How stochastic network calculus concepts help green the power grid

Abstract—The renewable energy generation such as solar and wind will constitute an important part of the next generation grid. As the variations of renewable sources may not match the time distribution of load, energy storage is essential for grid stability. Supplemented with energy storage, we investigate the feasibility of integrating solar photovoltaic (PV) panels and wind turbines into the grid. To deal with the fluctuation in both the power generation and demand, we borrow the ideas from stochastic network calculus and build a stochastic model for the power supply reliability with different renewable energy configurations. To illustrate the validity of the model, we conduct a case study for the integration of renewable energy sources into the power system of an island off the coast of Southern California. Performance of the hybrid system under study is assessed by employing the stochastic model, e.g., with a set of system configurations, the long-term expected Fraction of Time that energy Not-Served (FTNS) of a given period can be obtained. I

Distributed Control, Optimization, Coordination of Smart Microgrids:Passivity, Output Regulation, Time-Varying and Stochastic Loads

Microgrids are power distribution systems which are typically classified by Direct Current (DC) and Alternating Current (AC) networks. Nowadays, renewable generation sources and new loads such as Electric Vehicles (EVs) are largely used in power systems. Thus, due to the increased share of renewable generations and large scale introduction of new loads such as EVs, new control strategies are required to address the uncertainties of power networks. Due to the random and unpredictable diversity of load patterns, it is more realistic to consider dynamical or stochastic differential load models. In DC networks, in order to guarantee a proper and safe functioning of the overall network, the main goal is the voltage regulation. Thus, we propose controller schemes achieving voltage regulation and ensuring the stability of the overall DC network. Moreover, an important operational objective of AC networks is frequency regulation. Hence, we propose controller schemes achieving frequency regulation and ensuring the stability of the overall AC network. Furthermore, we propose an Energy Management Strategy (EMS) taking into account the load, power flow, and system operational constraints in a distribution network such that the cost of the Distributed Generations (DGs), Distributed Storages (DSs) and energy purchased from the main grid are minimized and the customers' demanded load are provided where the loads are considered stochastic generated by time-homogeneous Markov chain. Finally, we solve a microgird optimal control problem with taking into account the social behavior of the EV drivers via a corresponding real data set

Optimal battery charge/discharge strategies for prosumers and suppliers

We discuss the application of classical variational methods to optimal charging/discharging strategies for a prosumer or storage supplier, where the price of electrical power is known in advance. We outline how a classical calculus of variations approach can be applied to two related problems: (i) how can a prosumer minimise the cost of charging/discharging a battery, when the price of electrical power is known throughout the charging/discharging period? and (ii) how can an electricity supplier incentivise desired prosumer/storage supplier behaviour by adjusting the price

Optimizing DC Microgrid Systems for Efficient Electric Vehicle Battery Charging in Ain El Ibel, Algeria

In addressing the critical challenge of developing sustainable energy solutions for electric vehicle (EV) battery charging, this study introduces an innovative direct current (DC) microgrid system optimized for areas with high solar irradiance, such as Ain El Ibel, Djelfa. The research confronts two primary difficulties: maximizing solar energy utilization in the microgrid system and ensuring system stability and response accuracy for reliable EV charging. To tackle these challenges, the study presents two original achievements. Firstly, it develops a neural network-enhanced Maximum Power Point Tracking (MPPT) controller, which is further optimized with Particle Swarm Optimization (PSO) to increase the efficiency of solar energy capture. Secondly, it refines the system's reliability through the advanced calibration of a Fractional Order Proportional-Integral (FOPI) controller using the Grey Wolf Optimization (GWO) technique, marking a notable improvement in microgrid system stability and response accuracy. The integration of a solar panel array, battery storage, and a supercapacitor, coupled with these advanced optimization techniques, exemplifies a significant leap forward in enhancing efficiency and reliability of EV battery charging through renewable energy sources. Comprehensive simulation and evaluation of the system underscore its superiority over conventional methods, demonstrating the effectiveness of combining neural network-based optimization with PSO and GWO. This breakthrough not only advances the field of renewable energy, particularly for solar-powered EV charging stations, but also aligns with global efforts towards sustainable transportation solutions

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

DEVELOPMENT OF DIGITAL AUTOMATION SYSTEM FOR THE UTP INTEGRATED RENEWABLE ENERGY POWER GENERATION

Renewable Energy (RE) got advantages over the conventional fuel energy generation. The only obstacle emerge is the supportability to the load demand and intermittency of power. This problem makes the natural resources are wasted for many years. Many approached had been applied from previous study. However, it required a high cost to develop the system. Home or commercial sector cannot afford to invest in implementing this technology. In conjunction of increasing the efficiency of power extraction, the centralized system are yet to be developed. The innovation of low cost system are still available to challenge. In this study, an approached proposed is to use the board from the National Instrument myRIO-1900 Reconfigurable I/O (RIO) and LabView software for commercially approach. By implementing this board and software, the integrated system of many sources from renewable energy are expected to develop

Quality of Service and Associated Communication Infrastructure for Electric Vehicles †

Transportation electrification is pivotal for achieving energy security and emission reduction goals. Electric vehicles (EVs) are at the forefront of this transition, driving the development of new EV technologies and infrastructure. As this trend gains momentum, it becomes essential to enhance the quality of service (QoS) of EVs to encourage their widespread adoption. This paper has been structured with two primary aims to effectively address the above timely technological needs. Firstly, it comprehensively reviews the various QoS factors that influence EVs’ performance and the user experience. Delving into these factors provides valuable insights into how the QoS can be improved, thereby fostering the increased use of EVs on our roads. In addition to the QoS, this paper also explores recent advancements in communication technologies vital for facilitating in-formation exchanges between EVs and charging stations. Efficient communication systems are crucial for optimizing EV operations and enhancing user experiences. This paper presents expert-level technical details in an easily understandable manner, making it a valuable resource for researchers dedicated to improving the QoS of EV communication systems, who are tirelessly working towards a cleaner, more efficient future in transportation. It consolidates the current knowledge in the field and presents the latest discoveries and developments, offering practical insights for enhancing the QoS in electric transportation. A QoS parameter reference map, a detailed classification of QoS parameters, and a classification of EV communication technology references are some of the key contributions of this review paper. In doing so, this paper contributes to the broader objectives of promoting transportation electrification, enhancing energy security, and reducing emissions