## 1 Introduction  
The reduction of pollutant emissions is one of the current main targets fixed by international authorities. A lower energy need in the residential and tertiary sectors can help to achieve this goal, as it represents one of the dominant energy consuming sectors in industrialized societies. However, the adoption of a defined energy system still depends on technical and financial evaluations, while environmental aspects are not generally regarded as design goals. A review of the open literature on these topics shows that the current research works can be grouped into three major groups:  
* research focusing on the optimization of the operation of energy systems, ranging from the optimization of a single component, to the operation of the overall DG system;
* research dealing with the optimization of the system synthesis; and
* research focusing on synthesis, design and operation optimization. Each group can be further subdivided, considering single and multi-objective optimization targets. Over the last decade an increasing number of papers dealing with energy system optimization have been produced [1]. One of the first optimization models was developed by [PERSON] in 1992 [2], and consists of a linear programming model to minimize the operating cost of an energy supply system for local Swedish utilities. In 1997 he presented a linear programming model called MODEST [3] for the minimization of capital and operation costs of energy supply and demand side management. [PERSON] et al. [4] proposed an optimization model for aiding the design of a mixed energy production system, including heat pump based district heating, conventional boilers and decentralized heat pump. [PERSON] et al. [5] in 2002 proposed a method for optimal structural design, to determine the structures of energy supply systems in consideration of their multi-period operation. [PERSON] [6] has recently presented the MIND method, a decision support for optimization of industrial energy systems. For a general overview of models, methods and applications of multi-utility energy systems, the authors refer readers to extended reviews presented recently [1; 7; 8; 9; 10]. Among a large quantity of research presented in this field, some common points can be outlined:  
* almost all models rely on linear programming or mixed integer linear programming (MILP). However, some approaches based on meta-heuristics (simulated annealing, genetic algorithms, etc.) have been proposed, but they present some difficulties concerning the determination of search parameters and the judgment about optimality [11; 12; 13]. * the research normally focus only on specific targets: operation or synthesis optimization, economic and/or environmental optimization, unit or district heating network (DHN) optimization, etc. To deal with optimization of DG energy systems, including DHCN and thermal storage, and focusing on different objectives (economic rather than environmental), it is necessary to consider all aspects at the same time, and not in successive steps. This is because the operation optimization strongly affects the optimal synthesis of the system and, in addition, the economic optimum does not correspond to the environmental one. Some recent papers seem to go in this direction, performing a single objective optimization, generally economic: Chinese proposed a MILP model for the optimization of a DHCN in a DG context [14], [PERSON] and [PERSON] [15] presented a structural and operational optimization of a DG energy system. [PERSON] et al. [16] performed the optimization of sizing and operation of a DH system, focusing on the technological options for the TS. [PERSON] et al. [17] optimized DHC systems, focusing on the alternative between absorption and compression chillers. [PERSON] and [PERSON] [18] defined a model for determining the optimal capacity and operation of seven combined cooling, heating and power (CCHP) systems in the heating and cooling network of a residential complex located east of Theran. Finally, [PERSON] et al.