\r\n\tThe proposed subtitles are \r\n\t• Municipal Solid waste landfills \r\n\t• Industrial waste landfills \r\n\t• Hazardous waste landfills \r\n\t• Global approaches and technologies \r\n\t• Legal and economic aspects
",isbn:"978-1-83768-352-9",printIsbn:"978-1-83768-351-2",pdfIsbn:"978-1-83768-353-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"94513c9322631c6de257373b093d7d3a",bookSignature:"Dr. Suriyanarayanan Sarvajayakesavalu",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12042.jpg",keywords:"Landfills, Generation, Recycling, Disposal, Liquid, Gaseous, Radioactive, Methods, Techniques, Practices, Solid Waste Generation",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 8th 2022",dateEndSecondStepPublish:"July 6th 2022",dateEndThirdStepPublish:"September 4th 2022",dateEndFourthStepPublish:"November 23rd 2022",dateEndFifthStepPublish:"January 22nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"5 hours",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Active researcher and academician specialized in Environmental Monitoring.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"237021",title:"Dr.",name:"Suriyanarayanan",middleName:null,surname:"Sarvajayakesavalu",slug:"suriyanarayanan-sarvajayakesavalu",fullName:"Suriyanarayanan Sarvajayakesavalu",profilePictureURL:"https://mts.intechopen.com/storage/users/237021/images/system/237021.png",biography:"Prof. Dr. Suriyanarayanan Sarvajayakesavalu, MSc, MPhil, Ph.D., is Deputy Director Research of Vinayaka Mission's Research Foundation (VMRF) - Deemed to be University. Prior to this position, Dr. Suriyanarayanan served as a post-doctoral fellow at the University of Turin, Italy, and a project coordinator, faculty, and head (I/c) at the Department of Water and Health, JSS Academy of Higher Education and Research (formerly Jagadguru Sri Shivarathreeshwara University), Mysuru, India. He also served as a science officer for SCOPE Beijing office, Research Center for Eco-Environmental Sciences (RCEES), Chinese Academy of Sciences, from April 2015 to March 2017. He also serves as a Nodal Officer for SCOPE India activities under the JSS Academy of Higher Education and Research. He has research experience in the areas of environmental monitoring, radiation ecology, and environmental microbiology. He is a recipient of the Young Scientist research grant award from the Science and Research Board (SERB), Department of Science and Technology, Government of India. He also secured the prestigious visiting scientist fellowship from the Chinese Academy of Sciences in 2016–2017. He is an Associate Editor of Environmental Development Journal by Elsevier.",institutionString:"Vinayaka Missions University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444315",firstName:"Karla",lastName:"Skuliber",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/444315/images/20013_n.jpg",email:"karla@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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\n
1. Introduction
\n
Even though the use of wind generators for converting wind energy into electricity is beneficial from the environmental standpoint, their consideration in the active power dispatch makes the already complex task of achieving power system controllability even more demanding. Consequently, the quantification of the effects that large-scale integration of wind generation will cause on the network is a very important matter that requires special attention when planning and operating an electrical power system. Arguably, power flow analysis is the most popular computational calculation performed in a power system’s planning and operation, and this study has been selected to quantify the electrical response of wind generators when they are integrated in Flexible AC Transmission Systems.
\n
Mathematical models of several types of wind generators have been developed in which their active and reactive power outputs are obtained based on the steady-state equivalent representation of the induction machine. The power injection method is then used to include these models into the power flow formulation, which is solved by using a sequential approach to obtain an operating point of the power system. In this approach, only the network’s state variables are calculated through a conventional power flow algorithm, while a subproblem is formulated for updating the state variables of wind generators as well as their power injections at the end of each power flow’s iteration.
\n
Instead of using the power injection concept, another way of representing a wind generator is by means of an equivalent variable impedance expressed in terms of the slip of the generator and its rotor and stator winding parameters. This impedance is included in the system’s admittance matrix, and the network nodal voltages are computed through the power flow analysis. Based on these voltages, the air-gap power of the wind generator is calculated and used to iteratively compute the value of the slip of the induction generator that produces the match between the air-gap power and the mechanical power extracted from the wind.
\n
In general terms, all the methods discussed above share the characteristic of using a sequential approach to calculate the state variables of the wind generators, and none of them considers the integration of FACTS controllers in the network where the WECSs are embedded.
\n
A fundamentally different approach for the modelling of WECS, within the context of the power flow problem, is a method that simultaneously combines the state variables associated with the wind generators, the FACTS controllers and the transmission network in a single frame-of-reference for a unified iterative solution through a Newton-Raphson (NR) technique. From the convergence standpoint, the unified method is superior to the sequential one because the interaction between the network, FACTS controllers and wind generators is better represented during the iterative solution. Furthermore, it arrives at the solution with a quadratic convergence regardless of the network size. Hence, the key contribution of this work is to provide a comprehensive and general approach for the analysis of power flows in Flexible AC Transmission Systems containing wind generators in a unified single-frame of reference.
\n
\n
\n
2. Power flow including FACTS controllers and wind generators
\n
The unified approach suggested in [1] is extended to compute the power flow solution of a power system containing FACTS controllers and WECS furthermore, the approach is represented by a single set of nonlinear power flow mismatch equations f(X\n \n AC\n , X\n \n F\n , X\n \n WG\n \n )=0, where X\n \n AC\n is a vector of all nodal voltage magnitudes and angles, X\n \n F\n stands for the state variables of the FACTS controllers and X\n \n WG\n\n is a vector of all state variables associated with the wind generators. The linearised power flow mismatch equations corresponding to the wind farms are then combined with those associated with the FACTS controllers and the rest of the network, as given by (1), which are solved iteratively by the NR method:
where ΔR\n \n F\n and ΔR\n \n WG\n represent the mismatch equations of the FACTS controllers and wind generators, respectively. The NR method starts from an initial guess for all the state variables and updates the solution at each iteration i until a predefined tolerance is fulfilled. In this unified solution, all the state variables are adjusted simultaneously in order to compute the steady-state operating condition of the power system. Hence, this method has strong convergence characteristics.
\n
\n
\n
3. Modelling of FACTS devices
\n
Among all FACTS devices used to improve the steady-state performance of power systems [1], the Static Var Compensator (SVC) and Thyristor-Controlled Series Capacitor (TCSC) are the controllers considered.
\n
\n
3.1. Static VAR compensator
\n
An integrated SVC and step-down transformer model is obtained by combining the admittances of both components Y\n \n T-SVC\n \n =\n Y\n \n T\n + Y\n \n SVC\n as proposed in [1]. The linearised power flow equations are given by (2) considering the firing angle α\n \n SVC\n of the SVC as the state variable within the NR method
The TCSC firing angle power flow model is represented as an equivalent series reactance which is associated with the firing angle α\n \n TCSC.\n This reactance can be expressed as [1] (see Appendix):
Assuming the TCSC controls the active power flowing from bus k to bus m to a specified value of\n \n \n \n P\n \n k\n m\n \n \n s\n p\n \n \n \n \n , the set of linearised power flow equations is
Wind generators are categorized according to how they operate when they are connected to the grid. The Fixed-Speed Wind Generators (FSWG) are thus named so because their speed is mainly set according to the system’s frequency [2]. In this category are the Stall-Regulated Fixed-Speed Wind Generators (SR-FSWG) and the Pitch-Regulated Fixed-Speed Wind Generators (PR-FSWG). A variant of the previous models is the semi-variable speed wind generator (SSWG), which uses a wound-rotor induction generator with an external resistor added to the rotor circuit in order to achieve a power regulation when wind speeds are above the rated one [3]. Also, variable-speed wind generators are being employed worldwide with the doubly-fed induction generator being the most used. However, another emergent topology that is being widely accepted is the wind generator based on a Permanent Magnet Synchronous Generator (PMSG) with a full-scale converter in which the gearbox can be omitted [4].
