Sizing Of Renewable Energy System Research Articles

Metaheuristic Algorithms for Optimal Sizing of Renewable Energy Systems in Smart Grids

This research thoroughly examines the effectiveness of several metaheuristic algorithms in optimizing the dimensions of renewable energy systems in smart grids. It focuses on the urgent need for sustainable and efficient integration of energy. This study examines several optimization strategies by analyzing simulated datasets that represent renewable energy production profiles, energy consumption patterns, and battery storage performance. The data illustrates variations in the availability of renewable energy, with solar energy fluctuating between 500 kWh and 600 kWh, wind energy ranging from 280 kWh to 320 kWh, and hydro energy varying from 180 kWh to 220 kWh. Moreover, energy consumption patterns remain stable sectors, with throughout consumption levels ranging from 400 kWh to 430 kWh, 450 kWh to 480 kWh, and 600 kWh to 630 kWh, respectively. The examination of battery storage performance indicates that the charging efficiency ranges from 90% to 94% and the discharging efficiency ranges from 85% to 89%. Additionally, the depth of discharge ranges from 80% to 84% and the cycle life spans from 2000 cycles to 2400 cycles. By using several metaheuristic algorithms, the research produces a wide range of ideal size arrangements for solar panels, wind turbines, hydro turbines, and battery capacity. These suggested solutions exhibit variances that span from 3.23% to 20%. The results highlight the susceptibility of these algorithms to optimization goals, underlining the need of selecting appropriate algorithms that align with particular limitations and aims. The study's results illuminate the potential of metaheuristic algorithms in attaining effective and sustainable integration of renewable energy systems inside smart grids. This paves the path for informed decision-making and future developments in renewable energy management methods.

Improved Convolutional Neural Network and Heuristic Technique based on Forecasting and Sizing of Hybrid Renewable Energy System

Aims & Objective: The fast depletion of fossil fuels and the growing awareness of environmental protection has become a concerning topic. Because of this fact, the researchers are working for a long time to generate electrical energy sources due to the intermittent nature of unconventional energy sources such as solar, wind geothermal, tidal, and biomass as a sustainable, cost-effective, and environmentally friendly alternative for conventional energy sources. These systems are interconnected and full-fill demands as well as energy storage, which subsequently formed a complex hybrid renewable energy system. Hence, forecasting of energy generation, sizing of equipment is essential for the economic feasibility of a complex hybrid system. Also necessary for the design analysis. Methodology: In this research article, the proposed Functional Link Convolutional Neural Network (FLCNN) is applied to forecast the energy generation from the hybrid solar and wind energy system. Also, the Jaya algorithm has been applied to find the optimal sizing of the solar and wind based hybrid renewable energy system. Results & Discussion: The proposed method is simple in design and implementation, and it also reduces computational complexity and time. The proposed FLCNN technique has been compared with various other Machine Learning (ML) methodology, such as Convolutional Neural Network (CNN), Random Forest (RF), and Xg-Boost. In sizing, Jaya is compared with other heuristic techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Cat Swarm Optimization (CSO). Conclusion: The proposed FLCNN and Jaya optimization techniques successfully applied for tasks like energy forecasting and sizing of the renewable energy system.

Novel Methodology for Sizing a Single U-Tube Ground Heat Exchanger Useful at the Early Design Stage

Renewable energy system (RES) is an environmentally friendly source of energy. A suitable design of RES is crucial to implement an energy-efficient building such as a zero energy building (ZEB). The significance of appropriate decision-making for the successful implementation of energy-efficient buildings has been increasing. In addition, the identification of the sizing of RES is equally important for architects or HVAC engineers. In this study, a novel sizing method for a single U-tube ground heat exchanger (GHE) is proposed. A transient thermal analysis for a single GHE is performed by considering ground temperature recovery effect as well as other major design parameters. The results are used to design the proposed sizing method and were verified by transient simulations for different design cases. Additionally, it was observed that the coefficient of variation of root mean square error (CV(RMSE)) for all ten design cases was lower than 15% during the heating and cooling seasons. Thus, the proposed design method can be used for sizing a GHE in the early design stage.

Optimal sizing of renewable energy systems in a Microgrid considering electricity market interaction and reliability analysis

This paper addresses the optimal sizing of renewable energy systems (RESs) in a microgrid (MG), where the MG participates in the electricity market. A novel method for reliability analysis is proposed in this study to deal with the high penetration of RESs. In this framework, the MG is considered as a price maker, having a two-direction relation with the electricity market. RESs, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cells, are optimally sized based on the reliability index, and the results are evaluated before and after the MG involvement in the electricity market. The results show a 3.6% decrease in the total cost of the microgrid as a result of the transactions with the electricity market. Furthermore, the efficiency of the proposed approximate reliability method is verified, where the reliability of the MG is evaluated with less computational complexity and acceptable accuracy.

