Search

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

The increase in energy demand makes it urgent to explore green energy sources. And wind energy is one of the most promising renewable energy sources. In this paper, a rotating hybrid nanogenerator with a combination of a direct-current triboelectric nanogenerator (DC-TENG) and an electromagnetic generator (EMG) is designed; the design concept is to harvest wind energy. First, an optimized ternary DC-TENG is designed based on the ternary dielectric triboelectrification effect. A soft contact structure is designed, which can effectively increase the frictional area as well as reduce the frictional resistance. Then, by introducing the EMG unit, the output power of the device and the speed of charging the capacitor are increased. The high output voltage of the DC-TENG and the high output current of the EMG complement each other to produce excellent power output. At 180 rpm/min, the output power of DC-TENG and EMG can reach 1.78 mW and 215 mW, respectively. With a simple power management circuit design, the device is successfully implemented to power small electronic devices. By charging a suitable capacitor, the device can be used as a power supply to power the sensor. Together with the sensor and power management circuit, it forms a self-powered sensing system that can drive the sensor for continuous operation. This work provides a simple, convenient, and reliable design that offers new insights into energy harvesting in nature. • A ternary dielectric triboelectrification effect triboelectric nanogenerator. • A soft contact structure is designed to minimize frictional resistance. • Introduction of an electromagnetic generator to reduce the capacitor charging time. • Energy supply for the continuous operation of the self-powered sensing systems. [ABSTRACT FROM AUTHOR]

The rapidly growing state of energy consumption and the urgency of mitigating the harmful effects of climate change have accelerated our transition to renewable energy. This paper proposes a multi-level framework for exploring the global competition to develop renewable energy technologies. Through a combination of text mining techniques and by analyzing a corpus of patents covering six renewable energy technologies from 1970 to 2019, we reveal the lifecycle of these technologies, the original sources of these technologies, their potential markets, and the different supply and demand patterns of the major countries/regions involved. The results show that solar and wind energy are currently the most widely used renewable energy sources, yet biomass is the only energy source still in its growing stage. China's patents have seen a rapid upward trend, spurred on by strong policy promotion, which runs counter to the decrease in patent applications seen in most other major countries in this area. However, China's technology imports still far outweigh its exports, indicating that there is still a sizeable gap between China and some of the more developed countries like the United States, Germany, France, and the United Kingdom in terms of high-quality, high-value patents. Accordingly, China, the United States, and Japan are currently the leading in the world's renewable energy markets. That said, the market potential of Europe, Canada, and Australia should not be underestimated. These and more themes are discussed from a political, economic, environmental, and global governance perspective. This paper provides useful insights into the global renewable energy technology landscape, particularly the competitive dynamics at play. The aim is to guide policy makers or managers on the next steps in our progress toward applying and using renewable energy at scale. [ABSTRACT FROM AUTHOR]

Directive 2009/28/EC of the European Union (EU) defines a target of production from renewable energy (RE) for all the EU-28 countries to be achieved by the end of 2020. Spain accepted a target of at least 20% of gross final consumption of energy (GFCoE) from renewable energy sources (RES), which is not expected to be reached. This is because, on the one hand no new RE plants have been commissioned in Spain since 2012 and, on the other, the RE auctions launched by the government in the last two years are not sufficient to cover the lack of installed capacity to reach this goal. The aim of this paper is to analyse the role of wind energy (WE) in the production of RE to meet the 2020 target in Spain. This research presents a model to assess the combination of the repowering of current wind farms (WFs) with the commissioning of new ones, and with the use of other RES like hydro, solar and solid biofuels, proposing optimal ways to develop WE in Spain until 2020. Results show the most suitable combinations of repowered and new WFs according to the different scenarios expected. The findings of this study reveal that, in the most conservative forecast, a minimum repowering level of 46% in combination with new WE installed capacity would be required to reach the target. Finally, a sensitivity analysis provides an assessment of the scenarios including the evolution of other RES, giving the resultant energy mix in accordance with the repowering level. [ABSTRACT FROM AUTHOR]

