Design Of Energy Systems Research Articles

Comparing Energy Harvesting Efficiency between Solar Panels: Tracking the Sun vs Ground-Mounted 40 WP Panels

This experiment aimed to evaluate the energy efficiency of a solar tracking panel compared to a ground-fixed solar panel. A 40 WP solar panel with a time-tracking mechanism was used to monitor the sun's movement from morning to evening, while a static 40 WP ground-fixed solar panel was also studied for comparison.The study also included the energy consumption of a 12 V DC motor responsible for orienting the tracking panel to face the sun. Throughout the day, the tracking panel's position was continuously adjusted to align with the sun's position, facilitated by a microcontroller and a gyroscope. The energy consumed by the motor during the tracking process was accounted for. The net energy output of the tracking panel was computed by subtracting the energy expended in tracking from the total energy collected. Following the experimentation, the collected data was analyzed to determine the energy efficiency of the tracking panel in contrast to the ground-fixed panel. The findings revealed that the tracking panel exhibited a 15 percent increase in energy efficiency compared to the ground-fixed panel. These results emphasize the potential benefits of solar tracking technology in optimizing energy capture from solar panels. The implications of these findings are valuable for advancing the design and implementation of solar energy systems, offering increased efficiency and sustainability.

Optimal design and techno-economic analysis of hybrid renewable energy systems: A case study of Thala city, Tunisia

ABSTRACT This study explores the techno-economic feasibility of, both off-grid and on-grid, hybrid renewable energy systems for remote rural electrification in Thala City, located in the highest region of Tunisia, using wind and biomass resources. Employing Hybrid Optimization of Multiple Energy Resources based on different scenarios includes grid-connected and stand-alone configurations with pumped storage hydropower and lead acid battery storage while minimizing the levelized cost of energy, the net present cost, and greenhouse gas emissions. The optimal configuration wind/biomass/pumped-hydro storage/Converter grid-connected, minimizes the levelized cost of energy (LCOE) and net present cost (NPC), resulting in a cost of 501,540 US$ and LCOE of 0.042 US$/kWh. Notably, 7% of electricity is generated from olive mill waste, 69% from wind turbines, and 24% is purchased from the grid. This hybrid system emits 342 tons/year of CO2, 76% less than a grid-alone system, contributing to an annual CO2 reduction of 1000 tons.

Generation and validation of comprehensive synthetic weather histories using auto-regressive moving-average models

As energy system design moves to more complex methods of optimisation including machine learning there is a significant need for more weather data than is available. One method to solve this is using synthetic data models such as the auto-regressive moving-average (ARMA) model which has been frequently utilised to create such data. This paper looks at extending the ARMA algorithm to generate solar components through the use of clearsky detrending, maintaining vector relationships and by leveraging physical relationships. The method for the creation of entirely synthetic weather data files including key weather variables for energy system analysis is presented. Furthermore, a detailed comparison of energy system simulations utilising both real and synthetic data is made using NREL’s System Advisor Model. Whilst good agreement is made for the solar variables, and other weather variables, ARMA methods often fail to capture the standard deviation and skew of annual weather distributions. Vector-ARMA is shown to maintain correlations between variables and thus generate data sets that perform similarly in energy system design. It is finally shown that the ARMA method fails to preserve day-today correlations in weather variables and thus over-predicts optimal energy storage by 21% for a residential solar application.

