Urban wastewater sector is being pushed to optimize processes in order to reduce energy consumption without compromising its quality standards. Energy costs can represent a significant share of the global operational costs (between 50 and 60 ) in an intensive energy consumer. Pumping is the largest consumer of electrical energy in a wastewater treatment plant. Thus, the optimal control of pump units can help the utilities to decrease operational costs. This work describes an innovative predictive control policy for wastewater variable-frequency pumps that minimize electrical energy consumption, considering uncertainty forecasts for wastewater intake rate and information collected by sensors accessible through the Supervisory Control and Data Acquisition system. The proposed control method combines statistical learning (regression and predictive models) and deep reinforcement learning (Proximal Policy Optimization). The following main original contributions are produced: i) model-free and data-driven predictive control; ii) control philosophy focused on operating the tank with a variable wastewater set-point level; iii) use of supervised learning to generate synthetic data for pre-training the reinforcement learning policy, without the need to physically interact with the system. The results for a real case-study during 90 days show a 16.7 decrease in electrical energy consumption while still achieving a 97 reduction in the number of alarms (tank level above 7.2 meters) when compared with the current operating scenario (operating with a fixed set-point level). The numerical analysis showed that the proposed data-driven method is able to explore the trade-off between number of alarms and consumption minimization, offering different options to decision-makers.
All the aforementioned works share the following characteristics: i) are focused in operational scheduling of water distribution systems; ii) do not cover real-time control of pumping units (i.e., continuous optimization) and are designed for fixed-speed motored pumps (i.e., integer variables); iii) the formulations rely in approximations of the hydraulic model. Moreover, in WWTP, long-term'' flexibility (i.e., load shifting) is lower in comparison to water distribution. Therefore, most of the potential energy savings are a result of short-term'' flexibility, i.e., energy optimization close to real-time of variable-speed pumps. For instance, the MPC proposed by van in @cite_2 , can only be applicable in practice under certain conditions: i) constant or known water inflow rate; ii) large water reservoirs that allow pump scheduling in a 24 hours window (e.g., considering different electricity tariffs); iii) binary (on off) control of pumps.
@cite_2
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This paper defines and simulates a closed-loop optimal control strategy for load shifting in a plant that is charged for electricity on both time-of-use (TOU) and maximum demand (MD). A model predictive control approach is used to implement the closed-loop optimal control model, and the optimization problem is solved with integer programming. The simulated control model yields near optimal switching times which reduce the TOU and MD costs. The results show a saving of 5.8 for the overall plant, and the largest portion of the saving is due to a reduction in MD. The effect of disturbances, model uncertainty and plant failure is also simulated to demonstrate the benefits of a model predictive control model.