Curses, Tradeoffs, and Scalable Management:Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case’s relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP

Assessment of water resources management strategy under different evolutionary optimization techniques

Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (∈-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ∈-DSEA's results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value

Impacts of climate change on hydropower generation and developing adaptation measures through hydrologic modeling and multi-objective optimization

The climate change resulting from anthropogenic factors is driving governments and policy-makers to provide additional thrust on renewable energy. Hydropower, which is the dominant renewable component of the energy-mix, is also under threat due to the changing climate conditions. The present study aims to quantify the impact of climate change on hydropower generation, the associated revenues and subsequently suggest the adaptation measures through adaptive reservoir management. A modeling chain consisting of hydrologic and hydropower simulation models is adopted to evaluate the impacts of projected climate change on hydropower generation. Calibrated hydrologic models forced with the climate data from various climate models have been widely employed for future streamflow projection. A reliable modelling framework should ensure the simulation of reality with limited uncertainty, thus enhancing its predictive ability. In the literature, the hydrologic model assessment is reported to be inadequate when carried out based on only statistical objectives or limited number of evaluation metrics. In the present research, the thrust is given on improving the hydrologic model simulation through model diagnostic assessment, incorporating hydrologic signatures and multi-objective model calibration. Multi-objective evolutionary algorithm (MOEA) is coupled with the hydrologic model, Soil and Water Assessment Tool (SWAT), to perform model calibration. The methodology was first tested for Saugeen River watershed in Southern Ontario and then applied to the Magpie River watershed model located in Northern Ontario. The uncertainties contributed by the hydrologic models have generally been given a lesser focus in climate change impact analysis. In the present research, the uncertainty emanating from model parameters was investigated and found to dominate during some periods. The accounting of hydrologic model uncertainty is found to be vital for providing an improved assessment. Steephill Falls hydroelectric project located on Magpie River in Northern Ontario is considered as a case study for assessing climate change impacts on hydropower. The results show that the annual generation is not considerably affected but there is a significant seasonal redistribution on energy production. The changes in the hydropower revenues compared to the present level for the four seasons viz., winter, spring, summer and autumn are estimated to be 21.1%, 18.4%, -13.4% and -15.9%, respectively, for mid-century and 23.1%, 19.5%, -20.1% and -22.9% for end-century scenarios. In order to reduce the vulnerability of hydropower system to climate change and consequently mitigate the impacts, it will be profitable for the project owners to provide suitable adaptation measures. Adaptive reservoir management through multi-objective optimization of reservoir level was found to be an effective approach to develop adaptation measures provided additional live storage is made available. It also reduced the vulnerability of the system to climate change by 24%. The seasonal alteration in the energy production will require the project owners to arrange modification in power purchase/sharing agreement with the buyers

DIAGNOSTIC ASSESSMENT AND ADVANCEMENT OF MULTI-OBJECTIVE RESERVOIR CONTROL UNDER UNCERTAINTY

This dissertation contributes to the assessment of new scientific developments for multi-objective decision support to improve multi-purpose river basin management. The main insights of this work highlight opportunities to improve modeling of complex multi-purpose water reservoir systems and opportunities to flexibly incorporate emerging demands and hydro-climatic uncertainty. Additionally, algorithm diagnostics contributed in this work enable the water resources field to better capitalize on the rapid growth in computational power. This opens new opportunities to increase the scope of the problems that can be solved and contribute to the robustness and sustainability of water systems management worldwide. This dissertation focuses on a multi-purpose reservoir system that captures the contextual and mathematical difficulties confronted in a broad range of global multi-purpose systems challenged by multiple competing demands and uncertainty. The first study demonstrates that advances in state of the art multiobjective evolutionary optimization enables to reliably and effectively find control policies that balance conflicting tradeoffs for multi-purpose reservoir control. Multiobjective evolutionary optimization techniques coupled with direct policy search can reliably and flexibly find suitable control policies that adapt to multi-sectorial water needs and to hydro-climatic uncertainty. The second study demonstrates the benefits of cooperative parallel MOEA architectures to reliably and effectively find many objective control policies when the system is subject to uncertainty and computational constraints. The more advanced cooperative, co-evolutionary parallel search expands the scope of problem difficulty that can be reliably addressed while facilitating the discovery of high quality approximations for optimal river basin tradeoffs. The insights from this chapter should enable water resources analysts to devote computational efforts towards representing reservoir systems more accurately by capturing uncertainty and multiple demands when properly using parallel coordinated search. The third study extended multi- purpose reservoir control to better capture flood protection. A risk-averse formulation contributed to the discovery of control policies that improve operations during hydrologic extremes. Overall this dissertation has carefully evaluated and advanced the Evolutionary Multiobjective Direct Policy Search (EMODPS) framework to support multi-objective and robust management of conflicting demands in complex reservoir systems

