scholarly journals Neuro-Fuzzy-Based Model Predictive Energy Management for Grid Connected Microgrids

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 900 ◽  
Author(s):  
Ahsen Ulutas ◽  
Ismail Hakki Altas ◽  
Ahmet Onen ◽  
Taha Selim Ustun
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Technology Use ◽  
Energy Use ◽  
Renewable Energy Sources ◽  
Solar Irradiation ◽  
Pv System ◽  
Inference System ◽  
Management Algorithm ◽  
Neuro Fuzzy

With constant population growth and the rise in technology use, the demand for electrical energy has increased significantly. Increasing fossil-fuel-based electricity generation has serious impacts on environment. As a result, interest in renewable resources has risen, as they are environmentally friendly and may prove to be economical in the long run. However, the intermittent character of renewable energy sources is a major disadvantage. It is important to integrate them with the rest of the grid so that their benefits can be reaped while their negative impacts can be mitigated. In this article, an energy management algorithm is recommended for a grid-connected microgrid consisting of loads, a photovoltaic (PV) system and a battery for efficient use of energy. A model predictive control-inspired approach for energy management is developed using the PV power and consumption estimation obtained from daylight solar irradiation and temperature estimation of the same area. An energy management algorithm, which is based on a neuro-fuzzy inference system, is designed by determining the possible operating states of the system. The proposed system is compared with a rule-based control strategy. Results show that the developed control algorithm ensures that microgrid is supplied with reliable energy while the renewable energy use is maximized.

scholarly journals Management of Renewable Energy Sources and Technologies for Turkey

2020 ◽  
pp. 26-36 ◽  
Author(s):  
H. H. Sahin ◽  
B. Yelmen ◽  
C. Kurt
Keyword(s):  
Renewable Energy ◽  
Energy Saving ◽  
Energy Management ◽  
Fossil Fuels ◽  
Energy Use ◽  
Renewable Energy Sources ◽  
Clean Energy ◽  
Energy Sources ◽  
Energy Use Efficiency ◽  
Use Efficiency

Utilization of alternative sources named as new and renewable energy sources; Due to technological development and difficulties in competing economically with traditional resources, it has not reached the desired level until today. Due to the rapid increase in energy consumption today; It is a fact proved by scientific findings that fossil fuels will be consumed in the near future. Therefore, in the development of countries; Providing timely, reliable, clean and uninterrupted energy, creating a market environment, in other words, successful implementation of energy management have become imperative. Energy has become one of the most important problems of the world countries today. As is known, the lifetimes of energy sources such as coal and oil are limited. In addition, due to the use of fossil fuels; It is a fact that global warming is increasing day by day. In the light of the data obtained, for our energy needs; alternative solutions should be found, renewable energy sources should be evaluated. The importance of renewable energy sources has increased as the problems related to environmental pollution increase, and projects related to them have started to get support. These energy sources can basically be classified as hydroelectric energy, wind energy, solar energy, geothermal energy, modern biomass energy and hydrogen energy. Economic and efficient operation of new and renewable energy sources should be transformed into a common understanding in order to provide clean energy. In this study, energy production methods from green energy sources, environmental relations and new technologies used with these energy sources are explained. It has been compared among energy sources; In Turkey, energy management issues are discussed, new and renewable clean energy use efficiency and energy saving and new strategies are determined. In addition, recommendations were made on energy use efficiency and energy saving measures in various sectors.

scholarly journals Research Survey on Different MPP Tracking Optimization Algorithms for Solar Photovoltaic System (SPS)

2021 ◽  
Vol 12 (6) ◽  
pp. 2666-2671
Author(s):  
R. Mohan Kumar, Dr. C. Kathirvel
Keyword(s):  
Renewable Energy ◽  
Direct Method ◽  
Renewable Energy Sources ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Operating Efficiency ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Pv System ◽  
Solar Pv System

Due to increase in global warming, it is required to choose an alternative renewable energy source for the electricity generation. Among various renewable energy sources (RES), photo-voltaic energy is one of the most accessible source of energies. But the conversion rate of solar PV cell is about 25 % to 40 % of solar irradiation level. In Solar Photovoltaic (PV) system, to improve and maximize the operating efficiency level, Maximum Power Point Tracking (MPPT) techniques were required. Because of the change in the level of solar irradiance, and the nature of dynamic temperature, this MPP tracking will be highly important to make the solar PV system (SPS) to operate at higher efficiency level. This MPPT method is mainly categorized into three different types such as direct method, indirect method and intelligent method. This paper will gives and overview about various MPPT methods employed for solar PV system. Various controlling algorithms were discussed in this section for a better understanding.

scholarly journals Research Survey on Various MPPT Performance Issues to Improve the Solar PV System Efficiency

