Two-factor Learning Curve Research Articles

Prediction of hydrogen consumption scale and hydrogen price based on LEAP model and two-factor learning curve

Under the dual-carbon target, hydrogen energy, as a zero-carbon secondary energy source, has great scope for replacing fossil feedstocks in the fields of energy, transportation and industry. However, the current research on the competitiveness of hydrogen energy in various fields is not sufficiently addressed. In this paper, we use the LEAP model to predict the future scale of hydrogen use and the two-factor learning curve to predict the trend of hydrogen price change from 2025 to 2050, using Qinghai Province as the research background. At the same time, considering the carbon emission reduction benefits and raw material costs, the competitiveness of hydrogen energy in energy, transportation, industry and other fields in the future is compared. The results show that: 1) The hydrogen load scale in Qinghai Province will grow fast from 2025 to 2030. From 2030 to 2040, it slows under the steady and basic scenarios but remains high under the accelerated one. By 2040, the consumption scales are 1.057 million, 649,000 and 442,000 tons respectively. 2) The price of hydrogen energy will drop rapidly from the current 28 CNY/kg to about 20 CNY/kg in the next 5 years. By 2040, the price of hydrogen energy will be reduced to about 17 CNY/kg. 3) In terms of hydrogen energy competitiveness, when carbon emissions are not taken into account, hydrogen energy is currently competitive in the transportation field. During 2032–2038, it will be competitive in the field of methanol synthesis. By 2040, hydrogen energy will not be competitive in the fields of ammonia synthesis and power/heating. When considering carbon emissions, the competitiveness of hydrogen energy in the transportation field will become greater. The competitive year in the field of methanol synthesis will be 1–2 years ahead. By 2040, it will not be competitive in the field of synthetic ammonia and power/heating, but the gap will be significantly reduced due to the consideration of carbon emissions.

Impact of policies on wind power innovation at different income levels: Regional differences in China based on dynamic panel estimation

Based on the expanded two-factor learning curve and adopting dynamic panel methods, we explored the driving effects of policies and policy interaction on wind power technology innovation (TIW) at different developmental levels using panel data for the period 2006–2017 in 29 provinces in China. To reveal regional heterogeneity, we classified the 29 Chinese provinces into two regions based on the 2017 per capita gross domestic product. The results indicated that public research and development policy plays the most significant role in driving TIW for all samples. We only confirmed the positive interaction effect of policies for middle-income provinces (Region 2). Additionally, the learning-by-doing effect for TIW in China was identified, though the magnitude of that effect was much smaller than that of the learning-by-searching effect. Finally, the regional differences in the impacts of different policy instruments provide new insights for future policy design to effectively promote TIW.

The policy effects of demand-pull and technology-push on the diffusion of wind power: A scenario analysis based on system dynamics approach

With the support of incentive policies, the diffusion of wind power has achieved tremendous achievements in China. However, behind this rapid expansion, it is raised a question: what are the driving forces behind the diffusion of wind power? And which policies are more efficient to improve the diffusion rate of wind power? To scientifically answer this question, this paper combines system dynamics and the two-factor learning curve to investigate the dynamic diffusion process of wind power. The results show that: (1) The demand-pull policy through feed-in tariff (FIT) policy can promote the diffusion of wind power; (2) The technology-push policy through R&D policy also promotes the diffusion of wind power; (3) A higher level of FIT is more efficient to expand the installed capacity of wind power, in addition, a higher level of R&D intensity is more efficient for cost reduction; (4) The coefficient of learning by doing of wind power is 0.0274 and the coefficient of learning by research of wind power is 0.2540, which indicates China's wind power industry is transitioning from price subsidies to technology innovation policies. The research results can enrich the research on the diffusion of wind power and provide a reference for other regions to study the diffusion of renewable energy.

