scholarly journals Political-Optimizer-Based Energy-Management System for Microgrids

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3119
Author(s):  
Vishnu Suresh ◽  
Michal Jasinski ◽  
Zbigniew Leonowicz ◽  
Dominika Kaczorowska ◽  
Jithendranath J. ◽  
...  
Keyword(s):  
Energy Management ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Solar Pv ◽  
Energy Management Strategy ◽  
Storage Unit ◽  
Hydro Power ◽  
Operations Planning ◽  
Power System Operations ◽  
Cost Of Energy

This paper presents an energy-management strategy based on a recently introduced Political Optimizer (PO) for a microgrid installation at Wroclaw University of Science and Technology. The aim of the study is to check the effectiveness of two recently introduced meta-heuristic algorithms at power-system-operations planning. The optimization algorithms were compared with other conventional meta-heuristics wherein performance tests were carried out by minimizing costs in an IEEE 30-bus system. The best performing algorithm was then used to minimize the Levelized Cost of Energy (LCOE) in a microgrid consisting of renewable energy sources such as solar PV panels, a micro-hydro power plant, a fuel cell with a hydrogen storage tank and a Li-ion storage unit.

scholarly journals Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 168 ◽  
Author(s):  
Dominic A. Savio ◽  
Vimala A. Juliet ◽  
Bharatiraja Chokkalingam ◽  
Sanjeevikumar Padmanaban ◽  
Jens Bo Holm-Nielsen ◽  
...  
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Renewable Energy Sources ◽  
Constant Current ◽  
Management Approach ◽  
Energy Management Strategy ◽  
Storage Unit ◽  
Charging Station ◽  
Hybrid Microgrid ◽  
Utility Grid

A hybrid microgrid-powered charging station reduces transmission losses with better power flow control in the modern power system. However, the uncoordinated charging of battery electric vehicles (BEVs) with the hybrid microgrid results in ineffective utilization of the renewable energy sources connected to the charging station. Furthermore, planned development of upcoming charging stations includes a multiport charging facility, which will cause overloading of the utility grid. The paper analyzes the following technical issues: (1) the energy management strategy and converter control of multiport BEV charging from a photovoltaic (PV) source and its effective utilization; (2) maintenance of the DC bus voltage irrespective of the utility grid overloading, which is caused by either local load or the meagerness of PV power through its energy storage unit (ESU). In addition, the charge controller provides closed loop charging through constant current and voltage, and this reduces the charging time. The aim of an energy management strategy is to minimize the usage of utility grid power and store PV power when the vehicle is not connected for charging. The proposed energy management strategy (EMS) was modeled and simulated using MATLAB/Simulink, and its different modes of operation were verified. A laboratory-scale experimental prototype was also developed, and the performance of the proposed charging station was investigated.

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.

Parametric Modeling and Optimal Control of a Combined Heating and Power System With Energy Storage

2020 ◽  
Author(s):  
Claudia Lucia De Pascalis ◽  
Stephanie Stockar
Keyword(s):  
Energy Storage ◽  
Power Systems ◽  
Power System ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
Parametric Modeling ◽  
Medium Size ◽  
Energy Management Strategy ◽  
Generation Cost

Abstract Cogeneration is a well-known and cost effective solution for generating power and heat within the same plant, leading to improved overall efficiency and reduced generation cost. Combined heating and power systems can facilitate the penetration of renewable energy sources in medium size applications through the integration of electric and thermal energy storage units. Due to the complexity of the plant as well as significantly variability in power demand and generation, the design and operation of such systems requires a systematic co-optimization of plant and controller for guaranteeing near optimal performance. In this scenario, this paper presents a physics-based parametric modeling approach for the characterization of the main components of a 1MW combined heating and power system that includes renewable sources, electric and thermal storage devices. To demonstrate the model flexibility and potential benefits achieved by an optimal sizing, the system energy management is optimized using Dynamic Programming. The operational costs for different configurations are compared showing that an optimization of the energy management strategy in conjunction with an improved system sizing lead to more than 6% of reduction in the operational cost.

scholarly journals Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings

2019 ◽  
Vol 9 (4) ◽  
pp. 792 ◽  
Author(s):  
Ibrar Ullah ◽  
Sajjad Hussain
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Energy Cost ◽  
Management System ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Smart Homes ◽  
Energy Management System ◽  
End User ◽  
Residential Sector

This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.

