Search Results (53)

The worldwide growing adoption of Light Electric Vehicles (LEVs) indicates that such technology might in the near future be decisive for improving the sustainability of transportation. The segment of LEVs has some peculiar features compared to electric mobility in general, which then deserve a devoted investigation. Stakeholders are called to implement the most appropriate technology depending on the context, by taking into account multi-faceted factors, which are the investigation object of this work. At first, a methodology is formulated for estimating the power and energy impact of LEVs recharging. Based on this, and assessed that the load constituted by LEVs is in general modest but might create some problems in lowly structured networks, it becomes conceivable to develop Charging Station (CS) technologies which are alternative to the grid connection at a point of delivery. Yet, it is fundamental to develop accurate methodologies for the techno-economic and environmental analysis. This work considers a use case developed at the University of Brescia (Italy): a CS operating off-grid, powered by PhotoVoltaics (PV). Its peculiarity is that it is transportable, which makes it more appealing for rural/remote areas or when the charging demand is highly not homogeneous in time. On these grounds, this work specializes to a context where the proposed solution might be more appealing: small isolated islands, in particular Favignana in Sicily (Italy). It is estimated that the adoption of the proposed off-grid CS is by far advantageous as regards the greenhouse gases emissions but it is more economically profitable than the grid connection only if the number of users per day is less than order of 200. Hence this work provides meaningful indications on the usefulness of off-grid CS powered by PV in peculiar contexts and furnishes a general method for their techno-economic and environmental assessment. Full article

The Partial Power Processing (PPP) concept has garnered attention as it enables the down-sizing of converter and component ratings. Unlike conventional power processing, PPP addresses a portion of the transferred power, leading to a reduction in conversion losses. Throughout this paper, the state of the art of isolated and non-isolated DC-DC converter topologies will be revised. Partial Power Converter (PPC) systems represent one of the main streams of PPP, which, based on isolation requirements and converter connections, can further be divided into isolated converters, such as: Input-Parallel-Output-Series (IPOS), Input-Series-Output-Parallel (ISOP), and, Input-Series-Output-Series (ISOS), or non-isolated converters. This work intends to evaluate and differentiate the characteristics of each type of topology while developing analytically possible connections that may require further research and reviewing metrics that help in fair comparisons of different PPC arrangements, operating under different conditions. A thorough revision is provided for DC-DC converter topologies due to their increased importance in present-day applications, such as energy storage, Electric Vehicles (EVs), and Photo-Voltaics (PVs). Full article

Mismatching operating conditions affect the energetic performance of PhotoVoltaic (PV) systems because they decrease their efficiency and reliability. The two different approaches used to overcome this problem are Distributed Maximum Power Point Tracking (DMPPT) architecture and reconfigurable PV array architecture. These techniques can be considered not only as alternatives but can be combined to reach better performance. To this aim, the present paper presents a new algorithm, based on the joint action of the DMPPT and reconfiguration approaches, applied to a reconfigurable Series-Parallel-Series architecture, which is suitable for domestic PV application. The core of the algorithm is a deterministic cluster analysis based on the shape of the current vs. voltage characteristic of a single PV module combined with its DC/DC converter to perform the DMPPT function. Experimental results are provided to validate the effectiveness of the proposed algorithm and to demonstrate evidence of its major advantages: robustness, simplicity of implementation and time-saving. Full article

Fluctuant irradiance conditions constitute a challenge in front of a proper battery charging process, when originated from a PhotoVoltaic Array (PVA). The behavior of the PVA under such conditions (i.e., reflected by a disturbed PV characteristic curve) increases the complexity of the total available power’s extraction process. This inconvenient fact yields eventually to a decreased overall efficiency of PV systems, especially with the presence of imprecise power-electronics involved circuits. Accordingly, the purpose of this paper is to design a complete battery solar charger, with Maximum Power Point Tracking ability, emerged from a PVA of 1.918 kWp, arranged in Series-Parallel topology. The targeted battery is of Lithium-Ion (Li-I) type, with 24 VDC operating voltage and 150 Ah rated current. The design began by configuring an interleaved synchronous DC-DC converter to produce a desired voltage level, with low inductor ripple current and low output ripple voltage. The DC-DC converter is in turns condemned by a modified Perturb and Observe (P&O) algorithm, to ensure efficient maximum power tracking. Progressively, the design encountered a layout of the bi-directional DC-DC converter to ensure safe current charging values for the battery. Under the same manner, the role of the bi-directional converter was to plug the battery out of the system, in case when the Depth of Discharge (DoD) is below 25%, thus sustaining the life span of the battery. The entire setup of the proposed sub-systems then leads to the relatively fastest, safest, and most reliable battery charging process. Results show an effectiveness (in terms of PV power tracking) ranging from 87% to 100% under four swiftly changing irradiance conditions. Moreover, this paper suggested the design’s future industrialization process, leading to an effective PV solar charger prototype. Full article

