Abstract

Abstract

Abstract

Abstract

This paper deals with a procedure capable to build the scenario tree associated with a parking lot equipped with several bidirectional charging stations for plug-in electric vehicles (EVs). The scenario tree is conceived to be implemented in multistage stochastic optimization models for day-Ahead energy management systems of microgrids or, more in general, power distribution networks. Specific operating rules relevant to the initial energy available in the cars entering the parking lot are defined, as well as the other typical constraints (such as maximum number of available charging stations, size of the EVs batteries, and power ratings of charging stations). Moreover, the model reproduces also the coupling between the operation of the parking lot with the power production of a photovoltaic unit. The tree generation procedure at first generates a number of equiprobable scenarios by using day-Ahead forecasts and the probability distributions characterizing the expected deviations with respect to the forecasts; then similar scenarios are grouped on the basis of the chosen criteria and of the number of stages. A specific procedure is finally proposed and applied in order to guarantee the existence of feasible solutions.

Abstract

The development of microgrids has enabled prosumers to develop optimal power management and demand response (DR) strategies. Based on this idea, virtual prosumers and microgrids can be created in a traditional city structure, exploiting microgrids’ ability to operate connected or disconnected to the grid. Therefore, the development of more complex energy control and management systems becomes necessary. In this thesis, an easy to apply and computationally efficient energy management system (EMS) has been developed in Matlab. It is based on a hierarchical multi-agent system aiming to minimize the operating cost of a microgrid, that consists of a group of buildings, renewable energy sources (RES), energy storage systems (ESS) and electric vehicles (EVs). The examined optimization problem is solved using the particle swarm optimization algorithm (PSO), that ensures the optimum use of every microgrid’s component. Based on the results obtained by applying the algorithm to the operation of the microgrid, both in the time frame of one day and one week, cost savings of 40% can be achieved while all the microgrid’s energy needs and operation constraints are met.

Abstract

The work to detect the fault in a micro-network is presented, where an island-type micro-network implemented in simulink/Matlab is simulated. Which has a photovoltaic system, an energy storage system and a generation system that is in synchronism with the network. The ground faults of each phase of the photovoltaic system are simulated and the intelligent diagnosis of the fault is carried out with the analysis of the residual output of each of the system phases. The residual output is the result of the neuro-fuzzy algorithm, which are developed through the application of the ANFIS model, the output that is activated with any of the implemented algorithms allows locating the fault and disconnecting the system where the fault occurred. The results obtained with the Wilcoxon statistical test are compared, showing that the system with neural heuristics is slightly better.

Abstract

Microgrids play a crucial role in integrating various components such as combined heat and power (CHP) plants, distributed energy storage systems and renewable energy resources (RER). The integration of CHP plants in microgrids allows for the efficient utilization of electricity and heat energy while minimizing greenhouse gas emissions. Additionally, the presence of RER in microgrids contributes to the overall sustainability and reduces reliance on traditional fossil fuel-based generation. However, the increasing penetration of RER and the presence of CHP plants introduce operational challenges in the distributed power system. The intermittency of RER, particularly wind energy sources, combined with fluctuating load demands, creates uncertainties and impacts the system's stability and efficiency. To address the optimal scheduling of cogenerated power plants with intermittent RER, probabilistic programming techniques are employed. In this context, the scenario-based probabilistic method and the water cycle optimization algorithm (WCOA) are applied for the optimal dispatch of resources. The WCOA algorithm is chosen for its novelty and its ability to handle the uncertain behavior of wind power and load demand effectively. The application of the WCOA algorithm in this study results in considerably optimized operational costs compared to previous optimization techniques employed by other researchers. The algorithm achieves an overall saving of approximately 18% in operational costs. This highlights the effectiveness of the WCOA algorithm in addressing the challenges posed by the uncertainties in wind power and load demand, ultimately leading to improved operational efficiency and cost savings in microgrid systems.