Cleaner Energy Systems最新文献

This study presents the optimal design and operation of a proposed hybrid renewable energy system (HRES) for the electrification of a residential building in rural areas in Pakistan. The main contributions of this study are twofold. Firstly, it develops a size optimization model based on the particle swarm optimization (PSO) technique to determine the optimal configuration for two hybrid renewable energy systems (HRES), including both grid-tied and off-grid modes, integrating wind and photovoltaic (PV) systems with battery storage. The optimal configuration is determined by minimizing the levelized cost of electricity, using local meteorological and electricity load data, along with technical specifications of the main HRES components. Secondly, dynamic simulations of two HRES configurations are conducted, using MATLAB Simulink, ensuring the optimal energy balance between multiple energy sources and the load at each operation hour. To meet an annual electrical demand of 131.035 MWh, the grid-tied HRES yields 146.081 MWh annually, with solar contributing 68.85 MWh and wind 77.272 MWh. Conversely, the off-grid system generates 133.533 MWh annually, with solar and wind output power at 43.932 MWh and 89.601 MWh, respectively. The grid-tied system achieves an LCOE of approximately 0.29 $/kWh, with optimal wind turbine and PV capacities of 11 kW and 29 kW, respectively. While in off-grid configuration, the off-grid scenario exhibits an LCOE of 0.91 $/kWh, with optimal capacities of 10 kW for wind turbine, 20 kW for PV, and 2437.5 AH for batteries. The findings provide insights relevant to diverse locations, emphasizing the importance of local meteorological and geographical data. Multiple case studies ensure the robustness and applicability of the proposed system under varying conditions.

Sri Lanka as a country has tremendous potential for harnessing energy from renewable sources such as solar, wind, and hydro. However, as of 2018, only 39 % of Sri Lanka's energy generation capacity was harnessed through renewable energy sources. The continuous increase in electrical energy demand and the drastic increase in vehicle population over the past few years have resulted in much of its annual income being spent on purchasing fossil fuels from foreign countries. This has placed the country's future at risk due to the predicted shortage of fossil fuel reserves and in release of an unexpected level of harmful emissions to the environment. In the meantime, Sri Lanka also has an ambitious plan of achieving Net Zero by 2050. The study conducted a systematic review followed by a time series analysis to first identify the present state of the renewable energy progress of the country and through the time series analysis recognize any discrepancies in these efforts. The initial findings revealed the lack of coordination amongst relevant institutions and contrasting government policies such as the increase in investment for non-renewable energy resources as well as backing away from providing initial investment needed to boost the usage of renewable sources for businesses and smaller entities. The study further identified sectors such as transportation and non-renewable power generation activities as the two main barriers deterring the country from having a feasible plan for its efforts for net zero by 2050. From a non-governmental perspective, the study also recognized the knowledge gap and lack of awareness in the wider population of the long-term benefits of switching to renewable sources.

The growing energy demand in contemporary societies, coupled with the environmental detriments of conventional energy sources, necessitates a shift towards sustainable alternatives such as solar energy. However, the efficiency of solar energy systems is contingent upon various factors including surface orientation, tilt angle, geographic location, climatic conditions, solar irradiation, humidity, and temperature. Nevertheless, dust deposition on the active surfaces of solar energy systems remains the primary factor that highly impacts the system's energy yield, profitability, and efficiency. This paper provides a comprehensive review of the impact of environmental dust accumulation on the performance of solar energy systems that comprise photovoltaic, flat plate collectors, concentrating solar collectors, or solar chimneys. The objectives of this paper extend to consider economic consequences and the cleaning cost due to dust accumulation on the active surfaces of solar energy systems. The annual revenue loss due to dust accumulation was estimated at up to 35 % for 20 % of solar radiation reduction due to dust accumulation and the cleaning costs ranged from 0.016 to 0.9 $/m² worldwide, depending on system type, location, and cleaning technique. The present study offers distinctive perspectives on the topic and provide valuable information to policymakers, researchers, end-users, and stakeholders in the solar energy industry.

