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{"references": ["Mustafaqulov A. A., Arziqulov F. F., Jumanov A. O'zbekiston Jizzax viloyatining tog'li hududlarida muqobil energiya manbalaridan foydalanish / / Stajyor: elektron. ilmiy. journe. 2020. \u2116 41(170). Pochta manzili: https://internauka.org/journal/science/internauka/170 (murojaat sanasi: 11.02.2021).", "Akhmedovich, M. A., & Fazliddin, A. (2020). Current State of Wind Power Industry. The American Journal of Engineering and Technology, 2(09), 32-36. https://doi.org/10.37547/tajet/Volume02Issue09-05", "Narimanov B. A., Arzikulov F. F. qayta tiklanadigan energiya manbalari, iqlim o'zgarishi oqibatlarini yumshatish va barqarorlik masalalari / / Universum: texnika fanlari: elektron. ilmiy. journe. 2020. 10(79). Pochta manzili: https://7universum.com/ru/tech/archive/item/10841 (murojaat sanasi: 20.02.2021).", "Abbasi, Tasneem, and S. A. Abbasi. Renewable energy sources: Their impact on global warming and pollution. PHI Learning Pvt. Ltd., 2011.", "Edenhofer, Ottmar, et al., eds. Renewable energy sources and climate change mitigation: special report of the intergovernmental panel on climate change. Cambridge University Press, 2011.", "Energy Action Plans and Progress Reports (European Commission, Joint Research Centre, Institute for Energy and Transport via E. Fermi) // Renewable and Sustainable Energy Reviews, 2015. Vol. 51. November. P. 969-985.", "\u0410\u043b\u043b\u0430\u0435\u0432 \u041a.\u0420. \u00ab\u042d\u043d\u0435\u0440\u0433\u043e\u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u0438 \u0432\u043e\u0437\u043e\u0431\u043d\u043e\u0432\u043b\u044f\u0435\u043c\u044b\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438 \u044d\u043d\u0435\u0440\u0433\u0438\u0438\u00bb \u00ab\u041f\u0440\u043e\u0431\u043b\u0435\u043c\u044b \u044d\u043d\u0435\u0440\u0433\u043e - \u0438 \u0440\u0435\u0441\u0443\u0440\u0441\u043e\u0441\u0431\u0435\u0440\u0435\u0436\u0435\u043d\u0438\u044f\u00bb \u0421\u043f\u0435\u0446\u0432\u044b\u043f\u0443\u0441\u043a, 2011\u0433.", "T.Yu. Yunusov \u00abEnergiya ishlab chiqarishning bugungi kuni va kelajagi\u00bb \u00abFan va texnologiya\u00bb, Toshkent \u2013 2012y., 140 bet"]} The world is rapidly becoming a global village due to the growing daily energy demand of the entire population around the world, while the earth in its form cannot change. There is a growing need for energy and related services to meet human needs for social and economic development, well-being, and health. A return to renewable energy to mitigate the effects of climate change is a great approach that must be sustainable to meet the energy demand of future generations. The study proposed some measures and policy recommendations that, when considered, would help achieve the goal of using renewable energy, thus reducing emissions, mitigating the effects of climate change, and ensuring a clean environment, as well as clean energy for all and the future generations.

This article presents the issues of using solar collectors in solar heating systems and traditional heating systems.

The information presented in this work is of great practical importance. With its help, the production process of sorbents for wastewater treatment becomes easier and does not require a lot of time, thereby helping the ecological development of the Republic of Uzbekistan. The experiments carried out during this work allow us to open the prospects of using natural clay materials, thiourea and polyethyleneimine for efficient and ash treatment of waste water of industrial enterprises.

In this article, using waste paper, its chemical processing and restoration of its properties were studied. In addition, in order to expand its strength and fields of use, it was exposed to natural mineral fibers and the properties of composite materials were studied.

This zenodo archive includes the Temoa code and data used to produce the publication by Patankar et al. (2020) titled "Substitution between Energy Efficiency and Electricity in an Energy System Optimization Model." Tools for Energy Model Optimization and Analysis (Temoa) is an open source energy system model.

US energy system development consistent with the Paris Agreement will depend in part on future fuel prices and technology costs, which are highly uncertain. Energy system optimization models (ESOMs) represent a critical tool to examine clean energy futures under different assumptions. While many approaches exist to examine future sensitivity and uncertainty in such models, most assume that uncertainty is resolved prior to the model run. Policy makers, however, must take action before uncertainty is resolved. Robust optimization represents a method that explicitly considers future uncertainty within a single model run, yielding a near-term hedging strategy that is robust to uncertainty. This work focuses on extending and applying robust optimization methods to Temoa, an open source ESOM, to derive insights about low carbon pathways in the United States. Compared to the naïve scenario that ignores future uncertainty, expected savings associated with the robust strategy are 12% and the standard deviation in expected costs is 8% lower in the robust solution. The robust technology deployment strategy entails more diversified technology mixes across all of the energy sectors modeled. For details on how to run the model, please visit the project website: http://temoacloud.com

This zenodo archive includes the Temoa code and data used to produce Energy Storage Options for North Carolina. Temoa was used to evaluate the use of storage for bulk energy time shifting and peak capacity deferral, as described in Section 6.5. The data files are split into the capacity expansion and operational versions of the data. To begin, the capacity expansion runs are conducted to estimate the 2030 electricity generation mix in North Carolina. The optimal installed capacity is then fixed in the operational model, and the operational model is run to determine how storage with a fixed installed capacity and duration affects the objective function value. The change in objective function value, corresponding to the change in total system cost, is used to assess the net benefit associated with using storage for bulk energy time shifting and peak capacity deferral. (See report for more details.) The primary difference between the capacity expansion and operational versions of the model are the number of time slices. In the capacity expansion model, electricity supply and demand is balanced over a representative 24-hour period for each of the four seasons, for a total of 96 time slices per year. The operational model includes 8760 time slices per year, thereby representing every hour of the year, which allows us to better capture the value of storage. In terms of workflow, the capacity expansion runs were conducted with an input sqlite database file. Here we include the raw text-based sql file. It can be converted to a binary sqlite database file with the freely available sqlite program: https://www.sqlite.org. The results from the capacity expansion runs were automatically stored in the output tables associated with the same sqlite database. To run the operational version, capacity results are extracted from the capacity expansion sqlite file, and added to an input DAT file. These input DAT files are text-based files located in the scenario-specific folders for the operational runs. (Note that Temoa can run with either a sqlite or DAT file specified as input.) Each scenario folder for both the capacity expansion and operational runs also contain the config file used to run the model. Each model run was executed from the command line with the following syntax: $ python temoa_model/ --config=temoa_model/<config_file_name> Note that the specific instance of the Temoa source code we ran corresponds to git commit hash 06edf7b883cee44ebd1129803f2145687c46bea0. We verified the model code works with Pyomo versions 5.3 and 5.5. For details on how to run the model, please visit the project website: http://temoaproject.org/.

The article presents the frequency regulation of an asynchronous electric motor and thus energy saving has been achieved. Frequency control is energy-saving, so adjusting the frequency of the induction motor signal improves efficiency and reduces power consumption.