Determining the Transmission Capacity of Existing Transmission Lines Under High Wind Generation Conditions

Determining the transmission capacity of existing transmission lines is determine by the conductor current. Transmission line can withstand current below thermal standpoint limit to avoid irreparable damage to conductor. The maximum value of current can be determined with static approach (STR – Static Thermal Rating) and dynamic approach (DTR - Dynamic Thermal Rating). STR is defined by simple calculations and does not change often throughout the year where the DTR is calculated for in time conditions taking into account atmospheric conditions, conductor geometry and conductor current. Most common approach to calculate conductor temperature is done by applying IEEE standard (IEEE 738, 2012) or CIGRE (TB601, 2014). Most congestion in the transmission network occurs during the higher production from wind farms when the wind have significant speed. It is to be expected that similar meteorological conditions (wind speed and direction) will occur on transmission lines in the immediate geographical area of wind power plants. In this paper, analysis of relations between atmospheric parameters (wind speed and direction, ambient temperature and solar radiation) and ampacity is described as functional dependence. Taking historical weather data from meteorological stations, atmospheric conditions on transmission line corridors and taking account the frequency of occurrence of individual meteorological variations ampacity of conductor will be determined. For such determined conditions that are influenced by a certain parameters variation of ampacity have a different rating scales. The obtained results will provide an insight into the current ampacity and the possibilities of the transmission line capacity during the high engagement of wind power plants

Algorithm for short-term technical planning losses of electricity in the transmission network

Veličina gubitaka u prijenosnoj mreži je jedan od pokazatelj ekonomičnosti njena rada. Svaki operator sustava posvećuje posebnu pozornost prognoziranju i upravljanjem gubitcima električne energije u svojoj mreži. Da bi se osiguralo pravovremeno i ekonomično pokriće tih gubitaka, potrebne su prognoze gubitaka. Prognoze gubitaka su složen problem, zbog velike količine različitih i teško predvidivih parametara koji utječu na iznos gubitaka. Dužnost operatora prijenosnog sustava je osiguravanje energije za pokriće gubitaka, zbog čega postoji poseban interes prema problemu. Unatoč većem interesu, prognoziranje gubitaka postaje kompleksniji problem, zbog sve veće penetracije obnovljivih izvora energije, posljedično i većim tokovima u prijenosnoj mreži, te deregulacije tržišta koja unose dodatne nesigurnosti i nepredvidivosti. Zbog velikog broja parametara i kompleksnosti problema, pristup prognozi gubitaka često se okreće prema tehnikama strojnog učenja. Tehnike strojnog učenja daju dobre rezultate upravo u problemima s velikim brojem parametara koje treba istovremeno uzeti u obzir. Posljednji iskoraci u razvoju algoritama strojnog učenja pokazuju iznimne rezultate u pogledu performansi i raspona problematike gdje se takvi algoritmi primjenjuju. Posebna kategorija problema gdje ovakvi algoritmi pokazuju svoj puni potencijal su problemi uspostave funkcionalne veze između velikog broja utjecajnih parametara i skupa izlaznih varijabli. Problem planiranja tehničkih gubitaka spada u kategoriju takvih problema. Naime, dosadašnje analize planiranja gubitaka pokazale su da na gubitke utječu razni parametri poput opterećenja dalekovoda, korone, puzajućih struja, meteoroloških faktora, ponašanje konzuma, radni dan, tokovi u mreži vezani uz velike prekogranične transfere energije uslijed velike hidrologije u okruženju i slično. Kada se uvaže povijesni podaci navedenih parametara skup ulaznih parametara postaje značajan i teško rješiv standardnim tehnikama. Upravo ovakav problem pripada kategoriji problema rješivih strojnim učenjem. Predmet istraživanja ovog rada je mogućnosti kratkoročnog prognoziranja gubitaka električne energije u prijenosnoj mreži metodama strojnog učenja za potrebe operatora prijenosnog sustava, odnosno za potrebe planiranja i vođenja elektroenergetskog sustava. Napravit će se analiza obrade prikupljenih podataka prikladnih za modele strojnog učenja, te će se na temelju tih podataka opisati izrađeni modeli za prognoziranje kratkoročnih gubitaka kroz programski alat za automatiziranu izradu prognoza kratkoročnih gubitaka. U drugom dijelu rada bi se analizirali financijski efekti koji su dobiveni novom metodom prognoziranja kratkoročnih gubitaka i nabave istih.The size of losses in the transmission network is one of the indicators of the economy of its operation. Each system operator pays special attention to forecasting and managing electricity losses. We need loss forecasts to ensure timely and economic coverage of these losses. Loss forecasts are a complex problem due to many different and challenging to predict parameters that affect the number of losses. The Transmission System Operator has to provide energy to cover losses, so there is a particular interest in the problem. Despite the growing interest, loss forecasting is becoming a more complex problem due to the increasing penetration of renewable energy sources, higher flows in the transmission network, and market deregulation, which introduces additional uncertainties and unpredictability. Due to many parameters and the complexity of the problem, the approach to loss forecasting often turns to machine learning techniques. Machine learning techniques give good results precisely in problems with a large number of parameters that need to be taken into account simultaneously. The latest steps in the development of machine learning algorithms show exceptional results in terms of performance and the range of issues where such algorithms are applied. A particular category of problems where such algorithms show their full potential is the problem of establishing a functional connection between many influential parameters and a set of output variables. The problem of planning technical losses falls into the category of such problems. Namely, previous analyzes of loss planning have shown that losses are affected by various parameters such as transmission line load, corona, creeping currents, meteorological factors, consumption, working day, network flows related to large cross-border energy transfers due to high hydrology and the like. When the parameters' historical data take into account, the set of input parameters becomes significant and challenging to solve with standard techniques. This problem belongs to the category of problems that can be solved by machine learning. The subject of this paper is to investigate the possibilities of short-term forecasting of electricity losses in the transmission network by machine learning methods for transmission system operators and for the needs of planning and managing the power system. I will analyze the processing of collected data suitable for machine learning models, and based on these data, the developed models for short-term loss forecasting will be described through a software tool for automated short-term loss forecasting. The second part of the paper will analyze the economic effects obtained by the new method of predicting short-term losses and their acquisition

