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COMPUTATIONAL MODELS USED FOR MINIMIZING THE NEGATIVE IMPACT OF ENERGY ON THE ENVIRONMENT
Oprea Diana
Dept. of Electrical Engineering, Czech Technical University,
Prague, Czech Republic
Abstract. Optimizing energy system is a problem that is extensively studied for many years by scientists. This problem can be studied from different views and using different computer programs. The work is characterized by one of the following calculation methods used in Europe for modelling, power system optimization. This method shall be based on reduce action of energy system on environment. Computer program used and characterized in this article is GEMIS.
Keywords: pollution, greenhouse gases, climate change, renewable energy.
MODELE DE CALCUL, CARE SUNT UTILIZATE LA MINIMIZAREA IMPACTULUI NEGATIV AL ENERGETICII ASUPRA MEDIULUI AMBIANT
OPREA Diana
Facultatea Energiei Electrice, Universitatea Tehnica Ceha Prague, Republica Ceha
Rezumat. Optimizarea sistemului energetic este o problema ce de multi ani este intens studiata de oamenii de stiinta. Aceasta problematica poate fi studiata din diferite viziuni §i cu ajutorul diferitor programe de calcul. in lucrarea ce urmeaza este caracterizata una din metodele de calcul utilizate in Europa pentru modelarea, optimizarea sistemului energetic. Aceasta metoda se bazeaza pe criteriul mic§orarii actiunii sistemului energetic asupra mediului ambiant. Programul de calcul utilizat si caracterizat in lucrare este - GEMIS.
Cuvinte-cheie: poluare, gaze cu efect de sera, schimbari climaterice, surse regenerabile de energie.
ВЫЧИСЛИТЕЛЬНЫЕ МОДЕЛИ, ИСПОЛЬЗУЕМЫЕ ДЛЯ МИНИМИЗАЦИИ НЕГАТИВНОГО ВОЗДЕЙСТВИЯ ЭНЕРГЕТИКИ НА ОКРУЖАЮЩУЮ СРЕДУ
Опря Диана
Электротехнический факультет Чешского Технического Университета,
Прага, Чехия
Аннотация. Оптимизация энергетической системы является проблемой, которая широко изучается учеными в течение многих лет. Эта проблема может быть рассмотрена с различных точек зрения и с использованием различных компьютерных программ. В данной работе рассматривается один из методов расчета, используемых в Европе для моделирования и оптимизации энергетических систем. Метод, использованный в работе, основывается на снижении воздействия энергетической системы на окружающую среду. В данной работе используется компьютерная вычислительная программа - GEMIS. Ключевые слова: отходы, выбросы парниковых газов, изменения климата, возобновляемые источники энергии.
The national economy is a set of interrelated institutions and their activities in the field of material production, distribution, changes in consumption and production within a relatively closed group, tied to a specific territory. This system is an integral part (subsystem) of the world economic system, or its constituent units. Development of power system can not be solved without respecting its relationship to the system environment; the simple representation is indicated in Figure 1.
The use of electricity inseparably accompanies each manufacturing and non-human activity. It is an increasingly important part of the energy economy - the share of electricity in final consumption of all forms of energy is constantly increasing.
Power system is modelled to calculate the optimum work. Optimizing the development of production systems is one of the most complex optimization problems.
Figura 1. Gross structure of the system environment of the modelled system: NE - national economy, EM - energy management, CHS - centralized heat supply, ES - electricity system,
VM - water management
System approach to solving this task assumes particular allocation of the production system from the parent of the national economy. This requires an assessment of national economy as a complex system, clearly formulate the objectives of its functioning, establish a hierarchical arrangement of its subsystems and identify effective ways of managing it. Research and experiments in this direction leads, for the formulation of optimization criteria for the development of national economy, combining spectral and regional aspects of optimization problems, creating effective links between systems at different hierarchical levels, etc. Excluding complex (large) energy systems of the system of national economy is based on the concept of the relationship of energy and the economy:
- energy is a part of the national economy, on which level is the most common cover such complex tasks such as determine optimum rates of development of national economy and the dynamics of meeting the needs of society, determining the relationship between consumption and accumulation, to determine the optimal structure of the industry, etc;
- energy production - as well as other subsystems (cross-industry complexes) of the national economy - an independent object of study.
Naturally, the principle applies here as well as feedback and the result of optimizing energy development could affect the decision back to the superior level of development of the whole national economy.
Development of the system over time is one of the basic features of any economic system, as the requirements of the quantitative utility values still rising. These requirements can be met only a continuous process of implementing a large number of diverse actions in the operation of existing systems and their elements, their reconstruction and development of new systems and components (objects). The aim of optimization is to determine the development of rational composition and sequence of these measures and the timely provision of conditions necessary for their implementation.
Optimization of energy development must be based on the concept of energy as a complex system, which leads to the considerable volume of optimization problems that usually can not be solved without the use of methods of mathematical modeling and computer.
With the current state of the possibilities of computer technology and especially with regard to the stability of numerical solutions and the limited accuracy of identification of parameters for models of physical reality, for each specific problem, you must create a special model designed for solving only a certain type of problems [2].
