Co-optimization of wastewater treatment plants interconnected with smart grids

Faegheh Moazeni and Javad Khazaei

Applied Energy, 2021, vol. 298, issue C, No S0306261921005808

Abstract: Wastewater treatment plants (WWTPs) and smart grids of urban systems are in a strong nexus. Energy consumption of WWTPs will directly impact the economy of the smart grids, while the energy forecast, marketing, and resource allocation of smart grids will affect the economy of WWTPs. As such, these two critical infrastructures need to be operated and managed cooperatively to maximize the mutual economic benefits. In this paper, a mixed integer nonlinear programming (MINLP) co-optimization model is developed to address the day-ahead economic dispatch problem of smart grids embedded with interdependent links among wastewater treatment plants and smart grids. The energy demand of WWTPs is optimized by minimizing the energy consumption of the aeration module, mixed liquor pumping, primary clarifier’s influent pumping, secondary clarifier’s sludge pumping, and mixing devices that are integrated in the dynamic economic dispatch problem of smart grid. Additionally, biochemical oxygen demand (BOD) and total Kjedhal nitrogen (TKN) concentrations of the treated wastewater is also incorporated in the economic dispatch problem to ensure of the high quality of the WWTP’s effluent. Electricity consumption of the buildings is also added as a constraint to the economic dispatch, serving as a dynamic load for smart grid. Case studies are conducted to examine the impact of wastewater flow rate, influent BOD and TKN concentrations, battery efficiency, and end-of-day battery’s state of the charge constraints on economic dispatch of the smart grid. The results show that total operational cost and energy consumption of the integrated WWTP-smart grid is increased only by 3.4% and 1.75%, respectively, as the daily influent flow rate and BOD & TKN concentrations are increased from their minimum to maximum thresholds.

Keywords: Economic dispatch formulation; Demand response; Mixed integer nonlinear programming; Critical infrastructure; Energy management; Resource allocation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2021.117150

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