There is an increasing need for capable models in the forecast of the output of solar photovoltaic panels. These models are vital for optimizing the performance and maintenance of PV systems. There is also a shortage of studies on forecasts of the output power of solar photovoltaics sites in the absence of meteorological data. Unlike common methods, this study explores numerous machine learning algorithms for forecasting the output of solar photovoltaic panels in the absence of weather data such as temperature, humidity and wind speed, which are often used when forecasting the output of solar PV panels. The considered models include Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Transformer. These models were used with the data collected from 50 different solar photo voltaic sites in South Korea, which consist of readings of the output of each of the sites collected at regular intervals. This study focuses on obtaining multistep forecasts for the multi-in multi-out, multi-in uni-out and uni-in uni-out settings. Detailed experimentation was carried out in each of these settings. Finally, for each of these settings and different lookback and forecast lengths, the best models were also identified. Full article

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case. Full article

Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices. Full article

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is evaluated through a proposed method of levelized cost of energy (LCOE) for clustered generation portfolios. Effective renewable economics is applied to assess resource adequacy, annual carbon emissions, renewable capacity factor, over generation, and cost to build between thermal dispatch scenarios with incremental increases in VRE penetration. Solar PV and wind generation temporally complement one another in the region studied, and the combination of the two is beneficial to renewable energy integration. Furthermore, replacing older coal units with cleaner and agile natural gas units increases renewable hosting capacity and provides further pathways to decarbonization. Minute-based chronological simulations enable the assessment of renewable effectiveness related to weather-related variability and of complementary technologies, including energy storage for which a sizing procedure is proposed. The generally applicable methods are regionally exemplified for Kentucky, USA, including eight scenarios with four major year-long simulated case studies and 176 subcases using high performance computing (HPC) systems. Full article

This article is written within the European Project “THREE-Lanka” which has the aim of modernizing the higher education related to Renewable Energy (RE) in Sri Lanka. The paper presents the outcomes of analysing various incentive schemes to stimulate RE development. In Europe, there was substantial growth in RE installation through generous incentives in the first years. Then, to regulate this growth, in recent years, the auction system has been introduced to improve the competition among companies that install RE plants. In Sri Lanka, on the other hand, the main energy tariff policies focus on the spread of PhotoVoltaics (PV) through contributions based on the electricity fed into the grid. This paper provides an updated view of the evolution of the energy tariff policies in the relevant European countries with respect to Sri Lanka, covering some recent policy developments. Within the Sri Lankan framework, four case studies involving residential, commercial, and industrial users are outlined to suggest better mechanisms (in the case of not adequate current incentive tariff) for supporting the deployment of grid-connected PV systems in a wide power range. Such knowledge transfer in the THREE-Lanka project will demonstrate the enormous potential RE capacity in a developing country, still depending on fossil fuels but willing to follow the path towards sustainability. Full article

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow problems. Simulation results show that the proposed optimal TEP method combined with forecasting solar PV power generation using the LSTM can reduce the total system cost of the transmission expansion by 9.12% compared with the cost obtained by the TEP using solar radiation from statistical data. Full article