The industrial sector is a major energy consumer worldwide. Much of this consumption is due to air conditioning systems. In regions with extreme temperature conditions, the electricity consumption of these air conditioning systems increases significantly. This study was carried out with the objective of calculating the total energy consumption of the factory and identifying methods to decrease it. Furthermore, an enhanced energy system is suggested to lower energy consumption. This study was carried out with the objective of calculating the total energy consumption of the factory and identifying methods to decrease it. Furthermore, an enhanced energy system is suggested to lower energy consumption. It is also evident that the cooling load decreases by 21,661 kWh when thermal insulation is applied to the walls. Utilizing double-glazed windows for the skylight roof can lead to a reduction in the cooling load by 822 kWh. Additionally, the use of Light-Emitting Diode (LED) bulb lamps in the factory can further decrease the cooling load by up to 14,717 kWh.

This work proposes a non-islanded cogeneration energy management center (EMC) that can be used to displace grid-level natural gas turbine systems and natural gas combustion systems for heat. The design of the proposed EMC included a weighted multi-objective optimization aimed at minimizing: i) natural gas consumption; ii) capital costs; iii) utility costs; and iv) unmet thermal demand. The decision variables consisted of the existence and capacity of the equipment comprising the EMC, including: i) a natural gas boiler; ii) an internal combustion engine that generates heat and electricity; and iii) a hot water thermal storage system. Four resulting candidates EMC designs were then compared with the status-quo (SQ) in an eco-technoeconomic analysis; The SQ draws electricity from the grid and heating for dwellings come from natural gas boilers. Emissions at grid level change which alternative is favored. The findings showed that, for a system that serves 4–5 dense urban city blocks over a 20-year lifetime, the SQ system had cumulative levelized costs of 9.6 million USD for the final consumer, while the levelized costs of the EMC designs ranged from 12.9 to 15.1 million USD. In terms of emissions, the SQ emitted 959 tonnes of CO_2eq per year, while the EMC system produced around 500 tonnes of CO_2eq per year depending on the year, yielding a CCA varying between 364 and 653 USD/tonneCO_2eq

The transition from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs) is imperative to achieve the goal of reducing transport-related greenhouse gas emissions by 90 % in 2050. As urbanization intensifies, vehicle miles in urban environments increase and cities already consume 75 % of global energy, there is a pressing need for efficient charging infrastructure (CI) placement tailored to urban environments. Accordingly, this paper conducts a systematic literature review to outline prevailing research and derive requirements for a future CI model adaptable to urban environments. Analysis of N = 57 studies underscores the necessity for agent-based demand models to capture the intricate behaviors of EV drivers, which are currently underrepresented due to their data-heavy nature (n = 28 flow-based; n = 18 node-based). Furthermore, with a projected surge of 800 % in CI installations in Europe by 2030, strategic placement according to demand and urban-specific requirements is paramount. Still, multi-periodicity considerations are largely absent in current literature (n = 50). Geometric segmentation is presented as a solution to mitigate partial coverage issues. Ultimately, agent-based models, coupled with geometric segmentation, emerge as pivotal requirements for future CI models in urban environments, facilitating the transition to EVs, aligning with emission reduction targets, ensuring resource efficiency, and fostering urban sustainability.

Plant factory is one of the controlled environment agriculture forms with huge potential to alleviate food crisis, but the high energy cost restricts its widespread adoption. Numerous researches have explored various factors for energy-saving in plant factories in their settings, but there is a lack of analysis of the importance of these factors in energy saving. In this work, the energy-saving effect assessment of various factors in the container plant factory is investigated. Four cities (Harbin, Taiyuan, Shanghai, and Guangzhou), three plant densities (cultivation area: floor area=100 %, 150 %, and 200 %), and two temperature/humidity setpoints (20/22 ℃, 60/70 %, and 16/22 ℃, 50/95 %) are selected as operating conditions to cover different weather conditions and plant heat loads. The energy-saving effect of each factor is calculated using a random forest algorithm based on large amounts of energy simulation data. We identify that envelope overall heat transfer coefficient (U), air conditioner coefficient of performance (COP), and light efficacy (Efficacy) are three factors that have the largest impact on energy-saving in plant factories, in which light efficacy is the most important factor. Simultaneous optimization of these three factors could possibly reduce electricity consumption by ∼50 % compared to the base case. Finally, employing weight-light intensity correlation, the minimum specific energy consumption is approximately 4.76 kWh/kg lettuce fresh weight. This study utilizes advanced machine learning methods to sort out important factors and shows that significant energy reduction may be achieved by optimizing dominant factors, which gives a general guidance for future designers to build energy-efficient plant factories.

