Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution

Tu, Qiang; Betz, Regina; Mo, Jiao; Fan, Ying; Liu, Yu

doi:10.1016/j.apenergy.2019.05.039

Cited by 105 publications

(34 citation statements)

References 39 publications

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“…However, variability and intermittency issues associated to the performance of wave resources are not considered. In China [13], LCOE is used to estimate the on-shore wind power that will decline by 13.91% from 0.40 RMB/kWh in 2016 to 0.34 RMB/kWh in 2025. Although on-shore wind power achieves the grid parity with the carbon pricing of 10, 35, and 60 RMB/kWh for 2019, 2017, and 2016, respectively, by comparing the on-grid price of coal-fired power and LCOE.…”

Section: Least-cost Evaluation For Decentralized Electrification In Sgmentioning

confidence: 99%

“…A cost-effective energy planning model is used to compare the energy generation capacity of a CES with the options of DES that often consider solar with battery storage [2]. brown Several energy planning models were proposed in the literature to provide cost-effective solutions to classes of DES for different countries [12][13][14][15][16][17][18]; however, their focus was on isolated geographical locations. Although, the authors of [19] have proposed a model for energy planning for multiple SSA locations.…”

Section: Introductionmentioning

confidence: 99%

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Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa

Dahunsi

Almogren

Javaid

et al. 2020

Entropy

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The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d'Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.

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Section: Least-cost Evaluation For Decentralized Electrification In Sgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa

Dahunsi

Almogren

Javaid

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…Many researchers mentioned the energy economic issues related to the TGR [14,22,27,28]. First, tariff growth variation has an effect on the economic value of grid parity.…”

Section: Various Approaches For Better Economic Competitiveness Evalumentioning

confidence: 99%

“…First, tariff growth variation has an effect on the economic value of grid parity. Both the LCOE and tariff have to be considered simultaneously [14,27,28]. Second, the economic value of electrical energy storage is affected by tariff growth variation.…”

Section: Various Approaches For Better Economic Competitiveness Evalumentioning

confidence: 99%

Economic Competitiveness Evaluation of the Energy Sources: Comparison between a Financial Model and Levelized Cost of Electricity Analysis

Sung

Jung

2019

Energies

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The levelized cost of electricity (LCOE) is used widely to compare the economic competitiveness of the energy mix. This method is easy to understand and simple to apply, which makes it preferable for many energy policymakers. However, the method has several disadvantages from the energy business perspective. First, the LCOE approach does not consider revenue, and a high-interest rate usually correlates with the tariff growth rate. Thus, if a high-interest rate increases the cost, that high rate increases the revenue, which can affect economic competitiveness. Second, the LCOE does not consider different stakeholders. Equity investors and loan investors have different interests depending on different financial indicators, which influence the same energy sources’ differential economic attractiveness. This study analyzes and compares the LCOE, Project Internal Rate of Return (Project IRR), Equity Internal Rate of Return (Equity IRR), and Debt Service Coverage Ratio (DSCR) of an illustrative wind, coal, and nuclear power project using Monte-Carlo simulations. The results show that energy sources’ economic competitiveness can vary depending on financial indicators. This study will help energy policymakers develop more economically realistic energy portfolios.

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“…A modelling method for wind power production and its unpredictability is proposed by analysing the technical limitations of a series of wind turbines, and then the generation cost of wind power and its effect on carbon dioxide emission are discussed [3]. e LCOE for future wind power is estimated with the learning curve method, and the results show that the LCOE for onshore wind power will decrease by 13.91% from 0.40 yuan/kWh in 2016 to 0.34 yuan/kWh in 2025 [4]. An LCOE estimation method taking audit information into account is proposed [5].…”

Section: Introductionmentioning

confidence: 99%

Research on the Influence of Development Scenarios on the OLCOE of Wind Power: A Case Study of China

Liang

Zhang

Liu

et al. 2020

Mathematical Problems in Engineering

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To achieve a high penetration of renewable energy, wind power development in China has gradually moved to diverse manifestation (e.g., centralized onshore, low wind speed, and offshore wind power). However, preexisting studies regarding wind power cost neglect to consider the respective characteristics of different development scenarios. In this paper, the overall levelized cost of energy (OLCOE) model is established for different scenarios. Taking China’s wind farm data as an example, the impact of development scenarios and wind power permeability on OLCOE and its cost components is quantitatively analysed. The results show that, (1) in the low penetration scenario, low wind speed power has the best economy and is beneficial to the conventional units; (2) the large-scale development of offshore wind power requires a reduction in the cost of offshore wind turbines and submarine cables; and (3) at present, onshore centralized wind power has economic advantages, but there is little room for its cost reduction.

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Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution

Cited by 105 publications

References 39 publications

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa

Economic Competitiveness Evaluation of the Energy Sources: Comparison between a Financial Model and Levelized Cost of Electricity Analysis

Research on the Influence of Development Scenarios on the OLCOE of Wind Power: A Case Study of China

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