Creating a special model that will well represent a specific physical phenomenon, and examined one particular model and reality according to the following procedure: (Fig.2)
In the formulation of the task is necessary, if possible, as accurately define how transient or steady-state process is subject to review and consider the physical facts are crucial for him and vice versa can be neglected.
Figura 2: The steps of modelling a power system.
Every step in the method of calculation shown in fig. 2 has its importance, and each of them have some variations and complications.
When choosing a mathematical model is necessary to choose a model that is as simple as possible, while respecting all relevant variables and solved according to a group of transient or steady-state processes. Another model of power equipment is certainly a need to resolve a shock wave transients (model with distributed parameters), the other for electromagnetic or electromechanical transients (model with lumped parameters respectively, model considering the equations of motion). Another model will be used for symmetrical three-phase phenomena, another for the unbalanced. One model will be used for symmetrical three-phase phenomena, the other for unsymmetrical. Large selection of options providing calculation circuits with rotating machines. In them you can choose and combine different coordinate systems with many possible ways of converting rotor quantities to the stator or vice versa, and also to calculate the relative or named variables. The choice of the model must meet the following basic requirements:
a) simplicity,
b) compliance with all relevant physical facts for this story,
c) if possible easy identification of parameters,
d) a good numerical stability of the system.
The identification of parameters for the selected model can proceed in essentially two different ways: a) measurements, b) calculation. It is best to compare and combine both approaches. It is highly desirable that the appointment of the parameters in the mathematical model, most correspond to the actual parameters of the modelled reality. Measurement and calculation are always burdened with error, identification of some parameters from a mathematical point of view very poorly conditioned task (the difference between two large numbers determine the order number smaller). If the results of measurements and calculations of these great values loaded error, it may happen that in determining any of the parameters we arrive at the result in terms of physical reality absurd (negative resistance and inductance, coil binding factor greater than 1, etc.). Therefore it is necessary to constantly check the correctness of specific parameters, and these fundamental mistakes to avoid. The same is needed, particularly in models sensitive to inaccuracies entered input values, these values in several further steps to correct. Such models already respond to a small input imprecision big mistake in the result, or make it completely wrong, or stop the course of numerical calculation.
Nowadays it can be modelled much attention the development of electricity networks. Modelling guns for different objectives and are used to different methodologies. For example, PhD I. Lencz, optimized according to EC calculating minimum cost in the EC. Another example of the use of computational models can be found in the European electricity industry. The Power Engineering represented EURELECTRIC address in 2005, The Role of Electricity Project, focused on the time horizon to 2050. She qualified to develop a vision of the future role of electrification in relation to designated attributes of electricity supply. The work was dealt with in three blocks: a) the future demand for electricity and Electro technology, b) development of technologies of energy supply, c) modelling of energy systems. The solution was involved in a number of renowned institutions. When solutions were used in two models, the PRIMES model for the period to 2030 and Prometheus model for the target year 2050 [3].
The modelling of long-term quantified scenarios were used in the project "The Role of Electricity" two models. PRIMES model, used for the period to 2030 is characterized by a detailed interpretation of European countries (EU-25) and is ready for a description of future EU members. PROMETHEUS model to the outlook to 2050 treats Europe as a single unit, but as part of the world's energy system and markets. Models respect the details of the economic sector, and energy systems and energy technologies. Both models describe the resulting greenhouse gas emissions while respecting the impact of energy policy instruments in a broad sense (efficiency standards, taxes, subsidies, support for Mark, etc.).
In terms of the existence of many computer programs that work with the problems of modelling and optimizing the development of EC, is an opportunity to use their own software instead of development.
Next, we will indicate some energy mixes, through the linear program GEMIS, we get the emission values for each mix.
GEMIS is a database system: It offers environmental and cost data for energy, material, and transport systems, including their life-cycles. The environmental data cover air emissions (S02, NOx, particulates, CO, HC1, HF, H2S, NH3, NMVOC), greenhouse gases (C02, CH4, N2G, HFC, PFC, SF6), liquid effluents (AOX, BOD, COD, N), solid wastes (ashes etc.), and land-use. The cost data concern investment, fixed annual, and variable cost, as well as externality factors for air emissions, and GHG. Further data are stored for "meta" information: comments and description, references, data quality indicators, location and statistical grouping.
GEMIS is an analysis system: It determines full life-cycle impacts of energy, transport, and material technologies. In addition to the totals, GEMIS also gives the individual contributions of all processes to a calculated result (breakdown), and can determine results for selected system boundaries (e.g. a special location, in- or exclusion of material acquisition, crediting).
GEMIS is an evaluation tool: It evaluates deviations from multiple objectives (trade-offs), e.g., costs vs. emissions, or emissions vs. land use. It further calculates C02 and S02 equivalents, troposphere zone precursor equivalents, and the total resource use (cumulative energy and raw material requirements). Because of the modular approach of the database ("unit" processes), the sensitivity of any result can be determined quickly by copying original data, and adjusting key parameters - within seconds, GEMIS then calculates the new results which can be compare immediately with the original data.
