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Cities are progressively heightening their climate aspirations to curtail urban carbon emis- sions and establish a future where economies and communities can flourish within the Earth’s eco- logical limits. Consequently, numerous climate initiatives are being launched to control urban car- bon emissions, targeting various sectors, including transport, residential, agricultural, and energy. However, recent scientific literature underscores the disproportionate distribution of climate poli- cies. While cities in the Global North have witnessed several initiatives to combat climate change, cities in the Global South remain uncovered and highly vulnerable to climate hazards. To address this disparity, we employed the Balanced Iterative Reducing and Clustering using the Hierarchies (BRICH) algorithm to cluster cities from diverse geographical areas that exhibit comparable socio- economic profiles. This clustering strives to foster enhanced cooperation and collaboration among cities globally, with the goal of addressing climate change in a comprehensive manner. In summary, we identified similarities, pa􏰀erns, and clusters among peer cities, enabling mutual and generaliza- ble learning among worldwide peer-cities regarding urban climate policy exchange. This exchange occurs through three approaches: (i) inner-mutual learning, (ii) cross-mutual learning, and (iii) outer-mutual learning. Our findings mark a pivotal stride towards a􏰀aining worldwide climate ob- jectives through a shared responsibility approach. Furthermore, they provide preliminary insights into the implementation of “urban climate policy exchange” among peer cities on a global scale.

Materials:Rice straw, pine sawdust and Phoenix Tree's leaf were selected as the main biomass of this study. Algorithms and methods:Coats-Redfern integral method,Doyle method,Distribution Activation Energy Model (DAEM): The database contains all the original data, intermediate data and final results used in the paper. Fig. 1 was schematic diagram of WRT-3P high temperature TGA and gas flow routes Fig. 2 was influence of particle size on biomass pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 3 was influence of heating rate on different biomass (rice straw, pine sawdust and Phoenix Tree's leaf) pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 4 was potassium concentration of initial and soaked rice straw Fig. 5 was influence of K+ on rice straw pyrolysis kinetics (a) TG curves (b) DTG curves Fig. 6 was the relationship between and 1/T of three kinds of biomass with a particle size of 0.150 - 0.180 mm at different heating rates. (a) 5℃/min (b) 10℃/min (c) 20℃/min (d) 40℃/min Fig. 7 was the apparent activation energy of biomass pyrolysis obtained by DAEM.

The data is solar incident radiation on building skins ( four facades and roof) of 4 cities together with the analysis for a whole year.

Abstract The equation for the temperature distribution in a uranium rod under transient conditions is subjected to the finite Hankel transform, to produce a formal relationship between the physical parameters of rate of heat production, rate of heat removal and the temperature at any point in the rod. The form of solution is then applied to find the changes in uranium surface and centre temperatures following arbitrary changes in the rates of heat production and heat removal. The method is an inverse one in that arbitrary forms are assumed for a function φ(t) which is introduced into the equations, and the resulting variations of uranium temperature and rates of heat production and removal are examined. Within this limitation, however, the temperature and reactivity changes within the reactor can be determined for a short period of time following a disturbance, during which the relatively small thermal capacity of the uranium makes it the dominating variable.

In order to achieve paramount economy, hybrid renewable energy sources are gaining importance, as renewable sources are costless. Over the past few years wind energy incorporation drew more consideration in the electricity market, as wind power took an affirmative role in energy saving as well as sinking emission pollutants. Recently developed Grey wolf optimizer (GWO) algorithm has conspicuous behavior for verdicting global optima, without getting ensnared in premature convergence. In the proposed research the exploitation phase of the grey wolf optimizer has been further improved using random exploratory search algorithm, which uses perturbed solutions vectors along with previously generated solution vectors. The paper presents a hybrid version of Grey Wolf Optimizer algorithm combined with random exploratory search algorithm (hGWO-RES) for the solution of combinatorial scheduling and dispatch problem of electric power systems. To validate the feasibility of the algorithm, the proposed algorithm has been tested on 23 benchmark problems. To verify the feasibility and efficacy of operation of proposed algorithm on generation scheduling and dispatch of electric power systems, small and medium scale power systems consisting of 7-, 10-, 19-, 20- and 40-generating units systems taken into consideration. Commitment and scheduling pattern has been evaluated with and without wind integration and it has been experimentally founded that proposed hybrid algorithm provides superior solution as compared to other recently reported meta-heuristics search algorithms.

