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Studying the influence of the demand response and dynamic characteristics of the battery energy storage on the configuration and optimal operation of battery energy storage system (BESS) in the Wind-Photovoltaic (PV)-Energy Storage (ES) hybrid microgrid. A demand response model that is based on electricity price elasticity is established based on the time-of-use price. Take the capital-operating cost and direct economic benefit of the BESS and the loss of abandoned photovoltaic and wind power as the optimization objective, an optimal configuration method that considers the dynamic characteristics of the BESS and the maximum absorption of photovoltaic and wind power is proposed while using particle swarm optimization to solve. The results show that the configuration results considering the demand side response of the microgrid BESS can obtain better economy and reduce the storage capacity requirement, and the result shows that the efficiency of BESS relates to the load of the system, the distributed generation (DG) characteristics, and the dynamic characteristics of BESS. Meanwhile, the capacity and power of the energy storage configuration increase as the DG permeability increases due to the reverse load characteristic of the wind power.

Abstract Heat pipes have been extensively applied in the domain of energy conversion and heat recovery application. A novel radial heat pipe with internally finned condenser has been proposed to recover waste heat from air conditioning units. Full mathematical description and computational fluid dynamics model were developed to predict the loop flow and thermal performance in a single radial heat pipe, respectively. The research focuses mainly on the effects of input power, filling ratio, pipe diameter, and velocity of the cooling water on the thermal performance and entropy generation rate of this novel heat pipe. Numerical results illustrate that average reductions in total thermal resistance with utilization of fins approximately achieved 8.69% and 14.78% for fins nf = 4 and nf = 8, respectively. Similarly, entropy generation rate shows an average decrease of 19.4% and 37.5% for identical operations. Additionally, the effect of internally finned condenser on the multiphase fluid flow has been comprehensively analyzed, including the break of the condensed film, generation of bubbles and nucleation boiling. The results further indicate that incorporation of internal fins has a significant influence on enhancing thermal homogenization and waste thermal recovery efficiency of a radial heat pipe. Case results agreed well with experimental data within average deviation being no more than 10%.

With the development of new energy, large-scale wind power grid and photovoltaic bring challenges to the reliable operation of the system. Based on the actual situation, this paper introduces LOLP for evaluating the reliability of the system, analyzes the uncertain factors of the system after the new energy grid connected to the network, and models the uncertainties of the unit fault outage, the load forecast deviation, the wind power forecast deviation and the forecast photovoltaic deviation respectively. Based on considering the uncertainty factors, the optimization model of spinning reserve capacity considering the access of new energy is established. Taking the IEEE 39-bus standard test system as an example, the results show the reliability and economy of the proposed model, effectively optimize the allocation of spinning reserve capacity after new energy is connected to the network, and provide technical guidance for optimal scheduling of spinning reserve capacity.

In this paper we seek to understand the impact of expanded use of soybean oil biodiesel to address biofuel mandates on global vegetable oil markets, and in particular on the demand for palm oil. An open-economy equilibrium model is derived to investigate the market effects of biodiesel expansion on related energy and vegetable oil markets. The model is calibrated to represent the recent benchmark data in calendar year 2011. The simulation estimates suggest that the expanded use of soy oil for biodiesel in the US will have considerable impacts on world vegetable oils markets. The majority of the vegetable oil replacement is likely to occur through substitution of palm oil under a wide range of plausible elasticity values on the demand for vegetable oil and the demand substitution between soy oils and palm oils.

The energy resource is an essential input of economic growth, which has an important impact on the ecological environment and social welfare. From the perspective of social welfare, considering the radial and non-radial characteristics of different input and output indicators, and the inseparability of the energy input and undesirable output, this study employs the non-separable hybrid DEA (Data Envelopment Analysis) model to evaluate the total energy efficiency of Chinese provinces between 2012 and 2016. Furthermore, this study calculates the energy saving and emission reduction potentials of China. The results reveal that the average total factor energy efficiency in China from 2012 to 2016 is 0.694, which means that there are still 30.6% energy efficiency losses. There is great potential for China to save energy, reduce pollutant emissions, and increase the output of social welfare. There are great differences in the total factor energy efficiency among provinces. The average energy saving potential of the whole country is 60.5%. If the energy efficiency of all provinces can reach the frontier, the whole country can save more than half of the energy consumption. The highest national average emission reduction potential is SO2, followed by dust, CO2, and NOX. The implication of the conclusion is that in the development of regional economy, we cannot sacrifice the social welfare and sustainable development and take the growth rate of GDP as the only objective. Different energy saving and emission reduction policies should be put forward according to the characteristics of different provinces.

