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A promising alternative to either chemical or sensible heat energy storage systems is the latent heat thermal energy storage (LHTES) due to its superiority in many design aspects such as the high storage capacity as well as chemical stability and relatively low cost. Due to its potentials to overcome the problems of instability and intermittency of energy through using phase change material (PCM), latent heat energy storage has been used in a variety of practical applications. However, most of the phase change materials possess poor thermal conductivity resulting in modest charging/discharging rate. To overcome this deficit, high porosity metal foam is used to improve the overall thermal conductivity of the phase change materials leading to enhancing the heat transported, and hence, promoting the PCM melting and solidification.This proposal has been utilised to improve the performance of a latent heat thermal storage system having a parallel/staggered tube-bundle structure filled with paraffin as a PCM, where an open-cell copper foam is compounded to the paraffin wax. The PCM unit is charged/discharged using a relatively hot/cold water stream flowing across the tube-bundle units. The feasibility of such a configuration is examined numerically through simulating the proposed PCM-metal foam composite units and their surrounding shell computationally. The ANSYS Fluent CFD commercial code has been employed to solve the volume averaged Navier-Stokes equations taking into account the local thermal non-equilibrium expected to occur between the solid metal foam matrix and the paraffin phase-change material. The impact of some design and operating parameters on the charging/discharging performance has been tested including the water flow strength as well as the tube-bundle configuration. The currently proposed design of LHTES system has been found not only easy to configure but practically efficient as well, where the charging/discharging rate can be remarkably boosted through wise selection of design parameters.

Energy performance contracting is increasingly employed to promote energy conservation toward sustainable development. This study characterizes these services in China through an analysis from 2011 to 2020 that reveals imbalances in the energy services company (ESCO) sector with respect to trade route, customer base, and energy conservation technology. More specifically, analyses of inter- and intra-province trade networks show that Beijing was the lead provider of ESCO projects, whereas Hebei was the lead customer. Jiangsu, Liaoning, and Inner Mongolia have risen to be new significant players during the 13th Five Year Plan (FYP) period (2016–2020). Although a wide range of energy conservation measures were deployed, ESCO projects were most prevalent in the industrial sector, with particular emphasis on residual heat, pressure and gas recovery. However, the building sector become an increased focus for ESCO projects during the 13th FYP. We further find that national guidelines alone may not lead to significant advances in energy conservation without additional and complementary provincial support. Such heterogeneity, especially at the provincial level, may help explain observed imbalances in ESCO projects. While prior studies usually focus on national guidelines, the more general implication and call of our study is that greater attention should be placed on subnational (provincial) guidelines around energy conservation.

Centralized routing is beneficial as it provides a global network view to plan and achieve the best and effective path for real-time traffic. With the rapid development of software defined networking, centralized routing has aroused more and more concern of researchers. An effective centralized load balancing routing (LBR) for wireless mesh network considering energy harvesting is proposed in this letter. LBR can provide flexible and optimal routes for real-time traffic. To minimize the long-term weighted sum cost which includes load and energy condition, LBR considers the queue length, channel condition, energy cost and energy harvesting. The problem of making dynamic decisions based on real-time traffic conditions is solved by the dynamic programming approach. Simulation results demonstrate the advantages of LBR.

As land use leaves massive tracts of land vacant for recovery, restoration must undergo a substantial shift to incorporate a complexity perspective beyond the traditional community, biodiversity or functional views. With an interaction-function perspective, we may be able to achieve ecosystems with better chances to adapt to current environmental changes and, especially, to climate change. We explore combined approaches that include still unused and underexplored techniques that will soon go mainstream and produce massive amounts of information to address the complexity gap. As we understand how complexity reassembles after the end of agriculture, we will be able to design actions to restore or enhance it at unprecedented spatial scales while increasing its adaptability to environmental changes.

This paper investigates the power loss minimization problem of solar DC nanogrids that are designed to provide energy access to households in off-grid areas. We consider nano-grids with distributed battery storage energy systems and that are enabled by multi-port DC-DC converters. As the nano-grids are not connected to the national grid and have batteries and converters distributed in each household, addressing the power loss problem while ensuring supply-demand balance is a challenge. To address the challenge, we propose a novel quasi-consensus based distributed control approach. The proposed approach consists of two algorithms namely, incremental loss consensus algorithm and voltage consensus algorithm. The incremental loss consensus algorithm is proposed to optimally schedule the battery charge/discharge operation while ensuring that supply-demand balance and the battery constraints are satisfied. The voltage consensus algorithm is proposed to determine optimal distribution voltage set points which act as optimal control signals. Both algorithms are implemented in a distributed manner, where minimal information exchange between households is required to obtain the optimal control actions. Simulation results of a solar DC nano-grid with five interconnected households verify the effectiveness of the proposed approach at addressing the nano-grid power loss problem.

The file contains all 162 Intended Nationally Determined Contributions submitted to the UNFCCC, up to and including the 1st May 2016. These have been analysed and coded according to a range of 'concepts' identified by the coders.

Cryptocurrencies have gained a lot of attention in recent years, mostly due to their decentralized manner of operation and their growth in value. However, a major drawback most of them possess is their high energy consumption. Current solutions to this problem have significant l imitations: bringing back centralization and/or substituting the required energy with, e. g., storage space. This paper aims to address the problem by investigating the use of a two-level deep reinforcement learning (RL) model to design incentive policies for green mining in cryptocurrencies. This is done by modeling one such energy-intensive cryptocurrency system and creating an RL environment. Finally, by running simulations in an RL environment, we develop and test incentive policies, according to which cryptocurrency participants who primarily use renewable energy for their mining operations are more likely to add new blocks to the blockchain. Our results show that even when the green score of each crypto miner (determined by their use of green energy sources) has relatively small importance (up to 0.3) in their selection probability, miners still shift towards green mining in order to increase their chance of being picked to validate cryptocurrency transactions and receive the corresponding rewards.