Reservoir operation using a robust evolutionary optimization algorithm

In this research, a significant improvement in reservoir operation was achieved using a state-of-the-art evolutionary algorithm named Borg MOEA. A real-world multipurpose dam was used to test the algorithm's performance, and the target of the reservoir operation policy was to fulfil downstream water demands in drought condition while maintaining a sustainable quantity of water in the reservoir for the next year. The reservoir's performance was improved by increasing the maximum reservoir storage by 14.83 million m(3). Furthermore, sustainable water storage in the reservoir was achieved for the next year, for the simulated low flow condition considered, while the total annual imbalance between the monthly reservoir releases and water demands was reduced by 64.7%. The algorithm converged quickly and reliably, and consistently good results were obtained. The methodology and results will be useful to decision makers and water managers for setting the policy to manage the reservoir efficiently and sustainably

Optimum socio-environmental flows approach for reservoir operation strategy using many-objectives evolutionary optimization algorithm

Water resource system complexity, high-dimension modelling difficulty and computational efficiency challenges often limit decision makers' strategies to combine environmental flow objectives (e.g. water quality, ecosystem) with social flow objectives (e.g. hydropower, water supply and agriculture). Hence, a novel Optimum Social-Environmental Flows (OSEF) with Auto-Adaptive Constraints (AAC) approach introduced as a river basin management decision support tool. The OSEF-AAC approach integrates Socio-Environmental (SE) objectives with convergence booster support to soften any computational challenges. Nine SE objectives and 396 decision variables modelled for Iraq's Diyala river basin. The approach's effectiveness evaluated using two non-environmental models and two inflows' scenarios. The advantage of OSEF-AAC approved, and other decision support alternatives highlighted that could enhance river basin SE sectors' revenues, as river basin economic benefits will improve as well. However, advanced land use and water exploitation policy would need adoption to secure the basin's SE sectors

Hybrid Optimisation Algorithms for Two-Objective Design of Water Distribution Systems

Multi-objective design or extended design of Water Distribution Systems (WDSs) has received more attention in recent years. It is of particular interest for obtaining the trade-offs between cost and hydraulic benefit to support the decision-making process. The design problem is usually formulated as a multi-objective optimisation problem, featuring a huge search space associated with a great number of constraints. Multi-objective evolutionary algorithms (MOEAs) are popular tools for addressing this kind of problem because they are capable of approximating the Pareto-optimal front effectively in a single run. However, these methods are often held by the “No Free Lunch” theorem (Wolpert and Macready 1997) that there is no guarantee that they can perform well on a wide range of cases. To overcome this drawback, many hybrid optimisation methods have been proposed to take advantage of multiple search mechanisms which can synergistically facilitate optimisation. In this thesis, a novel hybrid algorithm, called Genetically Adaptive Leaping Algorithm for approXimation and diversitY (GALAXY), is proposed. It is a dedicated optimiser for solving the discrete two-objective design or extended design of WDSs, minimising the total cost and maximising the network resilience, which is a surrogate indicator of hydraulic benefit. GALAXY is developed using the general framework of MOEAs with substantial improvements and modifications tailored for WDS design. It features a generational framework, a hybrid use of the traditional Pareto-dominance and the epsilon-dominance concepts, an integer coding scheme, and six search operators organised in a high-level teamwork hybrid paradigm. In addition, several important strategies are implemented within GALAXY, including the genetically adaptive strategy, the global information sharing strategy, the duplicates handling strategy and the hybrid replacement strategy. One great advantage of GALAXY over other state-of-the-art MOEAs lies in the fact that it eliminates all the individual parameters of search operators, thus providing an effective and efficient tool to researchers and practitioners alike for dealing with real-world cases. To verify the capability of GALAXY, an archive of benchmark problems of WDS design collected from the literature is first established, ranging from small to large cases. GALAXY has been applied to solve these benchmark design problems and its achievements in terms of both ultimate and dynamic performances are compared with those obtained by two state-of-the-art hybrid algorithms and two baseline MOEAs. GALAXY generally outperforms these MOEAs according to various numerical indicators and a graphical comparison tool. For the largest problem considered in this thesis, GALAXY does not perform as well as its competitors due to the limited computational budget in terms of number of function evaluations. All the algorithms have also been applied to solve the challenging Anytown rehabilitation problem, which considers both the design and operation of a system from the extended period simulation perspective. The performance of each algorithm is sensitive to the quality of the initial population and the random seed used. GALAXY and the Pareto-dominance based MOEAs are superior to the epsilon-dominance based methods; however, there is a tie between GALAXY and the Pareto-dominance based approaches. At the end, a summary of this thesis is provided and relevant conclusions are drawn. Recommendations for future research work are also made