1,721,078 research outputs found
Optimal planning and economic evaluation of trigeneration districts
No full textTrigeneration, or combined cooling, heat and power (CCHP), is the process
by which electricity, heating and cooling are simultaneously generated from
the combustion of a fuel. Trigeneration systems for serving the electricity, thermal
and cooling loads in residential districts are a possible solution to enhance energy
efficiency, reduce fossil fuel consumption and increase the use of renewable energy
sources in the residential sector. Technical, economical and financial issues have to
be taken into account when planning a trigeneration system, or when expanding an
existing generation system. In this chapter a two-step decision support procedure is
presented for analysing alternative system configurations. The first step is based on
a mixed integer linear programming model that allows to describe the system components
in great detail and computes the annual optimal dispatch of the distributed
generation system with a hourly discretization, taking into account load profiles, fuel
costs and technical constraints. The optimal dispatch is then used for the economic
evaluation of the investment, taking into account prices of commodities, taxation,
incentives and financial aspects. The procedure allows to compare alternative plant
configurations and can be used as a simulation tool, for assessing the system sensitivity
to variations of model parameters (e.g. incentives, ratio debt/equity,...)
Decision support models for short term hydro-thermal resource scheduling of a price-taking power producer
No full textIn this paper we develop a decision support procedure for the short-term hydro-thermal resource scheduling problem of a power producer who operates in the liberalized electric energy market and aims at maximizing his own profit. The resources owned by the producer are hydroelectric plants and/or thermoelectric plants. It is assumed that weekly discharge of seasonal basins and maintenance plans of hydro and thermal plants have been determined so as to maximize medium-term (one year) profit. In the short-term horizon (one week or ten days), the power producer has to solve the unit commitment problem for the thermal units and the dispatchment problem for the available hydro plants and the committed thermal units, aiming at maximizing short-term profit. His decisions must be compatible with both technical constraints, i.e. inherent the production technologies, and market constraints. The procedure proposed in this paper is based on a mixed integer linear programming model, where the objective function represents the total profit and the constraints describe the hydro system, the thermal system and the market. The thermal system is modelled in great detail as it allows start-up and shut-down manouvres in every hour of the planning horizon, taking into account minimum up-time and downtime constraints as well as ramp-up and ramp-down constraints. The power producer is assumed to be unable to influence the market price, therefore energy prices are parameters exogenous to the decision model and the optimal schedule is determined on the basis of price forecasts
An Efficient Code for the Minimization of Highly Nonlinear and Large Residual Least Squares Functions
No full textA new code for solving the unconstrained least squares problem is given, in which a Quasi-NEWTON approximation to the second order term of the Hessian is added to the first order term of the Gauss-Newton method and a lineseareh based upon a quartic model is used. The new algorithm is shown numerically to be more efficient on large residual problems than the Gauss-Newton method and a general purpose minimization algorithm based upon BFGS formula. The listing and the user’s guide of the code is also given
Decision support models for scheduling conventional and renewable resources of a power producer in the liberalized electric energy market
No full textBilevel programming models for power generation capacity expansion planning with revenue adequacy constraints
Get PDFAn NLP model for evaluating the impact of Italian liberalized electric energy market rules on independent power producers
Get PDFThis paper presents a procedure for simulating the hourly bidding process of an Independent Power Producer (IPP), who aims at maximizing his own profit in the competitive context resulting from the electric energy market liberalisation. In the new context, market rules define how the IPP interacts with other competing power producers and with the Market Operator in the process of producing and transmitting electricity as well as of determining the
”market clearing price”. Aim of the work is to study how market rules affect these interactions, in order to detect conditions under which the interaction results happen to be in contrast with the liberalisation aim, i.e. the improvement of economic efficiency. Therefore the model developed in this paper is thought of as a tool for analysing how market rules affect the IPP profits and for detecting whether the IPP may exert market power. In the numerical experiments three cases are discussed, with reference to IPPs of different dimensions
Optimization and Forecasting Models for Electricity Market and Renewable Energies
Get PDFThis thesis presents different optimization and forecasting models, with the focus on energy markets and renewable energy sources. The analysis approach is related to models for wind and solar power forecasts and those for electricity prices forecasts. The first study explores a Principal Component Analysis in combination with two post-processing techniques for the prediction of wind power and of solar irradiance produced over two large areas. The Principal Component Analysis is applied to reduce the datasets dimension. A Neural Network and an Analog Ensemble post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction.
The second work explores two different techniques for the prediction of the Italian day-ahead electricity market prices. The predicted Italian prices are the zonal prices and the uniform purchase price (Prezzo Unico Nazionale or PUN). The study is conducted using 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 and a Support Vector Regression are applied on the different predictors to obtain the final forecasts. Different predictors’ combinations are analysed to find the best forecast. The results show that the best configuration is obtained using all the predictors together and applying the Neural Network to find the forecasted prices
On the convergence of krylov linear equation solvers
No full textIn this paper we show that the reduction in residual norm at each iteration of CG and GMRES is related to the first column of the inverse of an upper Hessenberg matrix that is obtained from the original coefficient matrix by way of an orthogonal transformation. The orthogonal transformation itself is uniquely defined by the coefficient matrix of the equations and the initial vector of residuals. We then apply this analysis to MINRES and show that, under certain circumstances, this algorithm can exhibit an unusual (and very slow) type of convergence that we refer to as oscillatory convergence
Modelli matematici e algoritmi risolutivi per la pianificazione dell'espansione della generazione e della trasmissione con elevate quote di generazione rinnovabile
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