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For more than 40 years, Mathematical Programming is the traditional tool for energy planning at the national or regional level aiming at cost minimization subject to specific technological, political and demand satisfaction constraints. The liberalization of the energy market along with the ongoing technical progress increased the level of competition and forced energy consumers, even at the unit level, to make their choices among a large number of alternative or complementary energy technologies, fuels and/or suppliers. In the present work we develop a modelling framework for energy planning in units of the tertiary sector giving special emphasis to model reduction and to the uncertainty of the economic parameters. In the given case study, the energy rehabilitation of a hospital in Athens is examined and the installation of a cogeneration, absorption and compression unit is examined for the supply of the electricity, heating and cooling load. The basic innovation of the given energy model lies in the uncertainty modelling through the combined use of Mathematical Programming (namely, Mixed Integer Linear Programming, MILP) and Monte Carlo simulation that permits the risk management for the most volatile parameters of the objective function such as the fuel costs and the interest rate. The results come in the form of probability distributions that provide fruitful information to the decision maker. The effect of model reduction through appropriate data compression of the load data is also addressed. © 2009 Elsevier Ltd. All rights reserved.

This article presents a methodological approach for the formulation of control strategies capable of reducing atmospheric pollution at the standards set by European legislation. The approach was implemented in the greater area of Thessaloniki and was part of a project aiming at the compliance with air quality standards in five major cities in Greece. The methodological approach comprises two stages: in the first stage, the availability of several measures contributing to a certain extent to reducing atmospheric pollution indicates a combinatorial problem and favors the use of Integer Programming. More specifically, Multiple Objective Integer Programming is used in order to generate alternative efficient combinations of the available policy measures on the basis of two conflicting objectives: public expenditure minimization and social acceptance maximization. In the second stage, these combinations of control measures (i.e., the control strategies) are then comparatively evaluated with respect to a wider set of criteria, using tools from Multiple Criteria Decision Analysis, namely, the well-known PROMETHEE method. The whole procedure is based on the active involvement of local and central authorities in order to incorporate their concerns and preferences, as well as to secure the adoption and implementation of the resulting solution. © 2006 Springer Science+Business Media, Inc.

The European policy target of substantially increasing the share of renewable energy in electricity generation, in combination with national subsidization support schemes, has strongly motivated private investors towards this business sector. In Greece, this interest is particularly apparent in the case of wind energy exploitation, due to a favourable legislative framework and the important wind potential available in several Greek regions. In such endowed regions, a very high number of applications were submitted to the competent authority, most of which compete for the same most attractive (windy) sites. The selection among these applications is a multicriteria problem that has been solved with the support of a Decision-Aid tool combining the multicriteria classification method ELECTRE-TRI with Integer Linear Programming. The developed approach takes into account both, the performances of the applications to the evaluation criteria, as well as a number of technical and policy constraints.

This paper aims at identifying the factors that have influenced changes in the level of industrial CO2 emissions. By means of an algebraic decomposition method the observed changes are analyzed into four different factors: output level, energy intensity, fuel mix and structural change. The application study refers to the industrial sector of European Union countries. The results show that CO2 emissions are possible to decrease without negatively affecting economic growth. In this sense, they confirm the decoupling of economic growth from energy consumption realized in the developed countries and prove that this detachment holds true also for the atmospheric emissions associated with energy use. (C) 2000 Elsevier Science B.V. All rights reserved. JEL classification: Q40.

This paper aims at identifying the factors that have influenced changes in the level of industrial CO2 emissions. By means of an algebraic decomposition method the observed changes are analyzed into four different factors: output level, energy intensity, fuel mix and structural change. The application study refers to the industrial sector of European Union countries. The results show that CO2

Recognizing that the industrial sector is one of the major sources of environmental impacts, there is a strong effort towards "cleaner" production. In this context, the European Union strongly encourages the adaptation of Best Available Techniques (BATs) which practically constitute recommended techniques for each one of the steps in the manufacturing process (Directive 96/61 EC). In the current case study, we examine the industrial sector of the greater Athens area under the prism of BATs introduction. The aim of the analysis is to find the mixture of BATs for the entire industrial sector that satisfies as much as possible the economic criteria (expressed by the Net Present Value of the projects) and the environmental criteria (quantified by the emission reduction in major pollutants). The obtained multi-objective optimization problem is addressed using two methods: (1) goal programming and (2) generation of the Pareto optimal solutions using the epsilon-constraint method, followed by an interactive filtering procedure in order to select the most preferred Pareto optimal solution (BAT mixture). The computational tool for implementing these methods is also described and the results from the application in the industrial sector of die greater Athens area are presented. (C) 2007 Elsevier B.V. All rights reserved.