- Email: [email protected]
Reliability pricing method based on consumer-centric reliability metrics with the integration of energy storage system
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)4160–4165 000–000 www.elsevier.com/locate/procedia
10th th
International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
ReliabilityThepricing methodSymposium based on consumer-centric reliability 15th International on consumer-centric District Heating and Cooling Reliability pricing method based on reliability metrics with the integration of energy storage system metrics with the integration of energy storage system Assessing the feasibility of using the heat demand-outdoor a a a Shouxiang Wanga, Chenrui Zhaoa, Liyang Liua*, Songguo Xiebb temperature function a long-term district demand Shouxiang Wang for , Chenrui Zhao , Liyang Liu *,heat Songguo Xie forecast a
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China Keya,b,c LaboratorybState of Smart of Ministry Education, Tianjin Grid Tianjin Electricaof Power Company, Tianjin 300010,Tianjin China 300072, a Grid b University, c China b State Grid Tianjin Electric Power Company, Tianjin 300010, China
a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract
Abstract The application of energy storage system (ESS) has been proven of great potential in improving the reliability of distribution The application of energy storage pricing system (ESS) has been proven of great in improving the several reliability of distribution network. However, the reliability mechanism considering ESS is potential not yet available. Firstly, consumer-centric Abstract network. reliability pricingdifferent mechanism considering ESS isfor notreliability yet available. several of consumer-centric reliability However, metrics arethe adopted to evaluate demands of consumers and theFirstly, improvement reliability after reliability metrics are adopted to evaluate different demands of consumers for reliability and the improvement reliability the installation ESS. Based these metrics, a new of reliability proposed to reflect theof value of ESSafter on District heatingof networks are on commonly addressed inmethod the literature as onepricing of the is most effective solutions for decreasing the the installation of reliability ESS. Based on these metrics, a new method of distribution reliability pricing to reflect of ESS the on consumer-centric improvement. The IEEE RBTS bus-2 systemisisproposed used as test systemthetovalue demonstrate greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat consumer-centric reliability improvement. The IEEE bus-2pricing distribution system reliability is used asservices test system tothe demonstrate the effectiveness of the the proposed method conditions in realizing theRBTS differential different integration of sales. Due to changed climate and building renovationofpolicies, heat demand in the with future could decrease, effectiveness of thenetwork. proposed method in realizing the differential pricing of different reliability services with the integration of ESS to distribution prolonging the investment return period. ESS to distribution network. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright © 2018 Elsevier Ltd. All rights reserved. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a caseth study. The district is consisted of 665 © 2019 The Published by Elsevier Ltd. Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. International Applied Selection and peer-review under of scientificThree committee the 10 (low, buildings that access vary inarticle both under construction andthetypology. weatherofscenarios medium,Conference high) and on three district This is an open the CC period BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). Peer-review responsibility of the scientific of ICAE2018 The 10ththe International Conference on Applied Energy. renovation under scenarios were developed (shallow,committee intermediate, deep). To– estimate error, obtained heat demand values were Energy (ICAE2018). compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: energy storage system, consumer-centric, reliability metrics, reliabilty pricing The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Keywords: energy storage system, consumer-centric, reliability metrics, reliabilty pricing (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and With thescenarios integration of energyOnstorage system into distribution network, the power renovation considered). the other hand,(ESS) function intercept increased for 7.8-12.7% persupply decade reliability (dependingof onthe the With the integration energy storagecould system (ESS) into distribution network, thetopower reliability of the distribution network hasof been increasingly improved, makes it also possible meet the different reliability coupled scenarios). The values suggested be used towhich modify the function parameters for the supply scenarios considered, and distribution hasheat been improved, which makes itpricing also possible to meet different reliability reliability demands of network consumers. In thisincreasingly context, through the corresponding mechanism, thethe reasonable improve the accuracy of demand estimations.
