mpc approach and the compressive sensing method for the large-scale communication network with low connectivity and bandwidth limitation is investigated. numerical results show the more resilient operation of mgs against simultaneous threats to the cyber-physical infrastructures. in this case, although the system performance level decreases, this decrease is lower than the non-resilient case against these types of simultaneous threats.

peer-to-peer energy sharing for enhancing networked microgrids resilience considering threats to data availability

this paper studies the simultaneous resilience enhancement of networked microgrids (nmgs) operation in a peer-to-peer way against extreme weather events and threats to data availability (da). applying the model predictive control (mpc) method and dynamic usage of energy storage helps microgrids (mgs) to mitigate the uncertainties of events impacts and increase their adaptation ability by rescheduling at each time step. however, despite the decentralized implementation, da threats, like a denial of service attack or mgs’ communication network damage due to the main event impact, cause communication network islanding and result in incorrect convergence of consensus values for energy sharing. hence, mgs share the prespecified preamble vectors along with shared energy values using the same communication protocol to overcome the above problems. furthermore, the impact of reducing the length of shared data by utilizing the mpc approach and the compressive sensing method for the large-scale communication network with low connectivity and bandwidth limitation is investigated. numerical results show the more resilient operation of mgs against simultaneous threats to the cyber-physical infrastructures. in this case, although the system performance level decreases, this decrease is lower than the non-resilient case against these types of simultaneous threats.