Economic and environmental impacts of public investment in clean energy RD&D

Learning curve theory has been adopted for investigating the relationship between technological learning and technology cost developments. The aim of this paper is to explore the impacts of public investment in clean energy research, development, and demonstration (RD&D) on future technology cost developments by using a two-factor learning curve approach. Learning-by-deploying and learning-by-researching were chosen as the main sources of learning. The focus is on onshore wind and solar photovoltaics in the United States of America. By using publicly available data, we estimated learning-by-deploying rates of 31.4% and 27.6%; and learning-by-researching rates of 2.3% and 4.7% for onshore wind and solar PV, respectively. By adopting a logistic curve approach, an additional $1322 and $819 mil. were forecast to be spent by 2050 in RD&D for onshore wind and solar PV, respectively. We explored the plausible long-term effects of diverse RD&D investment scenarios on electricity generation and greenhouse gas (GHG) emissions using a system dynamics model. The findings reveal that public investment in RD&D for clean energy technologies may play a key role in the pace of capital cost reductions and technology diffusion. However, relatively little long-term effects of RD&D efforts alone were found on market dynamics and GHG emissions.

Can the incentives polices promote the diffusion of distributed photovoltaic power in China?

Government incentive policies play an important role in the promotion of distributed photovoltaic power. However, which policy is more effective for the diffusion of distributed photovoltaic power? This is a question that needs to be answered. Based on this, we combined the two-factor learning curve and system dynamics model to study the dynamic diffusion process of China's distributed photovoltaic power (DSP). The results show that (1) the coefficients of learning by doing and learning by researching for DSP are 0.0435 and 0.2971 respectively. This indicates that technological innovation caused by R&D expenditures in the DSP is the driving force for cost reduction. (2) Both demand-pull and technology-push policies contribute to the diffusion of DSP; (3) the effect of FIT policy on the diffusion of distributed photovoltaic technology is more significant than that of R&D policy; and the reduction of production cost of photovoltaic power industry by R&D policy is more significant than FIT policy.

Measurement and key influencing factors of the economic benefits for China’s photovoltaic power generation: A LCOE-based hybrid model

2020 may be redefining China’s photovoltaic power generation (PPG) development. This research is an attempt to extract the key influencing factors and analyze the main driving forces to improve the economic benefits of China’s PPG and thus a lower-cost access to the grid as soon as possible. In this research, the relationship analysis model of PPG economic benefits based on decision lab analysis method (DEMATEL) and interpretative structural model (ISM) is constructed, and thereby accumulated research and development (R&D) funds and accumulated installed capacity are extracted from the set as the key factors. Then, the levelized cost of energy (LCOE) model based on the two-factor learning curve is established, and a measurement and calculation on the basis of the assumption of the East China Power Grid is conducted. Recommendations are offered on the grounds of the comparisons in the development status of PPG economic benefits between different regions of China. Finally, through the sensitivity analysis of key influencing factors with LCOE as a variable, it can be found that a 5% more investment in vital influencing factors could produce the average decrease of about 0.0184 yuan/kWh in LCOE.

Using real options to value capacity additions and investment expenditures in renewable energies in India

We calculate the overall policy value of installed capacity additions and investment expenditures in wind and solar energies in India. Recent increases in capacity additions and investments by both the public and private sectors along with government support schemes have made these energies more competitive with traditional fuels like coal in generating electricity. We use a two-factor learning curve to model the decline in prices of wind and solar energies. Employing a real options approach with global coal prices as the stochastic variable we find the overall value of promotion policies in renewables to be sufficiently large. The trigger price of coal for investment in renewable energies today for electricity generation suggest early to immediate deployment of additional wind and solar technologies as the optimal policy. With the option to deploy for a longer time horizon, continuing with promotion policies of capacity additions and investment expenditures in renewables is more valuable at high global coal prices.

Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model

Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment.