Multi-objective and multi-criteria decision making for Technoeconomic optimum design of hybrid standalone renewable energy system

The major obstacle in designing of the wind turbine/photovoltaic/battery storage system lies in the task of choosing the most optimum solution while simultaneously considering techno-economic objectives. Consequently, this study provides a distinctive amalgamation of multi-objective multi-perspective and group multi-criteria decision-making methodologies to ascertain collections of Pareto optimum configurations and to weight, rank, and pick up the most desirable optimum solutions that represent best trade-off between conflicting objectives. In this paper, the multi-objective Dragon fly algorithm is developed based on mutation scheme and crossover tactic algorithm is advanced to handle a lack of diversity and poor convergence, while non-dominated sorting and crowding distance strategy is addressed to store the best found optimum solutions in the achieve. Loss of load probability, excess energy, and life cycle cost are conflicting techno-economic objectives solved by multi-objective method. The optimum Pareto front solutions are ranked based on hybrid multi-criteria decision-making method for determining the most favorable solution for the WT/PV/BS system. The experimental outcomes indicated that the developed approach has a distinct performance in constructing a collection of Pareto front solutions in terms of diversity, converge, and convergence. Moreover, it demonstrates outstanding results when compared to well-organized multi-objective optimization methods. The future direction can be accomplished by applying the proposed hybrid sizing methodology for solving gird-connected system along with electric vehicle based on techno-economic scenarios.

Landlord–Tenant Dilemma: How Does the Conflict Affect the Design of Building Energy Systems?

To achieve climate goals, the European Union needs to increase building renovation rates. In owner-occupied buildings, energy cost savings provide financial incentives for renovation. However, 30% of all Europeans live in rented property, where conflicting stakeholder interests arise. Landlords are responsible for renovation decisions (building envelope and energy system) and the corresponding investments. Tenants face rising rents and only slightly benefit from falling energy costs. The literature calls this conflict the landlord–tenant dilemma. However, publications lack a quantification, leaving gaps in understanding its impact on technology choices and the heat transition. To address this, we incorporate the perspectives of landlords and tenants in a model-based approach for optimized technology choice (mixed-integer linear program). We compare optimal individual technology choices with the total cost optimum (including costs for landlords and tenants) for renovation decisions. Additionally, we examine how changes in the regulatory framework affect the economically driven landlord’s technology choice. Our study reveals that total costs and emissions are up to 60% and 283% higher for landlords deciding for rented houses compared to owner-occupied properties. Current approaches to solve the dilemma partly favor the development of climate-friendly energy systems. However, the renovation of the building envelope and operation costs are mostly disregarded in the decisions of landlords.

The effect and value of end-use flexibility in the low-carbon transition of the energy system

Flexibility at end-use level can lower both the costs of end-use sectors, such as the building sector and the investment and operational costs of the electricity sector. For planning purposes, it is however a need to understand how end-use flexibility influences the design of the future energy system. This paper analyses the role and value of end-use flexibility in the Norwegian low carbon energy system transition towards 2050. This is done by using a stochastic energy system model, IFE-TIMES-Norway, to quantify how end-use flexibility impacts the energy system design and the corresponding sectoral profits and costs. The results demonstrate that facilitating a techno-economic implementation of end-use flexibility lowers the cost of the energy transition towards 2050 between BEUR 4.4 and BEUR 8.3. This is primarily because end-use flexibility ensures a better match between local PV production and demand, lowers the capacity expansion needs of the electricity grid and increases profits from international electricity trade. Further, the results show that end-use flexibility reduces the need for hydrogen and thermal storage, where hydrogen storage capacity is lowered by 25 %–66 % in 2050, depending on storyline.

