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Highlights • A DR of power credit model was established. • Virtual RTP was introduced based on power credit scheme. • Considering consumer comfort while optimizing cost. • Cost & uncertainty analysis was conducted to prove rationality of the model. Abstract In view of real-time pricing implementation difficulties and certain deficiencies of the time-of-use price in China, a novel virtual real-time electricity pricing scheme is proposed based on power credit incentive program in this paper, which can help to achieve better results for peak load shifting than the conventional time-of-use price-based scheme. In addition, it can also work as virtual real-time pricing mechanism. The aim of this study from the perspective of load scheduling of residential appliance device, focuses on the optimization of cost and willingness to change of the user, and further examining the feasibility and effectiveness of electricity pricing mechanism in detail. Finally, the application prospect of the electricity pricing mechanism in demand response is evaluated and the performance of the model in our study established with illustration from our simulation results. [ABSTRACT FROM AUTHOR]

Many current studies on smart grid in electricity market are indicative of the key role it plays in electricity generation, distribution, retailing and end-user management. Demand response programs (DRPs) can be used to lower high energy prices in wholesale electricity markets, and ensure the security of power systems when at risk. The concern of most researchers in this field is to further unearth the potential of smart grid in the direction of demand response (DR) through enhanced demand side management (DSM) centering on the behavior of the end-user. Our model proposes a more effective way in using incentive based demand response program to help residential customers derive more benefits from smart grid. The constrained non-linear programming (CNLP) model optimizes residential consumption of electricity by shaving of load at peak and increasing of load at off-peak to help generators reduce production cost at peak times and increase revenue at of off-peaks. The model uses a credit function to regulate consumption and reward end-users for load shedding and load shifting at peaks and also at off-peaks reward end-users for increasing load. The simulated results show that, high consumption appliances are best used at dawn, midday, and at night, if consumers want to cut down cost. The corresponding effect is that, generating and distribution companies derive the right revenue from their investments by not producing beyond their capacity during peaks at high cost and maintain constant power supply in and environmentally friendly manner. [ABSTRACT FROM AUTHOR]

Electricity power is a basic industrial component which plays an important role in the economy of a nation. In this paper, the correlations evolution of electricity prices among 50 states and the District of Columbia are studied based on random matrix theory (RMT) Four regime shifts are identified from January 1990 to August 2014 in the U.S. residential, commercial and industrial electricity markets. Then, the genetic algorithm (GA) is applied to analyze the clusters of evolution. The results show that, the correlations of electricity prices increased continually in the three departments. However, it decreased in 2012 which further confirms its sensitivity to fuel market. Besides, four regime shifts exist in the three departments though the different times of occurrence caused by price level. And, the fluctuation of community evolution is consistent with four regime shifts. The final part is a summary of the research analyzed and results. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

From June 2014 to January 2015, the slumped price of crude oil mainly be caused by the increasing shale gas in the U.S. The market of crude oil was altered by the variation of natural gas trade patterns. It implies that the international fossil energy trade is a multilayer structure, and each layer is a complex system with numerous countries and complicated relations. In this paper, we build the international fossil energy trade multilayer network (ETMN), and study the evolutionary characteristics of networks during 2002–2013. The generalization of several important indicators, including degree distribution, community, stability of communities and the time-varying evolution of main countries’ importance were discussed. Our conclusions suggest that: Firstly, the ETMN and three energy-specific networks including coal, oil, and natural gas display the scale-free characteristic in un-weighted and weighted networks. However, it shows that even if a few countries have major trading partners, there are not always a few countries that play critical roles in trade intensity. Secondly, the natural gas network has the largest number of communities and stability compared with networks of coal and oil, and the volatility of stability lagged one year than the other two networks for the specific form of pipeline transportation. Thirdly, studying on the stability of networks shows that geopolitical environment is the most important influenced factor, but the status of renewable and new energy increases with its development obviously. Fourthly, the evolutionary characteristics of three major countries’ importance, including United States, China and Japan, are analyzed in detail. At last, some policy suggestions were pointed out according to the results. [ABSTRACT FROM AUTHOR]

• A demand response of evaluation model is established. • Interactive real-time pricing mechanism based on an evaluation model is introduced. • The optimal operation scheme of appliances is optimized. Electricity price mechanism is an important means to implement demand response, especially in the home energy management system. A reasonable pricing mechanism therefore can stimulate the enthusiasm of residential users and as well balance power supply and demand effectively. From the perspective of residential users, this paper establishes a residential user evaluation system based on an evaluation model by selecting indicators related to user characteristics and electricity consumption data, and as well proposes a new interactive real-time pricing mechanism. A constrained multi-objective optimization model is then constructed, and the optimal operation scheme of each appliance is optimized. Numerical simulation and case studies show that, the optimization model can accurately schedule operations of household appliances. Besides, under the action of interactive real-time pricing, the electricity load fluctuation rate and electricity cost are significantly reduced compared with other comparative cases. The results confirm that, interactive real-time pricing aside reducing the cost of energy for users, can also play an active role in reducing peak loads and increase off-peak load, thereby stabilizing the load fluctuation. [ABSTRACT FROM AUTHOR]

