scholarly journals Optimal Operation Control of PV-Biomass Gasifier-Diesel-Hybrid Systems Using Reinforcement Learning Techniques

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2632 ◽  
Author(s):  
Alexander N. Kozlov ◽  
Nikita V. Tomin ◽  
Denis N. Sidorov ◽  
Electo E. S. Lora ◽  
Victor G. Kurbatsky
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Optimization Problems ◽  
Combustion Engine ◽  
Renewable Energy Sources ◽  
Biomass Gasification ◽  
Optimal Operation ◽  
Total System ◽  
Producer Gas ◽  
Markov Decision

The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive progress has been made in the gasification technologies development during the last decade. This paper deals with the control and optimization problems for an isolated microgrid combining the renewable energy sources (solar energy and biomass gasification) with a diesel power plant. The control problem of an isolated microgrid is formulated as a Markov decision process and we studied how reinforcement learning can be employed to address this problem to minimize the total system cost. The most economic microgrid configuration was found, and it uses biomass gasification units with an internal combustion engine operating both in single-fuel mode (producer gas) and in dual-fuel mode (diesel fuel and producer gas).

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 Optimal Configuration and Sizing of Seaport Microgrids including Renewable Energy and Cold Ironing—The Port of Aalborg Case Study

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 431
Author(s):  
Nur Najihah Abu Bakar ◽  
Josep M. Guerrero ◽  
Juan C. Vasquez ◽  
Najmeh Bazmohammadi ◽  
Muzaidi Othman ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Maximum Load ◽  
Optimal Operation ◽  
Economic Feasibility ◽  
Photovoltaic System ◽  
Design Stage ◽  
Component Size

Microgrids are among the promising green transition technologies that will provide enormous benefits to the seaports to manage major concerns over energy crises, environmental challenges, and economic issues. However, creating a good design for the seaport microgrid is a challenging task, considering different objectives, constraints, and uncertainties involved. To ensure the optimal operation of the system, determining the right microgrid configuration and component size at minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system for a seaport microgrid with optimally sized components. The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system, and cold ironing facilities. The seaport architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering important parameters such as solar global horizontal irradiance, temperature, and wind resources. Finally, the best configuration is analyzed in terms of economic feasibility, energy reliability, and environmental impacts.

scholarly journals Integrated Optimization Design of Combined Cooling, Heating, and Power System Coupled with Solar and Biomass Energy

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 687 ◽  
Author(s):  
Lizhi Zhang ◽  
Fan Li ◽  
Bo Sun ◽  
Chenghui Zhang
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Optimization Design ◽  
Design Method ◽  
Renewable Energy Sources ◽  
Optimal Operation ◽  
Biomass Energy ◽  
System Structure ◽  
Energy Sources ◽  
Combined Cooling

The combined cooling, heating, and power (CCHP) systems coupled with solar energy and biomass energy can meet the needs of island or rural decentralized and small-scale integrated energy use, which have become increasingly popular in recent years. This study presents a renewable energy sources integrated combined cooling, heating, and power (RES-CCHP) system, driven by a biogas fueled internal combustion engine (ICE) and photovoltaic (PV) panels, which is different from the traditional natural gas CCHP system. Owing to the solar energy volatility and the constraint of biomass gas production, the traditional optimization design method is no longer applicable. To improve the energetic, economic and environmental performances of the system, an integrated design method with renewable energy capacity, power equipment capacity and key operating parameters as optimization variables is proposed. In addition, a case study of a small farm in Jinan, China, is conducted to verify the feasibility of the proposed RES–CCHP system structure and the corresponding optimal operation strategy. The results illustrate that the implementation of the optimal design is energy-efficient, economical and environmentally-friendly. The values of primary energy saving ratio, annual total cost saving rate and carbon emission reduction ratio are 20.94%, 11.73% and 40.79%, respectively. Finally, the influence of the volatility of renewable energy sources on the optimization method is analyzed, which shows that the RES–CCHP system and the method proposed are robust.

Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles

Energy ◽  
2019 ◽  
Vol 186 ◽  
pp. 115841 ◽  
Author(s):  
Mustafa Ata ◽  
Ayşe Kübra Erenoğlu ◽  
İbrahim Şengör ◽  
Ozan Erdinç ◽  
Akın Taşcıkaraoğlu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Optimal Operation ◽  
Energy Sources

scholarly journals The Impact of Powering an Engine with Fuels from Renewable Energy Sources including its Software Modification on a Drive Unit Performance Parameters

2019 ◽  
Vol 11 (23) ◽  
pp. 6585 ◽  
Author(s):  
Markiewicz ◽  
Muślewski
Keyword(s):  
Renewable Energy ◽  
Internal Combustion Engines ◽  
Combustion Engine ◽  
Renewable Energy Sources ◽  
Internal Combustion ◽  
Energy Sources ◽  
Exhaust Emission ◽  
Combustion Engines ◽  
Performance Parameters ◽  
The Impact

The application of fuels from renewable energy sources for combustion engine powering involves a great demand for this kind of energy while its production infrastructure remains underdeveloped. The use of this kind of fuel is supposed to reduce the emission of greenhouse gases and the depletion of natural resources and to increase the share of renewable energy sources in total energy consumption and thus support sustainable development in Europe. This study presents the results of research on selected performance parameters of transport by internal combustion engines including: power, torque, the emission of sound generated by the engine, the content of exhaust components (oxygen O2, carbon monoxide CO, carbon dioxide CO2, nitrogen dioxide NO2), and the content of particulate matter (PM) in exhaust emission. Three self-ignition engines were tested. The fuel injection controllers of the tested internal combustion engines were additionally adjusted by increasing the fuel dose and the load of air. The material used in the tests were mixtures of diesel oil and fatty acid methyl esters of different concentration. A statistical analysis was performed based of the results. The purpose of the work was to develop a resulting model for assessing the operation of engines fueled with biofuel and diesel mixtures while changing the vehicle's computer software. A computer simulation algorithm was also developed for the needs of the tests which was used to prognose the state of the test results for variable input parameters.

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