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
B. Pakkiraiah ◽  
G. Durga Sukumar
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Electrical Energy ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Maximum Output ◽  
Solar Pv ◽  
Pv System ◽  
Pv Panel ◽  
Solar Pv System

Nowadays in order to meet the increase in power demands and to reduce the global warming, renewable energy sources based system is used. Out of the various renewable energy sources, solar energy is the main alternative. But, compared to other sources, the solar panel system converts only 30–40% of solar irradiation into electrical energy. In order to get maximum output from a PV panel system, an extensive research has been underway for long time so as to access the performance of PV system and to investigate the various issues related to the use of solar PV system effectively. This paper therefore presents different types of PV panel systems, maximum power point tracking control algorithms, power electronic converters usage with control aspects, various controllers, filters to reduce harmonic content, and usage of battery system for PV system. Attempts have been made to highlight the current and future issues involved in the development of PV system with improved performance. A list of 185 research publications on this is appended for reference.

scholarly journals Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2929
Author(s):  
Rami Alamoudi ◽  
Osman Taylan ◽  
Mehmet Azmi Aktacir ◽  
Enrique Herrera-Viedma
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Hospital Performance ◽  
System Capacity ◽  
Design Parameters ◽  
System Response ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Pv System ◽  
Inference System

One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self-consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m2, the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.

scholarly journals The Energy System of an Ecovillage: Barriers and Enablers

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 682
Author(s):  
Zita Szabó ◽  
Viola Prohászka ◽  
Ágnes Sallay
Keyword(s):  
Renewable Energy ◽  
Spatial Structure ◽  
Energy Management ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Energy Sources ◽  
Energy Management System ◽  
Production And Consumption ◽  
Ecologically Sustainable

Nowadays, in the context of climate change, efficient energy management and increasing the share of renewable energy sources in the energy mix are helping to reduce greenhouse gases. In this research, we present the energy system and its management and the possibilities of its development through the example of an ecovillage. The basic goal of such a community is to be economically, socially, and ecologically sustainable, so the study of energy system of an ecovillage is especially justified. As the goal of this community is sustainability, potential technological and efficiency barriers to the use of renewable energy sources will also become visible. Our sample area is Visnyeszéplak ecovillage, where we examined the energy production and consumption habits and possibilities of the community with the help of interviews, literature, and map databases. By examining the spatial structure of the settlement, we examined the spatial structure of energy management. We formulated development proposals that can make the community’s energy management system more efficient.

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 Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2151
Author(s):  
Feras Alasali ◽  
Husam Foudeh ◽  
Esraa Mousa Ali ◽  
Khaled Nusair ◽  
William Holderbaum
Keyword(s):  
Renewable Energy ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Golden Ratio ◽  
Clean Energy ◽  
Energy Sources ◽  
Percentage Error ◽  
Ann Model ◽  
Demand Forecast ◽  
Pv System

More and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN model.

scholarly journals Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building

Smart Cities ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 471-495
Author(s):  
Viktor Stepaniuk ◽  
Jayakrishnan Pillai ◽  
Birgitte Bak-Jensen ◽  
Sanjeevikumar Padmanaban
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Storage Tank ◽  
Residential Buildings ◽  
Renewable Energy Sources ◽  
Hot Water ◽  
Active Management ◽  
Worst Case ◽  
And Storage ◽  
Storage Units

The smart active residential buildings play a vital role to realize intelligent energy systems by harnessing energy flexibility from loads and storage units. This is imperative to integrate higher proportions of variable renewable energy generation and implement economically attractive demand-side participation schemes. The purpose of this paper is to develop an energy management scheme for smart sustainable buildings and analyze its efficacy when subjected to variable generation, energy storage management, and flexible demand control. This work estimate the flexibility range that can be reached utilizing deferrable/controllable energy system units such as heat pump (HP) in combination with on-site renewable energy sources (RESs), namely photovoltaic (PV) panels and wind turbine (WT), and in-house thermal and electric energy storages, namely hot water storage tank (HWST) and electric battery as back up units. A detailed HP model in combination with the storage tank is developed that accounts for thermal comforts and requirements, and defrost mode. Data analytics is applied to generate demand and generation profiles, and a hybrid energy management and a HP control algorithm is developed in this work. This is to integrate all active components of a building within a single complex-set of energy management solution to be able to apply demand response (DR) signals, as well as to execute all necessary computation and evaluation. Different capacity scenarios of the HWST and battery are used to prioritize the maximum use of renewable energy and consumer comfort preferences. A flexibility range of 22.3% is achieved for the scenario with the largest HWST considered without a battery, while 10.1% in the worst-case scenario with the smallest HWST considered and the largest battery. The results show that the active management and scheduling scheme developed to combine and prioritize thermal, electrical and storage units in buildings is essential to be studied to demonstrate the adequacy of sustainable energy buildings.

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