Table 1. Scenarios of electricity productions in the EC
Structure of electricity production [TWh] Scenarios I, year 2020 Scenarios II, year 2020 Scenarios III, year 2020
Brown coal 33.41 28.91 28.42
Coal 7.58 7.60 5.58
Liquid fuels 0.18 0.18 0.18
Gaseous fuels 7.95 8.85 5.95
Nuclear fuel 29.58 29.58 39.23
RSE 8.65 13.00 8.42
Total 87.36 88.13 87.78
There are shown several scenarios for energy mix in Table 1. As a basis, all three scenarios have the same total power; see table 1 and table 2.
Scenario 'Mix_conferenta'
^ Meta data 131 Comment I Options Data
Options
Energy
demand [GWh]
Material demand [kg]
MIX CR scenar I MIX CR scenar II MIX CR scenar III
8.754E+4 0
8.351E+4 0
8,755E+'4 0
Table 3 is indicated in each energy source is composed an energy mix that was made. Scenario I, will be considered as a reference energy mix. Scenario II is an energy mix that is based on the percentage of renewable energy than in the other two scenarios, but also obtained from coal power remained about the same as in the baseline. Scenario III is based on nuclear energy, which is the highest percentage of energy produced is based on energy produced by nuclear stations. Results for all three examples we can see in table
According to the results shown in fig.3 1, we see that the optimal scenario is the third, which is the percentage of energy produced by nuclear stations. Due to the minimum amount greenhouse gas emissions to such types of energy sources. Case I, the reference, that uses all sources of energy proportional and results in this case are with the bigger emissions of greenhouse gases.
Table 3. A detailed presentation of the energy mix for scenario I.
I;;4 Scenario 'Mix_confe renta'
* Meta data
l-=l Comment
I Options
Data
Selected option ^ ^ 1 MIX CR scenar I
Energy | Materials | Persons | Freight | Residue Money 1
Enerov source TGWhl
qas-CC-CZ-OT-ZP 5.95E+3
hydro power plant-CZ-small 3E+2
hydro-powerplant-CZ-pumpinq CEZ 2E+3
hydro power plant-without pumpinq-CEZ 9E+2
biogas (double-cropMCE-cogen ATC-2010/en 1E+3
Xtra\biomass-pressinq to cobs-CZ 9E+2
liqnite-ST-CZ-HU 4x200 3.341E+4
qas-rmix-CZ-2010 1.1E+3
coal-ST-CZ-CU 4x200 7.58E+3
nuclear-5T-CZ-Dukjovany 1.5E+4
nuclear-5T-CZ-T emelin 1.45E+4
solar-PV-amorph-lTanned-DE-2000 1E+3
wind mill-CZ 1.5E+3
wood-coqen-CZ-ORC th 2.4E+3
Sum 8.754E+4
Table 4. Results of greenhouse gas emissions for linear program GEMIS for all three
energy mixes.
1 Results: greenhouse gas emissions
Scope
local all other
@ total
Option
[kg]
C02 equivalent
CO 2
CH4
N20
MIX CR scenar I
MIX CR scenar II
MIX CR scenar I
5.G7052E+10 |5.43497E+10 ¡7.20534E+7 ¡1.83588E+6
5.2336E+10 4.99629E+10 7.41111E+7 1.71079E+6
4.61852E+10 4.43872E+10 5.3295E+7 I
Fig.3. Graph representation for results of greenhouse gas emissions for linear program
GEMIS for all three energy mixes
Conclusions
Reducing emissions, including CO2 from combustion processes can be achieved by increasing the use of nuclear energy and renewable energy sources. On the issue of reducing greenhouse gas emissions, there is no answer to deal with everything; there is no perfect energy source that it could provide. Each source from the sun after oil, coal from the nucleus, the gas from the wind, has its advantages and disadvantages. Each country faces the challenge of how to create a balanced energy policy. Energy mix visions and strategies are determining an important part of our world’s future prosperity and welfare. Choices made now are important for future generations.In conclusion we would say that reducing greenhouse gas emissions is a long process. It is hard to say what will be in 2050 under the Kyoto Protocol, will be possible to reduced emissions by 90% or not, but in our hands are to decrease the emissions and to reduce them by real quantity. The survey made in the article given the energy mix can not be based only on nuclear power or renewable only for reducing greenhouse gas emissions. It is hard to imagine any state will have a power system based on only one energy source. A great significance is the energy mix composition suitable and reliable for a state.
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Acknowledgements
Research described in the paper was supervised by prof. Jin Tuma, FEE CTU in Prague.
About the author
Diana OPREA was born in 1984, in Chisinau, Republic of Moldova. In 2005 was graduated from Technical University of Moldova, Energy Faculty, electrical drives; 2006 graduated Master degree in Energy Engineering Department; Chisinau. In 2008 Graduated from Academy of Economic Sciences, International relationships; correspondence branch, Chisinau, Moldova. E-mail: opryadia@fel.cvut.cz, opreadiana2003@mail.ru.