The energy supply–demand security and climate change has continued to be problematic, making it significant and necessary to investigate embodied energy flow, particularly in a large and fast-growing developing country like China. One of the effective approaches is the energy/emissions embodied in bilateral trade (EEBT) aiming to locate the destination of energy bi-directionally to evaluate how energy flow between producer and consumer sectors. However, in addition to the flow of energy and resources, the topological structure and impact of underlying components from a system science perspective are equally important for policy-making. This study therefore constructs an energy embodied in trade network (EETN) model to track multi-layer primary energy flow by integrating the EEBT approach and complex network analysis. The embodied coal, oil, natural gas, and non-fossil fuels associated with China’s 30 provinces/municipalities are quantified at the provincial level. By the joint analysis of the network-oriented metrics, the EETN model elicits the possibility of understanding the heterogeneity distribution of different types of energy flow and the potential impact of province-specific policy interventions. We explain how resource endowment, economic growth, income inequality, cross-provincial industrial transfer, and infrastructures affect China’s provincial energy embodiments as well as the clustering features. Other findings and policy recommendations are also presented. [ABSTRACT FROM AUTHOR]

• A virtual real-time electricity price optimization model based on the credit mechanism is established. • Incorporate the balancing cost of electricity and the stability cost of the grid into the cost of electricity production. • A matching degree index is proposed. Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply–demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users' power load curve and distributed energy output curve. Taking a multi-microgrid system in a China's industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users' total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid. [ABSTRACT FROM AUTHOR]

This paper explores the optimization scheme of carbon trading in China based on a novel energy-saving and emission-reduction (ESER) system with carbon price constraints. With the aid of nonlinear dynamics theory, the dynamics behavior of the novel system is discussed. Genetic algorithm and back propagation neural network is used to identify the quantitative coefficients according to the statistical data of the second period in European Union (EU). Taking the actual situation in EU for instance, the variables which are sensitive to carbon trading are detailedly researched. Enlightened by the EU’s experience, an optimal road of China ’ s carbon trading is put forward. The results show that carbon emissions could be controlled by carbon trading. The investment to carbon trading hampers economic growth in the near future, and ESER technical progress is negatively correlated with carbon trading in the long run. Demand and supply relationship is closely related to carbon price, both are the important issues in carbon trading system. Excessive government control and extortionate carbon price will deliver the opposite effect and even fatal influence on carbon trading system. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the effect of energy construction adjustment on the energy-saving and emission-reduction (ESER) dynamical evolution system. Based on the nonlinear dynamics theory, the dynamic behavior of the novel system is discussed. The quantitative coefficients of the actual system are identified with the aid of genetic algorithm-back propagation neural network. Scenario analysis results show that, energy construction adjustment could effectively control energy intensity. To clarify this further, an example of 4D ESER system with new energy constraints is demonstrated. Investigation results show that, government control can effectively control energy intensity, while brings inhibiting impact on economic growth and people’s livelihood. Economic investment is the key variable affecting energy construction adjustment and ESER, the ESER system will crash when the investment is too low. Energy construction adjustment could effectively reduce energy intensity. However, the ESER system will also crash if the development of energy construction adjustment is too fast. The interesting thing is that the ESER system should be pulled back to steady state as the investment getting bigger. Energy intensity could be controlled in expected range by taking adequate measures. Full use of the role of energy structure adjustment should be made to promote the development of new energy, while government control is used only when necessary. [ABSTRACT FROM AUTHOR]

This paper attempts to explore carbon tax pilot in Yangtze River Delta (YRD) urban agglomerations based on a novel energy-saving and emission-reduction (ESER) system with carbon tax constraints, which has not yet been discussed in present literature. A novel carbon tax attractor is achieved through the discussion of the dynamic behavior of the new system. Based on the genetic algorithm-back propagation neural network, the quantitative coefficients of the actual system are identified. The scenario analysis results show that, under the same tax rate and constraint conditions, the ESER system in YRD urban agglomerations is superior to the average case in China, in which the impacts on economic growth are almost the same. The former’s energy intensity is lower and the shock resistance is stronger. It is found that economic property of YRD urban agglomerations is the main cause for the ESER system of YRD urban agglomerations being superior. In the current YRD urban agglomerations’ ESER system, energy intensity cannot be adjusted to an ideal level by commercialization management and government control; however, it is under effective control of carbon tax incentives. Therefore, strengthening the economic property of YRD urban agglomerations and effective utilization of carbon tax incentives could perfectly control energy intensity, without obvious potential negative impact on economic growth. [ABSTRACT FROM AUTHOR]

Highlights: [•] Use non-linear dynamical method to model the ESER system with carbon tax constraints. [•] The energy-saving and emission-reduction attractor with carbon tax constraints is obtained. [•] The concept of turning point of energy intensity is put forward for the first time. [•] The impacts of carbon tax on energy intensity and economic growth are described vividly. [•] The best time to levy carbon tax in China is found within the framework of the four-dimensional system. [ABSTRACT FROM AUTHOR]

• The optimal effect of health welfare spillover is obtained. • The level of economic output and social welfare level are simultaneously optimal. • Reveal the evolutionary relationship among spillover effect, transfer payment, carbon tax, etc. Based on the Over Lapping Generation Model, this paper concerns the impact of environmental pollution and residents' health status on economic activities, and analyzes the relationship between tax revenue distribution proportion, economic activities and residents' social welfare. Through modeling, health welfare is introduced into welfare function, and time allocation proportion is introduced into health function and consumption budget. It is proved that the level of economic output and social welfare level can simultaneously optimal. Given the tax rate, we explore the optimal allocation ratio of carbon tax revenue when the per capita output of the economic steady-state equilibrium is optimal and the social welfare of the residents is optimal. Moreover, the evolutionary relationship among spillover effect, transfer payment, carbon tax, etc. is revealed. The stability analysis of qualitative conclusion is carried out and corresponding policies are put forward. It is proved that the optimal point of health welfare spillover effect exists. Then, under the steady-state equilibrium, in this paper the optimal effect of health welfare spillover is obtained. [ABSTRACT FROM AUTHOR]