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications. Full article

Buildings play an important role to meet Sustainable Development Goals, especially regarding the use of resources and greenhouse gas emissions. They are increasingly designed with energy-efficient solutions regarding their operations, while the related use of natural resources is still insufficiently considered. In this article, a methodology in Building Information Modeling is proposed to measure the resource and climate footprints of buildings’ heating systems. The methodology is applied to a case study building in Germany. The studied heating systems include a gas condensing boiler, ground-source heat pump, ground-source heat pump with a photo-voltaic system and air-source heat pump backed up with a gas boiler. Next to the operational energy, the production and transport of the heating systems were also studied. Results show that heating system operations have the largest impact and that the variant of ground-source heat pump combined with photovoltaics (GSHP + PV) has the lowest impact. In comparison with the gas boiler (GB), savings of 75%, 47%, 80%, and 84% are addressed to climate, material, energy, and land footprints, respectively, while the water footprint of GSHP + PV is 73% higher than that of GB. Full article

This review emphasizes the role and performance of versatile DC-DC converters in AC/DC and Hybrid microgrid applications, especially when solar (photo voltaic) PV is the major source. Here, the various converter topologies are compared with regard to voltage gain, component count, voltage stress, and soft switching. This study suggests the suitability of the converter based on the source type. The merits of a coupled inductor and interleaved converters in micro gird applications are elucidated. The efficiency and operating frequencies of converts for different operating modes are presented to determine the suitable converters for inductive and resistive loads. The drawbacks of converters are discussed. Finally, the mode of operation of different converts with different grid power sources and its stability and reliability issues are highlighted. In addition, the significance of the converter’s size and cost-effectiveness when choosing various PV source applications are discussed. Full article

Due to industrialization and an exponential increase in population, the demand for vehicles has increased in Kuwait. Utilizing fossil fuels to power vehicles that are in high demand has posed various environmental and medical implications. Considering the emission-free feature of electric vehicles (EVs), moving towards “EV readiness” is the need of the hour. This can be further enhanced by adopting alternative sustainable technologies such as photo voltaic (PV) charging of EVs, to potentially eliminate or significantly reduce the reliance on fossil fuel while simultaneously decreasing harmful emissions. As Kuwait does not manufacture cars, it imports all vehicles and their parts, including internal combustion engines (ICEs) and EVs. To find out the challenges to uplifting Kuwait’s “EV readiness” this study delved into the performance of a typical EV car in Kuwait’s weather conditions. This includes the investigation of parameters that influence the energy requirements in an electric vehicle, the change in the energy requirement in relation to several driving scenarios, and the efficiency of the EV battery. The results indicate that a significant amount of energy is being wasted for battery conditioning, which drastically reduces the distance covered during summer. The energy required for air-conditioning and battery conditioning are both positively correlated to the ambient temperature, while the time required to charge the battery has no relationship with the time of the day, traffic, or trip length. Additionally, the paper discusses some major challenges, such as lack of awareness, inadequate charging stations, and absence of policy. The paper recommends the most vital areas to be focused on for meeting the above challenges to make the transition to an “EV ready” state. Full article

The demand for electricity is rapidly growing and renewable energy sources such as solar, wind and tidal energy can compensate the demand to a substantial level. Among these, solar energy is abundant, scalable and is cheaper. The generated energy can be used in an efficient way if the DC output is directly supplied to the load instead of converting it to AC. Every electrical system is capable of operating in DC and, for example, energy efficient Light Emitting Diode (LED) lights have become popular as they provides more lumens with less power consumption and also can be directly operated from DC. LED lighting system in large commercial buildings has irradiance levels which vary sigificantly during operation. Extracting maximum power from the energy system and maintaining constant voltage output at different loads is another challenge. This paper proposes a solar Photo Voltaic (PV)-based energy system including Battery Energy Storage System (BESS) for supplying LED lamps to a commercial building through a modified high gain Luo converter. The Perturb and Observe control algorithm has been used for maximum power extraction from a PV cell whereas PI (Proportional Integral) controllers maintain constant output voltage from PV–BESS against different irradiance levels. To supply the desired voltages to the LED lighting system, a modified high gain Luo converter is designed. To make the output voltage constant at different load currents, PI and Sliding Mode Controllers (SMC) are designed with the help of the state-space average model. It is found that the sliding mode controller outperforms the PI controller in terms of behavior in the transient period and tracking capability. The system is simulated using MATLAB/Simulink^®. The Sliding Mode Controller has a 95% less transient period and is 75% faster in tracking capability when compared to other controllers. The system could be incorporated with the PV source to obtain green energy. Full article