Search Results (25)

Fuel cells (FCs) are widely used in various applications such as transportation, vehicles, and energy storage, as well as in commercial and residential buildings. The FC is connected to the grid via an inverter, which converts DC power to AC power for integration with the AC grid. Thus, it is essential to adjust the gain of the inverter’s controllers to improve FC performance and the quality of the power generated by the FCs. In this work, a fractional-order PID (FOPID) controller is used to control an inverter where the FOPID’s gain settings are determined optimally to improve the performance of the current controller of the solid-oxide fuel cell (SOFC). The optimal parameters of the FOPID are obtained using a newly developed and efficient algorithm called beluga whale optimization (BWO). To highlight the efficiency of the proposed optimization approach, the obtained results are compared with particle swarm optimization (PSO) and the conventional active power controller (APC). The findings of this paper demonstrate that the SOFC achieves significantly superior performance when the FOPID controller is optimally tuned using BWO across all performance metrics related to the FC inverter. PSO also yields good results, ensuring smooth system operation and good performance. Based on the results, the output current from the SOFC using the BWO and PSO algorithms aligns well with the reference current, whereas the APC exhibits poor performance in tracking reference current changes in two cases. Specifically, the APC introduces a delay of approximately one second (0.5 to 0.6 s), resulting in poor control performance. This delay causes the system to deviate from the reference current control (RCC) by 10%, leading to poor performance. However, the proposed optimization algorithms effectively resolve this issue, offering a robust solution for enhanced current control. Full article

A pragmatic approach for Local Area Energy Planning to capture Whole System interactions and meet the dual goals of informing regulated infrastructure requirements while informing businesses and local authorities on building their business plans, is presented. Unlike existing approaches, the method presented in this paper aids market change by considering policy requirements and prioritisation, commercial relationships, place-based resources, processes and interfaces, people (skills and vulnerabilities), and energy vector interdependencies, and focuses on spatially distributed economic segments (e.g., agriculture, food logistics, etc.). The methodology promotes co-location opportunities for symbiotic clusters to avoid growth in resource-constrained regions (e.g., grid capacity), and presents a temporal visualisation method that connects policy, regulation, infrastructure, technology, place, and people. To provide a case study to design, evolve, and test the methodology, the Greater Lincolnshire Region’s Economic Zone in the UK is selected; specifically, the logistics segment. Adopting this type of Whole System approach provides business planning clarity and stakeholder confidence to drive the adoption of new technologies. It also identifies where inward investment for strategic locations is needed and develops an evidence base for policy lobbying and influencing. Full article

In recent years, PV power plants have been widely used on the roofs of commercial buildings with grid connections, primarily to enhance self-consumption in distributed energy systems. In addition, installing PV plants on commercial buildings’ roofs is becoming increasingly important, especially in crowded cities where land is limited. Since the Sun is an intermittent energy source, PV power plants cause frequency and voltage fluctuations in the grid. The way to avoid this problem is to install PV plants together with battery storage systems. Battery storage systems prevent frequency and voltage fluctuations in the grid and provide economic benefits. This article presents the sizing and techno-economic analysis of a factory building’s rooftop PV system with a battery. The amount of energy produced by the PV plant, PV temperature, and irradiation were recorded in a data logger obtained by various sensors. These real-time measurements were continuously collected and analyzed to evaluate system performance and assess seasonal variations.Load demand data were collected through an automatic meter reading system. The installed capacity of the PV power plant is 645 kW. The optimum battery capacity determined for this factory is 130 kW for 5 h. Techno-economic analysis was carried out using metrics such as the payback period, net present value, and levelized cost of energy. As a result of the analysis using various input variables, LCOE, NPV, and PBP were determined as 0.1467 $/kWh, 4918.3 $, and 7.03 years, respectively. Full article

