Uncertainty quantification for wind farm power generation

Accurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.

Constraints upon the response of fish and crayfish to environmental flow releases in a regulated headwater stream network

In dry climate zones, headwater streams are often regulated for water extraction causing intermittency in perennial streams and prolonged drying in intermittent streams. Regulation thereby reduces aquatic habitat downstream of weirs that also form barriers to migration by stream fauna. Environmental flow releases may restore streamflow in rivers, but are rarely applied to headwaters. We sampled fish and crayfish in four regulated headwater streams before and after the release of summer-autumn environmental flows, and in four nearby unregulated streams, to determine whether their abundances increased in response to flow releases. Historical data of fish and crayfish occurrence spanning a 30 year period was compared with contemporary data (electrofishing surveys, Victoria Range, Australia; summer 2008 to summer 2010) to assess the longer-term effects of regulation and drought. Although fish were recorded in regulated streams before 1996, they were not recorded in the present study upstream or downstream of weirs despite recent flow releases. Crayfish (Geocharax sp. nov. 1) remained in the regulated streams throughout the study, but did not become more abundant in response to flow releases. In contrast, native fish (Gadopsis marmoratus, Galaxias oliros, Galaxias maculatus) and crayfish remained present in unregulated streams, despite prolonged drought conditions during 2006-2010, and the assemblages of each of these streams remained essentially unchanged over the 30 year period. Flow release volumes may have been too small or have operated for an insufficient time to allow fish to recolonise regulated streams. Barriers to dispersal may also be preventing recolonisation. Indefinite continuation of annual flow releases, that prevent the unnatural cessation of flow caused by weirs, may eventually facilitate upstream movement of fish and crayfish in regulated channels; but other human-made dispersal barriers downstream need to be identified and ameliorated, to allow native fish to fulfil their life cycles in these headwater streams.

Optimal coordination of G2V and V2G to support power grids with high penetration of renewable energy

Electric vehicles (EVs) have recently gained much popularity as a green alternative to fossil-fuel cars and a feasible solution to reduce air pollution in big cities. The use of EVs can also be extended as a demand response tool to support high penetration of renewable energy (RE) sources in future smart grid. Based on the certainty equivalent adaptive control (CECA) principle and a customer participation program, this paper presents a novel control strategy using optimization technique to coordinate not only the charging but also the discharging of EV batteries to deal with the intermittency in RE production. In addition, customer charging requirements and schedules are incorporated into the optimization algorithm to ensure customer satisfaction, and further improve the control performance. The merits of this scheme are its simplicity, efficiency, robustness and readiness for practical applications. The effectiveness of the proposed control algorithm is demonstrated by computer simulations of a power system with high level of wind energy integration.

A digital signal processor controlled smart battery charger for photovoltaic power systems

An off-grid photovoltaic power system requires an energy storage system, especially batteries, for mitigation of variability and intermittency problems, and for assured service reliability and availability. The longevity and reliability of such batteries depend on the effectiveness of the charging system. This paper presents the modelling, simulation and hardware implementation of a four-stage switch-mode charger based on the single-ended primary inductance converter. The digital signal processor based controller implements algorithms for the system's power balance control, maximum power point tracking to improve charging speed and efficiency, four-stage optimal charging, and system's protection. The protection algorithm provides over-charge, overdischarge, over-temperature and short circuit protection capabilities. The proposed system has the following advantages: ability to continuously charge the batteries even at reduced solar irradiation, higher efficiency, and use of adaptive thermally compensated set points for optimum performance. A prototype is built and experimental results are presented to validate the simulation results.