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The inherent uncertainty in water demand poses significant challenges to water distribution systems' (WDSs) efficiency and quality. This study introduces a model predictive control framework tailored for real‐time optimal operation of WDSs under uncertain demand and maximum water age constraints to ensure water quality requirements. The methodology presented utilizes a scenario‐based energy‐cost optimization approach to account for demand uncertainties. As water age is unmeasurable, a model linearly related to some measured/observed network variables (e.g., flows, water levels, etc.) is proposed to infer water age values. Then, a scenario‐based mixed‐integer linear programming problem is formulated and solved repeatedly online to adjust operational strategies for minimizing energy operation costs while satisfying water age constraints. The outcome of this model is a feasible operation scheme for all demand scenarios, providing a cost‐effective decision that meets water age limits. The proposed model is tested on a real‐world‐based test case and validated through a series of sensitivity analyses. Plain Language Summary The uncertainty in water demand makes it hard to maintain the efficiency and quality of water distribution systems. This study presents a control framework designed for the real‐time optimal operation of these systems, taking into account unpredictable demand and water age requirements to ensure water quality. The method uses scenario‐based optimization to handle demand uncertainties. Since water age can't be directly measured, the study proposes a model that estimates it based on certain observable variables in the network. A scenario‐based mixed‐integer linear programming problem is then repeatedly solved online to adjust operational strategies, aiming to minimize costs while meeting water age requirements. This model provides a practical operation plan for all demand scenarios, offering a cost‐effective solution that adheres to water age limits. The proposed model is tested on a real‐world scenario and validated through various sensitivity analyses. Key Points The paper presents a real‐time control framework for water distribution systems under demand uncertainty It uses scenario‐based optimization to minimize costs and maintain water quality Validation on the C‐Town network model effectively performed under varying demand conditions

s To extract optimal reservoir operation policies, it is important to consider different objectives simultaneously. In this study, by applying a meta-heuristic, multi-objective optimization approach, real-time optimal operation rules of Karoon4 dam are extracted as two-objectives by considering performance criteria of the reservoir as objective functions. The rules are extracted by relating water release to storage volume and inflow with simple linear equations for two states of real-time operation, which are called dependent on forecast state and independent of forecast state. In the dependent on forecast state, inflow volume is considered during the current period and in the independent of forecast state, inflow volume is considered during the period before the operation. In fact, by associating water release in each period to inflow during a past period, inflow forecasting is employed. Multi-objective optimization results of conflicting objectives of reliability and vulnerability in hydropower generation of Karoon4 are exhibited as a Pareto curve by employing the non-dominated sorting genetic algorithm II (NSGA-II). Each point on the Pareto curve represents an optimal operation policy. Actually, based on the priority and desired criterion of the reservoir operator, for the value of any criterion, the optimal value of another criterion and its optimal operation policy can be extracted by using a Pareto curve. Maximum reliabilities of Pareto curves in first and second states of real-time operation are 60.83 and 60.00 %, respectively, with corresponding minimum vulnerabilities of 8.52 and 9.08 %. Although the dependence of reservoir release in each period to inflow during the previous period (i.e., independent of forecast state) improves the values of objective functions compared with the dependent on forecast state, the difference is insignificant. Since the independent of forecast state of real-time operation does not depend on inflow forecasting, the small difference is negligible and so this state seems more efficient. Also, a comparison of results of long-term operation with real-time operation shows that bcomputed real-time operation rules are flexible and accurate.

This study proposes an integrated framework that combines real‐time simulation with offline optimization to evaluate and enhance the operational performance of demand‐responsive transit (DRT) systems. Using the Kalamazoo Metro DRT as a case study, the Transportation Analysis and Mobility Optimization System (TAMOS) is employed to replicate dynamic booking behavior and vehicle dispatch logic. These real‐time operations are benchmarked against a static capacitated vehicle routing problem with time windows (CVRPTW), solved using Google OR‐Tools (v9.6) with the PARALLEL_CHEAPEST_INSERTION strategy to minimize fleet mileage while respecting vehicle capacity and time window constraints. Results show that the current fleet of 41 vehicles achieves a 74% service rate with an average pickup delay of 19.6 min. In contrast, the optimized CVRPTW solution fulfills 100% of trip requests with only 22 vehicles, assuming a relaxed pickup delay of 10 min. However, reducing the allowable delay to 5 min lowers trip feasibility to 65%, underscoring the operational sensitivity to temporal thresholds. The dual‐model approach illustrates how integrating real‐time simulation with optimization can quantify trade‐offs between service quality and operational efficiency. Additionally, the study introduces several enhancements to the OR‐Tools solver, including dynamic time windows, passenger‐level detour constraints, and integration with the Google Maps API for real‐world travel time matrices, improving model realism and decision relevance. The proposed framework is adaptable to various urban contexts and scalable across international settings, offering practical guidance for transit agencies in fleet sizing, delay tolerance, and service design under dynamic demand conditions.

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.

To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.

Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations.