Abstract The assessment of the time and frequency connectedness between cryptocurrencies and renewable energy stock markets is of key interest for portfolio diversification. In this paper, we utilize weekly data from 07 August 2015 to 26 March 2021 to document the dynamics and portfolio diversification from a fresh cryptocurrencies-renewable energy perspective. Our time-frequency domain spillovers results reveal that renewable energy stocks are the main spillover contributors in the connectedness system and the short-run spillovers dominate their long-run counterparts. Furthermore, investors can gain more profits through short-run transactions in our portfolio design and we can optimize portfolios by investing a large portion in cryptocurrencies. A fascinating fact is that the COVID-19 pandemic can reverse the effectiveness of our hedging strategy.

Abstract In this work, we examine the use of oil filled tubular optical concentrators coupled with a model organic bulk heterojunction photovoltaic: poly-3-hexathiophene-[6,6]-phenyl-C61-butyric-acid methyl-ester (P3HT:PCBM) to create a photovoltaic–photothermal hybrid solar collector. The organic photovoltaic cells were fabricated onto one half of a tubular light pipe and then silicone oil was flowed inside the pipe. This allows solar energy in the visible wavelengths to be effectively converted into electricity by photocell while simultaneously; the silicone oil captures the infrared radiation (IR) part of the spectrum as heat energy. The VIS–IR power conversion efficiency for this model organic system, under normally incident AM1.5G illumination was found to be: PCE ∼ 28%, which is combined by the photovoltaic efficiency (PCE ∼ 2%) and the photothermal efficiency (PCE ∼ 26%). We further show that the oil filled tube, acts as a passive optical element that concentrates the light onto the photovoltaic and thereby increases its overall efficiency but also the range of incident angles in which the light is efficiently captured.

Abstract As the increasing penetration of distributed renewable energy sources, the inherent fluctuation of their output power aggravates more control burden for the power system. Based on the direct load control, the regulation potential of distributed flexible loads can be developed to relieve this pressure. This paper proposes a real-time two-stage optimization model for multiple thermostatically controlled load groups to smooth the power fluctuation in distribution network. The upper-level evaluates the load regulation capacity and suppresses the net exchange power fluctuation; the lower-level further minimizes the regulation costs and makes the target demand curves for each load group, which can guide the subsequent load power tracking. Besides, within the load group, a proportion aggregation method is applied to reflect its external power regulation characteristics, while the decomposition program assigns the regulation task to each terminal user. For the complex problem established above, a hierarchical and iterative solving strategy is proposed to obtain the approximate optimal solution. The simulation results show that the proposed approach can effectively decrease the net exchange power fluctuation as well as regulation costs.

With the development of renewable energy technology and communication technology in recent years, many residents now utilize renewable energy devices in their residences with energy storage systems. We have full confidence in the promising prospects of sharing idle energy with others in a community. However, it is a great challenge to share residents’ energy with others in a community to minimize the total cost of all residents. In this paper, we study the problem of energy management and task scheduling for a community with renewable energy and residential cogeneration, such as residential combined heat and power system (resCHP) to pay the least electricity bill. We take elastic and inelastic load demands into account which are delay intolerant and delay tolerant tasks in the community. The minimum cost problem of a non-cooperative community is extracted into a random non-convex optimization problem with some physical constraints. Our objective is to minimize the time-average cost for each resident in the community, including the cost of the external grid and natural gas. The Lyapunov optimization theory and a primal-dual gradient method are adopted to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on State-action-reward-state-action (Sarsa) Algorithm, in the condition that each residence in the community is able to communicate with its neighbors by a central controller. Finally, extensive simulations are presented to validate the proposed algorithms by using practical data.