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FORMATION OF A SYSTEM OF INDICATORS FOR ASSESSING THE ECONOMIC EFFICIENCY OF ELECTRIC GRID SYSTEMS WITH ELEMENTS OF ARTIFICIAL INTELLIGENCE Lukovenko A.S (Russian Federation) Email: Lukovenko557@scientifictext.ru
Lukovenko Anton Sergeevich - Candidate of Sciences Technology, Master's Degree Student, DEPARTMENT OF ORGANIZATION AND MANAGEMENT OF SCIENCE-INTENSIVE INDUSTRIES, ENGINEERING AND ECONOMIC INSTITUTE RESHETNEV SIBERIAN STATE UNIVERSITY OF SCIENCE AND TECHNOLOGY, Electrician for maintenance of220 KV substation Taiga, BRANCH
JSC «FGC UES» KRASNOYARSK ENTERPRISE MES SIBERIA, KRASNOYARSK
Abstract: the paper deals with the methods of assessing the economic efficiency of investment projects. Economic efficiency (EE) of any enterprise is characterized by such financial indicators as profit or profitability. Methods are described for the evaluation of EE. The new methods of economic efficiency - the device controlled (flexible) AC power systems (FACTS) in conjunction with an artificial neural network (ANN). The calculation of the implementation of the pilot project using ANN for transformer substations is given.
Keywords: economic efficiency, innovation project, artificial neural network.
ФОРМИРОВАНИЕ СИСТЕМЫ ПОКАЗАТЕЛЕЙ ОЦЕНКИ ЭКОНОМИЧЕСКОЙ ЭФФЕКТИВНОСТИ ФУНКЦИОНИРОВАНИЯ ЭЛЕКТРОСЕТЕВЫХ КОМПЛЕКСОВ С ЭЛЕМЕНТАМИ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА Луковенко А.С. (Российская Федерация)
Луковенко Антон Сергеевич - кандидат технических наук, магистрант, кафедра организации и управления наукоемкими производствами, Инженерно-экономический институт Сибирский государственный университет науки и технологий им. академика М.Ф. Решетнёва, электромонтер по обслуживанию ПС 220 кВ Тайга, филиал
ПАО «ФСКЕЭС» Красноярское предприятие МЭС Сибири, г. Красноярск
Аннотация: в работе рассматриваются методы оценки экономической эффективности инвестиционных проектов. Экономическую эффективность (ЭЭ) любого предприятия характеризуют такие финансовые показатели, как прибыль или рентабельность. Описаны методы оценки ЭЭ. Приведен новый метод экономической эффективности - устройство управляемых (гибких) систем электропередач переменного тока (FACTS) совместно с искусственной нейронной сетью (ИНС). Приведен расчет реализации по внедрению пилотного проекта применением ИНС для трансформаторных подстанций. Ключевые слова: экономическая эффективность, инновационный проект, искусственная нейронная сеть.
In modern market conditions, making a conclusion about the economic efficiency of the enterprise, it should be borne in mind that it should not only meet the planned level, but be higher or equal to the efficiency of other market participants.
Given the high level of competition, a modern enterprise needs to implement a system based on a constant procedure of evaluation and implementation of measures to improve the level of efficiency.
1. Characteristics of the main methods of assessing the economic efficiency of production activities of energy enterprises
The economic efficiency of any enterprise is characterized by financial indicators, such as profit or profitability, when determining them, one should focus on the long-term development of the enterprise, but taking into account the results of past periods. Today, there are many methods and approaches to the definition of EE enterprise (Fig. 1).
Fig. 1. Methods for evaluating the effectiveness of innovative projects
The most widespread was the traditional financial model, which began to be used at the beginning of the last century, improved with the development of accounting methods and is still widely used.
The essence of this model is to remove from external influence and assess the economic efficiency of the enterprise based on the calculation of internal performance indicators. The main indicators of efficiency growth in this model is the increase in profits achieved by reducing costs. The analysis of the state of the enterprise is carried out on the basis of reporting data of previous periods, the efficiency of future periods is directly dependent on the previously achieved results.
Methods of evaluation of investment projects may not be uniform in all cases, since investment projects vary greatly in terms of the scale of costs, the timing of their useful use, as well as useful results [1].
The complex assessment of economic efficiency of the investment project is carried out on the basis of the following indicators:
- Net present effect characterizes the absolute result or the final effect of investment activity (in den. ed.).
- NPV-profit of the project, reduced to the present value.
- NPV-the discounted value of the project, defined as the sum of the discounted values of revenues less costs received in each year during the life of the project is determined by the expression 1.
