Global assessment of biomass suitability for ironmaking — opportunities for co-location of sustainable biomass, iron and steel production and supportive policies

Iron and steel production processes are amongst the biggest industrial contributors to the global carbon emissions, and national as well as international obligations are set to drive their significant emission reductions. One of the possible strategies is to partially substitute fossil fuels used during the iron ore reduction process by sustainably-sourced biomass. The extent of the opportunities for such fuel switching, however, varies for each country. Theoretically, biomass into ironmaking should be only supported for countries which present co-location of sustainably domestically sourced biomass in sufficient quantity, a substantial iron and steel industry and supportive national policies. Using a multi-criteria global suitability assessment approach developed in this research, the status of countries’ steel industry, sustainable biomass resources and supportive policies were examined for top 40 steel production countries via the blast furnace ironmaking route. The results highlight those countries with significant potential to use domestically sourced biomass for such application and advance the efficient use of the limited biomass resources from the global perspective. Specifically countries such as Canada, Sweden, China, USA and France were identified as the most suitable, but other countries present opportunities that could be overcome if the corresponding barriers are identified

Controllo di una rete di trasmissione di un bene di consumo

La presente invenzione riguarda il settore dell’elaborazione dati. Più specificamente, tale invenzione riguarda il controllo di reti di trasmissione

Comparison of the economic impact of different wind power forecast systems for producers

Deterministic forecasts of wind production for the next 72 h at a single wind farm or at the regional level are among the main end-users requirement. However, for an optimal management of wind power production and distribution it is important to provide, together with a deterministic prediction, a probabilistic one. A deterministic forecast consists of a single value for each time in the future for the variable to be predicted, while probabilistic forecasting informs on probabilities for potential future events. This means providing information about uncertainty (i.e. a forecast of the PDF of power) in addition to the commonly provided single-valued power prediction. A significant probabilistic application is related to the trading of energy in day-ahead electricity markets. It has been shown that, when trading future wind energy production, using probabilistic wind power predictions can lead to higher benefits than those obtained by using deterministic forecasts alone. In fact, by using probabilistic forecasting it is possible to solve economic model equations trying to optimize the revenue for the producer depending, for example, on the specific penalties for forecast errors valid in that market. In this work we have applied a probabilistic wind power forecast systems based on the "analog ensemble" method for bidding wind energy during the day-ahead market in the case of a wind farm located in Italy. The actual hourly income for the plant is computed considering the actual selling energy prices and penalties proportional to the unbalancing, defined as the difference between the day-ahead offered energy and the actual production. The economic benefit of using a probabilistic approach for the day-ahead energy bidding are evaluated, resulting in an increase of 23% of the annual income for a wind farm owner in the case of knowing "a priori" the future energy prices. The uncertainty on price forecasting partly reduces the economic benefit gained by using a probabilistic energy forecast system

Forecasting Italian electricity market prices using a Neural Network and a Support Vector Regression

This work explores two different techniques for the prediction of the Italian day-ahead electricity market prices, the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted over a 2-year long period, with hourly data of the prices to be predicted and a large set of variables used as predictors (i.e. historical prices, forecast load, wind and solar power forecasts, expected plenty or shortage of hydroelectric production, net transfer capacity available at the interconnections and the gas prices). A Neural Network (NN) and a Support Vector Regression (SVR) are applied on the different predictors to obtain the final forecasts. Different predictors' combinations are analyzed in order to find the best forecast. We compare the NN and SVR to two less sophisticated post-processing methods, i.e. a linear regression (LR) and the persistency (P)