A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems

Pumping of water for agriculture can be a flexible component of electric demand. In this study, a framework that involves scenario based stochastic programming models is developed to examine the effect of load shifting on the renewable energy system sizing for agricultural load. With the help of this framework, alternative load shifting policies are evaluated to observe how the intrinsic flexibility of agricultural load reduces the amount of investments while designing a renewable system. Using real data from India’s Gujarat region, solar and wind cases are evaluated separately to understand the coherency between these sources and the agricultural demand. The value of using a dispatchable source to help with the intermittency of the renewable sources in the systems is discussed. It is also shown that energy storage can be a convenient control mechanism for the integration of renewables; however, is an expensive substitute for demand response programs for agricultural load. Benchmarks for the incentive amounts that can be provided for alternative load shifting policies are presented.

Environmental feasibility of incorporation of electric taxis in South Africa

Public transport is very influential industry affecting our society due to its significant environmental and social impact. Policy makers have seen electric vehicle as a tool to reduce greenhouse gas emissions. Cleanliness of power from the electricity grid plays a vital role in environmental impact in the transition to electric vehicle. In countries like China, USA, India etc., majority of electricity generation is still dominated by fossil fuel fired generators. Thus, in some cases, the environmental impact of electric vehicle may be more adverse in comparison to conventional internal combustion engine vehicles (ICEV). This paper tries to study the environmental effect of incorporation of 100 Electric Vehicle taxies in Johannesburg (JNB), South Africa. Firstly, the current electricity generation scenario and vehicles standards of SA with its carbon emission is discussed. Secondly, it is shown that with the current generation mix the adoption of EV is not sustainable. Finally, penetration of Renewable Energy (RE) system at charging stations is proposed with Renewable Energy Fraction (REF) of 40% and 58% to make carbon mission from EV equivalent to Euro III and Euro V standards of petrol ICEV. The optimised size of RE system is found using the Electric System Cascade Analysis.

Techno-Economic Optimization of a Stand-alone PV/PHS/Battery systems for very low load situation

In much of the sizing problems of hybrid renewable energy system addressed so far, the emphasis has mostly been on optimizing the cost of energy and finding capacity of the feasible components. However there is hardly any literature on the optimal sizing of renewable energy system (RES) for very low load situation. The objective of this paper is to study the feasibility of pumped hydro storage (PHS) based photovoltaic (PV) RES for a very low load (maximum demand less than 30 kW) and also to study the integration of battery storage into the PV/PHS system, only to optimise and make the whole system cost effective and storage practically realizable. The modelling for components sizing of the RES is first discussed. Then the individual RESs are techno-economically optimized taking levelized cost of energy (COE) as the objective function at 100% reliability, i.e. 0% unmeet energy (UE) condition. In this, three optimization algorithms, namely genetic algorithm (GA), Firefly Algorithm (FA), and Grey Wolf Optimization (GWO) are implemented for optimal sizing of the PV and the storage units , and their performances are compared by applying them to an institutional academic block in India. It is shown that combined PV, PHS, and battery based RES is the optimal solution for the given low load situation. And GWO is the best optimization algorithm in terms of convergence rate as well as the COE and reliability. The results also demonstrate that utilizing a small battery bank with the PHS greatly reduces the upper reservoir capacity , with least generation of excess energy . Further, the optimal solution has also improved the low load factor of the academic block. Thus the present research contributes as a useful reference to the sizing problem of RES for a very low load situation by solving using a single resource PV based RES with two different reliable storage systems .

Development of IREOM model based on seasonally varying load profile for hilly remote areas of Uttarakhand state in India

An Integrated Renewable Energy Optimization Model (IREOM) model has been developed for sizing and optimization of renewable energy systems based on seasonal variation in the load profiles of the study area. An attempt has been made to develop correlations between renewable energy system sizes and their capital cost for the user specified system sizes. The developed correlations were used for the analysis of IREOM model using user specified system sizes and compared with manufacturer specified system sizes. The cluster of seven unelectrified villages having micro-hydro power, biomass, wind and solar energy resources in the state of Uttarakhand, India has been considered for the implementation of IREOM model. Based on the results obtained from the proposed model, suitable sizes of renewable energy systems have been suggested.