\n
Mathematical modelling of FSWG, SSWG and PMSG-based wind generators for power flow studies is addressed below. In reference [5], the models of fixed- and semi-variable speed wind generators are suitably derived for power flow analysis and can be readily integrated in the formulation presented herein. For this reason, only a brief description of these models is given next.
\n
\n
4.1. Fixed-speed wind generators
\n
This generator is directly connected to the network through a step-up transformer, and its final operating point depends upon the electrical frequency as well as the nodal voltage at the generator’s terminals. The generated reactive and active powers are determined by Equations (5) and (6), respectively, and the stator and rotor currents of the induction generator can be expressed according to Equations (7) and (8) [5] :
where s is the machine’s slip, V is the terminal voltage, and the constants from A to W are as given in the Appendix. Furthermore, the power converted from mechanical to electrical form (P\n \n conv\n ) can be computed by using Equation (9), where R\n \n 2\n represents the rotor resistance
\n ω\n \n s\n is the angular synchronous speed [rad/s],
\n β is the pitch angle [degrees],
\n ω\n \n T\n is the angular speed of the turbine [rad/s],
and the constants c\n \n 1\n to c\n \n 9\n are the parameters of the wind turbine’s design.
\n
Thus, assuming that the SR-FSWG is connected at bus k, the power mismatches equations are (11)-(13), and the set of linearised equations that has to be assembled and combined with the Jacobian matrix and the power mismatch vector of the entire network is shown in Equation (14) [5]:
where P\n \n g\n (V,s) and Q\n \n g\n (V,s) are given by (6) and (5), respectively, P\n \n LK\n and Q\n \n LK\n represent the active and reactive powers drawn by the load at bus k, respectively, and P\n \n k\n \n \n cal\n and Q\n \n k\n \n \n cal\n are active and reactive power injections given by
Since this wind generator has a blade pitch angle mechanism which actuates to limit the power extracted from the wind [7], the generated active power P\n \n g,pr\n can be obtained from its power curve and is considered constant at a value \n \n \n \n P\n \n g\n ,\n p\n r\n \n \n s\n p\n \n \n \n \n through the iterative process; however, the reactive power Q\n \n g,pr\n =Q\n \n g\n needs to be calculated [5]. Therefore, the internal power equilibrium point in the wind generator has to be computed by Equation (17):
where P\n \n losses,s\n and P\n \n losses,r\n\n are the three-phase stator and rotor power losses, respectively, and the core losses in the induction machine are neglected. As mentioned previously, the set of linearised power flow mismatch equations regarding the PR-FSWG is given by Equations (18)-(21) when the generator is connected at bus k:
In this type of generator, the slip of the induction machine cannot be regarded as the state variable because of the external resistance R\n \n ext\n added in the rotor circuit [5]. Hence, a total rotor circuit resistance, R\n \n x\n = (R\n \n 2\n +R\n \n ext\n\n ) /s, is considered as a single-state variable associated with the rotor circuit, which is adjusted to satisfy the power mismatch equations during the NR power flow calculation [5,8]. Hence, the reactive and active powers, the stator and rotor currents as well as the power converted from mechanical to electrical form will be dependent functions on R\n \n x\n and can be expressed as
where the constants A´, C´, E´, H´, K´, M´ and T´ are given in the Appendix. Since the generated active power \n \n \n \n P\n \n g\n ,\n s\n s\n \n \n \n \n \n is set to a fixed value \n \n \n \n P\n \n g\n ,\n s\n s\n \n \n s\n p\n \n \n \n \n obtained from the wind generator power curve, and assuming no core losses, the mechanical power of the induction generator P\n \n m,ss\n can be estimated as follows:
where the internal energy balance in the induction machine ΔP\n \n WT3\n = -(P\n \n m,ss\n - P\n \n conv\n ) is derived by using Equations (26)-(27).
\n
\n
\n
4.3. PMSG-based wind generator
\n
This type of wind generator possesses a PMSG and a full-rated converter to connect the generator to the network, resulting in complete speed and reactive power control [9]. Hence, all the generated power is supplied to the power system through a machine-side converter and grid-side converter. The schematic diagram of this topology is shown in figure 1(a). Reactive power support is one of the characteristics that make this machine attractive for wind power production. In this case, the inclusion of the explicit representation of the wind generator step-up transformer is considered, which allows for direct voltage magnitude control at the high-voltage side of the transformer. The proposed model for power flow studies is shown in the figure 1(b) in which P\n \n g,pmsg\n represents the output power set by the wind generator power curve for a given wind speed, V\n \n msc\n and V\n \n gsc\n are the voltage at the machine-side converter and grid-side converter terminal, respectively, and Z\n \n st\n is the step-up transformer impedance.
\n
Figure 1.
PMSG-based wind generator: (a) schematic diagram, (b) proposed model for power flow studies.
\n
The power flow equations for the PMSG-based wind generator are derived assuming the following voltage at the grid-side converter terminal: V\n gsc\n = V\n \n gsc\n \n e\n \n jδgsc\n . Based on Fig. 1(b), the active and reactive powers flowing from the grid-side converter terminal to the k-th bus are
For the active and reactive powers at bus k, the subscripts gsc and k are exchanged in Equations (32) and (33). Therefore, the NR-based power flow formulation is given by
Note that Equation (36) represents the power constraint in the AC/DC/AC converter in which active power losses are neglected.
\n
\n
\n
\n
5. Case studies with wind farms and FACTS devices
\n
This section shows how the proposed approach performs when considering a power system having FACTS devices and wind generators.
\n
\n
5.1. Five-bus test system with FSWGs, SSWGs and a SVC
\n
The typical five-bus test system is used to provide an example with the inclusion of a wind farm consisting of ten SR-FSWGs, ten PR-FSWGs and ten SSWGs operating at a wind speed of 16 m/s with which all wind generators are injecting their maximum power. Additionally, a SVC is placed at bus five in order maintain its terminal voltage magnitude at 1 pu. The conventional generators are set to control voltage magnitudes at 1 pu. Parameters of the wind farm are given in the Appendix.
\n
Figure 2.
Modified five-bus test system used to incorporate a SVC and a wind farm composed of several wind generator models.
\n
In order to show the effect of wind farms and a SVC in the operation of the power system, the following three scenarios are presented: (a) the base case where the wind farm and SVC are not considered, (b) the case where only the wind farm is running and (c) the case where the SVC is connected at the bus at which the wind farm is connected in order to provide voltage support. The results regarding each case are reported in Table I.
