Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linea...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Okawa, Yoshihiro [verfasserIn] Namerikawa, Toru

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Smart gird real-time pricing AC model of power grid

Anmerkung:	© South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015

Übergeordnetes Werk:	Enthalten in: Journal of control theory and applications - Guangzhou, 2003, 13(2015), 2 vom: Mai, Seite 90-100
Übergeordnetes Werk:	volume:13 ; year:2015 ; number:2 ; month:05 ; pages:90-100

Links:	Volltext

DOI / URN:	10.1007/s11768-015-4131-5

Katalog-ID:	SPR022315969

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allfields	10.1007/s11768-015-4131-5 doi (DE-627)SPR022315969 (SPR)s11768-015-4131-5-e DE-627 ger DE-627 rakwb eng Okawa, Yoshihiro verfasserin aut Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213 Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 13(2015), 2 vom: Mai, Seite 90-100 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:13 year:2015 number:2 month:05 pages:90-100 https://dx.doi.org/10.1007/s11768-015-4131-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 13 2015 2 05 90-100
spelling	10.1007/s11768-015-4131-5 doi (DE-627)SPR022315969 (SPR)s11768-015-4131-5-e DE-627 ger DE-627 rakwb eng Okawa, Yoshihiro verfasserin aut Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213 Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 13(2015), 2 vom: Mai, Seite 90-100 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:13 year:2015 number:2 month:05 pages:90-100 https://dx.doi.org/10.1007/s11768-015-4131-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 13 2015 2 05 90-100
allfields_unstemmed	10.1007/s11768-015-4131-5 doi (DE-627)SPR022315969 (SPR)s11768-015-4131-5-e DE-627 ger DE-627 rakwb eng Okawa, Yoshihiro verfasserin aut Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213 Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 13(2015), 2 vom: Mai, Seite 90-100 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:13 year:2015 number:2 month:05 pages:90-100 https://dx.doi.org/10.1007/s11768-015-4131-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 13 2015 2 05 90-100
allfieldsGer	10.1007/s11768-015-4131-5 doi (DE-627)SPR022315969 (SPR)s11768-015-4131-5-e DE-627 ger DE-627 rakwb eng Okawa, Yoshihiro verfasserin aut Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213 Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 13(2015), 2 vom: Mai, Seite 90-100 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:13 year:2015 number:2 month:05 pages:90-100 https://dx.doi.org/10.1007/s11768-015-4131-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 13 2015 2 05 90-100
allfieldsSound	10.1007/s11768-015-4131-5 doi (DE-627)SPR022315969 (SPR)s11768-015-4131-5-e DE-627 ger DE-627 rakwb eng Okawa, Yoshihiro verfasserin aut Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015 Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213 Namerikawa, Toru aut Enthalten in Journal of control theory and applications Guangzhou, 2003 13(2015), 2 vom: Mai, Seite 90-100 (DE-627)529093456 (DE-600)2299595-X 1993-0623 nnns volume:13 year:2015 number:2 month:05 pages:90-100 https://dx.doi.org/10.1007/s11768-015-4131-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 AR 13 2015 2 05 90-100
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author	Okawa, Yoshihiro
spellingShingle	Okawa, Yoshihiro misc Smart gird misc real-time pricing misc AC model of power grid Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
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topic_title	Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid Smart gird (dpeaa)DE-He213 real-time pricing (dpeaa)DE-He213 AC model of power grid (dpeaa)DE-He213
topic	misc Smart gird misc real-time pricing misc AC model of power grid
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title	Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
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title_full	Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
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doi_str_mv	10.1007/s11768-015-4131-5
title_sort	distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
title_auth	Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
abstract	Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015
abstractGer	Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015
abstract_unstemmed	Abstract This paper discusses a distributed decision procedure for determining the electricity price for a real-time electricity marketin an energy management system. The price decision algorithm proposed in this paper derives the optimal electricity pricewhile considering the constraints of a linearized AC power grid model. The algorithm is based on the power demand-supplybalance and voltage phase differences in a power grid. In order to determine the optimal price that maximizes the social welfare distributively and to improve the convergence speed of the algorithm, the proposed algorithm updates the price through the alternating decision making of market participants. In this paper, we show the convergence of the price derived from our proposed algorithm. Furthermore, numerical simulation results show that the proposed dynamic pricing methodology is effective and that there is an improvement in the convergence speed, as compared with the conventional method. © South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2015
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title_short	Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid
url	https://dx.doi.org/10.1007/s11768-015-4131-5
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Distributed dynamic pricing based on demand-supply balance and voltage phase difference in power grid

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