OPTIMAL DESIGN OF ENERGY COMMUNITIES
Multi-objective design of multi-vector energy hubs integrated with electric mobility charging systems and acting as an energy community
The present thesis has the aim to develop a tool based on prescriptive analytics to perform the optimal design of several multi-vector energy hubs, integrated with electric mobility charging infrastructures, jointly acting as a local energy community through a posteriori multi-objective function. In Chapter 1 after having introduced the scope of the study, the justification of its relevance, and the main objectives, a brief summary of the publications of the author and his main activities during the PhD program course is reported. In Chapter 2 the energy transition is introduced, underlining the EU environmental targets by 2030 and the main energy trends which the energy sector is facing. Then the main incentive policies which are used to reach the environmental targets are reported and briefly analysed. The focus is moved on the newly introduced concepts of energy communities and collective self-consumers at the EU and at the State Member level. The preliminary implementation of the EU directives in Italy and Spain are evaluated and commented. Finally, the concept of microgrid and nanogrid is reported, as an actual and real representation of integrated energy systems characterized by multiple energy demands and different technologies. Chapter 3 recalls the concept of traditional design and compare it with optimal design. After a brief introduction on the different analytics techniques (descriptive, predictive, prescriptive) the focus is moved to the MILP (Mixed-Integer Linear Programming) problem as a tool of prescriptive analytics which can be used to perform the optimal design. Finally, a review of the state of the art of optimal design algorithms and case studies are reported and the main contributions of the present work are underlined. Chapter 4 introduces the first step towards this thesis objective. At first a deterministic mathematical model capable of performing the optimal design of a single-vector (electricity) energy hub integrated with EVs (Electric Vehicles) infrastructure is reported and applied to the case of a single-family dwelling. The considered technologies are photovoltaic, electric storage systems and charging infrastructures. Later the complexity of the model is increased, by proposing a stochastic mathematical model capable of performing the optimal design of a single-vector energy hub integrated with EVs infrastructure. The model is applied to the Mensa building of the Savona Campus of the University of Genova. Several objective functions are considered and the results are reported and commented. Chapter 5 increases the complexity of the study by introducing a deterministic mathematical model to perform the optimal design of a multi-vector energy hub. Several energy demands are considered (electricity, space heating and cooling, domestic hot water) and the portfolio of technologies is significantly expanded involving electric and thermal RES (Renewable Energy Sources), micro cogeneration units, trigeneration units, conversion units (reversible heat pumps), electric and thermal storage systems and EVs charging infrastructures. A multi-objective function is implemented. The model is applied to the entirety of the Savona Campus of the University of Genova. Chapter 6 reports the final and complete version of the developed mathematical model. This model is able to perform the optimal design of several multi-vector energy hubs, integrated with EVs charging stations, jointly acting as an energy community. The model is then applied to the Opera Pia Engineering compound of the University of Genova through the analysis of two different cases. At first a purely virtual relationship between several hubs is considered similarly to the Italian implementation of the renewable energy community concept. Later, a physical relationship between hubs is investigated similarly to the Spanish implementation of the renewable energy community configuration. Finally, Chapter 7 reports the conclusions and possible future research activities
