Choosing secure water resource management plans inevitably requires trade‐offs between risks (for a variety of stakeholders), costs, and other impacts. We have previously argued that water resources planning should focus upon metrics of risk of water restrictions, accompanied by extensive simulation and scenario‐based exploration of uncertainty. However, the results of optimization subject to risk constraints can be sensitive to the specification of tolerable risk, which may not be precisely or consistently defined by different stakeholders. In this paper, we recast the water resources planning problem as a multiobjective optimization problem to identify least cost schemes that satisfy a set of criteria for tolerable risk, where tolerable risk is defined in terms of the frequency of water use restrictions of different levels of severity. Our proposed method links a very large ensemble of climate model projections to a water resource system model and a multiobjective optimization algorithm to identify a Pareto optimal set of water resource management plans across a 25 years planning period. In a case study application to the London water supply system, we identify water resources management plans that, for a given financial cost, maximize performance with respect to one or more probabilistic criteria. This illustrates trade‐offs between financial costs of plans and risk, and between risk criteria for four different severities of water use restrictions. Graphical representation of alternative sequences of investments in the Pareto set helps to identify water management options for which there is a robust case for including them in the plan.

This paper proposes a reduced ferrite IPT system for EV charging using ferrite-based soft magnetic composites (SMCs). IPT pads typically consist of a significant amount of ferrite to improve field shaping for better magnetic coupling between the primary and the secondary pads. However, the costly and brittle nature of ferrite leaves the IPT pads vulnerable to harsh roadway conditions. The IPT pad design in this paper either replaces ferrite with SMCs or removes the ferrite to reduce system cost and improve mechanical robustness. The design methodology uses a genetic algorithm to optimise the geometry of the primary magnetic core layer with a combination of SMC and ferrite using ANSYS Maxwell based on the magnetic coupling, ferrite volume and magnetic leakage fields. The paper experimentally validates the optimised pad that reduces ferrite volume by 63% compared to a typical IPT pad that uses only ferrite. The optimised design has 11% coupling reduction, but also reduces magnetic field leakage by 8%. When considering end-to-end DC-DC efficiency, the reduction with the optimised system is no more than 0.65%. The minimal deterioration is due to the optimised placement of SMC in low B-field regions.

We present a nonlinear integer programming formulation for fitting a spline-based regression to two-dimensional data using an adaptive knot-selection approach, with the number and location of the knots being determined in the solution process. However, the nonlinear nature of this formulation makes its solution impractical, so we also outline a knot selection heuristic inspired by the Remes Exchange Algorithm, to produce good solutions to our formulation. This algorithm is intuitive and naturally accommodates local changes in smoothness. Results are presented for the algorithm demonstrating performance that is as good as, or better than, other current methods on established benchmark functions.