Valuing investment decisions of renewable energy projects considering changing volatility

Volatility is an important parameter when evaluating investments using the real options method. For renewable energy investments, the volatility of cash flow continuously changes, because as new information and knowledge are gathered, there is less foreseen variation. This paper proposes an extended recombining trinomial tree model, where the changing volatility is used to generate transition probabilities. The changing volatility is generated using a consolidation process where multiple random variables, including the market price of electricity, carbon price, and lending rate, are integrated into a low-dimension stochastic process. A two-factor learning curve is used to model the changes of investment cost. We apply the proposed model to analyze solar photovoltaic (PV) power generation investment in China. The results show volatility with changing feature. Compared with constant volatility, changing volatility may advance investment decisions and change the project value. Complete grid parity policy in the solar PV industry is infeasible because the opportunity cost brought by the option of delaying cannot be offset. The changing volatility may produce a lower and equally effective subsidy level compared with constant volatility. A carbon emission trading scheme is helpful in advancing investments in renewable energy, which is reflected in improvements in project value, advancements in investment decisions, and reductions in the required subsidy level. • Propose an extended recombining trinomial tree model considering changing volatility. • Integrate multiple random variables into a low-dimension process to generate the changing volatility. • Analyze the effects of changing volatility on project value, investment decision and the needed subsidy.

Is it time to launch grid parity in the Chinese solar photovoltaic industry? Evidence from 335 cities

This study evaluates grid parity in the Chinese solar PV industry by calculating the unsubsidized unit profits (UUPs) of solar PV projects in 335 Chinese cities. Furthermore, the effects of technological advances and various electricity price mechanisms on grid parity are explored. We consider the differences between centralized grid-connected and distributed projects. Two-factor learning curves are used to forecast technological progress and two kinds of electricity price mechanisms—government-regulated and market-based—are also brought into analyses. The results indicate the infeasibility of implementing complete grid parity regardless of the type of project. However, centralized grid-connected projects in 5 cities and distributed projects in 129 cities could implement pilot grid parity. On the whole, centralized grid-connected project has a lower benefit performance than distributed projects. Resource Zone I, which consists of Ningxia Province and several western cities in Gansu, Xinjiang and Inner Mongolia, has the best benefit performance of three resource areas. Technological advances and variations in electricity prices under both mechanisms could improve the UUPs but raising them above zero in all cities would still be impossible. The market prices of electricity under market-based mechanisms may generally have greater impacts than do government-regulated mechanisms on the benefit performances throughout the whole nation.

The economic synergies of modelling the renewable energy-water nexus towards sustainability

This study investigates the role of the water-energy-nexus in transition plans to achieve a future system with a higher share of renewable energy and lower excess water extraction. Applying a novel nexus approach, an interactive multi-period model is proposed to study the synergies of sustainable water and energy supply planning, taking into account operational flexibility of the water sector and its compatibility with inherent intermittency of variable renewable resources. For investigating synergies in designing both energy systems and water systems together, the path of technology cost reduction is assumed to be a function of experience and knowledge and estimated using two-factor learning curves. The learning-by-searching rate was higher than the learning-by-doing rate for utility-scale wind, photovoltaic and reverse osmosis desalination technologies, ensuring research & development investment can play a chief role in the development of these industries. The levelized cost and capacity factor of photovoltaic and wind resources in the Middle East were calculated using data from 81 projects. The results showed that planning the water and energy sectors as endogenous parts of one integrated system brings potential synergies. The levelized cost of variable renewable energy decreased by 20% and the levelized cost of desalinated water dropped 4%.

An Integrated Planning Framework for Sustainable Water and Energy Supply

This study aims to reveal the economic, technical, and environmental impacts of different system configurations (centralized or decentralized, components, and technologies) on transition plans to achieve a higher share of renewable energy and desalination supplies for regions facing water scarcity. The main contribution of this research is the comparative evaluation of on-grid decentralized or distributed renewable-powered desalination systems for sustainable water and energy supply planning. Applying a novel nexus approach, an interactive multi-period planning model is developed to highlight synergies and to identify conflicts of planning both energy and water sectors at the same time as endogenous subsystems of one overall system. For studying these synergies in this study, the pace of technology deployment and the path of decline in overall costs are assumed to be a function of experience and knowledge as two-factor learning curves. Using data from 81 projects, the levelized cost and capacity factor of utility-scale photovoltaic and wind supplies in the Middle East were calculated. The results indicate that a scenario with a decentralized water sector and renewable-powered multiple-effect distillation technology has the best overall performance among the proposed scenarios.