\n
\n
\n
\n
\n
\n
\n
\n Results\n
\n
\n Scenario\n
\n
\n
\n
\n (a)\n
\n
\n (b)\n
\n
\n (c)\n
\n
\n
\n
\n V1\n \n
\n
1.000
\n
1.000
\n
1.000
\n
\n
\n
\n V2\n \n
\n
1.000
\n
1.000
\n
1.000
\n
\n
\n
\n V3\n \n
\n
0.971
\n
0.971
\n
0.977
\n
\n
\n
\n V4\n \n
\n
0.971
\n
0.971
\n
0.979
\n
\n
\n
\n V5\n \n
\n
0.967
\n
0.966
\n
1.000
\n
\n
\n
\n V6\n \n
\n
---
\n
0.945
\n
0.981
\n
\n
\n
\n Pg,WF\n \n
\n
---
\n
25.349
\n
25.341
\n
\n
\n
\n Qg,WF\n \n
\n
---
\n
-10.802
\n
-9.857
\n
\n
\n
\n Qg,SVC\n \n
\n
---
\n
---
\n
36.897
\n
\n
Table 1.
Power flow simulation results with wind farm and SVC.
\n
The simulated wind farm is a reactive power consumer since it lacks a reactive power control as seen from Table 1. When no SVC is considered, its reactive power absorption exceeds 10 MVAr, resulting in a low-voltage magnitude at node six. On the other hand, when the SVC is set in operation, not only the low-voltage side of the wind farm transformer is boosted, but also the system voltage profile.
\n
Since all wind generators are operating at the same wind speed, clearly the state variables calculated for each group of wind generators corresponding to each model will result in the same value. Furthermore, if the system voltage profile changes, as occurs with the inclusion of the SVC, another operating point is found at each wind generator as shown in Table 2.
\n
\n
\n
\n
\n
\n
\n \n
\n
\n Scenario\n
\n
\n SR-FSWG\n
\n
\n PR-FSWG\n
\n
\n SSWG\n
\n
\n SVC\n
\n
\n \n
\n
\n s\n
\n
\n s\n
\n
\n Rx\n \n
\n
\n \n αSVC (deg)\n \n
\n
\n
\n
(b)
\n
-0.00506
\n
-0.00679
\n
-67.69437
\n
---
\n
\n
\n
(c)
\n
-0.00467
\n
-0.00613
\n
-75.16952
\n
136.35024
\n
\n
Table 2.
Computed wind generators and SVC state variables.
\n
\n
\n
5.2. Five-bus test system with PMSG-based wind generators and a TCSC
\n
In this case, a PMSG-based wind farm is located at bus 4 with 30 wind generators operating at a rated wind speed, i.e. 15 m/s. Be aware that each PMSG-based wind generator provides reactive power support by controlling its terminal voltage magnitude. Also, a TCSC is placed for controlling the active power flowing through the transmission line connected between nodes 4 and 5 at P\n \n 4-5\n = 20 MW, as shown in figure 2. The next scenarios are analyzed: (a) the base case where the wind farm and TCSC are not considered, (b) the power system including only the PMSG-based wind farm and (c) the network having simultaneously the TCSC and the wind farm. The results are reported in Table 3.
\n
Figure 3.
Modified five-bus test system used to incorporate a TCSC and a PMSG-based wind farm.
\n
The system voltage profile is improved when the PMSG-based wind farm is integrated to the grid as seen from Table 3. This is mainly due to two reasons; there is a redistribution of power flows in the network, e.g. the load connected at node four is being supplied locally by the wind farm. On the other hand, the PMSG-based wind farm is providing voltage support, resulting in a voltage magnitude of 1 pu at the low-voltage side of the wind farm transformer even when the TCSC is set in operation to increase the power transfer to 20 MW in the line connecting nodes 4 and 5. The wind generators and TCSC state variables estimated by the NR algorithm for each scenario are the following: scenario (b) V\n \n gsc\n = 1.017 pu, δ\n \n gsc\n = 9.071o, and scenario (c) V\n \n gsc\n = 1.016 pu, δ\n \n gsc\n = 8.862o, α\n \n TCSC\n = 143.499o.
\n
\n
\n
\n
\n
\n \n
\n
\n Results\n
\n
\n Scenario\n
\n
\n \n
\n
\n (a)\n
\n
\n (b)\n
\n
\n (c)\n
\n
\n
\n
\n V1\n \n
\n
1.000
\n
1.000
\n
1.000
\n
\n
\n
\n V2\n \n
\n
1.000
\n
1.000
\n
1.000
\n
\n
\n
\n V3\n \n
\n
0.971
\n
0.984
\n
0.984
\n
\n
\n
\n V4\n \n
\n
0.971
\n
0.987
\n
0.988
\n
\n
\n
\n V5\n \n
\n
0.967
\n
0.973
\n
0.971
\n
\n
\n
\n V6\n \n
\n
---
\n
1.000
\n
1.000
\n
\n
\n
\n Pg,WF\n \n
\n
---
\n
60.000
\n
60.000
\n
\n
\n
\n Qg,WF\n \n
\n
---
\n
2.566
\n
2.218
\n
\n
\n
\n P1-2\n \n
\n
89.683
\n
46.792
\n
45.883
\n
\n
\n
\n P1-3\n \n
\n
40.639
\n
20.252
\n
21.206
\n
\n
\n
\n P2-3\n \n
\n
24.821
\n
11.565
\n
13.114
\n
\n
\n
\n P2-4\n \n
\n
28.076
\n
9.279
\n
11.253
\n
\n
\n
\n P2-5\n \n
\n
55.032
\n
45.465
\n
41.053
\n
\n
\n
\n P4-3\n \n
\n
-18.686
\n
13.628
\n
11.171
\n
\n
\n
\n P4-5\n \n
\n
6.258
\n
15.590
\n
20.000
\n
\n
Table 3.
Power flow simulation results with wind farm and TCSC.
\n
\n
\n
\n
6. Discussion
\n
This chapter has put forward the NR-based power flow algorithm which is capable of computing the steady-state operating point of electric networks containing WECS and FACTS devices. The solution problem is formulated in a single-frame of reference, resulting in an efficient iterative solution. Additionally, a PMSG-based wind generator model for power flow studies is presented, which allows for direct voltage magnitude control at the high-voltage side of the wind generator transformer. Numerical examples have shown that FACTS controllers are a practical alternative to integrate WECS into power systems without degrading their operative performance.
The data for each wind farm is (on a base power of 100 MVA): wind farm step-up transformer impedance is 0.2 pu, and the impedance of each wind generator transformer is 4.1667 pu. Also, the data of each wind generator model are given in Table 4\n
\n
\n
\n
\n
\n
\n \n
\n
\n
\n SR-FSWG\n
\n
\n PR-FSWG\n
\n
\n SSWG\n
\n
\n \n
\n
\n Z1\n \n
\n
0.0027 + j0.025
\n
0.0 + j0.09985
\n
0.00269 + j0.072605
\n
\n
\n
\n Z2\n \n
\n
0.0022 + j0.046
\n
0.00373 + j0.10906
\n
0.002199 + j0.04599
\n
\n
\n
\n Zm\n \n
\n
j1.38
\n
j3.54708
\n
j1.37997
\n
\n
\n
\n Vnom\n
\n
690
\n
690
\n
690
\n
\n
\n
\n Pnom\n
\n
900
\n
600
\n
1000
\n
\n
Table 4.
Wind generator data.
\n
where Z\n \n 1\n is the stator impedance [Ω], Z\n \n 2\n is the rotor impedance [Ω], Z\n \n m\n is the magnetizing impedance [Ω], V\n \n nom\n is the rated voltage [V] and P\n \n nom\n is the rated power of the wind generator [kW].
