Blockchain-Enabled Carbon and Energy Trading for Network-Constrained Coal Mines With Uncertainties

In this paper, a blockchain-enabled distributed market framework is proposed for the bi-level carbon and energy trading between coal mine integrated energy systems (CMIESs) and a virtual power plant (VPP) with network constraints. To maximize the profits of these two entities and describe their complicated interactions in the market, the bi-level trading problem is formulated as a Stackelberg game considering integrating the energy market and the cap-and-trade carbon market mechanism. Meanwhile, in the CMIES, energy recovery units and belt conveyors can be flexibly scheduled and the pumped hydroelectric storage in the VPP is scheduled for energy management. To tackle uncertainties from PV outputs, the joint trading, and the energy management is solved by the distributionally robust optimization (DRO) method. In addition, for participants' privacy, the alternating direction method of multipliers (ADMM) - based DRO algorithm is applied to solve the trading problem in a distributed framework. Further, the Proof-of-Authority (PoA) blockchain is deployed to develop a safe and anonymous market platform. Finally, case studies along with numerous comparison cases are conducted to verify the effectiveness of the proposed method. Simulation results indicate that the proposed method can effectively reduce the system operation cost and regional carbon emission, reduce the conservativeness and protect the privacy of each participant.

Automated summary

In this paper, a blockchain-enabled distributed market framework is proposed for the bi-level carbon and energy trading between coal mine integrated energy systems (CMIESs) and a virtual power plant (VPP) with network constraints. The bi-level trading problem is formulated as a Stackelberg game, considering integrating the energy market and the cap-and-trade carbon market mechanism, in order to maximize the profits of these two entities and describe their complicated interactions in the market. The proposed method effectively reduces the system operation cost and regional carbon emission, while protecting the privacy of each participant.