Commissioning of Cogeneration Power Plant

U ovom diplomskom radu opisano je puštanje u rad kogeneracijskog postrojenja. U radu su korišteni podaci i mjerenja za stvarno kogeneracijsko postrojenje Spin Valis Internacional. Ispitivanja su rađena za pokusni rad elektrane prema planu i programu ispitivanja u pokusnom radu. Prvo je obrađena analiza mogućnosti priključka korisnika na elektroenergetsku mrežu, gdje su definirane moguće varijante priključka. Mreža je analizirana prema naponskim prilikama, tokovima snage, gubitcima u mreži i kratkospojnim prilikama, te na temelju tih analiza odabran je optimalan priključak. Zatim je obrađen utjecaj postrojenja na kvalitetu mrežu, gdje se ispitivala frekvencija i napon prije i poslije priključenja elektrane na mrežu. Obveza ispitivanja je bila osigurati uvjete i mjere za rad na siguran način sukladno Mrežnim pravilima i njegovim podzakonskim aktima. Nakon pojedinog ispitivanja zapisani su rezultati ispitivanja i provjerava udovoljavaju li traženim kriterijima. Ukoliko su rezultati ispitivanja zadovoljavajući (unutar granica tolerancije) konstatira se uspješan završetak ispitivanja. Opisano je ishođenje građevinske dozvole i plan izgradnje elektrane. Mjerenja su izvršena u programskom paketu Digsilent Powerfactory 14.This thesis describes the commissioning of the cogeneration plant. We have used data and measurements for real cogeneration plant Spin Valis International. Tests were conducted to test run the plant on schedule and test program in trial period. The first deals with the analysis of the possibilities of user connection to the grid, which defines possible variants of connection. The network is analyzed under voltage conditions, power flows, losses in the network and short-conditions, and based on these analyzes selected the optimal connection. Then the processed plant impact on the quality of the network, where they examined the frequency and voltage before and after the connection of the power plant to the grid. The obligation of the project was to provide the conditions and measures for work in a safe manner in accordance with the Grid Code and its bylaws. After each test recorded the results of tests and checks for compliance with the required criteria. If the test results are satisfactory (within tolerance limits) notes the successful completion of the tests. Described is obtaining a building permit and plan to build a power plant. Measurements have been made in the application pack DIgSILENT Powerfactory 14

Algorithm for short-term technical planning losses of electricity in the transmission network