CP CT? — TC
NPV = -IC; NPV = £Ct ' (1)
t=0 (1 + r) where IC is the cost of an investment project;
(1 + r)
t=0
CF-cash proceeds from the investment project;
t-the period of receipt of funds or implementation of costs of the investment project; n - duration of the investment project.
Economic sense: the organization can make any decisions of an investment nature, the level of profitability of which is lower than the current value of the indicator "cost of capital", which means either WACC, if the source of funds is not exactly identified, or the value of the target source. It can be calculated by formula 2.
^ CF ^CF - IC
So^R-- K - Ic^iR -0 (2)
If the value of IRR exceeds the cost of capital raised to Finance the project, then such a project is considered as profitable, and Vice versa.
The project payback period is the period during which the initial investment costs are recovered, or the number of periods (calculation steps, for example, years) during which the accumulated amount of estimated future income flows will be equal to the amount of the initial investment [2].
The algorithm of its calculation is as follows:
- If the income is distributed evenly over the years, the payback period is calculated by dividing the one-time costs by the amount of annual income.
- If the profit is unevenly distributed, the payback period is calculated by direct calculation of the number of years during which the investment will be repaid with income.
Initial investment Average for the period income
n
PP = min n, wherein ^ CF ^ IC
t-1
Advantages: the simplicity of the calculation that allows you to use it for small firms with a small cash flow, as well as for rapid evaluation of projects in resource constraints.
One of these new, developing approaches is a method based on fuzzy logic and the use of artificial intelligence, in particular, neural networks together with devices controlled (flexible) AC power systems (FACTS).
2. The economic efficiency of the electric grid complex with application of devices managed (flexible) systems AC power FACTS and ANN
FACTS is one of the most promising electric grid technologies, the essence of which is that the electric network from a passive device of electric power transport is converted into a device actively involved in the management of the modes of operation of power systems, and in the case of a violation of the stability of the power system is self-repairing [3]. Recovery is associated with the use of ins as a control algorithm.
The field of application of FACTS devices are system-forming and distribution electric networks of power systems, intersystem electric communications. The technical feasibility of the use of certain FACTS devices should be established on the basis of the results of calculations of the steady-state regimes, the stability of the power system and transients at normalized disturbances in the power system [4].
In order to increase the transmitted power along the section of the electric network (for example, inter-system communication), it is possible to consider as alternative options the construction of an additional power line in conjunction with the technical means ensuring its normal operation, or to use the capacity of existing power lines by installing FACTS devices in the electric network for this purpose together with the ins.
In Tab. 1 the stages of implementation of the project on implementation of the pilot project of reliability forecasting with application of ins for transformer substations are presented.
Table 1. Implementation Steps for implementation of the pilot project offorecasting the reliability with the application of ANN for transformer substations
Term Stages Pessimistic (Rub.) Optimistic (Rub.)
The first quarter of 2019 and the fourth quarter 2019 1. Revision of the package Programmer's work: 300 000 Computer programmer: 70 000 Database license (SQL MS): 200 000 Programmer's work: 500 000 Computer programmer: 100 000 The license for the database (ORACLE):
The first quarter of2020 -The second quarter of2020 2. Equipment purchase Server: 200 000 Client computer Server: 500 000 Client computer: 50 000 (X11)
2020 III quarter -IV quarter of 2021 3. Implementation of the pilot package Overhead: For RSC: 100 000 (X11) For electric power company: 300 000 Overhead: For RSC: 150 000 (X11) For electric power company: 500 000
Subtotal 2500000 4200000
Conclusion:
1. It is determined that the main advantages of the ins in forecasting is the collection and processing of data without time constraints, the ability to obtain data directly from the EPS, as well as the ability to take into account a variety of parameters that do not consist in functional communication.
2. The study of the theoretical foundations of economic efficiency evaluation allowed us to conclude that economic efficiency is one of the most important criteria for assessing the success of the enterprise.
3. The main principle of the development of the system of performance indicators and formulation of its essence in absolutely all degrees of economic management is the correspondence of the final result and the result (income), taking into account the used and spent resources.
References / Список литературы
1. Kamchatova E.Yu. Methodology of economic efficiency assessment of electric grid facilities // Transport business of Russia, 2011. № 2. P. 172-174.
2. Neshitoy A.S. Investments: textbook for universities. M.: Publishing and trading Corporation «Dashkov and Co», 2009. 372 p.
3. Lukovenko A.S. Analysis of domestic and international experience in the use of controlled AC power in intelligent electrical networks // Energy of a single network, 2018. № 5 (41). P. 31-38.
4. Khomyakova O.A. Possibilities of artificial neural networks as a device for predicting the consumption of electric energy in railway transport enterprises, 2013. № 2. P. 264-266.
