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In this paper a novel generalised modelling framework for multi-energy systems containing storage elements is combined with a multi-objective model predictive control. The control objective is to both minimise the economic cost of importing energy into the system as well as providing the service of smoothing the profile of the electrical energy imported thus levelling the load and reducing peak demand. The capabilities of the approach are demonstrated by its application to a representative case study based on two buildings at the University of Manchester. Results show that model predictive control is capable of simultaneously addressing minimisation of economic cost as well as smoothing the electrical energy import.

In this paper a novel generalised modelling framework for multi-energy systems containing storage elements is combined with a multi-objective model predictive control. The control objective is to both minimise the economic cost of importing energy into the system as well as providing the service of smoothing the profile of the electrical energy imported thus levelling the load and reducing peak demand. The capabilities of the approach are demonstrated by its application to a representative case study based on two buildings at the University of Manchester. Results show that model predictive control is capable of simultaneously addressing minimisation of economic cost as well as smoothing the electrical energy import.

Two studies conducted in Guadeloupe (West Indies) and Réunion (Indian Ocean) islands were designed to investigate the benefits of producing sugarcane as an energy crop and to assess the influence of agroclimatic factors on energy efficiency, respectively. In this context, it is essential to know the low heating value of the dry above-ground biomass (LHVd, MJ/kg) and its energy yield (EY, MJ/m2) in order to select the best varieties and set up a payment method for growers. Eighteen Poaceae (sugarcane and Erianthus) cultivars were compared under wet tropical environmental conditions in Guadeloupe. Three sugarcane cultivars were studied in four contrasting environments in Réunion. The partition sampling and biomass measurement procedures were identical at both locations. Low heating value (LHV) predictions were achieved using near-infrared reflectance spectroscopy (NIRS) after specific calibration (Guadeloupe), or arithmetically after lignocellulosic compound prediction (Réunion). In both studies, LHV variability was very low and slightly dependent on the site, cultivar and above-ground biomass components (millable stalks and tops, and green and dead leaves). Considering the overall dry above-ground biomass (DAB, kg/m2), the LHVd was calculated by averaging 159 samples (mean 16.65 MJ/kg) in Guadeloupe and 315 samples (mean 16.45 MJ/kg) for Réunion. An excellent linear relationship between the DAB and its EY, regardless of cultivar, age and environment, was found (n = 474 and R² = 0.99). Sugarcane energy content assessment could thus be simplified by measuring the DAB, while enabling development of a faster method of payment for growers based on the DAB measurement and the correlation between DAB and EY. Finally, the findings of this study should allow growers to rapidly determine the commercial value of their sugarcane crops, and also enable purchasers to assess the amount of recoverable energy. (Résumé d'auteur)

Two studies conducted in Guadeloupe (West Indies) and Réunion (Indian Ocean) islands were designed to investigate the benefits of producing sugarcane as an energy crop and to assess the influence of agroclimatic factors on energy efficiency, respectively. In this context, it is essential to know the low heating value of the dry above-ground biomass (LHVd, MJ/kg) and its energy yield (EY, MJ/m2) in order to select the best varieties and set up a payment method for growers. Eighteen Poaceae (sugarcane and Erianthus) cultivars were compared under wet tropical environmental conditions in Guadeloupe. Three sugarcane cultivars were studied in four contrasting environments in Réunion. The partition sampling and biomass measurement procedures were identical at both locations. Low heating value (LHV) predictions were achieved using near-infrared reflectance spectroscopy (NIRS) after specific calibration (Guadeloupe), or arithmetically after lignocellulosic compound prediction (Réunion). In both studies, LHV variability was very low and slightly dependent on the site, cultivar and above-ground biomass components (millable stalks and tops, and green and dead leaves). Considering the overall dry above-ground biomass (DAB, kg/m2), the LHVd was calculated by averaging 159 samples (mean 16.65 MJ/kg) in Guadeloupe and 315 samples (mean 16.45 MJ/kg) for Réunion. An excellent linear relationship between the DAB and its EY, regardless of cultivar, age and environment, was found (n = 474 and R² = 0.99). Sugarcane energy content assessment could thus be simplified by measuring the DAB, while enabling development of a faster method of payment for growers based on the DAB measurement and the correlation between DAB and EY. Finally, the findings of this study should allow growers to rapidly determine the commercial value of their sugarcane crops, and also enable purchasers to assess the amount of recoverable energy. (Résumé d'auteur)

AbstractReview: 105 refs.

AbstractReview: 105 refs.

Abstract In this paper, a medium-term load forecasting model is proposed, intended to be used for the extrapolation of loads containing a small proportion of air conditioners. The main feature of the proposed model is the decomposition of weekly load observations into two independent components: trend line and seasonal variations. The trend line is described by the combination of the polynomial regression and the first-order autoregressive model. The parameters of this model are estimated with the aid of the generalized least-squares method. The seasonal component is modeled via a discrete model, where the weather sensitive cycle is separated from the weather insensitive one. A linear regression model is further proposed in order to associate weather sensitive variations with the exogenous weather variable (temperature). This procedure enabled a simple and efficient convolution of all load components into a single forecasting model. The whole methodology is verified on a real-life example of the peak-load consumption within the power system of former Yugoslavia.

Abstract In this paper, a medium-term load forecasting model is proposed, intended to be used for the extrapolation of loads containing a small proportion of air conditioners. The main feature of the proposed model is the decomposition of weekly load observations into two independent components: trend line and seasonal variations. The trend line is described by the combination of the polynomial regression and the first-order autoregressive model. The parameters of this model are estimated with the aid of the generalized least-squares method. The seasonal component is modeled via a discrete model, where the weather sensitive cycle is separated from the weather insensitive one. A linear regression model is further proposed in order to associate weather sensitive variations with the exogenous weather variable (temperature). This procedure enabled a simple and efficient convolution of all load components into a single forecasting model. The whole methodology is verified on a real-life example of the peak-load consumption within the power system of former Yugoslavia.

Abstract Rigid ceramic filters have emerged as the most promising technology for cleaning of hot gases due to their resistance to attack by aggressive gases and high temperatures. Their potential may be further extended by using them in combination with injection of a dry sorbent to remove acid gases and other chemical contaminants. A crucial factor in their successful utilisation is the ability of the cleaning pulse to remove the deposited cake effectively from the filter surface. In this study, laboratory experiments have been carried out using a single ceramic candle filter. The non-steady-state behaviour of the filter in the initial period of filtration was followed. ‘Patchy’ cleaning of the filter was observed and is consequently identified as the major cause of the inefficiency of filter cleaning indicated by the measured residual pressure drops. A simple correlation has been proposed to determine the cleaned fraction from pressure differences as a measure of the efficiency of cleaning. The conditioning curves have been successfully simulated using a recently developed probabilistic model and it has been shown that the model can also be applied to simulate the experimental results obtained in a pilot plant operating at high temperatures. The modelling results are consistent with the experimental observation that patchy cleaning with a thin residual dust layer in the cleaned areas can explain the conditioning behaviour.