Veličina gubitaka u prijenosnoj mreži je jedan od pokazatelj ekonomičnosti njena rada. Svaki operator sustava posvećuje posebnu pozornost prognoziranju i upravljanjem gubitcima električne energije u svojoj mreži. Da bi se osiguralo pravovremeno i ekonomično pokriće tih gubitaka, potrebne su prognoze gubitaka. Prognoze gubitaka su složen problem, zbog velike količine različitih i teško predvidivih parametara koji utječu na iznos gubitaka. Dužnost operatora prijenosnog sustava je osiguravanje energije za pokriće gubitaka, zbog čega postoji poseban interes prema problemu. Unatoč većem interesu, prognoziranje gubitaka postaje kompleksniji problem, zbog sve veće penetracije obnovljivih izvora energije, posljedično i većim tokovima u prijenosnoj mreži, te deregulacije tržišta koja unose dodatne nesigurnosti i nepredvidivosti. Zbog velikog broja parametara i kompleksnosti problema, pristup prognozi gubitaka često se okreće prema tehnikama strojnog učenja. Tehnike strojnog učenja daju dobre rezultate upravo u problemima s velikim brojem parametara koje treba istovremeno uzeti u obzir. Posljednji iskoraci u razvoju algoritama strojnog učenja pokazuju iznimne rezultate u pogledu performansi i raspona problematike gdje se takvi algoritmi primjenjuju. Posebna kategorija problema gdje ovakvi algoritmi pokazuju svoj puni potencijal su problemi uspostave funkcionalne veze između velikog broja utjecajnih parametara i skupa izlaznih varijabli. Problem planiranja tehničkih gubitaka spada u kategoriju takvih problema. Naime, dosadašnje analize planiranja gubitaka pokazale su da na gubitke utječu razni parametri poput opterećenja dalekovoda, korone, puzajućih struja, meteoroloških faktora, ponašanje konzuma, radni dan, tokovi u mreži vezani uz velike prekogranične transfere energije uslijed velike hidrologije u okruženju i slično. Kada se uvaže povijesni podaci navedenih parametara skup ulaznih parametara postaje značajan i teško rješiv standardnim tehnikama. Upravo ovakav problem pripada kategoriji problema rješivih strojnim učenjem. Predmet istraživanja ovog rada je mogućnosti kratkoročnog prognoziranja gubitaka električne energije u prijenosnoj mreži metodama strojnog učenja za potrebe operatora prijenosnog sustava, odnosno za potrebe planiranja i vođenja elektroenergetskog sustava. Napravit će se analiza obrade prikupljenih podataka prikladnih za modele strojnog učenja, te će se na temelju tih podataka opisati izrađeni modeli za prognoziranje kratkoročnih gubitaka kroz programski alat za automatiziranu izradu prognoza kratkoročnih gubitaka. U drugom dijelu rada bi se analizirali financijski efekti koji su dobiveni novom metodom prognoziranja kratkoročnih gubitaka i nabave istih.The size of losses in the transmission network is one of the indicators of the economy of its operation. Each system operator pays special attention to forecasting and managing electricity losses. We need loss forecasts to ensure timely and economic coverage of these losses. Loss forecasts are a complex problem due to many different and challenging to predict parameters that affect the number of losses. The Transmission System Operator has to provide energy to cover losses, so there is a particular interest in the problem. Despite the growing interest, loss forecasting is becoming a more complex problem due to the increasing penetration of renewable energy sources, higher flows in the transmission network, and market deregulation, which introduces additional uncertainties and unpredictability. Due to many parameters and the complexity of the problem, the approach to loss forecasting often turns to machine learning techniques. Machine learning techniques give good results precisely in problems with a large number of parameters that need to be taken into account simultaneously. The latest steps in the development of machine learning algorithms show exceptional results in terms of performance and the range of issues where such algorithms are applied. A particular category of problems where such algorithms show their full potential is the problem of establishing a functional connection between many influential parameters and a set of output variables. The problem of planning technical losses falls into the category of such problems. Namely, previous analyzes of loss planning have shown that losses are affected by various parameters such as transmission line load, corona, creeping currents, meteorological factors, consumption, working day, network flows related to large cross-border energy transfers due to high hydrology and the like. When the parameters' historical data take into account, the set of input parameters becomes significant and challenging to solve with standard techniques. This problem belongs to the category of problems that can be solved by machine learning. The subject of this paper is to investigate the possibilities of short-term forecasting of electricity losses in the transmission network by machine learning methods for transmission system operators and for the needs of planning and managing the power system. I will analyze the processing of collected data suitable for machine learning models, and based on these data, the developed models for short-term loss forecasting will be described through a software tool for automated short-term loss forecasting. The second part of the paper will analyze the economic effects obtained by the new method of predicting short-term losses and their acquisition

Algorithm for short-term technical planning losses of electricity in the transmission network

