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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 m) 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.

The increase penetration of renewable energy sources (RES) into the European power system has introduced a significant amount of variability and uncertainty in the generation profiles raising the needs for ancillary services as well as other tools like demand response, improved generation forecasting techniques and changes to the market design. While RES is able to replace energy produced by the traditional centralized generation, it cannot displace its capacity in terms of ancillary services provided. Therefore, centralized generation capacity must be retained to perform this function leading to over-capacity issues and underutilisation of the assets. Large-scale reversible hydro power plants represent the majority of the storage solution installed in the power system. This technology comes with high investments costs, hence the constant search for methods to increase and diversify the sources of revenue. Traditional fixed speed pump storage units typically operate in the day-ahead market to perform price arbitrage and, in some specific cases, provide downward replacement reserve (RR). Variable speed pump storage can not only participate in RR but also contribute to FRR, given their ability to control its operating point in pumping mode. This work does an extended analysis of a complete bidding strategy for Pumped Storage Power, enhancing the economic advantages of variable speed pump units in comparison with fixed ones.