A methodology for assessing the impact of salinity gradient power generation in urban contexts

The paper proposes a methodology to assess the potential impact of salinity gradient power technology in urban contexts. The idea to employ such energy source in urban contexts derives from the observation that, among the energy districts outputs, low-salinity treated wastewater can be used to produce electricity if a suitable source of high salinity feed (seawater of a salt-works) is also available. The methodology uses the HOMER software for assessing the district’s electric energy production, consumption and exchange with the main grid. Then, starting from the total gross surface and the number of inhabitants of the district, some possible realistic scenarios characterized by different wastewater flow rate are defined. Finally, for each scenario the size and the yearly energy production of the salinity gradient power system are calculated thanks to a simulator carried out by the same authors. An application example, considering three different scenarios, shows that urban density plays a crucial role in the process and that the most promising realistic scenarios are those including treated wastewater and brine and unlimited seawater and brine. The economic feasibility of the salinity gradient power technology is evaluated by a comparison with classical renewable technologies such as photovoltaic and wind systems

Chance-Constrained Optimization for MultiEnergy Hub Systems in a Smart City

The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.</p

Energy Management Systems and Potential Applications of Quantum Computing in the Energy Sector

The combined use of technologies plays a key role in the energy transition towards a green and sustainable economy, driven by the European Green Deal initiatives and the Paris Agreement to achieve climate neutrality in the European Union (EU) by 2050. Indeed, all viable solutions with no barriers to innovation should be considered if a fair, cost-effective, competitive, and green transition is to be ensured.Energy hubs enable the synergy of different forms of energy by exploiting their specific vir-tues. However, their management in an integrated context must be entrusted to automated manage-ment systems capable of making real-time decisions.This PhD thesis aims to assess the main potential applications of quantum computing to the energy sector in the current development scenario of quantum technologies, as well as provide the elements for modelling an energy hub and managing uncertainties.The thesis is organized as follows. Chapter 1 provides an introduction to energy manage-ment systems. The concept of an energy hub and its mathematical modelling are introduced in chap-ter 2. Chapter 3 introduces the fundamentals of energy supply. Chapter 4 examines potential use cases for quantum computing in the energy sector. Chapter 5 addresses the modelling of uncertain parameters. Chapter 6 concludes the thesis with a case study of two urban districts modelled as mul-ticarrier energy hubs connected by a multicarrier energy infrastructure providing electricity, gas and hydrogen. The conclusions are drawn in chapter 7. The appendices with additional insights enrich the thesis, which is full of comments and bibliographical references