According to standard writing class instructions, a surefire way of having one’s manuscripts ignored is to start off with a lengthy prologue We deliberately offend this golden rule and take a detour, treating our readers to a perhaps unusual account of a well-known piece of recent economic history — the ‘Dutch employment miracle’ of the 1980s and 1990s (Blanchard 2000; The Economist 2002) What was so miraculous to many was the sharp and sustained drop in the supposedly sclerotic Dutch unemployment rate, which had peaked at more than 11 per cent of the labour force in 1982 — a rate which was 21 percentage points higher than the average EU-15 unemployment rate in the same year By 1990, Dutch unemployment had come down to 51 per cent, a full 21 percentage points below the EU-15 unemployment rate in the same year, and it declined further to only 31 per cent in 2000, with the EU-15 unemployment rate stuck at 77 per cent; the Dutch managed to maintain the momentum, keeping unemployment down at 38 per cent of the labour force during the period 2000–10, a full 4 percentage points lower than the unemployment rate in the EU-15 This labour market success is generally ascribed to the Dutch socioeconomic model, colloquially known as the ‘Polder Model’

/pdf/wage-led-or-profit-led-supply-wages-productivity-and-867qf8m0us.pdf

Wage-led or Profit-led Supply: Wages, Productivity and Investment

A computer-implemented system and method for inferring operational specifications of a photovoltaic power generation system is provided. The operational specifications of a photovoltaic plant configuration can be inferred through evaluation of historical measured system production data and measured solar resource data. Preferably, the solar resource data includes both historical and forecast irradiance values. Based upon the location of the photovoltaic plant, a time-series power generation data set is simulated based on a normalized and preferably substantially linearly-scalable solar power simulation model. The simulation is run for a range of hypothetical photovoltaic system configurations. A power rating is derived for each system configuration by comparison of the measured versus simulated production data, which is applied to scale up the simulated time-series data. The simulated energy production is statistically compared to actual historical data, and the system configuration reflecting the lowest overall error is identified as the inferred (and optimal) system configuration.

Computer-implemented system and method for inferring operational specifications of a photovoltaic power generation system

Effective interconnection of stochastically variable renewable energy resources like solar PV (photovoltaic) systems to the grid requires meticulous attention to ensure system compatibility, stability and reliability The operating characteristics of such PV power plants need to become familiar to power systems engineers and operators Such familiarity can effectively be achieved only via system modeling, simulation and experimental studies This paper develops models for an integrated PV power plant which comprises PV array, SEPIC (single ended primary inductance converter) converter, bi-directional de-de converter, dc-ac converter, and battery energy storage system using Matlab/Simulink The models are used for simulation studies to investigate: 1) the response of the system to the ever-changing environmental variables, 2) the ability to track the maximum power point, 3) the role of the battery energy storage in the mitigation of voltage and power oscillations and 4) the role of the inverter in ensuring compliance to the grid requirements Experimentally, a DSP (digital signal processing) based control system for maximum power point tracking, dc bus voltage regulation, battery charge/discharge control, dc-ac converter control, system synchronization and protection is implemented and used for the validation of the simulation studies The simulation and experimental results are presented and discussed

Modeling, simulation and hardware implementation of a PV power plant in a distributed energy generation system

Large-scale solar photovoltaic (PV) generation is now a viable, economically feasible and clean energy supply option. Incentive schemes, such as the Feed-in-Tariff (FIT) in Ontario, have attracted large-scale investments in solar PV generation. In a previous work, the authors presented an investor-oriented planning model for optimum selection of solar PV investment decisions. In this paper, a method for determining the sensitivity indices, based on the application of duality theory on the Karush-Kuhn-Tucker (KKT) optimality conditions, pertaining to the solar PV investment model is presented. The sensitivity of the investors' profit to various parameters, for a case study in Ontario, Canada are presented and discussed and these are found to be very close to those obtained using the Monte Carlo simulation and finite-difference (individual parameter perturbation) based approaches. Furthermore, a novel relationship is proposed between the sensitivity indices and the investor's profit for a given confidence level to evaluate the risk for an investor in solar PV projects.

Sensitivity-Indices-Based Risk Assessment of Large-Scale Solar PV Investment Projects

The calculation of the variance of a correlation coefficient matrix for a photovoltaic fleet can be completed in linear space as a function of decreasing distance between pairs of photovoltaic plant locations. When obtaining irradiance data from a satellite imagery source, irradiance statistics must first be converted from irradiance statistics for an area into irradiance statistics for an average point within a pixel in the satellite imagery. The average point statistics are then averaged across all satellite pixels to determine the average across the whole photovoltaic fleet region. Where pairs of photovoltaic systems are located too far away from each other to be statistically correlated, the correlation coefficients in the matrix for that pair of photovoltaic systems are effectively zero. Consequently, the double summation portion of the calculation can be simplified to eliminate zero values based on distance between photovoltaic plant locations, substantially decreasing the size of the problem space.

Computer-implemented system and method for correlating satellite imagery for use in photovoltaic fleet output estimation

Planning studies are conducted both to incorporate design modifications to the transmission and distribution system and to ensure continued reliable operation of the system. Validated PV system models are required to account for the effect of existing PV generation, evaluate the potential impact of proposed projects, and assess technical options for integrating even larger PV generation capacity in the future. There is a need to improve PV system models used for all four aspects of distribution and transmission system planning: power flow, dynamic, short-circuit, and transient. Planning models need to be more accessible to meet technical and process requirements of the interconnection and regional planning processes. Some industry efforts are under way, notably by the WECC, to address gaps in PV system modeling. Further progress will require continued support by utilities, manufacturers, and software developers.

http://ieeexplore.ieee.org/iel5/8014/5753319/05753328.pdf

Model Makers

We participated in all tracks of the WMT 2022 efficient machine translation task: single-core CPU, multi-core CPU, and GPU hardware with throughput and latency conditions. Our submissions explores a number of several efficiency strategies: knowledge distillation, a simpler simple recurrent unit (SSRU) decoder with one or two layers, shortlisting, deep encoder, shallow decoder, pruning and bidirectional decoder. For the CPU track, we used quantized 8-bit models. For the GPU track, we used FP16 quantisation. We explored various pruning strategies and combination of one or more of the above methods.

M Behnke

Papers

Model Makers

Edinburgh’s Submission to the WMT 2022 Efficiency Task

Prover-M: A Simple Model to Project Disposal of Fine Sediments