Spectroscopic studies of metal complexes

Imperial Users onl

Subjective Performance Measures in Optimal Incentive Contracts

Objective measures of performance are seldom perfect. In response, incentive contracts often include important subjective components that mitigate incentive distortions caused by imperfect objective measures. This paper explores the combined use of subjective and objective performance measures in (respectively) implicit and explicit incentive contracts. Naturally, objective and subjective measures often are substitutes, sometimes strikingly so: we show that if objective measures are sufficiently close to perfect then no implicit contracts are feasible (because the firm's fallback position after reneging on an implicit contact is too attractive). We also show, however, that objective and subjective measures can reinforce each other: if objective measures become more accurate then in some circumstances the optimal contract puts more weight on subjective measures (because the improved objective measures increase the value of the ongoing relationship, and so reduce the firm's incentive to renege). We also analyze the use of subjective weights on objective performance measures, and provide case-study evidence consistent with our analyses.

Design and Evaluation of a Machine Learning Based Model for Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions

Usage of battery energy storage system (BESS) to facilitate demand response (DR) programs such as time of use (TOU) pricing can reduce utility bills for residential customers. However, using such a system for minimizing electric bills under these rate structures has the potential to cause an increase in emissions from the grid system. The increased emissions were majorly due to bulk energy storage of electricity produced by off-peak generators with higher emission rates and excess energy consumption due to battery inefficiency. BESS operating to optimize competing objectives to minimize utility cost and minimize CO2 emissions requires complex models that require an accurate forecast of future energy demand. These models get less effective as errors in demand forecasts increase. Demand forecasts for residential consumers are challenging due to high demand variability. Moreover, these models require computationally expensive mixed-integer linear programming (MILP) models in the day-to-day operation of BESS. In this work, a machine learning model (ML) was developed that attempts to predict an optimal battery schedule for an upcoming day based on easy to obtain information such as day of the week, month, previous day’s demand, average temperature, and relative humidity. The ML model’s utility bill and CO2 emission results were then compared to a no BESS scenario as well as a multi-objective optimization model based on perfect (OPT model) and forecasted (FORECAST model) demand data. The models were tested on two customers each from California and Arizona. The paired t-test comparison showed that the ML model results were not statistically different from the FORECAST model. The ML model was able to capture 65% of potential cost savings that could be generated from the OPT model. The model was also efficient in balancing the reduction of utility costs as well as CO2 emissions. Moreover, it requires less time and effort as is required for building and maintaining the FORECAST model

Holographic storage using shift multiplexing

We demonstrate theoretically and experimentally a new multiplexing method for volume holographic storage using a single reference beam that is composed of multiple plane waves or is a spherical wave. We multiplex the holograms by shifting the recording material or the recording/readout head. The volume properties of the recording medium allow selective readout of holograms stored in successive overlapping locations. High storage densities can be achieved with a relatively simple implementation by use of the new method

Production of case studies of the delivery of skills for learning, skills for life and skills for work

This report summarises the main themes to emerge from a study to highlight good practice in delivering practical, applied or vocational learning provision for all pupils