  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.

Systems and Control;Artificial Intelligence;Systems and Control