Accounting for water-, energy- and food-security impacts in developing country water infrastructure decision-making under uncertainty

Decision makers lack information and tools to help them understand non-revenue impacts of different water infrastructure investment and operation decisions on different stakeholders in developing countries. These challenges are compounded by multiple sources of uncertainty about the future, including climatic and socio-economic change. Many-objective trade-off analysis could improve understanding of the relationships between diverse stakeholder-defined benefits from a water resources system. It requires a river basin simulation model to evaluate the performance of the system resulting from different decisions. Metrics of performance can be defined in conjunction with stakeholders, relating the level of benefits they receive (monetised or otherwise) to flows or storages in the system. Coupling the model to a many-objective search algorithm allows billions of possible combinations of available decisions to be efficiently filtered to find those which maximise stakeholder benefits. Competition for water requires trade-offs, so a range of options can be generated which share resources differently. Uncertainties can be included in the analysis to help identify sets of decisions which provide acceptable benefits regardless of the future which manifests, i.e. perform robustly. From these options, decision makers can select a balance representing their preferences. This thesis reports the development of such a state-ofthe-art approach through applications in three real-world developing country contexts, with increasing levels of complexity and uncertainty. The first application in Brazil’s Jaguaribe Basin uses environmental and livelihoods indicators to help re-operate three existing dams. The second in Kenya’s Tana Basin adds new irrigation infrastructure investment options to decisions about re-operating a cascade of five existing dams in a more complex case. Finally robust portfolios of new hydropower investments are identified in Nepal’s Koshi Basin, accounting for climate and other uncertainties using a four-phased analytical approach. These applications confirm the approach’s utility and inform future research and practical use

Capacity expansion modelling to aid water supply investment decisions

Increasing population, economic development, and environmental changes imply that maintaining the water supply-demand balance will remain a top priority. Water resource systems may need to be expanded in order to respond to demand growth. Capacity expansion studies can be used to answer the question of what the optimal expansion size, timing and location of new infrastructure should be. This thesis develops and applies capacity expansion optimisation modelling approaches. We begin with the 'Economics of Balancing Supply and Demand’ (EBSD) planning framework used by the water industry since 2002 in England. The base model is formulated as a mixed integer linear programming optimisation model that selects the least cost annual schedule of supply and demand management options that meet forecasted demand over the planning horizon. Custom water saving profiles are allowed for demand management options. Multiple demand scenarios are considered to ensure the supply-demand balance is preserved under different demand conditions and that operating costs of selected options are accurately assessed. The base deterministic EBSD model is applied to the water companies of South East England (the WRSE area). Various extensions to the EBSD framework are then proposed and implemented. The model formulation is first expanded to incorporate a generic cost estimate for options not yet proposed in water company resources management plans. This allows to extend the WRSE network with new inter-company transfers for which costs are represented by a concave cost curve approximated by a piece-wise linear function. Considering additional interconnections allows evaluating the financial implications of further interconnectivity in the WRSE area. Next, an extension is proposed to improve the application of the stochastic version of the EBSD approach. The proposed method allows to identify the set of future capacity expansions that withstand uncertainty of supply and demand estimates and still achieve a required reliability. The method consists of an iterative process: at each iteration the EBSD optimisation model is run and the reliability of the solution set (supply-demand schedules) is tested under Monte Carlo simulation. Ad-hoc model constraints are introduced at each iteration to enable the EBSD model to exclude unreliable solutions identified at previous iterations. Next, the English price-cap regulatory process is represented within a modified EBSD model formulation. The model identifies future capacity expansions that maximise water company profit under constraints on the maximum price that can be charged to customers and on the allowed rate of return. The incentive schemes that the regulator uses to reward (or penalise) companies for out- (or under-) performance, are also represented. The goal is to help explain how the current regulatory system of incentives motivates water company investment decisions. Two further extensions are then presented in the appendixes. The first one allows the EBSD model formulation to be extended so that costs of activated schemes are accounted over the schemes’ useful life, beyond the typical 25-30 year planning horizon. This eliminates biased comparisons of schemes with different economic lifetimes. With the second extension, a diverse set of supply-demand schedules are generated, that solve the capacity expansion problem and are sufficiently ‘close’ (in terms of costs) to the least-cost solution. Generating multiple near-optimal solutions gives an idea of what alternative plans are available in addition to the leastcost one. This allows the consideration other un-modelled factors or strategic priorities in the decision making process

Incorporating declared capacity uncertainty in optimizing airport slot allocation

Slot allocation is the mechanism used to allocate capacity at congested airports. A number of models have been introduced in the literature aiming to produce airport schedules that optimize the allocation of slot requests to the available airport capacity. A critical parameter affecting the outcome of the slot allocation process is the airport’s declared capacity. Existing airport slot allocation models treat declared capacity as an exogenously defined deterministic parameter. In this presentation we propose a new robust optimization formulation based on the concept of stability radius. The proposed formulation considers endogenously the airport’s declared capacity and expresses it as a function of its throughput. We present results from the application of the proposed approach to a congested airport and we discuss the trade-off between the declared capacity of the airport and the efficiency of the slot allocation process

Smart management strategies of utility-scale energy storage systems in power networks

Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important issues of power networks at both distribution and transmission levels. The optimization strategies employed for ESS placement and sizing successfully improved the following aspects of power systems: performance and power quality of the distribution networks investigated, the frequency response of the transmission networks studied, and facilitation of the integration of renewable generation (wind and solar). This present research provides effective solutions to some real power industry problems including minimizationof voltage deviation, power losses, peak demand, flickering, and frequency deviation as well as rate of change of frequency (ROCOF). Detailed simulation results suggest that ESS allocation using both uniform and non-uniform ESS sizing approaches is useful for improving distribution network performance as well as power quality. Regarding performance parameters, voltage profile improvement, real and reactive power losses, and line loading are considered, while voltage deviation and flickers are taken into account as power quality parameters. Further, the study shows that the PQ injection-based ESS placement strategy performs better than the P injection-based approach (in relation to performance improvement), providing more reactive power compensations. The simulation results also demonstrate that obtaining the power size of a battery ESS (MVA) is a sensible approach for frequency support. Hence, an appropriate sizing of grid-scale ESSs including tuning of parameters Kp and Tip (active part of the PQ controller) assist in improving the frequency response by providing necessary active power. Overall, the proposed ESS allocation and sizing approaches can underpin a transition plan from the current power grid to a future one

On multiobjective combinatorial optimization and dynamic interim hedging of efficient portfolios

A dynamic portfolio policy is one that periodically rebalances an optimally diversified portfolio to account for time-varying correlations. In order to sustain target-level Sharpe performance ratios between rebalancing points, the efficient portfolio must be hedged with an optimal number of contingent claim contracts. This research presents a mixed-integer nonlinear goal program (MINLGP) that is directed to solve the hierarchical multiple goal portfolio optimization model when the decision maker is faced with a binary hedging decision between portfolio rebalance periods. The MINLGP applied to this problem is formed by extending the separable programming foundation of a lexicographic nonlinear goal program (NLGP) to include branch-and-bound constraints. We establish the economic efficiency of applying this normative approach to dynamic portfolio rebalancing by comparing the risk-adjusted performance measures of a hedged optimal portfolio to those of a naively diversified portfolio. We find that a hedged equally weighted small portfolio and a hedged efficiently diversified small portfolio perform similarly when comparing risk-adjusted return metrics. However, when percentile risk measures are used to measure performance, the hedged optimally diversified portfolio clearly produces less expected catastrophic loss than does its nonhedged and naively diversified counterpart