A two-factor learning model for private sector industrial firms

ABSTRACT The Cobb–Douglas form of the two-factor learning curve model has been the conventional choice in empirical studies of the relative importance of R&D versus capacity expansion for cost reduction in energy technologies. Most empirical studies have focused on the role of public R&D in the context of renewable energy technologies. In this study, we provide a rationale for considering a different model formulation of the tradeoff between R&D and production capacity expansion when studying technology development in the private sector and we compare it to the conventional Cobb–Douglas model. We then apply our model formulation to a particular emerging technology case study.

An Evaluation of Investment in a PV Power Generation Project in the Gobi Desert Using a Real Options Model

This paper presents a policy benefit model of a photovoltaic (PV) power generation project based on real options analysis (ROA) and the two-factor learning curve model. The main purpose is to examine the investment behavior of developing a PV project in the Gobi desert considering multiple uncertain factors. We take the environmental cost of desertification control into account for the first time in the literature. Four other uncertain factors are thermal power cost, PV power generation cost, carbon prices, and government subsidy. A binary tree method is applied to solve the proposed model, and we obtain both unit decision value and optimal investment time. Our baseline scenario illustrates that ROA is more effective than net present value (NPV) analysis when dealing with uncertainty. Our simulation results show that the government could suffer a loss in accordance with the existing subsidy policy when investing in a PV project. Therefore, the subsidy should be gradually reduced. Finally, the influence of the subsidy policy on decision value is discussed, and an appropriate subsidy is determined accordingly.

Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence

Cost evolution has an important influence on the commercialization and large-scale application of power technology. Many researchers have analyzed the quantitative relationship between the cost of power technology and its influencing factors while establishing various forms of technical learning curve models. In this paper, we focus on the positive effects of the policy on research and development (R&D) learning by summarizing and comparing four energy technology cost models based on learning curves. We explore the influencing factors and dynamic change paths of power technology costs. The paper establishes a multi-stage dynamic two-factor learning curve model based on cumulative R&D investment and the installed capacity. This work presents the structural changes of the influencing factors at various stages. Causality analysis and econometric estimation of learning curves are performed on wind power and other power technologies. The conclusion demonstrates that a “learn by researching” approach had led to cost reduction of wind power to date, but, in the long term, the effect of “learn by doing” is greater than that of “learn by researching” when R&D learning is saturated. Finally, the paper forecasts the learning rates and the cost trends of the main power technologies in China. The work presented in this study has implications on power technology development and energy policy in China.

Induced technological change and the timing of public R&D investment in the Japanese electricity sector considering a two-factor learning curve

The UNFCCC has stated that energy policies and measures to address climate change should be cost-effective to ensure global benefits at the lowest possible cost. To mitigate the bulk of carbon emission from the electricity sector, a large market penetration of renewable energy technologies with as low cost as possible is a key research topic. The energy-related R&D policy in Japan aims to achieve a green economy. In our study, based on this context, we demonstrated how to optimize the timing of public R&D investment within the framework of a bottom-up partial equilibrium model. The developed optimization model represents the Japanese electricity sector and minimizes the total system cost subject to an accumulated carbon emission constraint. Our main research focus is the role of R&D activity, especially in the innovation stage of renewable technologies. We employ a two-factor learning curve and quantify the impact of the learning effect on the dynamic diffusion of major renewable technologies. The study shows a dynamic technology transition in the Japanese electricity sector and the optimized R&D investment schedule for each renewable technology. With the first-best energy policy, an R&D budget of more than 2000 million USD would be allocated to PV in 2050, which corresponds to 45% of the energy-related Japanese R&D budget in 2050. Because some have criticized the uncertainty problems with dynamic simulations and learning models, supplemental sensitivity analyses are included.

