Stochastic Power-Water Coordination: Unlocking Flexibility in Hybrid RO Desalination Plants via Variable-Speed Pumps and Tank Mixing

TL;DR

This paper introduces a novel coordinated operation framework for hybrid desalination plants that leverages variable-speed pumps and tank management to enhance flexibility and reduce costs in power systems.

Contribution

It develops a detailed, linearized model of a hybrid RO desalination plant and proposes a stochastic optimization method for integrated scheduling with power systems.

Findings

Up to 6% reduction in operating costs.

Enhanced operational flexibility through proactive tank and salinity management.

Effective handling of uncertainties with the proposed stochastic optimization.

Abstract

Water desalination plants (DPs) are among the most critical infrastructures and largest electricity loads in water-scarce regions worldwide. Although reverse osmosis (RO) desalination is the most energy-efficient and dominant technology, it remains energy-intensive but can offer substantial flexibility potential for power systems. This paper proposes a coordinated operation framework for power systems and DPs that explicitly accounts for both systems' operational constraints and fully unlocks DP flexibility. To achieve this, a detailed DP model is developed, incorporating the characteristics of an actual high-pressure pump with variable-speed operation, on-off operation with flushing requirements, water quality constraints, and water dynamics and salt mixing in the storage tank. By proactively managing freshwater storage and tank salinity in a closed-loop coordinated scheduling framework, the operational flexibility of the DP is significantly enhanced. With appropriate simplification and linearization, the resulting coordinated scheduling problem is formulated as a tractable mixed-integer linear programming (MILP) model, and a two-step decomposed commitment-scheduling stochastic optimization (TDCSO) is proposed to efficiently address uncertainties. Case studies validate the proposed approach and demonstrate up to a 6% operating cost reduction.