Veličina gubitaka u prijenosnoj mreži je jedan od pokazatelj ekonomičnosti njena rada. Svaki operator sustava posvećuje posebnu pozornost prognoziranju i upravljanjem gubitcima električne energije u svojoj mreži. Da bi se osiguralo pravovremeno i ekonomično pokriće tih gubitaka, potrebne su prognoze gubitaka. Prognoze gubitaka su složen problem, zbog velike količine različitih i teško predvidivih parametara koji utječu na iznos gubitaka. Dužnost operatora prijenosnog sustava je osiguravanje energije za pokriće gubitaka, zbog čega postoji poseban interes prema problemu. Unatoč većem interesu, prognoziranje gubitaka postaje kompleksniji problem, zbog sve veće penetracije obnovljivih izvora energije, posljedično i većim tokovima u prijenosnoj mreži, te deregulacije tržišta koja unose dodatne nesigurnosti i nepredvidivosti. Zbog velikog broja parametara i kompleksnosti problema, pristup prognozi gubitaka često se okreće prema tehnikama strojnog učenja. Tehnike strojnog učenja daju dobre rezultate upravo u problemima s velikim brojem parametara koje treba istovremeno uzeti u obzir. Posljednji iskoraci u razvoju algoritama strojnog učenja pokazuju iznimne rezultate u pogledu performansi i raspona problematike gdje se takvi algoritmi primjenjuju. Posebna kategorija problema gdje ovakvi algoritmi pokazuju svoj puni potencijal su problemi uspostave funkcionalne veze između velikog broja utjecajnih parametara i skupa izlaznih varijabli. Problem planiranja tehničkih gubitaka spada u kategoriju takvih problema. Naime, dosadašnje analize planiranja gubitaka pokazale su da na gubitke utječu razni parametri poput opterećenja dalekovoda, korone, puzajućih struja, meteoroloških faktora, ponašanje konzuma, radni dan, tokovi u mreži vezani uz velike prekogranične transfere energije uslijed velike hidrologije u okruženju i slično. Kada se uvaže povijesni podaci navedenih parametara skup ulaznih parametara postaje značajan i teško rješiv standardnim tehnikama. Upravo ovakav problem pripada kategoriji problema rješivih strojnim učenjem. Predmet istraživanja ovog rada je mogućnosti kratkoročnog prognoziranja gubitaka električne energije u prijenosnoj mreži metodama strojnog učenja za potrebe operatora prijenosnog sustava, odnosno za potrebe planiranja i vođenja elektroenergetskog sustava. Napravit će se analiza obrade prikupljenih podataka prikladnih za modele strojnog učenja, te će se na temelju tih podataka opisati izrađeni modeli za prognoziranje kratkoročnih gubitaka kroz programski alat za automatiziranu izradu prognoza kratkoročnih gubitaka. U drugom dijelu rada bi se analizirali financijski efekti koji su dobiveni novom metodom prognoziranja kratkoročnih gubitaka i nabave istih.The size of losses in the transmission network is one of the indicators of the economy of its operation. Each system operator pays special attention to forecasting and managing electricity losses. We need loss forecasts to ensure timely and economic coverage of these losses. Loss forecasts are a complex problem due to many different and challenging to predict parameters that affect the number of losses. The Transmission System Operator has to provide energy to cover losses, so there is a particular interest in the problem. Despite the growing interest, loss forecasting is becoming a more complex problem due to the increasing penetration of renewable energy sources, higher flows in the transmission network, and market deregulation, which introduces additional uncertainties and unpredictability. Due to many parameters and the complexity of the problem, the approach to loss forecasting often turns to machine learning techniques. Machine learning techniques give good results precisely in problems with a large number of parameters that need to be taken into account simultaneously. The latest steps in the development of machine learning algorithms show exceptional results in terms of performance and the range of issues where such algorithms are applied. A particular category of problems where such algorithms show their full potential is the problem of establishing a functional connection between many influential parameters and a set of output variables. The problem of planning technical losses falls into the category of such problems. Namely, previous analyzes of loss planning have shown that losses are affected by various parameters such as transmission line load, corona, creeping currents, meteorological factors, consumption, working day, network flows related to large cross-border energy transfers due to high hydrology and the like. When the parameters' historical data take into account, the set of input parameters becomes significant and challenging to solve with standard techniques. This problem belongs to the category of problems that can be solved by machine learning. The subject of this paper is to investigate the possibilities of short-term forecasting of electricity losses in the transmission network by machine learning methods for transmission system operators and for the needs of planning and managing the power system. I will analyze the processing of collected data suitable for machine learning models, and based on these data, the developed models for short-term loss forecasting will be described through a software tool for automated short-term loss forecasting. The second part of the paper will analyze the economic effects obtained by the new method of predicting short-term losses and their acquisition

