Optimal energy management of small electric energy systems including V2G facilities and renewable energy sources

2012 ◽  
Vol 92 ◽  
pp. 50-59 ◽  
Author(s):  
C. Battistelli ◽  
L. Baringo ◽  
A.J. Conejo
2021 ◽  
Author(s):  
Nikita Tomin ◽  
Vladislav Shakirov ◽  
Aleksander Kozlov ◽  
Denis Sidorov ◽  
Victor Kurbatsky ◽  
...  

Author(s):  
Nikita Tomin ◽  
Vladislav Shakirov ◽  
Aleksander Kozlov ◽  
Denis Sidorov ◽  
Victor Kurbatsky ◽  
...  

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.


Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5095 ◽  
Author(s):  
Abdulla Al Wahedi ◽  
Yusuf Bicer

E-Mobility deployment has attained increased interest during recent years in various countries all over the world. This interest has focused mainly on reducing the reliance on fossil fuel-based means of transportation and decreasing the harmful emissions produced from this sector. To secure the electricity required to satisfy Electric Vehicles’ (EVs’) charging needs without expanding or overloading the existing electricity infrastructure, stand-alone charging stations powered by renewable sources are considered as a reasonable solution. This paper investigates the simulation of the optimal energy management of a proposed grid-independent, multi-generation, fast-charging station in the State of Qatar, which comprises hybrid wind, solar and biofuel systems along with ammonia, hydrogen and battery storage units. The study aims to assess the optimal sizing of the solar, wind and biofuel units to be incorporated in the design along with the optimal ammonia, hydrogen and battery storage capacities to fulfill the daily EV demand in an uninterruptable manner. The main objective is to fast-charge a minimum of 50 EVs daily, while the constraints are the intermittent and volatile nature of renewable energy sources, the stochastic nature of EV demand, local meteorological conditions and land space limitations. The results show that the selection of a 468 kWp concentrated photovoltaic thermal plant, 250 kW-rated wind turbine, 10 kW biodiesel power generator unit and 595 kWh battery storage system, along with the on-site production of hydrogen and ammonia, to generate 200 kW power via fuel cells can achieve the desired target, with a total halt of on-site hydrogen and ammonia production during October and November and 50% reduction during December.


2022 ◽  
pp. 60-94
Author(s):  
Khaled Dassa ◽  
Abdelmadjid Recioui

The smart grid is the aggregation of emerging technologies in both hardware and software along with practices to make the existing power grid more reliable and ultimately more beneficial to consumers. The smart grid concept is associated with the production of electricity from renewable energy sources (RES). For the distant isolated regions, microgrids (MG) with RES are offering a suitable solution for remote and isolated region electrification. The improper sizing would lead to huge investment cost which could have been avoided. The objective of this chapter is to review the state-of-the-art studies on the use of optimization techniques to renewable energy design and sizing. The chapter reviews the optimization techniques employed at different components of the microgrid including the energy sources, storage elements, and converters/inverters with their control systems.


2011 ◽  
pp. 61-64
Author(s):  
Mihály Dombi ◽  
István Kuti ◽  
Péter Balogh

The utilization of renewable energy sources (res) is crucial regarding to sustainable reconstruction of energy systems. The target is a balanced, sustainable development of Hungarian energy management considering equally the ecological, social and economic aspects. There are many different technologies of utilization of res varied by sources, conversion processes, size and products. The comparison of each technology and their sustainability assessment are required by the importance of efficient remodeling of energy infrastructure. The group of attributes was composed by numerous important parameters in the course of our analysis with the choice experiment (ce) methodology. The estimation of each attributes’ influence on the individual’s preferences and choices was possible by this method and the preferences of the statistical population was concluded. So thus the utility derived from each attribute was estimated. The result of the ce analysis for the population of experts is demonstrated in the current phase of our research.


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