Up-scaling, formative phases, and learning in the historical diffusion of energy technologies

The 20th century has witnessed wholesale transformation in the energy system marked by the pervasive diffusion of both energy supply and end-use technologies. Just as whole industries have grown, so too have unit sizes or capacities. Analysed in combination, these unit level and industry level growth patterns reveal some consistencies across very different energy technologies. First, the up-scaling or increase in unit size of an energy technology comes after an often prolonged period of experimentation with many smaller-scale units. Second, the peak growth phase of an industry can lag these increases in unit size by up to 20 years. Third, the rate and timing of up-scaling at the unit level is subject to countervailing influences of scale economies and heterogeneous market demand. These observed patterns have important implications for experience curve analyses based on time series data covering the up-scaling phases of energy technologies, as these are likely to conflate industry level learning effects with unit level scale effects. The historical diffusion of energy technologies also suggests that low carbon technology policies pushing for significant jumps in unit size before a ‘formative phase’ of experimentation with smaller-scale units are risky

Determinants of Renewable Energy Innovation: Environmental Policies vs. Market Regulation

This paper carries out a comprehensive analysis of renewable energy innovations considering four mechanisms suggested by innovation models: 1. policy-inducement; 2. market structure; 3. demand and social cohesion- mainly proxied by income inequality; 4. characteristics of country knowledge base. For OECD countries and years 1970-2005, we build a unique dataset containing time-varying information on quality-adjusted patent production in renewable energy, the latter being a function of environmental policies, green R&D, entry barriers, knowledge stock, knowledge diversity and income inequality. We develop count data models using the Generalized Method of Moments (GMM) to account for endogeneity of policy support. Our synthetic policy index positively affects innovations especially in countries with deregulated energy markets and low entry barriers. The effect of entry barriers and inequality is negative and of similar magnitude as that of policy. Product market liberalization positively affects green patent generation, especially so when ambitious policies are adopted, when the initial level of public R&D expenditures and when the initial share of distributed energy generation is high. Our results are robust to alternative specifications, to the inclusion of technology-specific effects and to the use of quality-adjusted patents as dependent variables. In the latter case, the estimated effect of lowering entry barriers and of knowledge diversity almost double on citation count relatively to patent count.renewable energy technology; patent; environmental policies; product market regulation; inequality

Determinants of Renewable Energy Innovation: environmental policies vs. market regulation

This paper carries out a comprehensive analysis of renewable energy innovations considering four mechanisms suggested by innovation models: 1. policy-inducement; 2. market structure; 3. demand and social cohesion- mainly proxied by income inequality; 4. characteristics of country knowledge base. For OECD countries and years 1970-2005, we build a unique dataset containing time-varying information on quality-adjusted patent production in renewable energy, the latter being a function of environmental policies, green R&D, entry barriers, knowledge stock, knowledge diversity and income inequality. We develop count data models using the Generalized Method of Moments (GMM) to account for endogeneity of policy support. Our synthetic policy index positively affects innovations especially in countries with deregulated energy markets and low entry barriers. The effect of entry barriers and inequality is negative and of similar magnitude as that of policy. Product market liberalization positively affects green patent generation, especially so when ambitious policies are adopted, when the initial level of public R&D expenditures and when the initial share of distributed energy generation is high. Our results are robust to alternative specifications, to the inclusion of technology-specific effects and to the use of quality-adjusted patents as dependent variables. In the latter case, the estimated effect of lowering entry barriers and of knowledge diversity almost double on citation count relatively to patent count.renewable energy technology, patent, environmental policies, product market regulation, inequality

The roles and potentials of renewable energy in less-developed economies

Increasing the renewable energy share in national energy mix remains one of the major energy policy goals across many economies. This paper assesses the roles and potentials of renewable energy sources in less-developed economies while citing Nepal as an example. Renewable energy has a significant role to play in the electrification of rural areas in developing economies and contribute towards sustainable development. Realizing full potentials of renewable, however, requires addressing both the associated demand-side and supply–side constraints. Innovative subsidies and tax incentives, adequate entrepreneurial support, strengthening institutional arrangement and promoting local community-based organizations such as the cooperatives are the necessary factors in promoting the green technologies in countries like Nepal. International factors such as large scale investment and adequate technology transfer are equally crucial to create a rapid spread and increase affordability of decentralised renewable energy technologies in less-developed economies.renewable; electrification; research and development

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Challenges to the Integration of Renewable Resources at High System Penetration

Successfully integrating renewable resources into the electric grid at penetration levels to meet a 33 percent Renewables Portfolio Standard for California presents diverse technical and organizational challenges. This report characterizes these challenges by coordinating problems in time and space, balancing electric power on a range of scales from microseconds to decades and from individual homes to hundreds of miles. Crucial research needs were identified related to grid operation, standards and procedures, system design and analysis, and incentives, and public engagement in each scale of analysis. Performing this coordination on more refined scales of time and space independent of any particular technology, is defined as a “smart grid.” “Smart” coordination of the grid should mitigate technical difficulties associated with intermittent and distributed generation, support grid stability and reliability, and maximize benefits to California ratepayers by using the most economic technologies, design and operating approaches

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions