A two-stage optimal energy management by using ADP and HBB-BC algorithms for microgrids with renewable energy sources and storages

2019 ◽  
Vol 21 ◽  
pp. 460-480 ◽  
Author(s):  
Mostafa Sedighizadeh ◽  
Amir Hosein Mohammadpour ◽  
Seyed Mohammad Mahdi Alavi
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Two Stage ◽  
Optimal Energy ◽  
Optimal Energy Management

Development and Optimal Energy Management of Community Microgrids with Flexible Renewable Energy Sources

2021 ◽  
Author(s):  
Nikita Tomin ◽  
Vladislav Shakirov ◽  
Aleksander Kozlov ◽  
Denis Sidorov ◽  
Victor Kurbatsky ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

Design and optimal energy management of community microgrids with flexible renewable energy sources

2021 ◽  
Author(s):  
Nikita Tomin ◽  
Vladislav Shakirov ◽  
Aleksander Kozlov ◽  
Denis Sidorov ◽  
Victor Kurbatsky ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Optimal Energy ◽  
Optimal Energy Management

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

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.

scholarly journals A Case Study in Qatar for Optimal Energy Management of an Autonomous Electric Vehicle Fast Charging Station with Multiple Renewable Energy and Storage Systems

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5095 ◽  
Author(s):  
Abdulla Al Wahedi ◽  
Yusuf Bicer
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Battery Storage ◽  
Charging Station ◽  
Optimal Energy ◽  
Production Of Hydrogen ◽  
Fast Charging ◽  
Optimal Energy Management ◽  
Fast Charging Station

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.

scholarly journals Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
V. V. S. N. Murty ◽  
Ashwani Kumar
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Optimal Energy ◽  
Simulation Results ◽  
Battery Energy ◽  
Microgrid Energy Management

AbstractMicrogrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.

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