demands of consumers. In this context, through the corresponding pricing mechanism, the reasonable reliability © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +86-22-27403740; fax: +86-22-27403740. * E-mail Corresponding Tel.: +86-22-27403740; fax: +86-22-27403740. address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.815
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Shouxiang Wang et al. / Energy Procedia 158 (2019) 4160–4165 Shouxiang Wang/ Energy Procedia 00 (2018) 000–000
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pricing can help to effectively reflect the influence of ESS and share the cost of ESS among consumers with different demands for power supply reliability. The existing reliability pricings mainly focus on the pricing of system reliability considering customers’ different reliability demands and evaluate the cost of reliability enhancement with the application of reserve capacity from the power supply side. Reference [1] presents a synthetically method of deciding on power price based on customers’ different reliability demands, and gives the function of reliability compensation with the technique of value analysis and distribution system’s reliability assessment, and also establishes the reliability power price and compensation mechanism which are seasoned with electricity market operation. Based on quantitative analysis to price elasticity, a new algorithm is presented in [2] to coordinate spot price and reliability. Authors in [3] study demand-price elasticity and its effect on nodal spot price and reliability of deregulated power systems using optimal power flow (OPF) and reliability evaluation techniques. Reference [4] evaluates the efficiency of power supply enterprise and builds an approach to regulate power supply price correlated to efficiency evaluation results to determine the biggest margin of raising price and customer interruption costs. A reliability price and compensation model for distribution network with distributed generation(DG) is proposed in [5], where the interests of both the power supplier and the customer is fully considered, and the investment cost is apportioned according to the customers’ reliability requirements to obtain the reliability price. However, the reliability pricing based on traditional reliability metrics can’t fully reflect the power supply reliability demands that individual consumers possess. In this paper, consumer-centric reliability metrics are used to evaluate the influence of ESS on the reliability of distribution network. Besides, considering the influence of ESS on different consumer’s reliability demands, a reliability pricing method based on the consumer-centric metrics is proposed. The IEEE RBTS bus-2 distribution system is studied to verify the feasibility and effectiveness of the proposed reliability pricing method. 2. Consumer-centric reliability metrics The existing reliability metrics mostly focus on the system side, which can’t fully reflect the power supply reliability that the consumers have perceived. In order to make up for the shortcomings of the traditional reliability evaluation metrics, this paper adopts the consumer-centric reliability metrics proposed by reference [6] to evaluate the improvement of reliability after the installation of ESS. i , x and Sm,i , x are firstly proposed in Reference [6], which represent the severity of an outage happened at interval i to consumer x and consumer-centric severity respectively. Ti ,0 +Ti ,x i , x Pi , x t Ti , x Px ,total Ti ,0
mean, x = i , x Ti , x
i
T i
i, x
T i
i, x
8760h
Sm,i , x i , x mean, x
(1) (2) (3)
The larger value of i , x represents that a severer outage happened at time interval I to consumer x and the larger value of Sm,i , x means an outage occurs when the specific consumer requires more power. Based on these two metrics, consumer-aware failure rate f x , aware , consumer-aware Customer Annual Interruption Duration Index
CAIDI x, aware and consumer-aware interruption average duration rx , aware for consumer x are defined as follows. Nx f x , aware Sm,i , x f m 1 Nx
CAIDI x , aware Tm Sm,i , x
(4) (5)
m 1
rx , aware
CAIDI x , aware f x , aware
(6)
Shouxiang Wang et al. / Energy Procedia 158 (2019) 4160–4165 Shouxiang Wang/ Energy Procedia 00 (2018) 000–000
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3
f x , aware refers to the failure rate that consumers are aware of in a year. Consumer-aware Customer Annual Interruption Duration Index CAIDI x, aware is proposed to quantitatively describe the annual interruption hours from the consumers’ side. A larger value of rx , aware represents a larger interruption average duration. The consumer-aware Expected Energy Not Served ( EENSaware, x ) for x which describes the amount of power not served expected by the consumers is defined as follows.
EENSaware, x
Nx
S m 1
m,i , x
Px ,total Tm
(7)
3. Reliability pricing method considering the application of ESS 3.1.Cost of ESS The capital recovery factor Cc(r,n) which represents the present value of an annuity is expressed as: r (1 r ) n (8) Cc (r , n) (1 r ) n 1 where r is the interest rate and n represents the operating life of ESS. The capital recovery factor Cc(r,n) is converted into daily cost, replacement cost, operating and maintenance cost, as shown below: 24 BAT (9) Ccap Cc (r , n)(CP Pmax CW Amax ) kiTa BAT Ccap
BAT Com
24 W W BAT (Crep Amax kSFF (r , LBAT ) Crep S kSFF (r , LBAT )) kiTa
C P(t ) C
P (t ) 0
o
W
M hourly P (t ) 0
(10) (11)
where Pmax and Amax are ESS power and capacity respectively. CP and Cw are their unit cost. ki is the capacity factor which represents that the unit cost of ESS varies slightly with its capacity. Ta is the operation hours of ESS per year. SBAT is the salvage value of ESS, kSFF refers to the sinking fund factor of ESS. LBAT is the life of ESS. Co and CM are the operation and maintenance cost respectively. Whourly is the empirical value of the self-discharge of ESS in the unit time which is 0.02% Amax normally. P(t)(t=1,2,3…,24) represents the charge-discharge power . 3.2. Reliability pricing with the integration of ESS According to the value engineering theory, the price function elasticity coefficient Epf is used to express the sensitivity of price P to function F. P / P0 E pf (12) F / F0 where P0 is the original base price of the system and F0 is a certain index representing the reliability . When the function changes from F0 to F0 F , the price changes from P0 to P0 P . Only when Epf is identical to 1, can we determine the orientation of function adjustment so as to taking into account the interests of both enterprises and consumers to achieve social equity. Hence it is derived that: F P P0 (13) F0 Epf is used to calculate the price of load point in distribution network, and the consumer-centric reliability metrics are included in the function. In this paper, CAIDI 0,aware is used to represent function F0. The formula of reliability price is shown as below:
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CAIDI x , aware CAIDI 0, x , aware P0 max P P (2 CAIDI x , aware ) P CAIDI x , aware CAIDI 0, x , aware x 0 0 CAIDI 0, x , aware (14) Px (CAIDI x , aware ) P P (2 CAIDI x , aware ) P CAIDI 0, x , aware
CAIDI x, aware is less than or equal to CAIDI 0, x, aware , which means that consumers enjoy power supply with higher reliability, they should be charged more for the better service. When the actual value of CAIDI x, aware is less than
CAIDI 0, x, aware but larger than CAIDI 0, x, aware , which indicates the actual reliability can’t satisfy the demands of consumers, the reliability price is set to be lower than normal situation. 4. Case study The IEEE-RBTS feeder F4 at bus-2 shown in Fig.1 is used to demonstrate the proposed reliability pricing method.