\n
The coefficients of Equation (10) are as follows: c\n \n 1\n = 0.5; c\n \n 2\n = 116; c\n \n 3\n = 0.4; c\n \n 4\n = 0.0; c\n \n 5\n = 0; c\n \n 6\n = 5; c\n \n 7\n =21; c\n \n 8\n = 0.08; and c\n \n 9\n = 0.035; β = 0. Also, for the PMSG-based wind generator, its rated voltage is 690 V, and its rated power is 2000 kW.
\n
\n
Acknowledgements
\n
The authors gratefully acknowledge the financial support granted to MSc. Luis M. Castro by the Consejo Nacional de Ciencia y Tecnología (CONACYT) México, and the University of Michoacán (U.M.S.N.H) for allowing him to undertake PhD studies. The authors gratefully acknowledge the financial support granted to Fuerte-Esquivel CR and Angeles-Camacho C by the FI and the II at the UNAM under the research project 2102.
\n
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/42641.pdf",chapterXML:"https://mts.intechopen.com/source/xml/42641.xml",downloadPdfUrl:"/chapter/pdf-download/42641",previewPdfUrl:"/chapter/pdf-preview/42641",totalDownloads:2251,totalViews:141,totalCrossrefCites:0,totalDimensionsCites:2,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:64,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"February 21st 2012",dateReviewed:"August 30th 2012",datePrePublished:null,datePublished:"March 20th 2013",dateFinished:"February 6th 2013",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/42641",risUrl:"/chapter/ris/42641",book:{id:"3105",slug:"modeling-and-control-aspects-of-wind-power-systems"},signatures:"E. Barrios-Martinez, L.M. Castro, C.R. Fuerte-Esquivel and C. Angeles-Camacho",authors:[{id:"42204",title:"Prof.",name:"César",middleName:null,surname:"Angeles-Camacho",fullName:"César Angeles-Camacho",slug:"cesar-angeles-camacho",email:"CangelesC@iingen.unam.mx",position:"Lecturer",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"151706",title:"Dr.",name:"Claudio Ruben",middleName:null,surname:"Fuerte-Esquivel",fullName:"Claudio Ruben Fuerte-Esquivel",slug:"claudio-ruben-fuerte-esquivel",email:"cfuerte@umich.mx",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"151708",title:"MSc.",name:"Esher",middleName:null,surname:"Barrios-Martinez",fullName:"Esher Barrios-Martinez",slug:"esher-barrios-martinez",email:"ebarriosm@ii.unam.mx",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"165797",title:"Dr.",name:"Luis M.",middleName:null,surname:"Castro",fullName:"Luis M. Castro",slug:"luis-m.-castro",email:"lcastro@faraday.fie.umich.mx",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Power flow including FACTS controllers and wind generators",level:"1"},{id:"sec_3",title:"3. Modelling of FACTS devices",level:"1"},{id:"sec_3_2",title:"3.1. Static VAR compensator",level:"2"},{id:"sec_4_2",title:"3.2. Thyristor-controlled series compensator",level:"2"},{id:"sec_6",title:"4. Modelling of wind generators",level:"1"},{id:"sec_6_2",title:"4.1. Fixed-speed wind generators",level:"2"},{id:"sec_6_3",title:"4.1.1. Stall-regulated fixed-speed wind generator",level:"3"},{id:"sec_7_3",title:"4.1.2. Pitch-regulated fixed-speed wind generator",level:"3"},{id:"sec_9_2",title:"4.2. Semi-variable speed wind generator",level:"2"},{id:"sec_10_2",title:"4.3. PMSG-based wind generator",level:"2"},{id:"sec_12",title:"5. Case studies with wind farms and FACTS devices",level:"1"},{id:"sec_12_2",title:"5.1. Five-bus test system with FSWGs, SSWGs and a SVC",level:"2"},{id:"sec_13_2",title:"5.2. Five-bus test system with PMSG-based wind generators and a TCSC",level:"2"},{id:"sec_15",title:"6. Discussion",level:"1"},{id:"sec_16",title:"Appendix",level:"1"},{id:"sec_17",title:"Acknowledgements",level:"1"}],chapterReferences:[{id:"B1",body:'\n Acha E, Fuerte-Esquivel CR, Ambriz-Perez H, Angeles-Camacho C. FACTS : Modelling and Simulation in Power Networks, Chichester: John Wiley & Sons; 2004.\n '},{id:"B2",body:'\n Hwang PI, Ahn SJ, Moon SI. Modeling of the Fixed Speed Wind Turbine Generator System for DTS. IEEE PES General Meeting, 2008: 1-7.\n '},{id:"B3",body:'\n Burnham DJ, Santoso S, Muljadi E. Variable Rotor-Resistance Control of Wind Turbine Generators. IEEE PES General Meeting, 2009: 1-6.\n '},{id:"B4",body:'\n Senjyu T, Yona A, Funabashi T. Operation Strategies for Stability of Gearless Wind Power Generation Systems. IEEE PES General Meeting, 2008: 1-7.\n '},{id:"B5",body:'\n Castro LM, Fuerte-Esquivel CR, Tovar-Hernández JH. A Unified Approach for the Solution of Power Flows in Electric Power Systems Including Wind Farms. Electric Power Systems Research, in press.\n '},{id:"B6",body:'\n Ackerman T. Wind Power in Power Systems, 1st ed. Chichester: John Wiley & Sons; 2005.\n '},{id:"B7",body:'\n Bianchi FD, De Battista H, Mantz RJ. Wind Turbine Control Systems - Principles, Modelling and Gain Scheduling Design, 1st ed. London: Springer-Verlan; 2006.\n '},{id:"B8",body:'\n Divya KC, Rao PSN. Models for wind turbine generating systems and their application in load flow studies. Electric Power Systems Research 2006: 76:844–856.\n '},{id:"B9",body:'\n Hong-Woo K, Sung-Soo K, Hee-Sang K. Modeling and control of PMSG-based variable-speed wind turbine. Electric Power Systems Research 2010:80:46-52.\n '}],footnotes:[],contributors:[{corresp:null,contributorFullName:"E. Barrios-Martinez",address:null,affiliation:'
Instituto de Ingeniería, Universidad Nacional Autónoma de México, UNAM, México
Instituto de Ingeniería, Universidad Nacional Autónoma de México, UNAM, México
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Prof. Dr.",name:"Somchai",middleName:null,surname:"Chatratana",fullName:"Somchai Chatratana",slug:"somchai-chatratana"},{id:"7123",title:"Dr.",name:"Bunlung",middleName:null,surname:"Neammanee",fullName:"Bunlung Neammanee",slug:"bunlung-neammanee"},{id:"9933",title:"Dr.",name:"Somporn",middleName:null,surname:"Sirisumrannukul",fullName:"Somporn Sirisumrannukul",slug:"somporn-sirisumrannukul"}]},{id:"9578",title:"Real-time Physical Simulation of Wind Energy Conversion Systems",slug:"real-time-physical-simulation-of-wind-energy-conversion-systems",signatures:"Iulian Munteanu, Antoneta Iuliana Bratcu, Seddik Bacha and Daniel Roye",authors:[{id:"13139",title:"Dr.",name:"Antoneta Iuliana",middleName:null,surname:"Bratcu",fullName:"Antoneta Iuliana Bratcu",slug:"antoneta-iuliana-bratcu"},{id:"51882",title:"Mr",name:"Seddik",middleName:null,surname:"Bacha",fullName:"Seddik Bacha",slug:"seddik-bacha"},{id:"122696",title:"Prof.",name:"Iulian",middleName:null,surname:"Munteanu",fullName:"Iulian Munteanu",slug:"iulian-munteanu"}]},{id:"9559",title:"Variability and Predictability of Large-Scale Wind Energy in the Netherlands",slug:"variability-and-predictability-of-large-scale-wind-energy-in-the-netherlands",signatures:"A.J. Brand, M. Gibescu