Energy is directly related to the economy and its demand would increase with development in the industrial, residential, transportation, and commercial sectors. It is projected that by 2050, the global energy demand will experience a two-fold increase. Currently, almost 85 % of the consumed energy globally is generated by burning non-renewable sources like oil, coal, and gas. Open car parking areas at universities, shopping malls, hospitals, etc. are unexploited areas that have enormous potential to generate renewable energy without disturbing the flora and fauna of the region. This study analyses the techno-economic feasibility of generating grid-connected energy using solar photovoltaic, PV panels on the parking lots of academic institutions and utilizes a part of it for charging the parked EVs . The best system is found to produce energy at a cost of 0.0529 USD/kWh, almost 54 % less compared to the grid. In the proposed design, 69 % of the total energy is produced by solar PV and almost 52.5 % is sold to the grid. The capacity factor of the PV system, at this location, is 19.8 %. The annual utility bill saving is around US $ 798,100. The internal rate of return and simple payback period are 7.35 % and 10.9 years. The proposed system architecture also caters to an EV charging capacity of 195 MWh, about 1.5 % of the total energy consumption, and a charging load of 578 kWh/day. This proposed PV energy system, for the parking areas, can be adopted in any other region with similar climatic conditions.

Energy harvesting systems can capture and convert small amounts of energy from various natural sources, contributing to offsetting the growing global energy challenges without incurring the depletion of additional energy resources. Human kinetic energy can be obtained from physical exercise - in indoor cycling, the athlete's metabolic energy converts into heat, enthalpy (to the environment), and work. This study reports on the concept development of the Green&Healthy Power system, an energy harvesting system that collects energy from physical activities. A case study based on an indoor bike generator as a conceptual innovation is presented. The user can select the intensity of effort in which the exercise occurs to produce electricity. Using situations based on literature results, the gains are evaluated by measuring aerobic endurance. The user is informed about the health benefits of the exercise (traditional indices), the energy produced, and the greenhouse gas emissions avoided. This study also reviews some aspects of precedent practices, highlighting the concept's challenging characteristics and setting out the initial formulation of the Green&Healthy Power system. This contribution defines research, development, and demonstration priorities for new ideas and technologies to improve energy efficiency and reduce the final consumption of primary energy. As results of the study case, initial figures indicate that 1000 people performing 20 min of activities on the energy harvesting system three times a week can avoid annual emissions of 180 kg CO₂ and produce 800 kWh/year of electricity, resulting in savings of R$ 800/year. Brazil has a population of 200 million inhabitants and if 1 % of the population carried out this activity, electricity generation could reach up to 1.6 TWh/month (= 19.2 TWh/year), representing 3.4 % of electricity flows within the Brazilian electricity matrix.

Renewable energy trading could be considered the next step in power trading's development. It is probable that individuals currently involved in power trading will need to upgrade their data collection, processing, and reporting systems. This article provides a comprehensive evaluation of renewable energy trading utilizing Blockchain technology. Initially, the paper examines country-specific renewable energy trading with a focus on India, China, the US, France, and Germany's renewable energy policies. Moreover, the paper presents potential renewable energy trading markets such as peer-to-peer, over the grid, and partially or fully independent microgrid's. This paper shows the appraisal of bond, commodity, derivative, and algorithm-based renewable energy trading using different Blockchain methods, including Ethereum and R3 Corda. It is find out during the renewable energy trading, proposers of bid, also include capital cost of the renewable energy power plant, salvage value after useful life of different component of renewable energy power plant. It is also find out proper trading is to be done with offering subsidies of up to 70 % of the capital cost, and with a 30 % viability gap finance (VGF) at this cost.