The universal technique of the current energy budget calculation is recommended for several periods of time. Basing on the data of annual energy budget and considering the plant's features, social, climatic and other conditions, correcting factors are calculated to estimate energy resources consumption for the day, week, month and year. Shortterm current energy budget supports important financial, trade, logistic, organizational and other control directions. The developed technique is applicable for subnational entities and industrial enterprises in order to increase level of energy resources consumption planning and forecasting as well as bills' optimization.
One of the promising ways to improve the reliability and efficiency of power supply for customers in the areas remote from central electrical grid is the use of hybrid power systems with renewable energy sources. The primary task of designing such systems is the unit commitment of the generating equipment that provides the optimal technical and economic indexes of the electric power system. The stochastic nature of generation and nonlinearity of the characteristics of power plants cause a high complexity of solving this problem, which, from a mathematical point of view, is formulated as an optimization problem. An accurate and reliable solution of this optimization problem increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. And it is a vital task of modern power industry. A probabilistic-statistical methods and models for the analysis of experimental data are used to construct climatic time series and graphs of electrical loads. In addition, to study the operating modes of the electric power system the MatLab system is used for the simulation and modeling, and an evolutionary particle swarm algorithm is used to solve the optimization problem. The original model of solar radiation is used as a part of this methodology. This model provides forecasting the key characteristics of solar radiation in any geographical point of Russia including the areas that have no results of routine actinometric observation. Weibull distribution function is used to forecast daily variations of wind speed. It enhances the validity of forecasting of electricity generation of wind-driven power plant at daily time interval. As a result of the research, a method of optimum unit commitment has been developed for the equipment of electric power systems based on renewable energy sources. The use of the particle swarm algorithm as a part of the methodology provides reliable and accurate determination of the extremum of the objective function, which increases the efficiency of design and operation of hybrid electric power systems with renewable energy sources. The method has been tested on practical examples of optimum unit commitment for the equipment of electric power systems of various configurations and has proven its effectiveness. The technique is implemented as a software application, which ensures the convenience of its practical application. The obtained results can be used by companies involved in the design and operation of electric power systems using renewable energy generating units.
Energy, financial, material and other interrelation set common presentation formed on enterprises can be sum in energy-financial flows. Such balances composition and analysis regular practice provides during several flows forming determine contradictions in time, put value of energy efficiency indexes. And, in case of their increasing quickly make management decision. In this way energy-financial balance is enterprise operating management.Fuel and energy balances are important and sustainable device of energy facility exploitation's efficiency control and analysis. The most interesting and less spread apart energy balances that are in monetary value [1].Such balance can be used for energy industry's analysis and planning, for quality control of energy-saving and energy resources utilization efficiency improvement measures, for production development programme energy efficiency values estimation. Balance shows energy resources production and consumption volumes of reasonable time, money resources income and expense, fuel storage, energy resources plant demands and utility networks losses, energy resources consumption volumes of organization units according to uses [1 -7].There can be designed a model for common whole set of energy, financial, material and other interrelations performance between energy resources packagers and consumers, where these interrelations are presented as flows simulated by network graphs [8].Energy flows form energy balance that remains as base for lots infrastructural solutions connected with development designing, planning and on-line controlling of areal economy.Energy balance, because of its physical nature severity, radically can be formed for any arbitrary short period and any arbitrary small site.Financial flows incidental with energy flows and expressing bought and sold energy resources cash compensation are formed differently. It is hugely complicated to build financial balance for short periods and small sites. Some energy flows do not have responsive financial flow, and some financial flows are generated not by energy flows, but by their realization conditions. Financial flows are greatly varying in time, and small sites financial flows generally do not have recordkeeping. Today standard coasts division into semi-variable (in power industry -fuel, energy, water and etc.) and semi-constant (amortization, salary, taxes and etc.) essentially does not change temporary inequality. Because of it supposed energy and financial flows spread pattern accordance generally in fact is out of a place.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.