Emission path planning based on dynamic abatement cost curve

Global climate change is one of the most serious environmental problems that has attracted an unprecedented global attention. The scientific community has recognized that human activity is mostly responsible for global climate warming. Therefore, states participating in the United Nations Framework Convention on Climate Change (UNFCCC) have initiated a series of negotiations on how to develop their own emission reduction targets. However, the realization of these targets is at the expense of economic development to a great extent. Therefore, an abatement plan is necessary to decide how and when to implement emission reduction targets. A marginal abatement cost curve (MACC), often used as an important quantitative tool to estimate abatement burden or evaluate abatement performance, should be considered carefully. In this paper, we enrich the traditional MACC by introducing the concept of an “immediate unit marginal abatement cost” to describe its intrinsic incremental abatement difficulty. This cost is proved to be agreed with the actual situation in many ways, and is therefore very suitable for practical use. Two-factor learning curve are considered in the model, which can go some way in improving the performance of the revised MACC. Using a parameterization method, the original problem can be converted into a tractable nonlinear programming problem. We use an algorithm to search for the approximate global optimal solution. The effectiveness and the efficiency of the algorithm are discussed respectively. In the results, we find that the abatement path distinguishes largely under different abatement targets. Some plausible reasons and policy advices are given.

The Impact of Learning-by-doing and Learning-by-searching on the Diffusion of Renewable Energy Technologies in the Japanese Electricity Sector

The mitigation of climate change is thought by many to be a significant environmental concern. In order to mitigate the bulk of carbon emissions from the electricity sector, large market penetration of renewable energy technologies is a key research issue. The goal of the paper is to address the timing of public RD investment for renewable technologies in the Japanese electricity sector. The model is cast as a dynamic bottom-up energy-system model and numerically optimized, minimizing a total system cost subject to an accumulated carbon emission constraint. Autonomous energy-efficiency improvement and two-factor learning curves describe the technological development process for each energy technology. With the two-factor learning curve, the model shows that the cost of electricity of renewable technologies can be reduced by up to 43% in the year 2100 if there is no constraint on public RD budget. Renewable energy sources requires substantial RD support in their initial phase of development; however, the investment pattern would switch to a path of reducing the amount after 2050. The budget of RD investment is strictly limited; thus, an important policy objective is to design a dynamic investment schedule that will induce sustainable innovations for green technologies.

Mitigation strategies and energy technology learning: An assessment with the POLES model

This paper explores various dimensions of the learning process for low-carbon technologies under different mitigation scenarios. It uses the POLES model, which addresses learning as an endogenous phenomenon with learning curves, and a set of scenarios developed as part of the AMPERE project. It represents an analytical effort to understand the learning patterns of energy technologies in various contexts and tries to disentangle the different dimensions of the relation between these patterns and the deployment process. One result is, surprisingly, that apparent learning may be slower in mitigation scenarios with accelerated technology deployment when using two-factor learning curves. Second, the R&D analysis clearly shows that reductions in R&D budgets have significant impacts on long term technology costs. Third, solar technology which is more constrained by floor costs in the model benefits more from major technological breakthroughs than wind energy. Finally, ambitious stabilization targets can be met with limited cost increases in the electricity sector, thanks to the impact of learning effects on the improvement in technology costs and performances.

An innovation-focused roadmap for a sustainable global photovoltaic industry

The solar photovoltaic (PV) industry has undergone a dramatic evolution over the past decade, growing at an average rate of 48 percent per year to a global market size of 31GW in 2012, and with the price of crystalline-silicon PV module as low as $0.72/W in September 2013. To examine this evolution we built a comprehensive dataset from 2000 to 2012 for the PV industries in the United States, China, Japan, and Germany, which we used to develop a model to explain the dynamics among innovation, manufacturing, and market. A two-factor learning curve model is constructed to make explicit the effect of innovation from economies of scale. The past explosive growth has resulted in an oversupply problem, which is undermining the effectiveness of “demand-pull” policies that could otherwise spur innovation. To strengthen the industry we find that a policy shift is needed to balance the excitement and focus on market forces with a larger commitment to research and development funding. We use this work to form a set of recommendations and a roadmap that will enable a next wave of innovation and thus sustainable growth of the PV industry into a mainstay of the global energy economy.