The rising demand for water and energy is driving the overuse of natural resources and contributing to environmental degradation. To address these challenges, the focus has shifted to low- and zero-emission technologies that utilize alternative sources of water and energy. Although such systems are commonly applied in residential, commercial, and industrial buildings, facilities along transportation routes generally depend on grid connections. This study aimed to enhance operational independence and reduce environmental impacts by modernizing the Rest Area Stobierna (RAS) along Poland’s S19 expressway, part of the Via Carpatia road. A comprehensive technical, economic, and environmental analysis was conducted using HOMER Pro software (3.18.3 PRO Edition) and a simulation model based on YAS operating principles. The proposed Hybrid Renewable Energy System (HRES) incorporates photovoltaic panels, battery storage, and a rainwater harvesting system (RWHS). Two configurations of the HRES were evaluated, a prosumer-based setup and a hybrid-island mode. Optimization results showed that the hybrid-island configuration was most effective, achieving a 61.6% share of renewable energy in the annual balance, a 7.1-year return on investment, a EUR 0.77 million reduction in Net Present Cost (NPC), and a 75,002 kg decrease in CO₂ emissions over the system’s 25-year lifecycle. This study highlights the potential of integrating renewable energy and water systems to improve sustainability, reduce operational costs, and enhance service quality in road infrastructure facilities, offering a replicable model for similar contexts. Full article

This paper presents the development of an Artificial Intelligence (AI)-based integrated dynamic hybrid PV-H₂ energy system model together with a reflective comparative analysis of its performance versus that of the commercially available HOMER software. In this paper, a novel Particle Swarm Optimization (PSO) dynamic system model is developed by integrating a PSO algorithm with a precise dynamic hybrid PV-H₂ energy system model that is developed to accurately simulate the hybrid system by considering the dynamic behaviour of its individual system components. The developed novel model allows consideration of the dynamic behaviour of the hybrid PV-H₂ energy system while optimizing its sizing within grid-connected buildings to minimize the levelized cost of energy and maintain energy management across the hybrid system components and the grid in feeding the building load demands. The developed model was applied on a case-study grid-connected building to allow benchmarking of its results versus those from HOMER. Benchmarking showed that the developed model’s optimal sizing results as well as the corresponding levelized cost of energy closely match those from HOMER. In terms of energy management, the benchmarking results showed that the strategy implemented within the developed model allows maximization of the green energy supply to the building, thus aligning with the net-zero energy transition target, while the one implemented in HOMER is based on minimizing the levelized cost of energy regardless of the green energy supply to the building. Another privilege revealed by benchmarking is that the developed model allows a more realistic quantification of the hydrogen output from the electrolyser because it considers the dynamic behaviour of the electrolyser in response to the varying PV input, and also allows a more realistic quantification of the electricity output from the fuel cell because it considers the dynamic behaviour of the fuel cell in response to the varying hydrogen levels stored in the tank. Full article

Over the past few decades, grid-connected photovoltaic systems (GCPVSs) have been consistently installed due to their techno-socio-economic-environmental advantages. As an effective solution, this technology can shave air conditioning-based peak loads on summer days at noon in hot areas. This paper assesses the effect of solely rooftop GCPVS installations on the peak load shaving of commercial buildings in arid regions, e.g., the Middle East and North Africa. To this end, the load profile of a large building with 470 kW of unshaved peak power in Mashhad, Iran (36.2972° N, 59.6067° E) is analyzed after commissioning an actual 51 kW GCPVS. The results of this experimental study, exploiting 15 min resolution data over a year, endorse an effective peak shaving of the GCPVS without employing a battery energy storage system, with 12.2–18.5% peak power shaving on a summer day at noon. The monthly GCPVS self-sufficiency is also 10.2%, on average. In accordance with the studied case’s results, this paper presents valuable insights and recommends actionable policies to regions with similar solar potential and electricity supply challenges, aiming to expedite GCPVS development. Full article

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers’ datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines. Full article

The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner’s economic benefit. Full article

