Cost estimate of electricity produced by TPV
22
Citation
7
Reference
10
Related Paper
Citation Trend
Abstract:
A crucial parameter for the market penetration of TPV is its electricity production cost. In this work a detailed cost estimate is performed for a Si photocell based TPV system, which was developed for electrically self-powered operation of a domestic heating system. The results are compared to a rough estimate of cost of electricity for a projected GaSb based system. For the calculation of the price of electricity, a lifetime of 20 years, an interest rate of 4.25% per year and maintenance costs of 1% of the investment are presumed. To determine the production cost of TPV systems with a power of 12–20 kW, the costs of the TPV components and 100 EUR kW−1el,peak for assembly and miscellaneous were estimated. Alternatively, the system cost for the GaSb system was derived from the cost of the photocells and from the assumption that they account for 35% of the total system cost. The calculation was done for four different TPV scenarios which include a Si based prototype system with existing technology (ηsys = 1.0%), leading to 3000 EUR kW−1el,peak, an optimized Si based system using conventional, available technology (ηsys = 1.5%), leading to 900 EUR kW−1el,peak, a further improved system with future technology (ηsys = 5%), leading to 340 EUR kW−1el,peak and a GaSb based system (ηsys = 12.3% with recuperator), leading to 1900 EUR kW−1el,peak. Thus, prices of electricity from 6 to 25 EURcents kWh−1el (including gas of about 3.5 EURcents kWh−1) were calculated and compared with those of fuel cells (31 EURcents kWh−1) and gas engines (23 EURcents kWh−1).Keywords:
Recuperator
Production cost
Electricity system
Electricity demand
Cite
Citations (47)
This paper presents the findings of our study on forecasting the levelized cost of electricity of different electricity generation technologies including renewable energy (solar PV, hydropower, biomass and wind) until 2030. Usually, the cost of electrical generation from renewable and conventional sources are compared by using the LCOE method (Levelized Cost Of Energy). In general, the LCOE is calculated based on the assumptions for each technology. In this paper, we evaluate the LCOE of the various electricity generation techniques for the case of Lebanon. The study predicts future evolution of these costs in Lebanon until 2030 according to specic assumptions related to each technology and taking into account the triple aspects : technical, economic and environmental. The generation costs don't depend only on investment, maintenance and fuel costs, but also on the cost of carbon emitted by each technology per unit of the electricity produced. We focus our analysis on these assumptions and the impact of their variability on the cost by using sensitivity analysis. Our study shows that by 2030, renewable generation technologies will remain more expensive than fossil-fueled technologies. However, within certain measures taken by the State, as integration of a carbon price, renewable energy resources could emerge strongly in the electricity generation investment planning and become much competitive.
Investment
Unit cost
Cite
Citations (0)
The marketization process of electricity purchase and power generation and its significance in China
Abstract China’s electricity purchases and power generation have been subject to deterministic catalogue tariffs for a long time. With the continuous improvement of China’s electricity market reform, more and more parts of electricity purchase and power generation use market-based methods to determine electricity prices. This paper reviews the marketization process in China’s electricity purchase and power generation. In view of whether or not to participate in the electricity market, this paper classifies different types of electricity purchase and power generation in China. Finally, it summarizes the significance of China’s electricity purchase and power generation marketization.
Marketization
Electricity retailing
Stand-alone power system
Electric Power Industry
Cite
Citations (1)
Recuperator
Economic shortage
Cite
Citations (0)
Economic cost is decisive for the development of different power generation. Life cycle cost (LCC) is a useful tool in calculating the cost at all life stages of electricity generation. This study improves the levelized cost of electricity (LCOE) model as the LCC calculation methods from three aspects, including considering the quantification of external cost, expanding the compositions of internal cost, and discounting power generation. The improved LCOE model is applied to three representative kinds of power generation, namely, coal-fired, biomass, and wind power in China, in the base year 2015. The external cost is quantified based on the ReCiPe model and an economic value conversion factor system. Results show that the internal cost of coal-fired, biomass, and wind power are 0.049, 0.098, and 0.081 USD/kWh, separately. With the quantification of external cost, the LCCs of the three are 0.275, 0.249, and 0.081 USD/kWh, respectively. Sensitivity analysis is conducted on the discount rate and five cost factors, namely, the capital cost, raw material cost, operational and maintenance cost (O&M cost), other annual costs, and external costs. The results provide a quantitative reference for decision makings of electricity production and consumption.
Capital cost
Cost driver
Cite
Citations (21)
Recuperator
Cite
Citations (42)
Sudan faces an electricity supply shortage despite its abundant natural resources. This paper aims to manage these resources for sustainable power generation to meet Sudan’s electricity demand. The sustainability assessment integrates quantitative analysis of power generation’s impacts on water, land, and greenhouse gas (GHG) emissions, in addition to the levelized cost of electricity (LCOE). Cost-effective, resource- and GHG emission-effective, and GHG-stringent scenarios are executed in this study to investigate the impact of different constraints on the sustainability of power generation in Sudan. The average LCOEAV for these three scenarios is 43.64–100.00 USD/MWh, with the lowest in the cost-effective scenario and the highest in the resource- and GHG emission-effective scenario. The LCOEAV for the stringent scenario is 32% higher than the cost-effective scenario. The two governmental and lowest-cost plans, which serve as the business-as-usual cases in this study, are optimized and comparatively evaluated. The sensitivity analysis is conducted by reducing each clean energy pathway to a minimum LCOE of 42.89 USD/MWh. Solar–photovoltaic (PV), wind, and hydroelectricity pathways are the most sensitive to the LCOE and can significantly contribute to Sudan’s total power generation if their costs are minimal. A rational scenario for power generation in Sudan is developed to improve sustainability performance and avoid the unreliability of the studied scenarios and cases. The rational average generation mix comprises 44% clean energy, 46% fossil fuels, and 10% imported electricity pathways.
Cite
Citations (1)
Carbon offset
Electricity system
Cite
Citations (17)
The aim of this study is forecasting of Turkey’s electricity generation and consumption for the period 2017-2027. To achieve this, Turkey’s electricity generation and consumption for the period 1996-2016 was modelled using Grey prediction method GM(1,1). Results showed that the small error probability (p) and posterior error ratio (C) values of GM(1,1) model for Turkey’s electricity generation were obtained as 0.12 and 0.97, respectively, and 0.11 and 0.97, respectively for Turkey’s electricity consumption. So, the level of established GM(1,1) models for Turkey’s electricity generation and Turkey’s electricity consumption is in good level. Additionally, mean absolute percentage error (MAPE) values of GM(1,1) models for Turkey’s electricity generation and consumption were obtained as 3.12% and 3.08%, respectively. Results of F-test showed that p-value of GM(1,1) model for Turkey’s electricity generation and consumption was 0.48. According to these results, GM(1,1) models are suitable for prediction of Turkey’s electricity generation and consumption. Furthermore, the average annual grow rates of Turkey’s electricity generation and consumption for the period 2017-2027 were forecasted as 5.25% and 5.58%, respectively. In addition to this, Turkey’s electricity generation and consumption were forecasted as 405310GWh and 344672GWh, respectively, for 2023.
Consumption
Cite
Citations (7)