Abstract Energy storage systems (ESS) are widely used in active distribution networks (ADN) to smoothen the drastic fluctuation of renewable energy sources (RES). In order to enhance the scalability and flexibility of ESS, a virtual energy storage system (VESS), which is composed of battery energy storage system (BESS), RES as well as flexible loads (FL), is developed in this paper to realize the functionalities of ESS in more cost-effective way in ADN. Aiming at achieving voltage regulation, dynamic pricing strategies based on system voltage condition are designed for VESS. A distributed real-time power management model containing dynamic pricing strategies is proposed to accomplish the voltage regulation and economic power sharing in VESS. Moreover, a set of distributed algorithms, over time-varying unbalanced directed networks, are designed for dynamic pricing strategies and optimal power management model. Furthermore, the convergence property, optimality and system voltage stability are explained by detailed mathematical analysis. Three various case studies which were ran on a real time digital simulator (OPAL-RT OP5600) were designed to validate the effectiveness of the strategy. Finally, simulation results show that the economic power dispatch and voltage regulation are achieved among VESS simultaneously, even in the presence of time-varying directed and unbalanced communication networks.

In electric power generation and distribution systems, three structures are capable of impacting energy quality: generation, distribution and loads. Currently, the diversity of the latter ranges from purely resistive loads such as heating systems to extremely nonlinear loads such as switched electronic power supplies. This has dramatically affected the quality of electric energy. The operation of these loads contributes to the reduction of the power factor of the installation, the injection of harmonic currents in the electrical network and the imbalance of currents and voltages between the phases in the case of three-phase systems. The implementation of a method capable of recognizing electrical loads and identifying them through algorithms can be used to manage the electric energy consumption, contributing a lot to the improvement of the quality of electric energy. In order to deal with uncertainty in the identification of these loads, probability distributions will be applied, which are able to handle uncertainty through a probabilistic modeling using Bayesian Networks.

Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

Torrefaction is considered as a method for producing biofuels with improved characteristics compared to those of the “raw” biomass (higher calorific value, moisture resistance, better grindability). The torrefaction process is an endothermic process that is usually carried out in a gaseous atmosphere in the absence of oxygen. To reduce the required heat input, it is proposed to employ the oxidative torrefaction and conduct the process in a fluidized bed agitated with flue gases containing less than 6% oxygen. Preliminary studies of the oxidative torrefaction of sunflower husks, including thermogravimetric analysis of the treated material, have shown that the heat treatment time for the biomass should be at least 5 min. A fluidized bed is a reactor with ideal mixing of the treated material where uniform treatment of raw material particles cannot generally be attained. To overcome this disadvantage of the fluidization technique and achieve the required residence time for biomass in a fluidized bed during a continuous torrefaction process, it was proposed to equip a torrefaction reactor with a series of vertical baffles spaced at 50 mm. These baffles induce a loop-like flow of the processed biomass from the inlet to the outlet of the reactor. To investigate the residence time for husk particles in the reactor, a tracer, which was colored to husk particles' color with a water-soluble dye which did not change the weight and size of the particles, was injected into the bed of uncolored particles. Tracer samples were taken every 30 s at the outlet of the reactor and were analyzed using a special procedure to determine the fraction of colored particles in each sample. This enabled us to gauge the time during which the colored particles injected into the fluidized bed reached the point of their discharge from the bed. Studies performed in a “cold” model of the reactor showed that a series of vertical baffles in the bed can provide the required residence time for biomass in a reactor including commercial reactors. Plates can provide the necessary biomass residence time in the reactor.