Fig.1.IEEE-RBTS feeder F4
In Fig.1, a microgrid with several types of DG including Photovoltaic (PVs), Wind Turbines (WTs), diesel generators and ESSs is connected to the grid through a PCC node. LP16-LP22 are load points. This paper assumes that the loads are of the same type with different reliability demands, and mainly focuses on the influence of ESS on the reliability pricing of LP21and LP22. The parameters of the loads including rate power and CAIDI 0, x, aware are presented in Table 1. Table 1.Load parameters Load number
P(kW)
CAIDI0,x,aware(h)
LP16
18000
1.20
LP21
3000
2.70
LP17
24000
3.10
Shouxiang Wang et al. / Energy Procedia 158 (2019) 4160–4165 Shouxiang Wang/ Energy Procedia 00 (2018) 000–000
4164
LP22
2500
3.45
LP18-19
8000
3.55
LP20
800
3.68
5
In order to demonstrate the impact of ESS on reliability pricing, three scenarios are discussed in this paper. Scenario 1 (S1) assumes no ESS is connected to the system. Scenario 2 (S2) assumes only 2 ESSs are connected to the system. And in scenario 3 (S3), 4 ESSs are considered. Parameters and reliability metrics of DG are presented in Table 2[9], in which MTBF represents mean time between failures, and MTTR is mean time to repair. Table 2.DG parameters DG
PV
WG
Diesel
ESS
Number
4
2
2
——
Rate power(kW)
92
100
227
120
MTBF(h)
900
1000
600
970
MTTR(h)
20
50
50
30
Cut-in/ 20 groups of 20 Specification
series 230Wp; CNPV-230M
FOR=0.03;
Failure rate
rated/
50
to start is
cut-out wind speeds (m/s):
12V/200Ah
0.2;
in series;
Time to
Min SOC is
start is 0.1h
4/12/25
4%
The results of CAIDI0,x,aware are shown in Table 3. The original CAIDIx,aware of LP21 and LP22 in S1 is lower than CAIDI0,x,aware shown in Table 1 which indicates that the actual reliability in S1 can’t meet their demand. It is obvious that the reliability increases in S2, S3 compared with S1, which indicates that the integration of ESS to distributed network has improved the reliability of LP21and LP22. Table 3. CAIDIx,aware in different scenarios Load number
S1
S2
S3
LP21
3.567
3.423
3.198
LP22
3.467
3.389
3.176
The results of reliability price with the integration of ESS in different scenarios are presented in Fig.2. Due to the application of ESS, the reliability of LP21 and LP22 improves which makes the reliability prices of LP21 and LP22 increase as well. Since more ESSs are integrated into the system in S3 which makes reliability of LP21 and LP22 improve closer to what they demand, the reliability price of each consumer is the highest among 3 scenarios. Meanwhile in 3 scenarios, the reliability of LP22 is higher than that of LP21, so LP22 should bear more expenses, which indicates that consumers who enjoy higher reliability should pay more.
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Fig.2.Reliability price in 3 scenarios
5. Conclusions In this paper, a reliability pricing method based on consumer-centric metrics with the integration of ESS is proposed. By sharing the investment and operation cost of ESS and power supply cost under different consumer-centric reliability level, the power supply requirements of consumers with different reliability demands can be satisfied. The case study indicates the effect of different amount of ESSs in the microgrid on the reliability pricing, and it also shows the effectiveness and practicability of proposed pricing method in realizing the differential pricing of different reliability services.