In the current energy usage scenario, the demands on energy load and the tariffs on the usage of electricity are two main areas that require a lot of attention. Energy forecasting is an ideal solution that would help us to better understand future needs and formulate solutions accordingly. Some important factors to investigate are the quantity and quality of smart grids as they are significantly influenced by the transportation, storage, and load management of energy. This research work is a review of various machine learning algorithms for energy grid applications like energy consumption, production, energy management, design, vehicle-to-grid transfers, and demand response. Ranking is performed with the help of key parameters and is evaluated using the Rapid Miner tool. The proposed manuscript uses various machine learning techniques for the evaluation of power quality performance to validate an efficient algorithm ranking in a grid-connected system for energy management applications. The use of renewable energy resources in grid-connected systems is more common in modern power systems. Universally, the energy usage sector (commercial and non-commercial) is undergoing an increase in demand for energy utilization that has substantial economic and ecological consequences. To overcome these issues, an integrated, ecofriendly, and smart system that meets the high energy demands is implemented in various buildings and other grid-connected applications. Among various machine learning techniques, an evaluation of seven algorithms—Naïve Bayes, artificial neural networks, linear regression, support vector machine, Q-learning, Gaussian mixture model, and principle component analysis—was conducted to determine which algorithm is the most effective in predicting energy balance. Among these algorithms, the decision tree, linear regression, and neural networks had more accurate results than the other algorithms used. As a result of this research, a proposal for energy forecast, energy balance, and management was compiled. A comparative statement of various algorithms concludes with results which suit energy management applications with high accuracy and low error rates. Full article

In this article, the optimal sizing of hybrid solar photovoltaic and battery energy storage systems is evaluated with respect to rooftop space and feed-in tariff rates. The battery scheduling is performed using a proposed rule-based energy management strategy. The rules are formulated based on the demand limit, PV export power limit, and state of charge of the battery. Furthermore, optimization modeling with initial choices of parameters and constraints in terms of solar photovoltaic and battery energy storage capabilities is developed to minimize the total net present cost. The hourly values of solar irradiance, air temperature, electrical loads, and electricity rates are considered the inputs of the optimization process. The optimization results are achieved using particle swarm optimization and validated through an uncertainty analysis. It is observed that an optimal photovoltaic and battery energy storage system can reduce the cost of electricity by 12.33%, including the sale of 5944.029 kWh of electricity to the grid. Furthermore, energy consumption, peak demand, and greenhouse gas emissions are reduced by 13.71%, 5.85%, and 62.59%, respectively. A comprehensive analysis between the variable and fixed data for the load, energy from PV, batteries, and the grid, and costs demonstrates that the optimal sizing of photovoltaic and battery energy storage systems with the best mix of energy from PV, batteries, and the grid provides the optimal solution for the proposed configuration. Full article

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods continues to decline. Precision in power measurement is an essential factor for the billing and management of power supply and demand. Moreover, it is challenging to build a supply plan for the power demand and to manage the billing for the power consumption. To solve these problems, this paper proposes a novel method based on Lock-in Amplifier (LIA) and Lock-in Amplifier Frequency-Locked Loop (LIA-FLL) to measure the power with high precision and accuracy. The proposed method first tracks the variations in the input signal frequency using LIA-FLL and generates the updated reference signals for LIA. After that, the LIA is used to extract the accurate amplitude of each frequency component. The proposed method results in accurate and precise measurement, even with harmonics and frequency fluctuations. The validity of the proposed method is verified by comparing the power measurement results with the classical method, FFT, and ZERA COM3003 (a commercially available power measurement reference instrument). Full article

