Distributed collaborative scheduling of combined heat and power systems based on quantized privacy preserving consensus
The conventional centralized scheduling proves ineffective in addressing the suboptimal real-time performance caused by the large-scale integration of energy devices.This paper introduces a distributed optimization scheduling scheme for complex communication environments within a combined heat and power(CHP)system,incorporating consensus algorithm.Regarding the privacy that exists in the decision-making information exchange process,an effective mask function is introduced to prevent the sensitive information of the system from being leaked during the cooperative scheduling process of various types of equipment.Based on this privacy scheme,taking into account the limited communication resources and distributed communication lag in the communication environment,a distributed optimization scheduling scheme based on a quantified privacy consistency algorithm is proposed.This scheme enables the rapid attainment of optimal solutions for the collaborative scheduling of energy devices.Additionally,we conducted a rigorous theoretical analysis on the convergence of the designed algorithm to ensure its feasibility.It is worth pointing out that the privacy quantification scheme designed in this paper is also suitable for distributed optimization scheduling based on the weighted average consensus algorithm.Finally,by experimenting with the CHP system model,we have verified that the proposed algorithm can minimize operating costs while maintaining a balance of supply and demand,and it demonstrates good convergence performance.
combined heat and powerenergy systemsquantized consensusdistributed schedulingprivacy protection
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