Development of a microgrid with renewable energy sources and electrochemical storage system integration

Beside the traditional paradigm of "centralized" power generation, a new concept of "distributed" generation is emerging, in which the same user becomes pro-sumer. During this transition, the Energy Storage Systems (ESS) can provide multiple services and features, which are necessary for a higher quality of the electrical system and for the optimization of non-programmable Renewable Energy Source (RES) power plants. A ESS prototype was designed, developed and integrated into a renewable energy production system in order to create a smart microgrid and consequently manage in an efficient and intelligent way the energy flow as a function of the power demand. The produced energy can be introduced into the grid, supplied to the load directly or stored in batteries. The microgrid is composed by a 7 kW wind turbine (WT) and a 17 kW photovoltaic (PV) plant are part of. The load is given by electrical utilities of a cheese factory. The ESS is composed by the following two subsystems, a Battery Energy Storage System (BESS) and a Power Control System (PCS). With the aim of sizing the ESS, a Remote Grid Analyzer (RGA) was designed, realized and connected to the wind turbine, photovoltaic plant and the switchboard. Afterwards, different electrochemical storage technologies were studied, and taking into account the load requirements present in the cheese factory, the most suitable solution was identified in the high temperatures salt Na-NiCl2 battery technology. The data acquisition from all electrical utilities provided a detailed load analysis, indicating the optimal storage size equal to a 30 kW battery system. Moreover a container was designed and realized to locate the BESS and PCS, meeting all the requirements and safety conditions. Furthermore, a smart control system was implemented in order to handle the different applications of the ESS, such as peak shaving or load levelling.

Modelling of ORC components and systems for low-medium temperature heat recovery applications with reduced environmental impact

Although its great potential as low to medium temperature waste heat recovery (WHR) solution, the ORC technology presents open challenges that still prevent its diffusion in the market, which are different depending on the application and the size at stake. Focusing on the micro range power size and low temperature heat sources, the ORC technology is still not mature due to the lack of appropriate machines and working fluids. Considering instead the medium to large size, the technology is already available but the investment is still risky. The intention of this thesis is to address some of the topical themes in the ORC field, paying special attention in the development of reliable models based on realistic data and accounting for the off-design performance of the ORC system and of each of its components. Concerning the “Micro-generation” application, this work: i) explores the modelling methodology, the performance and the optimal parameters of reciprocating piston expanders; ii) investigates the performance of such expander and of the whole micro-ORC system when using Hydrofluorocarbons as working fluid or their new low GWP alternatives and mixtures; iii) analyzes the innovative ORC reversible architecture (conceived for the energy storage), its optimal regulation strategy and its potential when inserted in typical small industrial frameworks. Regarding the “Industrial WHR” sector, this thesis examines the WHR opportunity of ORCs, with a focus on the natural gas compressor stations application. This work provides information about all the possible parameters that can influence the optimal sizing, the performance and thus the feasibility of installing an ORC system. New WHR configurations are explored: i) a first one, relying on the replacement of a compressor prime mover with an ORC; ii) a second one, which consists in the use of a supercritical CO2 cycle as heat recovery system.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.

A robotic platform for precision agriculture and applications

Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited. As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles. The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances. Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles. The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed. Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork. The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.