Modeling water resources management at the basin level: review and future directions

Water quality / Water resources development / Agricultural production / River basin development / Mathematical models / Simulation models / Water allocation / Policy / Economic aspects / Hydrology / Reservoir operation / Groundwater management / Drainage / Conjunctive use / Surface water / GIS / Decision support systems / Optimization methods / Water supply

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

Design of biomass value chains that are synergistic with the food-energy-water nexus: strategies and opportunities

Humanity’s future sustainable supply of energy, fuels and materials is aiming towards renewable sources such as biomass. Several studies on biomass value chains (BVCs) have demonstrated the feasibility of biomass in replacing fossil fuels. However, many of the activities along the chain can disrupt the food–energy–water (FEW) nexus given that these resource systems have been ever more interlinked due to increased global population and urbanisation. Essentially, the design of BVCs has to integrate the systems-thinking approach of the FEW nexus; such that, existing concerns on food, water and energy security, as well as the interactions of the BVCs with the nexus, can be incorporated in future policies. To date, there has been little to no literature that captures the synergistic opportunities between BVCs and the FEW nexus. This paper presents the first survey of process systems engineering approaches for the design of BVCs, focusing on whether and how these approaches considered synergies with the FEW nexus. Among the surveyed mathematical models, the approaches include multi-stage supply chain, temporal and spatial integration, multi-objective optimisation and uncertainty-based risk management. Although the majority of current studies are more focused on the economic impacts of BVCs, the mathematical tools can be remarkably useful in addressing critical sustainability issues in BVCs. Thus, future research directions must capture the details of food–energy–water interactions with the BVCs, together with the development of more insightful multi-scale, multi-stage, multi-objective and uncertainty-based approaches

Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

Optimization Methodologies in Complex Water Supply Systems for Energy Saving and a Correct Management under Uncertainty

Nowadays, the management of complex water supply systems needs to pay a close attention to economic aspects concerning high costs due to energetic management. Among them, the optimization of water pumping plants activation schedules is a significant issue when managing emergency and costly water transfers under drought risk. These problems are affected by a high uncertainty level, which is difficult to be faced. In this class of problems, uncertainty lies in water availability, demand behavior, electric prices and so on. Therefore, in order to provide a reliable solution, this research wants to develop some approaches of optimization under uncertainty, dealing with water resources management problems concerning multi-users and multi-reservoirs systems, especially referring to the definition of optimal activation rules for emergency pumping stations in drought conditions. In scarcity situations, the evaluation of different solutions is intimately related to the future water resource availability and the opportunity to provide water through the activation of emergency and costly water transfers. Hence, the water system optimization problem needs to deal with uncertainties particularly in treating the effectiveness of emergency measures activation to face droughts. The research analysis wants to assure simultaneously an energy saving and a correct management in complex water supply system under uncertainty conditions. The formulation of this problem highlights a complicated decision procedure, considering the requirements duality: to guarantee a complete water demands fulfilment respecting an energy saving policy. The obtained results should allow the water system’s authority to get a robust decision policy, minimizing the risk of wrong future decisions. A cost-risk balancing approach has been here developed to manage this problem, in order to balance the damages due to shortages of water and the energy-cost requirements of pumping plants. In a first step, the problem has been solved using a traditional Scenario Analysis Approach with a two stages stochastic programming. The obtained results using Scenario Analysis Approach were appreciable considering a limited number of historical scenarios characterized by a short time horizon. Nevertheless, in a second phase, when increasing the number of considered scenarios by generation of a new synthetic database in order to take into account the effect of climate and hydrological changes, some computational problems related to the dimensions of the model arose. Therefore, to solve these computational difficulties, it is been necessary to apply a specialized approach for optimization under uncertainty. Hence, a simulation model has been coupled with an optimization module using the Stochastic Gradient Methods. Testing the effectiveness of this proposal, an application of the modelling approach has been developed in a water-shortage prone area in South-Sardinia (Italy), characterized by Mediterranean climate and high annual variability in hydrological inputs to reservoirs. By applying the combined simulation and optimization procedure a robust decision strategy in pumping activation was obtained considering also the synthetic database