A smart load appliance application using a single compressor fridge-freezer to support grid operations

This paper describes a fridge-freezer smart load model, which responds to external signals from the wholesale electricity market to support grid operations while switching the fridge-freezer on and off to maintain optimum operations for the owner. The key parameters of the model are the appliance dimensions, thermal mass, the fridge and freezer thermal time constants and the compressor power consumption. The model demonstrates that control strategies help to minimise load at times when the grid is under stress from high demand, and shift some load to a lower wholesale price or when there is excess renewable power. Three control strategies are proposed, based on peak shaving and valley filling, price signals and wind availability.

Assessment of Domestic Load Suitable for Smart Consumer Load Participation

This study characterizes the domestic loads suitable to participate in the load participation scheme to make the power system more carbon and economically efficient by shifting the electricity demand profile towards periods when there is plentiful renewable in-feed.

A series of experiments have been performed on a common fridge-freezer, both completely empty and half full. The results presented are ambient temperature, temperature inside the fridge, temperature inside the drawer of the fridge, temperature inside the freezer, thermal time constants, power consumption and electric energy consumed.

The thermal time constants obtained clearly demonstrate the potential of such refrigeration load for Smart Customer Load Participation.

Domestic fridge-freezer load aggregation to support ancillary services

Grid operators and electricity retailers in Ireland manage peak demand, power system balancing and grid congestion by offering relevant incentives to consumers to reduce or shift their load. The need for active consumers in the home using smart appliances has never been greater, due to increased variable renewable generation and grid constraints. In this paper an aggregated model of a single compressor fridge-freezer population is developed. A price control strategy is examined to quantify and value demand response savings during a representative winter and summer week for Ireland in 2020. The results show an average reduction in fridge-freezer operating cost of 8.2% during winter and significantly lower during summer in Ireland. A peak reduction of at least 68% of the average winter refrigeration load is achieved consistently during the week analysed using a staggering control mode. An analysis of the current ancillary service payments confirms that these are insufficient to ensure widespread uptake by the small consumer, and new mechanisms need to be developed to make becoming an active consumer attractive. Demand response is proposed as a new ancillary service called ramping capability, as the need for this service will increase with more renewable energy penetration on the power system.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Finite element modelling of composite structures under crushing load

This paper details the theory and implementation of a composite damage model, addressing damage within a ply (intralaminar) and delamination (interlaminar), for the simulation of crushing of laminated composite structures. It includes a more accurate determination of the characteristic length to achieve mesh objectivity in capturing intralaminar damage consisting of matrix cracking and fibre failure, a load-history dependent material response, an isotropic hardening nonlinear matrix response, as well as a more physically-based interactive matrix-dominated damage mechanism. The developed damage model requires a set of material parameters obtained from a combination of standard and non-standard material characterisation tests. The fidelity of the model mitigates the need to manipulate, or "calibrate", the input data to achieve good agreement with experimental results. The intralaminar damage model was implemented as a VUMAT subroutine, and used in conjunction with an existing interlaminar damage model, in Abaqus/Explicit. This approach was validated through the simulation of the crushing of a cross-ply composite tube with a tulip-shaped trigger, loaded in uniaxial compression. Despite the complexity of the chosen geometry, excellent correlation was achieved with experimental results.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

A 1-D radial vaneless diffuser model accounting for the effects of aerodynamic blockage

The radial vaneless diffuser, though comparatively simple in terms of geometry, poses a significant challenge in obtaining an accurate 1-D based performance prediction due to the swirling, unsteady and distorted nature of the flow field. Turbocharger compressors specifically, with the ever increasing focus on achieving a wide operating range, have been recognised to operate with significant regions of spanwise separated flow, particularly at off-design conditions.

Using a combination of single passage Computational Fluid Dynamics (CFD) simulations and extensive gas stand test data for three geometries, the current study aims to evaluate the onset and impact of spanwise aerodynamic blockage in radial vaneless diffusers, and how the extent of the blocked region throughout the diffuser varies with both geometry and operating condition. Having analysed the governing performance parameters and flow phenomena, a novel 1-D modelling method is presented and compared to an existing baseline method as well as test data to quantify the improvement in prediction accuracy achieved.

An Investigation Into Improving Off-Design Performance in a Turbocharger Turbine Utilizing Non-Radial Blading

Off-design performance now plays a vital role in the design decisions made for automotive turbocharger turbines. Of particular interest is extracting more energy at high pressure ratios and lower rotational speeds. In this region of operation the U/C value will be low and the rotor will experience high values of positive incidence at the inlet. The positive incidence causes flow to separate on the suction surface and produces high blade loading at inlet, which drives tip leakage. A CFD analysis has been carried out on a number of automotive turbines utilizing non-radial fibred blading. To help improve secondary flows yet meet stress requirements a number of designs have been investigated. The inlet blade angle has been modified in a number of ways. Firstly, the blading has been adjusted as to provide a constant back swept angle in the span wise direction. Using the results of the constant back swept blading studies, the back swept blade angle was then varied in the span wise direction. In addition to this, in an attempt to avoid an increase in stress, the effect of varying the leading edge profile of the blade was investigated. It has been seen that off-design performance is improved by implementing back swept blading at the inlet. Varying the inlet angle in the span wise direction provided more freedom for meeting stress requirements and reduces the negative impact on blade performance at the design point. The blade leading edge profile was seen to offer small improvements during off-design operation with minimal effects on stress within the rotor. However, due to the more pointed nature of the leading edge, the rotor was less tolerant to flow misalignment at the design point.