This study investigates the role of rainfall forecast system characteristics in predictive real-time optimal operation (PRTOP) of urban drainage systems (UDSs). A simulation-optimization model is proposed integrating the stormwater management rainfall-runoff simulator, the harmony search optimization algorithm, and a rainfall forecasting module. This module generates different rainfall forecast scenarios depending on the forecast (time) horizon (FH) and the forecast type (perfect or imperfect). Five adaptive PRTOP models are compared to evaluate the relationships between the FH, forecast type, and the system’s relative regulating capacity (SRRC). The models are tested in a part of UDS of Tehran, capital of Iran. Results indicate that for the studied system, perfect knowledge of future rainfall is more beneficial for a specific range of the SRRC, equal to 80–90% of its upper bound. Besides, the performance of the PRTOP model improves with increasing the FH up to a certain point, and then having no further positive effect. Finally, the PRTOP model equipped with a hypothetical forecasting model where the forecast error is a nonlinear function of the forecast lead time still performs better than both a zero-FH reactive RTOP model and short forecast horizon PRTOP models.

The substantial challenge posed by inherent hydrological forecasting uncertainty has critical implications for the optimization of real-time reservoir operations. In response, this study introduces a stochastic framework explicitly devised to comprehensively quantify the ramifications of hydrological forecasting uncertainty, notably its temporal correlations, on the outcomes of real-time reservoir optimization and risk assessment. Furthermore, this framework seeks to delineate the pivotal influence of incorporating or neglecting these temporal dynamics on the eventual results, while concurrently elucidating the underlying mechanisms governing these discernible influences. The framework adopts a comprehensive approach to simulating hydrological forecast uncertainty through ensemble forecasts and scenario trees, employing three methods (two Monte Carlo sampling-based methods and one Gaussian copula method) to generate inflow forecast ensembles. To improve the adaptability to uncertainties in inflow forecasts, the framework incorporates a transformation of the generated ensembles into scenario trees, serving as input for a stochastic optimization model that derives the final optimal decision based on optimizing the expected value of the objective function for all scenarios. Additionally, a parallel differential evolution algorithm is proposed to solve the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China, where several numerical experiments are conducted to explore the effect of forecast uncertainty level and temporal correlation on real-time reservoir optimal operation. Results show that the temporal correlation of inflows must be considered in both inflow stochastic simulation and reservoir stochastic optimization to avoid overestimating or underestimating operational risk, potentially leading to operation failures. By examining the risk simulation surface, reservoir operators can evaluate the robustness of operational decisions and make more reliable final decisions.

Modern electrical power systems integrate renewable generation, with solar generation being one of the pioneers worldwide. In Latin America, the greatest potential and development of solar generation is found in Chile through the National Electric System. However, its energy matrix faces a crisis of drought and reduction of emissions that limits hydroelectric generation and involves the definitive withdrawal of coal generation. The dispatch of these plants is carried out by the system operator, who uses a simplified mechanism, called “economic merit list” and which does not reflect the real costs of the plants to the damage of the operating and marginal cost of the system. This inefficient dispatch scheme fails to optimize the availability of stored gas and its use over time. Therefore, a real-time redispatch model is proposed that minimizes the operation cost function of the power plants, integrating the variable generation cost as a polynomial function of the net specific fuel consumption, adding gas volume stock restrictions and water reservoirs. In addition, the redispatch model uses an innovative “maximum dispatch power” restriction, which depends on the demand associated with the automatic load disconnection scheme due to low frequency. Finally, by testing real simulation cases, the redispatch model manages to optimize the operation and dispatch costs of power plants, allowing the technical barriers of the market to be broken down with the aim of integrating ancillary services in the short term, using the power reserves in primary (PFC), secondary (SCF), and tertiary (TCF) frequency control.

Flood disaster has always been the key direction of urban governance and the real-time optimization of urban drainage system has become an important solution. At present, the control of gates in urban flood control system is dominated by rule-based control (RBC), while the research on real-time optimization control (RTC) based on gate opening is relatively few. This paper develops a real-time reservoir optimization model (RTROM) to solve the problem of urban flash flood control. The gate opening was discretized at an interval of 0.1 m as a decision variable, and the differential evolution algorithm (DE) was used to calculate the objective function of this study to obtain the optimal control strategy describing the operation of urban flood control system. The model was tested in the Jingdian Lake area in Fuzhou, China. The results show that in practice, the model can reduce the upstream flood flow by 48.2 m 3 /s, but also increase the discharge flow after a delay of 17 h. Jingdian Lake can store up to 126,000 m 3 of floodwater, and the storage capacity utilization rate has reached 68.5%. It shows that RTROM can maximize the effect of regional lake flood control. More importantly, it effectively lowered the water level of the downstream Qintin Lake by 0.53 m, reducing the flood risk faced by the Qintin Lake. Compared with rule-based control (RBC) model, RTROM is more effective in reducing urban flooding and can provide optimal operation strategies for the real-time operation of urban drainage systems.