ELECTRICAL ENGINEERING IN RENEWABLE ENERGY SYSTEMS: A REVIEW OF DESIGN AND INTEGRATION CHALLENGES

As the global pursuit of sustainable energy intensifies, the integration of renewable energy sources into existing power systems has become a critical focal point for electrical engineers. This review explores the challenges and advancements in the field of Electrical Engineering concerning the design and integration of renewable energy systems. The transition from traditional fossil fuels to renewable sources, such as solar, wind, and hydroelectric power, necessitates a comprehensive understanding of the intricate engineering aspects involved. The first section of the review delves into the design challenges faced by electrical engineers when developing efficient and reliable renewable energy systems. This encompasses the optimization of power generation from intermittent sources, the enhancement of energy conversion technologies, and the development of energy storage solutions to mitigate the inherent variability of renewables. Cutting-edge design methodologies and innovative materials are discussed to highlight the ongoing efforts to improve the performance and reliability of renewable energy systems. The second section focuses on the integration challenges encountered during the incorporation of renewable energy into existing power grids. Grid stability, power quality, and the management of decentralized energy sources pose significant hurdles. Electrical engineers are addressing these challenges through the implementation of advanced control systems, smart grid technologies, and energy management strategies. The review also explores the role of energy storage systems and the potential of emerging technologies like microgrids in facilitating seamless integration. Furthermore, the review examines the interdisciplinary nature of electrical engineering in the context of renewable energy, emphasizing the collaboration between electrical engineers, environmental scientists, and policymakers. The synergy between these disciplines is crucial for developing holistic solutions that address not only technical challenges but also environmental and regulatory considerations. This review provides a comprehensive overview of the design and integration challenges faced by electrical engineers in the realm of renewable energy systems. By understanding and overcoming these challenges, the global community can accelerate the transition towards a sustainable and resilient energy future.
 Keywords: Renewable energy, Energy Integration, Challenges, Electrical, Engineering, Review.

Renewable energy systems: Industrial and home best practise case studies

The use of renewable energies and energy efficiency improvement systems is sometimes constrained by the investment needed for devices and its installation. However, governments have developed policies to reduce the initial costs to end-users. The increasing number of renewable energy systems can help in the reduction of the global dependency on fossil energy sources, and simultaneously can help to ensure that the carbon dioxide emissions quota is not exceeded, aiming a reduction of the green-house effect. In this sense, this paper presents two best practise case studies. One of them is in the industrial context and other in the residence context, concerning the use of renewable energy systems and energy efficiency improvement strategies. The best practice case study in industrial context involves the renewable energy systems design in an automotive sector factory, making use of solar thermal panels and energy efficiency measures. The non-industrial case study covers the analysis of the energy efficiency improvement using a thermal energy recovery system from hot water used in daily baths or showers. The direct benefits (savings) and indirect (impacts) are analysed for both case studies.

Optimising mixed-portfolio hybrid Renewable systems with no or constrained grid connectivity

We present a method to optimise a hybrid renewable energy system design under various grid constraints, from entirely off-grid to strongly interconnected. The optimisation procedure is a simplified downward gradient method, and the variable used as the objective function is the equivalent annual cost of the system installation and operation. The results show how a good interconnection provides the opportunity to use renewable energy technology as an income stream without a need for energy storage. A balance of energy storage and local backup generation emerges as the optimum if the grid is severely constrained or without grid connection. It is always a balance of storage and backup, as the storage is most cost effective to balance supply and demand over short time scales of less than a week while back-up generation is better able to meet longer periods of insufficient renewable generation. Another key results is that a combination of renewable technologies, PV and wind, is always better here than one technology alone.

Optimal design and sizing of a hybrid energy system for water pumping applications

AbstractOne of the ways to increase the participation and penetration of renewable energy resources is to bring down the cost of these abundant resources for easy implementation and affordability. In this paper, a generalized reduced gradient (GRG) non‐linear optimization algorithm is implemented to solve a tri‐objective optimal design and sizing of a low‐cost hybrid mix consisting of a photovoltaic (PV) power plant, biomass power plant (BPP), and battery energy system for water pumping load applications in the University of Johannesburg, South Africa considering four different hybrids of biomass‐battery, PV‐battery, PV‐biomass, and PV‐biomass‐battery. The optimization model considers available energy and battery state of charge while minimizing least cost of energy (LCOE), carbon dioxide emission (tCO2eq), and loss of power supply probability (LPSP) including carbon tax incentive and penalty. The results when compared against particle swarm optimization (PSO) show the superiority of GRG over PSO with an optimal combination of PV‐biomass‐battery mix with optimal size of the PV power plant as 360.50 kW, the BPP 181.08 kW, and the battery size of 6,553.60 kWh giving a minimal optimal LCOE, CO2 emission and LPSP of 0.018 $/kWhr (with carbon tax), and 0.016 $/kWhr (without carbon tax), 28,067.73tCO2eq tCO2eq, and 1.7%, respectively. These values give a competitive advantage compared to the unit cost and values of CO2 emission and LPSP currently in the literature.

