Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

2021 ◽  
Vol 227 ◽  
pp. 113608
Author(s):  
Guozhou Zhang ◽  
Weihao Hu ◽  
Di Cao ◽  
Wen Liu ◽  
Rui Huang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Reverse Osmosis ◽  
Energy Management ◽  
Energy System ◽  
Data Driven ◽  
Learning Approach ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
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 Economical Evaluation and Optimal Energy Management of a Stand-Alone Hybrid Energy System Handling in Genetic Algorithm Strategies

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 233 ◽  
Author(s):  
Omar Mohammed ◽  
Yassine Amirat ◽  
Mohamed Benbouzid
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Hybrid System ◽  
High Reliability ◽  
Energy System ◽  
System Model ◽  
Optimization Approach ◽  
Hybrid Energy ◽  
Optimal Energy ◽  
Optimal Energy Management

Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.

scholarly journals Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

2019 ◽  
Vol 11 (22) ◽  
pp. 6293 ◽  
Author(s):  
Seunghyun Park ◽  
Surender Reddy Salkuti
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Storage Systems ◽  
Solar Pv ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Optimal Energy ◽  
Optimal Energy Management

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

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