Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network
This work addresses carbon emission reduction and operational efficiency in distribution networks for energy management, but it is incremental as it builds on existing multiagent and reinforcement learning methods.
The paper tackles low-carbon demand management in distribution networks by proposing a multiagent bi-level operation framework, where agents optimize load control and network dispatching under carbon emission constraints, and demonstrates effectiveness in case studies with IEEE 33-bus and 123-bus systems in terms of satisfying carbon constraints, ensuring safe operation, and preserving privacy.
This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.