and W.W. de Boer",authors:[{id:"126555",title:"Prof.",name:"Madeleine",middleName:null,surname:"Gibescu",fullName:"Madeleine Gibescu",slug:"madeleine-gibescu"}]},{id:"9573",title:"Variability of Wind and Wind Power",slug:"variability-of-wind-and-wind-power",signatures:"Joaquin Mur-Amada and Angel Bayod-Rujula",authors:[{id:"6231",title:"PhD.",name:"Joaquin",middleName:null,surname:"Mur Amada",fullName:"Joaquin Mur Amada",slug:"joaquin-mur-amada"},{id:"122691",title:"Dr.",name:"Ángel",middleName:null,surname:"Bayod-Rújula",fullName:"Ángel Bayod-Rújula",slug:"angel-bayod-rujula"}]},{id:"9580",title:"Impact of Real Case Transmission Systems Constraints on Wind Power Operation",slug:"impact-of-real-case-transmission-systems-constraints-on-wind-power-operation",signatures:"Francois Vallee, Olivier Deblecker, Jacques Lobry",authors:[{id:"2047",title:"Ir.",name:"Francois",middleName:null,surname:"Vallee",fullName:"Francois Vallee",slug:"francois-vallee"},{id:"5930",title:"Dr Ir",name:"Olivier",middleName:null,surname:"Deblecker",fullName:"Olivier Deblecker",slug:"olivier-deblecker"},{id:"31945",title:"Prof.",name:"Jacques",middleName:null,surname:"Lobry",fullName:"Jacques Lobry",slug:"jacques-lobry"}]},{id:"9561",title:"Wind Power at Sea as Observed from Space",slug:"wind-power-at-sea-as-observed-from-space",signatures:"W. Timothy Liu, Wenqing Tang and Xiaosu Xie",authors:[{id:"6457",title:"Dr.",name:"W. Timothy",middleName:null,surname:"Liu",fullName:"W. Timothy Liu",slug:"w.-timothy-liu"},{id:"126557",title:"Prof.",name:"Wenqing",middleName:null,surname:"Tang",fullName:"Wenqing Tang",slug:"wenqing-tang"},{id:"126558",title:"Prof.",name:"Xiaosu",middleName:null,surname:"Xie",fullName:"Xiaosu Xie",slug:"xiaosu-xie"}]},{id:"9567",title:"Methods and Models for Computer Aided Design of Wind Power Systems for EMC and Power Quality",slug:"methods-and-models-for-computer-aided-design-of-wind-power-systems-for-emc-and-power-quality",signatures:"Vladimir Belov, Peter Leisner, Nikolay Paldyaev, Alexey Shamaev and Ilja Belov",authors:[{id:"6477",title:"Dr.",name:"Ilja",middleName:null,surname:"Belov",fullName:"Ilja Belov",slug:"ilja-belov"},{id:"122662",title:"Prof.",name:"Vladimir",middleName:null,surname:"Belov",fullName:"Vladimir Belov",slug:"vladimir-belov"},{id:"122663",title:"Prof.",name:"Peter",middleName:null,surname:"Leisner",fullName:"Peter Leisner",slug:"peter-leisner"},{id:"122683",title:"Prof.",name:"Alexey",middleName:null,surname:"Shamaev",fullName:"Alexey Shamaev",slug:"alexey-shamaev"}]},{id:"9569",title:"Design of Robust Power System Stabilizer in an Interconnected Power System with Wind Power Penetrations",slug:"design-of-robust-power-system-stabilizer-in-an-interconnected-power-system-with-wind-power-penetrati",signatures:"Cuk Supriyadi A.N, I. Ngamroo, Sarjiya, Tumiran and Y.Mitani",authors:[{id:"6357",title:"Dr.",name:"Cuk Supriyadi",middleName:null,surname:"Ali Nandar",fullName:"Cuk Supriyadi Ali Nandar",slug:"cuk-supriyadi-ali-nandar"},{id:"122687",title:"Prof.",name:"Yasunori",middleName:null,surname:"Mitani",fullName:"Yasunori Mitani",slug:"yasunori-mitani"}]},{id:"9570",title:"Wind Power Impact on Power System Dynamic Performance",slug:"wind-power-impact-on-power-system-dynamic-performance",signatures:"Emmanuel S. Karapidakis",authors:[{id:"1109",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Karapidakis",fullName:"Emmanuel Karapidakis",slug:"emmanuel-karapidakis"}]},{id:"9571",title:"Wind Power: Integrating Wind Turbine Generators (WTG’s) with Energy Storage",slug:"wind-power-integrating-wind-turbine-generators-wtg-s-with-energy-storage",signatures:"Septimus van der Linden",authors:[{id:"6621",title:"Mr.",name:"Septimus",middleName:null,surname:"van der Linden",fullName:"Septimus van der Linden",slug:"septimus-van-der-linden"}]},{id:"9579",title:"Optimization of Spinning Reserve in Stand-alone Wind-Diesel Power Systems",slug:"optimization-of-spinning-reserve-in-stand-alone-wind-diesel-power-systems",signatures:"Fernando Olsina and Carlos Larisson",authors:[{id:"22068",title:"Dr.",name:"Fernando",middleName:null,surname:"Olsina",fullName:"Fernando Olsina",slug:"fernando-olsina"}]},{id:"9568",title:"Power Characteristics of Compound Microgrid Composed from PEFC and Wind Power Generation",slug:"power-characteristics-of-compound-microgrid-composed-from-pefc-and-wind-power-generation",signatures:"Shin’ya Obara",authors:[{id:"6937",title:"Prof. Dr.",name:"Shin'ya",middleName:null,surname:"Obara",fullName:"Shin'ya Obara",slug:"shin'ya-obara"}]},{id:"9572",title:"Large Scale Integration of Wind Power in Thermal Power Systems",slug:"large-scale-integration-of-wind-power-in-thermal-power-systems",signatures:"Lisa Goransson and Filip Johnsson",authors:[{id:"6996",title:"Professor",name:"Filip",middleName:null,surname:"Johnsson",fullName:"Filip Johnsson",slug:"filip-johnsson"},{id:"9080",title:"MSc",name:"Lisa",middleName:null,surname:"Goransson",fullName:"Lisa Goransson",slug:"lisa-goransson"}]},{id:"9576",title:"The Future Energy Mix Paradigm: How to Embed Large Amounts of Wind Generation While Preserving the Robustness and Quality of the Power Systems?",slug:"the-future-energy-mix-paradigm-how-to-embed-large-amounts-of-wind-generation-while-preserving-the-ro",signatures:"Ana Estanqueiro",authors:[{id:"6615",title:"Prof.",name:"Ana",middleName:null,surname:"Estanqueiro",fullName:"Ana Estanqueiro",slug:"ana-estanqueiro"}]},{id:"9565",title:"Environmental Impact of Modern Wind Power under LCA Methodology",slug:"environmental-impact-of-modern-wind-power-under-lca-methodology",signatures:"Eduardo Martinez Camara, Emilio Jimenez Macias, Julio Blanco Fernandez and Mercedes Perez de la Parte",authors:[{id:"121647",title:"Prof.",name:"Eduardo",middleName:null,surname:"Martinez Camara",fullName:"Eduardo Martinez Camara",slug:"eduardo-martinez-camara"}]},{id:"9566",title:"Wind-Solar Driven Natural Electric Hybrid Ventilators",slug:"wind-solar-driven-natural-electric-hybrid-ventilators",signatures:"N.A.Ahmed",authors:[{id:"6371",title:"Dr.",name:"Noor",middleName:null,surname:"Ahmed",fullName:"Noor Ahmed",slug:"noor-ahmed"}]}]}],publishedBooks:[{type:"book",id:"1289",title:"Solar Cells",subtitle:"Silicon Wafer-Based Technologies",isOpenForSubmission:!1,hash:"76fb5123cd9acbf3c37678c5e9bd056a",slug:"solar-cells-silicon-wafer-based-technologies",bookSignature:"Leonid A. 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1. Introduction
The science of decision support is foundational for every type of policy, and this work offer a proposal to analyze its role in energy policy.
