Abstract: To address the scheduling challenges brought by high energy consumption, insufficient resource coordination, and uncertainties in photovoltaic output and influent load in wastewater treatment plant (WWTP) clusters, this paper proposes a coordinated scheduling strategy for integrated "source-load-storage" WWTP clusters led by a plant-network integrated operator. The core of this strategy is a cooperative game framework based on asymmetric Nash bargaining theory, which decomposes the coordinated scheduling problem into two sub-problems: social cost minimization (P1) and fair allocation of cooperative benefits (P2). To handle uncertainties in P1, a two-stage (day-ahead and real-time) robust optimization model is established to ensure the robustness of the scheduling scheme. Case study results demonstrate that the proposed method not only significantly reduces the total operating cost of the cluster but also incentivizes active participation of each plant through a contribution-based benefit allocation mechanism. Moreover, it effectively mitigates uncertainty risks, exhibiting superior economic efficiency, fairness, and robustness.

Key words: wastewater treatment plant clusters, plant-network integration, source-load-storage, Nash bargaining, robust optimization