The rapid electrification of vehicles has led to a great increase in numbers of charging stations and a growing appetite for charging power, with stochastic charging behaviours heavily loading the electricity grid. The upcoming difficulties and increasing costs associated with electricity production will require a rapid development of smart grids and city networks. Smart micro-grids established in nearly zero-energy buildings (nZEB) are a promising strategy to support grid stability and resilience at a reduced cost. A significant amount of electricity storage capacity is necessary for optimal dispatch of the self-produced photovoltaic electricity. For office buildings, this capacity can be provided by the aggregate battery storage of the employees’ electric vehicles, which connect to the smart grid during working hours for charging and, if allowed, for discharging. An additional, fully controllable electricity source that is necessary to support an optimal micro-grid is the internal combustion engine-powered generator that is present in every commercial and office building as an emergency power supply. In the current study, a preliminary investigation of a smart micro-grid in a near zero-energy office building with a 218 kWp rooftop photovoltaic installation is carried out. The required electricity storage capacity is supplied by the employees’ electric vehicles, which stay connected to the building’s in-house chargers during working hours. The optimal rating of the natural gas-fuelled GenSet is determined based on a system’s operation and control study. Optimal dispatch of the different power sources to support the building’s autonomy and seasonal timing of electricity export to the grid is studied versus the electricity demand profiles of the electricity grid. Full article

In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation. Full article

Near zero energy buildings are increasing worldwide, exploiting low-carbon technologies in heating and electricity self-production. Commercial buildings are increasingly considered as candidates for the installation of smart micro-grids, which may profit from the added storage capacity of the batteries of employees electric vehicles, stationed during daytime in their charging lots. Smart exploitation of the interaction of these electricity sources and sinks may prove essential to address the complex electricity network demand patterns in today’s fast changing energy mixture. The interaction of an efficient office building’s energy system with a big rooftop photovoltaic installation and the aggregate storage capacity of 40 electric cars that are connected in the building’s charging lots is studied by means of transient simulation in TRNSYS environment. The 18-zone building’s heating, ventilation, and air conditioning system, the cars’ batteries, and photovoltaic systems’ interactions are analyzed on a monthly, seasonal, and hourly basis, against the respective demand curves of the Greek network. The results suggest that the specific system’s size may profitably support the operation of a smart micro-grid. The total annual electricity consumption of the building is computed to reach 112,000 kWh, or 20 kWh/m²y. The annual electricity needs of the 40 electric cars, amounting to 101,000 kWh, can be fully met with 30% of the photovoltaic electricity production. Thus, the building becomes a net exporter of electricity to the network, with maximum exported electricity occurring daily between 12:00 and 14:00, which is favorable to meeting the demand curve. Thus, the establishment of smart micro-grids in commercial buildings with large rooftop photovoltaic panels’ capacity and a significant number of electric cars in the employees’ car fleet is quite effective in this direction. Full article

Road vehicles are responsible for air pollution in Hong Kong, and electric vehicles (EVs) are a promising alternative to internal combustion engine vehicles as the city is transitioning to clean energy. In this work, EV adoption in Hong Kong is investigated and analyzed, including the global EV markets, present EV status in Hong Kong, local challenges facing EV development, suggestions for EV promotion in Hong Kong, emerging technologies, and decommissioning of batteries and EVs. The challenges of EVs include insufficient charging infrastructures, inadequate management of public charging facilities, difficulties in EV repair and maintenance, “dead mileage” during charging, unacceptable long charging times, and limited commercial EV models. Strategies such as providing incentives and bonuses for commercial EVs, offering high-power quick-charging facilities, actively developing commercial EVs, installing more charging infrastructures for private EVs, building connections among stakeholders, encouraging the participation of the private sector to promote fee-based services, and supporting the development of innovative technologies should be implemented to promote EVs in Hong Kong. Emerging technologies for EVs such as wireless charging, smart power distribution, vehicle-to-grid and vehicle-to-home systems, connected vehicles, and self-driving are discussed. Eco-friendly decommissioning of EV batteries can be realized by recycling and second-life applications. This paper serves as a reference and guide for the sustainable and smart evolution of the transportation sector in Hong Kong and other global large cities. Full article