An example of application of a particular machine learning (ML) technique to an energy policy problem is presented. It is important to understand the role of ML in energy and environmental analysis, for two solid reasons.
The first concerns the need to process large volumes of data and to elaborate and model complex relationships, typical of the energy analysis and of the environmental analysis. In this context, the use of AI (Artificial Intelligence) and machine learning is almost mandatory.
The second concerns the need to a concerted effort to identify how these tools may best be applied to tackle major problems of recent years, like climate change [1]: about this, CO2 emissions is key variable that we must control to achieve the global objective of mitigating damage for humanity.
This work has a specific goal. Using known tools from the scientific literature on energy generation costs, we intend to show how the use of a machine learning technique (the support vector machines, SVM) can produce a more accurate modeling of these costs.
The link with CO2 emissions is provided by the possibility of using the cost model in a cost-effectiveness analysis (C-E A), in which the cost is represented by the Levelised Cost of Energy (LCOE) and the effectiveness is represented by the CO2 emissions of the technologies considered per unit of energy produced.
The CO2 estimation is then obtained by selecting the best generation options according to the C-E A results.
The meaning of this work is the following.
Imagine that you are an energy analyst, in the public or private sector, and you need to use only one or just few variable/s (such as a forecast on the cost of natural gas), to estimate the costs of an electricity generation technology.
This task can be accomplished using a cost model of electricity generation in which a single piece of information can vary, leaving everything else unchanged (or imposing a certain trend on it).
The metric used is the indicator LCOE (Levelised Cost of Energy) provided by IEA (International Energy Agency), using 2020 data.
Once you have obtained a certain level of accuracy in estimate of energy cost, it is possible to move into a context of cost-effectiveness analysis, in which the best energy option in terms of Incremental Cost-Effectiveness Ratio (ICER) was selected to produce energy and, finally, provide a certain level of CO2 emissions for the time horizon in which such a technology is still the “best option”.
In other words, the estimate of energy cost and the cost-effectiveness analysis, allow us to trace the scenarios for electricity generation mix and, finally, calculate a quantitative forecast of the CO2 emitted.
The proposed work just intends to show the application of one of the existing machine learning techniques to the estimation of the LCOE, starting from some explanatory variables.
A linear model (LM) and an SVM are compared in the prediction of the LCOE value for a combined cycle gas plant (CCGT) with a focus on the fuel cost, Operation and Maintenance (O&M) cost and CO2 price using IEA data for Italy in 2020.
The work carried out intends to highlight the possibilities of applying machine learning techniques not only in the purely engineering aspects of energy systems, but also in the statistical-economic ones at a higher level of abstraction.
Some words about why to focus on power generation systems.
As countries work towards a low carbon world, it is crucial that policymakers, modelers, and experts have at their disposal reliable information on the cost of generation.
IEA [2] reports that the levelised costs of electricity generation of low-carbon generation technologies are more and more low the costs of conventional fossil fuel generation. Renewable energy costs continue their descent in recent years and their costs are now competitive with dispatchable fossil fuel-based electricity generation for many countries.
2. Methodology
This section presents the main tools used in this work: the LCOE methodology provided by IEA and the SVM, the used machine learning technique. Just before SVM presentations a very brief remind about ML and its use in energy systems and CO2 emissions estimates will be provided.
2.1 Levelised cost of energy
The Levelised Cost of Energy (LCOE) is the selected tool to measure the cost of an energy unit produced by the considered technologies. LCOE is a methodology described in the joint report by the International Energy Agency and the OECD (Organization for Economic Co-operation and Development) Nuclear Energy Agency (NEA) (now at the ninth edition in a series of studies on electricity generating costs) [1]. This report includes cost data on power generation from natural gas, coal, nuclear, and a broad range of renewable technologies.
The metric for plant-level cost chosen is the well-known levelised cost of electricity (LCOE) (IEA are now considering system effects and system costs with the help of the broader value-adjusted LCOE, or Levelised Cost of Value-Adjusted LCOE, VALCOE metric, here not considered).
The LCOE is widely considered as the principal tool for comparing the plant-level unit costs of different base load technologies over their operating lifetimes since indicates the economic costs of a technology family, not the financial costs of a certain projects in a certain market. Due to the equality between discounted average costs and the stable remuneration over lifetime electricity production LCOE recall the costs of electricity production in regulated electricity markets with stable tariffs than to the variable prices in deregulated markets.
Despite many limitations, LCOE has maintained its utility and appeal since it is a uniquely straightforward, transparent, comparable, and well understood metrics remaining a widely used tool for modeling, policy making and public debate.
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted revenues and the present value of the sum of discounted costs. Another way on the left-hand side one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:
PMWh The constant lifetime remuneration to the supplier for electricity;
MWh The amount of electricity produced annually in MWh;
1+r−t The real discount rate corresponding to the cost of capital;
Capitalt Total capital construction costs in year t;
O&Mt Operation and maintenance costs in year t;
Fuelt Fuel costs in year t;
Carbont Carbon costs in year t;
Dt Decommissioning and waste management costs in year t
PMWh is equal to levelised cost of electricity (LCOE).
Eq. (1) is the formula used here to calculate average lifetime levelized costs based on the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning and dismantling provided by OECD countries and selected non-member countries.
2.2 Machine learning
Machine learning (ML) is the field of artificial intelligence (AI) that provide methods to learn from data over time creating algorithms not being programmed to do so.
The literature about ML is relatively recent but is so vast that only some hint to review works can be made here, as an access point to this world1.
Machine learning approaches are normally categorized as in the follows.
Supervised machine learning, that trains itself on a labeled data set; unsupervised machine learning that uses unlabeled data with algorithms to extract the features required to label, sort, and classify the data in real-time, without human intervention; semi-supervised learning (SsL) namely a medium between supervised and unsupervised learning: SsL uses a smaller labeled data set during training and make classification and feature extraction from a larger, unlabeled data set; reinforcement machine learning is like supervised learning, but do not requires sample data for training (since using “trial and error” mode).