Solar Energy Systems Course for Energy Engineering Students in the Context of the European Higher Education Area (EHEA)

The Bologna Process is a voluntary intergovernmental European cooperation initiative that led to the creation of the named European Higher Education Area (EHEA). EHEA was formed to promote mobility, increase academic recognition and attract students and staff from around the world to Europe. In this framework, since 2009, the Barcelona College of Industrial Engineering (Escuela Universitaria de Ingeniería Técnica Industrial de Barcelona – EUETIB) of the Technical University of Catalonia – BarcelonaTech (UPC) is offering the 4-year Bachelor Degree in Energy Engineering since 2009 with a total number of ECTS credits of 240. Current article deals with the inclusion of the sizing and design of solar energy systems in the context of this degree. In particular, and although this topic was eventually abandoned in the initial degree curriculum, the paper deals with the development of a 3rd-year course, Energy Integration, that focuses on this topic.

Potential-risk and no-regret options for urban energy system design — A sensitivity analysis

This study identifies supply options for sustainable urban energy systems, which are robust to external system changes. A multi-criteria optimization model is used to minimize greenhouse gas (GHG) emissions and financial costs of a reference system. Sensitivity analyses examine the impact of changing boundary conditions related to GHG emissions, energy prices, energy demands, and population density. Options that align with both financial and emission reduction and are robust to system changes are called “no-regret” options. Options sensitive to system changes are labeled as “potential-risk” options.There is a conflict between minimizing GHG emissions and financial costs. In the reference case, the emission-optimized scenario enables a reduction of GHG emissions (−93%), but involves higher costs (+160%) compared to the financially-optimized scenario.No-regret options include photovoltaic systems, decentralized heat pumps, thermal storages, electricity exchange between sub-systems and with higher-level systems, and reducing energy demands through building insulation, behavioral changes, or the decrease of living space per inhabitant. Potential-risk options include solar thermal systems, natural gas technologies, high-capacity battery storages, and hydrogen for building energy supply.When energy prices rise, financially-optimized systems approach the least-emission system design. The maximum profitability of natural gas technologies was already reached before the 2022 European energy crisis.

Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %.

Course on Renewable Energies for Energy Engineering Students in the Framework of the European Higher Education Area (EHEA)

As it is well known, the Bologna Process is a voluntary intergovernmental European cooperation initiative that led to the creation of the named European Higher Education Area (EHEA). EHEA was formed to promote mobility, increase academic recognition and attract students and staff from around the world to Europe. On the other hand, since September 2016, the Technical University of Catalonia (Universitat Politècnica de Catalunya, UPC) – BarcelonaTech has a new Campus in Barcelona City, the Diagonal-Besòs Campus. In this new Campus appears the Eastern Barcelona School of Engineering (Escola d’Enginyeria de Barcelona Est, EEBE), with new Bachelor’s and Master’s Degrees. In this framework, also since September 2016, the EEBE is offering the new 4-year Bachelor’s Degree in Energy Engineering with a total number of 240 ECTS credits. Current article deals with the inclusion of the course about sizing and design of renewable energy systems in the context of this degree. In particular, and although this topic was eventually abandoned in the initial degree curriculum, the paper presents the development of a 3rd-year course, Renewable Energies, that focuses on this topic.

Intelligent Controller Design for a Sustainable Energy System

This paper discusses an intelligent control system design for a sustainable energy system. This work also described an optimal design and implementation of an efficient selfsustainable energy system with consideration of energy storage options for excess energy produced by renewable energy sources.

Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

The influence of global warming on building performance necessitates its consideration during the design phase. The conventional approach of separately designing the buildings and energy systems may not yield the globally optimal solution. To address this issue, this study proposes a coordinated optimization design approach for buildings and regional integrated energy systems (RIES) under future climate conditions. Firstly, a future regional load prediction model based on a Random Forest algorithm optimized by the Grey Wolf Optimizer is established. Next, a bi-level optimization model is proposed, with the upper level optimizing the building envelope to minimize costs and carbon emissions, and the lower level determining the optimal configuration capacity and operating strategy of RIES for cost minimization. Multi-objective grey wolf optimizer and CPLXE are employed to address the problem. Finally, the proposed method is validated using a case study of an office area in Jinan. The results show that the method can reduce costs by 14.6 % and carbon emissions by 50.93 % compared to the prototype building area with independent production systems, highlighting the potential of synergistic design between buildings and RIES. In addition, different optimization results were obtained under typical meteorological year, shared socioeconomic pathway (SSP) 245, and SSP585 climate scenarios, indicating the impact of future climate on building and RIES design. Therefore, it is necessary to consider climate change in future design work.

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions.
 The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

Hydrodynamic analysis of a heave-hinge wave energy converter combined with a floating breakwater

Research interest in breakwater design has increased recently due to the impetus to develop marine renewable energy systems, as breakwaters can be retrofitted to harness wave energy at the same time as attenuating it. This study investigates a novel system of attaching a hinge baffle under a floating breakwater. The floating breakwater itself acts as a heaving wave energy converter, and meanwhile the hinge rotation provides a second mechanism for wave energy harnessing. A computational model with multi-body dynamics was established to study this system, and a series of simulations were conducted in various wave conditions. Both wave attenuation performance and energy conversion ratio were studied, using an interdisciplinary approach considering both coastal engineering and renewable energy. In particular, the performance of the proposed system is compared with contemporary floating breakwater designs to demonstrate its advantage. Overall, a useful simulation framework with multi-body dynamics is presented and the simulation results provide valuable insights into the design of combined wave energy and breakwater systems.

A novel optimization approach for the design of a hybrid energy system based on a modified version of a subtraction-average-based optimizing method

A novel optimization approach for the design of a hybrid energy system based on a modified version of a subtraction-average-based optimizing method

EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations

With the increasing demand for renewable energy, solar power has emerged as a promising option for sustainable power generation. However, the effectiveness and efficiency of solar power systems rely on the ability to effectively manage their performance, making it essential to develop efficient control models. This paper proposes a novel ensemble predictive control model for solar deployments using bio-inspired optimizations to improve load-connected solar deployments’ performance. The proposed model integrates multiple control devices, including Maximum Power Point Tracker, Proportional-Integral-Derivative, Proportional-Integral, and Fuzzy Logic Controllers, to selectively control the solar Photovoltaic systems. The proposed model incorporates a predictive control operation utilizing an LSTM-GRU (Long Short-Term Memory-Gated Recurrent Unit) with the VARMA (Vector Auto-Regressive Moving Average) model, which can accurately predict the future power generation of the solar system. This feature can facilitate efficient energy management and increase the system’s performance for different use cases. Implement a SEPIC (Single Ended Primary Inductor Capacitor) converter design to improve the system’s overall efficiency levels. To validate the effectiveness of the proposed approach, the author conducted experiments using real-world data and compared the proposed results with other control strategies. The results demonstrate that the ensemble predictive control model based on bio-inspired optimizations outperforms the existing control models regarding accuracy, efficiency, and stability levels. The proposed model has the potential to significantly improve the performance of load-connected solar deployments, offering a more practical approach to solar power generation. The combination of predictive control operations with bio-inspired optimizations can facilitate the design of sustainable energy systems with higher efficiency and accuracy.