Hybrid Distributed Power Generation for an Isolated Rural Settlement in Masirah Island, Oman

2011 ◽  
Author(s):  
Saleh Al Saadi ◽  
Moncef Krarti
Keyword(s):  
Power Systems ◽  
Residential Buildings ◽  
Renewable Energy Sources ◽  
Electrical Energy ◽  
Small Community ◽  
Solar Pv ◽  
Cost Of Energy ◽  
Storage Batteries ◽  
Electrical Generation ◽  
The Cost

This paper summarizes the findings from a feasibility study of using renewable energy sources in combination with conventional power systems to meet the electrical requirements for an isolated island of Masirah in Oman. The study has been conducted to determine the best hybrid system to generate electrical energy needed for a small community of 500 residential buildings. A series of a simulation analyses have been carried out to evaluate and optimize different distribution technologies including photovolatics, wind and diesel for electrical generation in combination with storage batteries. It was found that the cost of energy could be reduced by as much as 48% compared to the cost for the baseline generation system currently used in the Masirah Island (i.e. diesel-driven generators). In particular, it was found that wind turbines in combination with storage batteries have a great impact in reducing the cost of generating electrical energy for the residential community. Moreover, solar PV panels were found unattractive under the current diesel price rates but could potentially become viable if the diesel prices increase. The paper outlines an optimal design for generating electricity for the community at lowest cost while minimizing carbon emissions.

scholarly journals Optimum Resilient Operation and Control DC Microgrid Based Electric Vehicles Charging Station Powered by Renewable Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4240 ◽  
Author(s):  
Khairy Sayed ◽  
Ahmed G. Abo-Khalil ◽  
Ali S. Alghamdi
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Solar Pv ◽  
Dc Microgrid ◽  
Charging Station ◽  
And Control ◽  
Ev Charging

This paper introduces an energy management and control method for DC microgrid supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed to manage and control power flow from renewable energy sources to EVs through DC microgrid. An integrated approach for controlling DC microgrid based charging station powered by intermittent renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system are considered decision parameters to be optimized. The optimized design of the renewable energy system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various renewable energy technologies for supplying EV charging stations to improve their performance are investigated. To evaluate the performance of the used control strategies, simulation is carried out in MATLAB/SIMULINK.

scholarly journals Energy Management Electronic Device for Islanded Microgrids Based on Renewable Energy Sources and Battery-based Energy Storage

2021 ◽  
Vol 41 (1) ◽  
pp. e83905
Author(s):  
Elkin Dario Granados Hernández ◽  
Nelson Leonardo Diaz Aldana ◽  
Adriana Carolina Luna Hernández
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Electronic Device ◽  
Renewable Energy Sources ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Energy Management System ◽  
Distributed Energy ◽  
Energy Management Strategy

Energy management systems are one of the most important components in the operation of an electric microgrid. They are responsible for ensuring the supervision of the electrical system, as well as the coordination and reliability of all loads and distributed energy resources in order for the microgrid to be operated as a unified entity. Because of that, an energy management system should be fast enough at processing data and defining control action to guarantee the correct performance of the microgrid. This paper explores the design and implementation of an energy management system deployed over a dedicated electronic device. The proposed energy management device coordinates the distributed energy resources and loads in a residential-scale islanded microgrid, in accordance with a rule-based energy management strategy that ensures reliable and safe operation of the battery-based energy storage system. A hardware-int-he-loop test was performed with a real-time simulation platform to show the operation of the electronic device

scholarly journals Optimal Configuration and Sizing of an Integrated Renewable Energy System for Isolated and Grid-Connected Microgrids: The Case of an Urban University Campus

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3527
Author(s):  
Navid Shirzadi ◽  
Fuzhan Nasiri ◽  
Ursula Eicker
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Capital Intensity ◽  
Vertical Axis ◽  
Energy System ◽  
Solar Pv ◽  
Grid Connection ◽  
Cost Of Energy ◽  
Solar Tracker ◽  
Cost Efficient

Although renewable technologies are progressing fast, there are still challenges such as the reliability and availability of renewable energy sources and their cost issues due to capital intensity that hinder their broad adoption. This research aims at developing a configuration-sizing approach to enhance the cost efficiency and sourcing reliability of renewable energies integrated in microgrids. To achieve this goal, various technologies were considered, such as solar PV, wind turbines, converters, and batteries for system configuration with minimization of net present cost (NPC) as the objective. Grid connection scenarios with up to 100% renewable contribution were analyzed. The results show that the integration of renewable technologies with some grid backup could reduce the levelized cost of energy (LCOE) to about half of the price of the electricity that the university purchases from the grid. Also, different kinds of solar tracker systems were studied. The outcome shows that by using a vertical axis solar tracker, the LCOE of the system could be reduced by more than 50 percent. This research can help the decision-maker to opt for the best scenarios for generating reliable and cost-efficient electricity.

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