With the rapid development of renewable energy, the proportion of wind power in power supply has grown significantly. Also, it influences the power price fluctuation. Since the marginal cost of wind power is almost zero, the growth of wind power's penetration rate has led to a crowding-out effect and challenged traditional power generation technology, substantially reducing the power price in the market. In perfect competition market, the real-time balance between supply and demand of power is regulated by the real-time power price fluctuation. So this paper models the intermittence of wind power as uncertain supply, and develops a capacity-investment model under the uncertainties of both supply and demand. We introduce priority dispatch elasticity to describe the crowding-out effect and take the power price to be endogenous in order to model the situation of high penetration rate. We compare the optimal wind power capacity decision under the strategy of priority grid connection and abandoned power management. Our results show that optimal capacity investment is inversely related to priority dispatch elasticity under both strategies. However, the profit from power generation is always higher when adopting the abandoned power management strategy. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Unsustainable and uncontrolled development of urbanization, as well as the expansion and increase of environmental challenges, has caused extensive activities in recent years to reduce environmental pollution and efficient use of energy resources, worldwide. One of the appropriate solutions to reduce carbon dioxide (CO 2) emission and the optimal use of energy sources is the construction of an industrial energy park (IEP). Given the pivotal role of energy parks in different energy networks, accurate techno-economic-environmental assessment of energy parks, along with the promoted energy conversion facilities and flexibility options has become a challenging task. To address this open issue, this paper focuses on the coordinated operation of IEPs with a variety of thermal, electrical, and cooling loads to satisfy economic and environmental benefits. The proposed combined cooling, heat, and power (CCHP)-based energy park is equipped with multiple energy conversion facilities, such as absorption and electric chiller, power-to-heat facility, multi-energy storage, as well as demand response (DR) program as the flexibility option to supply energy demands, while has energy exchanging (power and heat exchanged) with the corresponding markets. The main objectives of this paper are to minimize the total operation cost of the energy park and decrease CO 2 emission rates. The coordinated model is exposed to the fluctuation of wind power, electrical and heat loads, and power prices, so the hybrid stochastic/information gap decision theory (IGDT)-based robust approach is used to handle them. The technical implications of using the proposed strategy are: (1) Creating a robust platform for industrial park operators to actively participate in different energy markets and take advantage of existing economic opportunities to meet the required energy demands at the lowest cost; and (2) Encouraging industrial park operators to participate in DR programs to reduce operating costs, increase energy efficiency, and contribute to the development of energy network plans. The proposed stochastic-IGDT multi-objective model is examined on the sample park and numerical results are discussed for different cases. Results reveal that under the proposed coordinated approach, total operation cost and emission pollution are decreased up to 3.1%, and 2.2%, respectively. • Evaluating the operation of the CCHP-based IEP under coordinated strategy • Providing a hybrid IGDT/stochastic approach for operation of the IEP • Developing economic-environmental scheduling for IEP under hybrid approach • Integrating multi-carrier storage, DR, boiler, CHP, chiller, P2H, and RES in IEP [ABSTRACT FROM AUTHOR]

Renewable energy is one of the fastest growing segments of energy consumption and wind energy is one of the most widely used sources of renewable energy. There is, however, much less known about the main drivers of wind energy consumption at the country level. This paper uses the logarithmic mean Divisia index (LMDI) method to study the driving factors in wind energy consumption for a group of 17 countries (Australia, Canada, China, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom, and the United States) that are major consumers of wind energy. The renewable energy share component has the largest impact on wind energy consumption. Improvements in energy intensity are the largest driver of reductions in wind energy consumption. Wind energy consumption forecasts for each country in the business as usual (BAU) scenario for the years 2018–2025 show that compound annual growth rates (CAGRs) for wind energy consumption are highest for Canada, Sweden, China, and Germany. Countries that have high shares of renewable energy like Spain, Portugal, and Denmark have low forecast values of CAGRs. Wind energy forecasts are also calculated for a high growth rate scenario and a low growth rate scenario. Future increases in wind energy consumption are going to depend upon the continued increase in renewable energy share which in turn is affected by energy policy designed to promote fuel switching from fossil fuels to renewables. [ABSTRACT FROM AUTHOR]