About the machine learning algorithms for use with labeled data the regression algorithms (as linear and logistic regression); decision trees (based on a set of decision rules to perform classification); instance-based algorithms: it uses classification to estimate how likely a data point is to be a member of one group, or another based on its proximity to other data points.
Methods based for use with on unlabeled data are: clustering algorithms: (like K-means, TwoStep, and Kohonen clustering); association algorithms: (that find patterns in data by identifying ‘if-then’ relationships namely association rules); neural networks: (that create a layered network of calculations featuring an input layer, when data in; one or more hidden layer, where calculations are performed; and an output layer. Where each conclusion is assigned a probability); deep neural network that uses multiple hidden layers, each of which successively refines the results of the previous layer. Deep learning models are typically unsupervised or semi-supervised. Certain types of deep learning models—including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.
In this work, the machine learning approach used is the SVM one.
SVMs2 are machine learning algorithms built on statistical learning theory for structural risk minimization. In pattern recognition, classification, and analysis of regression, SVMs outperform other methodologies. The significant range of SVM applications in the field of load forecasting is due to its ability to generalize (also, local minima lead to no problems in SVM).
SVM was chosen, in this work, for the sake of simplicity, since the performed Support Vector Regression (SVR) [5], extremely easy to understand in comparing a traditional statistical tool with a competing machine learning based one.
Often, the available applications of SVM in the energy sector are oriented on the engineering side3 while in this work the approach is oriented in support decisions for energy policy field.
Using one of the possibilities offered by SVMs, namely the SVR, the follows show how it is possible to obtain more accurate forecasts of costs per unit of energy produced, using LCOE as a metric.
The best available accuracy is then used in a context of cost-effectiveness analysis.
In the following, a method to select among competing options (options that can be differ even for slight changes in some significant LCOE parameters), the one characterized by the best Incremental Cost-Effectiveness Ratio (ICER) is presented.
The possibility of making this choice during the lifetime of the plant leads to the possibility of identifying the best technology available, year by year, to get the corresponding profile of the associated CO2 emissions.
2.2.1 Machine learning for energy systems and CO2 emission estimation
The growing utilization of data collectors in energy systems has resulted in a massive amount of data accumulated (an increasing mass of mart sensors are now extensively used in energy production and energy consumption) leading to a continuous production of big data and, consequently, to a massive number of opportunities and challenges in decision support science.
Today, ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy, and speed or to provide an understanding on energy system functionality in the context of complex human interactions.
[7] propose a comprehensive review of essential ML to present the state of the art of ML models in energy systems and discuss their likely future trends.
Machine learning was used for estimate CO2 emission from energy systems in several context, using different approach. It is possible to recall, among an increasing number of works in recent years:
[8] about flexibility of the electricity demand, a machine learning algorithm developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions in Danish bidding zone DK2;
[9] an investigation on the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries;
[10] on the linkage between energy resources and economic development the focus of that work is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources with a higher predictive accuracy;
[11] about proposing a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs using a random forest machine learning method);
[12] on the relationship among iron and steel industries, air pollution and economic growth in China (using a Long Short Term Memory, LSTM, approach);
[13] on the forecasting of energy consumption related carbon emissions for the Beijing-Tianjin-Hebei region.
[14] on the uses of gray relational analysis to identify the factors that have a strong correlation with carbon emissions for China to reduce carbon emissions by studying prediction of carbon emissions (using LSTM).
[15] on the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities for the Peruvian Amazon (using deep learning models).
[16] on the use of a random forest machine learning regression workflow to map country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into, to create a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries as a transformative tool to quantify the climate change mitigation services that forests provide.
[17] To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, through a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040.
2.3 Our methodology
The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.
In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.
The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.4
To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.5
This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.
Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant’s lifetime.
First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.
The basic relationships of the model are:
PF=Power∗8760∗AVLF∗AAF100∗1−AuxPE2
ws=1−wdE3
ks=krft+EMRP∗BE4
i=wd∗kd+ws∗ksE5
d=i/1+iE6
dfi=∑j1/1+ijE7
icfinal=icfinal+icCnsT∗dfiE8
df=∑j1/1+djE9
icfinal=icfinal+icCnsT∗dfi1E10
dfi=∑j1/1+ijE11
Pro=Pro+PF∗dfE12
OM=FOM+VOM∗PF∗dfE13
Fue=CFue∗PF∗dfE14
CO2=PCO2∗PF∗dfE15
Cost=∑jOM+Fue+CO2E16
Decom=n∗Decom∗ProE17
LCOE=Power∗icfinal∗1000+Cost+Decom/ProE18
Where:
CC Cost of Capital (USD/MWh)
Power net capacity (MWe)
AVLFmin AVerage Load Factor min value (%)
AVLFmax AVerage Load Factor max value (%)
AAF Average Availability Factor (%)
AuxP Auxiliary Power (%)
Lifetime Time horizon of plant (years).
wdmin min weight of cost of debt on total cost (%)
wdmax max weight of cost of debt on total cost (%)
kdmin min value of debt rate (%)
kdmax max value of debt rate (%)
tmin min value of taxation (%)
tmax max value of taxation (%)
krftmin min value of free risk rate (%)
krftmax max value of free risk rate (%)
EMRPmin min value of Expected Market Risk Premium (%)
EMRPmax max value of Expected Market Risk Premium (%)
Bmin min value of Beta (%)
Bmax max value of Beta (%)
CnsTmin min value of Construction Time (years)
CnsTmax max value of Construction Time (years)
FOMmin Fixed Operation and Maintenance Costs min (USD*MWh)
FOMmax Fixed Operation and Maintenance Costs max (USD*MWh)
VOMmin Variable Operation and Maintenance Costs min (USD*MWh)
VOMmax Variable Operation and Maintenance Costs max (USD*MWh)
Cfuemin min value of Costs of Fuel (USD*MWh)
Cfuemax max value of Costs of Fuel (USD*MWh)
Effmin min value of Efficiency (%)
Effmax max value of Efficiency (%)
PCO2min min value of CO2 price (USD*MWh)
PCO2max max value of CO2 price (USD*MWh)
Decommin min value of Decommissioning (USD*MWh)
Decommax max value of Decommissioning (USD*MWh)
All other parameters are settled using the IEA values.
We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).
Table 1 describes the hypothesis used in this first step of the analysis.
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 1
Linear decreasing of 2% per year except every 6 years
constant
constant
Scenario 2
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Table 1.
Scenarios used for the regression of LCOE on fuel cost and O&M cost
3. Results
Figure 1 shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (Figure 2).
Figure 1.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
Figure 2.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
1,30E-14
8,39E-15
8,39E-17
SVM
5,25E-01
4,01E-01
4,01E-03
Tuned SVM
1,74E-03
1,54E-03
1,54E-05
with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (Figures 1 and 2).
Figure 3.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
Figure 4.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
3.87E+00
2.70E+00
2.70E-02
SVM
2.77E+00
1.59E+00
1.59E-02
Tuned SVM
2.61E+00
1.45E+00
1.45E-02
Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.
The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.
Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 3
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.
For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:
RMSE
MAE
MAPE
Linear Model
4.25878
3.49147
0.03491
SVM
2.70117
1.52912
0.01529
Tuned SVM
2.58541
1.52378
0.01524
In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.
Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).
In addition to this, let us imagine that the second type of plant has an average load factor of 94%.
Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.
The meaning of this operation is as follows:
to use systems with different characteristics (in this case we have changed the O&M costs and the load factor of a single technology family);
to calculate the ICER corresponding to each plant in a defined time interval (in this case, from when the LCOE starts to vary);
to calculate the degree of uncertainty on the value of the ICER thanks to the MAPE of the SVR, defining the variation range for the ICER6;
to select the technology that has the lowest ICER and then we calculate the corresponding emissions over the time horizon considered;
finally, to calculate the emissions profile corresponding to the winning technology, year by year.
CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).
Figure 5 illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.
4. Conclusions
ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).
In our case, the performed step was:
improving LCOE forecasting performance,
comparing multiple competing options by use of the ICER in Cost-Effectiveness Analysis;
consider the uncertainty about ICER using the MAPE (in this case, but is just an option) calculated by SVM;
choosing the best technology and calculating the CO2 emissions for it;
defining the trend of the CO2 emissions in the lifetime of the plant by step 4.
Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.
The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CO2 emissions, energy systems, machine learning, support vector machines, cost-effectiveness analysis, forecasting",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76238.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76238.xml",downloadPdfUrl:"/chapter/pdf-download/76238",previewPdfUrl:"/chapter/pdf-preview/76238",totalDownloads:147,totalViews:0,totalCrossrefCites:1,dateSubmitted:"November 12th 2020",dateReviewed:"March 26th 2021",datePrePublished:"April 12th 2021",datePublished:null,dateFinished:"April 12th 2021",readingETA:"0",abstract:"In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76238",risUrl:"/chapter/ris/76238",signatures:"Marco Rao",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Abdel Rahman Rehab O.",coverURL:"https://cdn.intechopen.com/books/images_new/10627.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-368-7",printIsbn:"978-1-83969-367-0",pdfIsbn:"978-1-83969-369-4",isAvailableForWebshopOrdering:!0,editors:[{id:"146384",title:"Dr.",name:"Marcos Sales Guerra",middleName:null,surname:"Tsuzuki",slug:"marcos-sales-guerra-tsuzuki",fullName:"Marcos Sales Guerra Tsuzuki"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology",level:"1"},{id:"sec_2_2",title:"2.1 Levelised cost of energy",level:"2"},{id:"sec_3_2",title:"2.2 Machine learning",level:"2"},{id:"sec_3_3",title:"2.2.1 Machine learning for energy systems and CO2 emission estimation",level:"3"},{id:"sec_5_2",title:"2.3 Our methodology",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_8",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Rolnick, D. and Donti, P. and H. Kaack, L.H., and Kochanski, K. and Lacoste, A. and Sankaran, K. and Ross, A. and Milojevic-Dupont, N., and Jaques, N., and Waldman-Brown, A., and Luccioni, A., and Maharaj, T., and Sherwin, E., and Mukkavilli, S.K., and Konrad P. K, and Carla Gomes, K., and Ng, A., and Hassabis, D., and C. Platt, J.C., and Creutzig, F., and Chayes, J. and Bengio, Y., Tackling Climate Change with Machine Learning, 2019'},{id:"B2",body:'IEA, Projected Cost of Generating Electricity 2020, IEA, 2020'},{id:"B3",body:'Saravanan and Sujatha, P., “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 945–949, doi: 10.1109/ICCONS.2018.8663155'},{id:"B4",body:'Wang, L. Support vector machines: theory and applications, Springer-Verlag, Berlin, 2005'},{id:"B5",body:'Awad M., Khanna R. (2015) Support Vector Regression. In: Efficient Learning Machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4'},{id:"B6",body:'Zendehboudi, A., Baseer, M.A., Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review, Journal of Cleaner Production, Volume 199, 2018, Pages 272-285, DOI: 10.1016/j.jclepro.2018.07.164'},{id:"B7",body:'Salimi, M., Mosavi, A., Faizollahzadeh, A.S., Amidpour, M., Rabczuk, T., and Shamshirband, S., State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies 2019, 12, 1301; doi:10.3390/en12071301'},{id:"B8",body:'Leerbeck, K., Bacher, P., Grønborg Junker, R., Goranović, G., Corradi, O., Ebrahimy, R., Tveit, A., Madsen, H., Short-term forecasting of CO2 emission intensity in power grids by machine learning, Applied Energy, Volume 277, 2020, DOI:10.1016/j.apenergy.2020.115527'},{id:"B9",body:'Magazzino, C., Mele, M., Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions, Renewable Energy, Volume 167, 2021, Pages 99–115 DOI: 10.1016/j.renene.2020.11.050'},{id:"B10",body:'Cogoljević, D., Alizamir, M., Piljan, I., Piljan, T., Prljić, K., Zimonjić, S. A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, Volume 495, 2018, Pages 211-214, DOI: 10.1016/j.physa.2017.12.082'},{id:"B11",body:'Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., Yamagata, Y. Mapping building carbon emissions within local climate zones in Shanghai, Energy Procedia, Volume 152, 2018, Pages 815-822, DOI: 10.1016/j.egypro.2018.09.195'},{id:"B12",body:'Mele, M., Magazzino, C. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China, Journal of Cleaner Production, Volume 277, 2020, 123293, DOI: 10.1016/j.jclepro.2020.123293'},{id:"B13",body:'Li, M.; Wang, W.; De, G.; Ji, X.; Tan, Z. Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm. Energies 2018, 11, 2475. https://doi.org/10.3390/en11092475'},{id:"B14",body:'Huang, Y., Shen, L., Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China, Journal of Cleaner Production, Volume 209, 2019, Pages 415-423, DOI: 10.1016/j.jclepro.2018.10.128'},{id:"B15",body:'Csillik, O. and Asner, G.P. 2020 Environ. Res. Lett. 15 014006'},{id:"B16",body:'Csillik, O., Kumar, P., Mascaro, J. et al. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci Rep 9, 17831 (2019). https://doi.org/10.1038/s41598-019-54386-6'},{id:"B17",body:'Niu, D. Wang, K., Wu, J., Sun, L., Yi Liang, Y., Xu, X., Yang, X. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network, Journal of Cleaner Production, Volume 243, 2020, 118558, DOI: 10.1016/j.jclepro.2019.118558'},{id:"B18",body:'Korovkinas, K., Danènas, P., Garsva, G., Support vector machine parameter tuning based on particle swarm optimization metaheuristic, Nonlinear Analysis: Modelling and Control, Vol. 25, No. 2, 266–281, DOI=10.15388/namc.2020.25.16517'},{id:"B19",body:'Botchkarev, A. A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms, Interdisciplinary Journal of Information, Knowledge, and Management, Volume 14, pp. 045–076, 2019, DOI=10.28945/4184'}],footnotes:[{id:"fn1",explanation:"Here we just remind a recent review of the state of art in machine learning techniques [3]."},{id:"fn2",explanation:"For a good introduction to this topic see [4]."},{id:"fn3",explanation:"See, for example [6]."},{id:"fn4",explanation:"Indeed, manual tuning is often considered as one of the most significant choice [18]."},{id:"fn5",explanation:"See [19] for a complete discussion about the used metrics."},{id:"fn6",explanation:"Namely, ICER max/min = ICER +/− ICER*MAPE."}],contributors:[{corresp:"yes",contributorFullName:"Marco Rao",address:"marco.rao@enea.it",affiliation:'
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
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