needs of everyone on average. We could thus move
to a vision (whose realization today is facilitated by
the digital revolution) in which the sustenance of the
communities was provided by the resources that one
was able to self-produce. In particular, in the case of
communities of users that are close even
geographically, the self-produced energy may also
depend on the agro-energy resources available
locally, thus also preserving biodiversity.
2 BACKGROUND
The starting point of the project is a software
developed in the framework of a previous project able
to carry out an optimal evaluatation of Hybrid
Renewable Energy System (HRES) investments
(Noh et al., 2016). The software is based on a
simulation approach that consider all the involved
subsystems (photovoltaics, small wind turbines, solar
thermal, heat pump, electric and thermal storages,
heating and cooling emitters, building envelope,
traditional back-up systems) and their mutual
interactions. A multi-objective optimization finds the
Pareto frontier that maximizes Net Present Value
(NPV) and minimizes CO
2
emissions in three relevant
scenarios, taking into account the high sensitivity to
the conventional fuel cost variation.
The output of the methodology may steer the
decision maker to a more in-depth analysis and
characterization of the critical variables and possibly
towards a more robust design choice. and people
behaviours with regards to energy use. Furthermore,
it can improve academic teaching programmes,
steering them towards a global vision.
2.1 Software Application Example
The medium-long term planning of the construction
of an integrated thermal and electrical energy
production system fed by renewable sources – also
known as Hybrid Renewable Energy Systems
(HRES) – is an interesting, albeit tricky, investment
decision, which occurs under heterogeneous
uncertainty. In fact, in recent years, the deregulation
of the electrical energy sector and the growing
attention towards environmental concerns have
significantly stimulated the energy production market
and, consequently, have both raised the attention of
investors and added new variables and constraints
that further complicate such investment decisions
(McGovern and Hicks, 2004). For example, see the
call for reduction of greenhouse gas emissions, the
new targets for penetration of Renewable Energy
Sources (RES) in the electricity generating mix, and
the Energy Performance of Buildings Directive
(European Parliament and Council, 2012), which
requires new buildings to be nearly zero-energy by
the end of 2020.
Such trends call for a robust and integrated
investment evaluation approach to deal with the
increasing complexity of the decision context, to
reliably model the dynamics of the subsystems
involved in an HRES (Electrical, Thermal
components and Buildings), and to control their deep
interconnections.
The software presents an integrated, multi-
objective, four-stage methodology to evaluate long-
term HRES investments by comparing different
system configurations, coping with the above-
mentioned issues. The methodology includes a
simulation-based optimization procedure that
integrates the electrical generation, the thermal
systems, and the building of the investigated HRES,
and provides useful information concerning the
investment choice and the analysis of the output
reliability. Furthermore, it considers the minimization
of the equivalent CO
2
emissions corresponding to the
possible HRES configurations.
Simulation-based procedures are among the most
adopted approach for investigating electric
production of HRES (Bernal-Agustín and
DufoLópez, 2009a; Zhou et al. 2010) and very
effective and favorable for building-energy systems
(Hamdy et al., 2013; Kapsalaki et al., 2012) and in
building energetic studies (ASHRAE, 2009; Nguyen
et al., 2014).
The analyzed case study is a small-size building
powered by a HRES – specifically, an offgrid thermo-
electric system). These systems usually have strict
budget limitations, which force designers to find the
optimal sizing of technologies in terms of cost-
benefits. In smallscale systems, accurate input data
are generally available and manageable, allowing the
simulation models to deepen the analysis of the
energy system. A peculiar case of such systems is an
autonomous system, i.e. an off-grid system, serving
both electric and thermal demand, which is typical of
rural areas, mountains, and small islands and quite
common far from the urban environment and socially
important for certain communities. An autonomous
system is an example of nearly Zero-Energy
Buildings (nZEBs), as sought by international energy
directives and initiatives.
Specifically, to apply and test the proposed
methodology, we chose a stand-alone farm hostel
located in Enna, in the south of Italy, at 931 m above
sea level. The climate is cold in winter (Heating