In this paper, deterministic and probabilistic optimal placement and sizing of wind turbines (WTs) in distribution networks are investigated with two objectives: reducing loss and improving voltage profile and stability index. A new improved meta-heuristic method, named β -chaotic sequence spotted hyena optimizer, is proposed to determine the optimal size and location of wind turbines. The proposed method is implemented on IEEE 33-bus and 69-bus radial distribution networks. The performance of the method is verified with finding the optimal location and size of WTs, which exhibited lower power loss and better minimum voltage and voltage stability index with more convergence speed in comparison with the conventional spotted hyena optimizer and particle swarm optimization, and previous studies. Additionally, the probabilistic placement and sizing of WTs are implemented considering the uncertainty of wind generation and the network demand based on Monte Carlo simulation. The results showed that losses increased, the voltage profile weakened, and the voltage stability index was reduced, compared with the deterministic method. For 33 bus network, the loss, minimum voltage, and voltage stability index in two WTs application are recorded as 28.79 kW, 0.9811, and 31.12 p. u, respectively, using the deterministic method, while the values of 34.56 kW, 0.9789, and 30.72 p. u are recorded using the probabilistic method. On the other hand, for 69 bus network, these values are recorded as 18.60 kW, 0.9833, and 67.13 p. u using the deterministic method, and 29.36 kW, 0.9794, and 65.62 p. u using the probabilistic method. Therefore, the resultsclearly show that the probabilistic method is more realistic and accurate than the deterministic method due to consideration of load and wind generation intrinsic changes with all possible probabilities, based on which the network operators can have a more accurate view of the impact of renewable resources on the network characteristics, hence making better decisions to improve them. • Deterministic and probabilistic optimal placement and sizing of wind turbines. • A new improved meta-heuristic method, named β -chaotic sequence spotted hyena optimizer. • Due to the consideration of the load, the proposed method is more realistic and accurate. • Better minimum voltage and voltage stability index with more convergence speed. [ABSTRACT FROM AUTHOR]

Nowadays, the high penetration of renewable energy sources (RESs) with non-uniformly distributed patterns has created unprecedented challenges for regional power systems to maintain system flexibility and reliability. These technical challenges obligate power system operators to curtail part of the produced renewable energy at various scheduling intervals. Motivated by these challenges, grid-connected energy hubs are seen as a way forward to boost system flexibility, decrease the rate of renewable power curtailment, and increase energy efficiency. However, simplified models may significantly affect the performance of the grid-connected energy hubs in practice. Hence, this paper proposes a holistic structure to determine the optimal coordinated operation of the grid-connected energy hubs and the regional power system by relying on the high penetration of wind power. In this regard, various fundamental challenges that have not yet been addressed in an integrated manner, including the C O 2 emission rate and the amount of curtailed renewable energy along with total operating costs of the integrated energy system, are among the main objectives of the optimization problem. The proposed structure is developed in the form of tractable mixed-integer nonlinear programming (MINLP) problem to handle the day-ahead security-constrained unit commitment (SCUC). The information-gap decision theory (IGDT)-based robust model is used for accurate modeling of wind power uncertainty. The characteristics of the proposed IGDT-based robust SCUC model and its benefits are investigated through several technical case studies conducted on the modified 6-bus and 24-bus test systems. The simulation results validate the effectiveness and feasibility of the proposed structure. According to the obtained results for the 6-bus test system, networked energy hubs can help the system operator to reduce the total operating cost, wind power curtailment cost, and C O 2 emission cost by 16.62%, 100%, and 30.44%, respectively, through utilizing up-to-date energy conversion facilities and energy storage systems as well as managing energy demands. It can be seen that the proposed strategy is a very effective step towards achieving a 100% renewable energy system. [ABSTRACT FROM AUTHOR]

Increasing the efficiency of an electric power system with cleaner productions is important for the fastest growing power industries. In this paper, hydro and wind renewable energy sources are considered to determine the optimal coordinated generation mode with high energy efficiency. In the proposed approach, the power efficiency is studied based on the quantification of economic benefits in the generation process. The economic benefit evaluating method is calculated by considering necessary items such as the regular operational cost, hydro regulation cost, power loss cost, plant investment and generation profit. From the operator's viewpoint, the mathematical expression of the hydro regulation cost is innovatively presented in the proposed method. To achieve the efficiency analysis, a new hydro-wind hybrid model is established using MATLAB/Simulink, which overcomes the previous limitation on data dependencies. Three real operating challenges, including the fluctuation of wind speed, the price volatility in market, and the influence of wind power permeation, are considered to reflect the relationship between the uncertain operating condition and optimal coordinated generation mode. Simulation results highlight the impact of real operating challenges on the hydro regulation cost, generation benefit and energy efficiency in the hybrid power system. The proposed model and method will result in a system with high efficiency and improved financial condition. Meanwhile, it will also benefit the policy development of renewable energy-based power industries. Image 1 • A novel simulation model of the hydro-wind power system is established. • Energy efficiency is studied from the perspective of power generation benefits. • A method for quantifying economic benefits from the power generation is presented. • Benefits optimized by the wind power penetration help reduce power wastage. • Benefits considering price volatility of power industries help policy development. [ABSTRACT FROM AUTHOR]