Commissioning of Cogeneration Power Plant

U ovom diplomskom radu opisano je puštanje u rad kogeneracijskog postrojenja. U radu su korišteni podaci i mjerenja za stvarno kogeneracijsko postrojenje Spin Valis Internacional. Ispitivanja su rađena za pokusni rad elektrane prema planu i programu ispitivanja u pokusnom radu. Prvo je obrađena analiza mogućnosti priključka korisnika na elektroenergetsku mrežu, gdje su definirane moguće varijante priključka. Mreža je analizirana prema naponskim prilikama, tokovima snage, gubitcima u mreži i kratkospojnim prilikama, te na temelju tih analiza odabran je optimalan priključak. Zatim je obrađen utjecaj postrojenja na kvalitetu mrežu, gdje se ispitivala frekvencija i napon prije i poslije priključenja elektrane na mrežu. Obveza ispitivanja je bila osigurati uvjete i mjere za rad na siguran način sukladno Mrežnim pravilima i njegovim podzakonskim aktima. Nakon pojedinog ispitivanja zapisani su rezultati ispitivanja i provjerava udovoljavaju li traženim kriterijima. Ukoliko su rezultati ispitivanja zadovoljavajući (unutar granica tolerancije) konstatira se uspješan završetak ispitivanja. Opisano je ishođenje građevinske dozvole i plan izgradnje elektrane. Mjerenja su izvršena u programskom paketu Digsilent Powerfactory 14.This thesis describes the commissioning of the cogeneration plant. We have used data and measurements for real cogeneration plant Spin Valis International. Tests were conducted to test run the plant on schedule and test program in trial period. The first deals with the analysis of the possibilities of user connection to the grid, which defines possible variants of connection. The network is analyzed under voltage conditions, power flows, losses in the network and short-conditions, and based on these analyzes selected the optimal connection. Then the processed plant impact on the quality of the network, where they examined the frequency and voltage before and after the connection of the power plant to the grid. The obligation of the project was to provide the conditions and measures for work in a safe manner in accordance with the Grid Code and its bylaws. After each test recorded the results of tests and checks for compliance with the required criteria. If the test results are satisfactory (within tolerance limits) notes the successful completion of the tests. Described is obtaining a building permit and plan to build a power plant. Measurements have been made in the application pack DIgSILENT Powerfactory 14

Commissioning of Cogeneration Power Plant

U ovom diplomskom radu opisano je puštanje u rad kogeneracijskog postrojenja. U radu su korišteni podaci i mjerenja za stvarno kogeneracijsko postrojenje Spin Valis Internacional. Ispitivanja su rađena za pokusni rad elektrane prema planu i programu ispitivanja u pokusnom radu. Prvo je obrađena analiza mogućnosti priključka korisnika na elektroenergetsku mrežu, gdje su definirane moguće varijante priključka. Mreža je analizirana prema naponskim prilikama, tokovima snage, gubitcima u mreži i kratkospojnim prilikama, te na temelju tih analiza odabran je optimalan priključak. Zatim je obrađen utjecaj postrojenja na kvalitetu mrežu, gdje se ispitivala frekvencija i napon prije i poslije priključenja elektrane na mrežu. Obveza ispitivanja je bila osigurati uvjete i mjere za rad na siguran način sukladno Mrežnim pravilima i njegovim podzakonskim aktima. Nakon pojedinog ispitivanja zapisani su rezultati ispitivanja i provjerava udovoljavaju li traženim kriterijima. Ukoliko su rezultati ispitivanja zadovoljavajući (unutar granica tolerancije) konstatira se uspješan završetak ispitivanja. Opisano je ishođenje građevinske dozvole i plan izgradnje elektrane. Mjerenja su izvršena u programskom paketu Digsilent Powerfactory 14.This thesis describes the commissioning of the cogeneration plant. We have used data and measurements for real cogeneration plant Spin Valis International. Tests were conducted to test run the plant on schedule and test program in trial period. The first deals with the analysis of the possibilities of user connection to the grid, which defines possible variants of connection. The network is analyzed under voltage conditions, power flows, losses in the network and short-conditions, and based on these analyzes selected the optimal connection. Then the processed plant impact on the quality of the network, where they examined the frequency and voltage before and after the connection of the power plant to the grid. The obligation of the project was to provide the conditions and measures for work in a safe manner in accordance with the Grid Code and its bylaws. After each test recorded the results of tests and checks for compliance with the required criteria. If the test results are satisfactory (within tolerance limits) notes the successful completion of the tests. Described is obtaining a building permit and plan to build a power plant. Measurements have been made in the application pack DIgSILENT Powerfactory 14