Acknowledgements This paper is supported by State Grid Corporation of China Research Program. References [1] Zhou Ping, Xie Kaigui, Zhou Jiaqi, et al.Reliability power price and compensation mechanism adapted to electricity market operation environment[J]. Automation of Electric Power Systems, 2004, 28(21): 6-11. [2] Li Sheng-hu, Ding Ming. Coordination to power system operational reliability in power market based on price elasticity[J]. Proceedings of CSEE, 2005, 25(24): 34-40. [3] Goel L, Qiuwei Wu, Peng Wang, et al. Reliability enhancement of deregulated power systems considering demand-price elasticity[C]. 2005 International Power Engineering Conference, 29 Nov-2 Dec, 2005, Singapore. [4] Zhou Ming, Tan Zhihua, Li Gengyin. An approach to regulate electricity supply price considering power supply reliability[J]. Power System Technology, 2011, 35(1):197-202. [5] Biyun Chen, Yu Zheng, Fuyong Zhang. Reliability price and compemsation mechanism of distribution network with distributed generation[C].2017 IEEE Conference on Energy Internet and Energy System Integration, 26-28 Nov, 2017, Beijing, China. [6] Shouxiang Wang, Liyang Liu, Lei Wu, et al. Consumer-centric spatiotemporal reliability assessment and compensation model with sensitive component analysis in smart grid[J].IEEE Transaction on Power Systems, 2018, 33(2):2155-2164. [7] MA Xiyuan, WU Yaowen, FANG Hualiang, et al. Optimal sizing of hybrid solar-wind distributed generation in an islanded microgrid using improved bacterial foraging algorithm[J]. Proceedings of the CSEE, 2011, 31(25): 17-25. [8] Greene N, Hammerschlag R, Small and clean is beautiful: exploring the emissions of distributed generation and pollution prevention policies, Electricity Journal, 2000, 13(5)：50-60 [9] S. Wang, Z. Li, L. Wu, S. Mohammad, and Z. Li, “New metrics for assessing the reliability and economics of microgrids in distribution systems,” IEEE Trans. Power Syst., vol.28, no.3, pp.2852-2861, Aug.2013.
ScienceDirect ScienceDirect
Energyonline Procedia 00 (2018) 000–000 Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)4160–4165 000–000 www.elsevier.com/locate/procedia
10th th
International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
ReliabilityThepricing methodSymposium based on consumer-centric reliability 15th International on consumer-centric District Heating and Cooling Reliability pricing method based on reliability metrics with the integration of energy storage system metrics with the integration of energy storage system Assessing the feasibility of using the heat demand-outdoor a a a Shouxiang Wanga, Chenrui Zhaoa, Liyang Liua*, Songguo Xiebb temperature function a long-term district demand Shouxiang Wang for , Chenrui Zhao , Liyang Liu *,heat Songguo Xie forecast a
Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China Keya,b,c LaboratorybState of Smart of Ministry Education, Tianjin Grid Tianjin Electricaof Power Company, Tianjin 300010,Tianjin China 300072, a Grid b University, c China b State Grid Tianjin Electric Power Company, Tianjin 300010, China
a
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
a
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract
Abstract The application of energy storage system (ESS) has been proven of great potential in improving the reliability of distribution The application of energy storage pricing system (ESS) has been proven of great in improving the several reliability of distribution network. However, the reliability mechanism considering ESS is potential not yet available. Firstly, consumer-centric Abstract network. reliability pricingdifferent mechanism considering ESS isfor notreliability yet available. several of consumer-centric reliability However, metrics arethe adopted to evaluate demands of consumers and theFirstly, improvement reliability after reliability metrics are adopted to evaluate different demands of consumers for reliability and the improvement reliability the installation ESS. Based these metrics, a new of reliability proposed to reflect theof value of ESSafter on District heatingof networks are on commonly addressed inmethod the literature as onepricing of the is most effective solutions for decreasing the the installation of reliability ESS. Based on these metrics, a new method of distribution reliability pricing to reflect of ESS the on consumer-centric improvement. The IEEE RBTS bus-2 systemisisproposed used as test systemthetovalue demonstrate greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat consumer-centric reliability improvement. The IEEE bus-2pricing distribution system reliability is used asservices test system tothe demonstrate the effectiveness of the the proposed method conditions in realizing theRBTS differential different integration of sales. Due to changed climate and building renovationofpolicies, heat demand in the with future could decrease, effectiveness of thenetwork. proposed method in realizing the differential pricing of different reliability services with the integration of ESS to distribution prolonging the investment return period. ESS to distribution network. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand Copyright © 2018 Elsevier Ltd. All rights reserved. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a caseth study. The district is consisted of 665 © 2019 The Published by Elsevier Ltd. Copyright ©Authors. 2018 Elsevier Ltd. Allresponsibility rights reserved. International Applied Selection and peer-review under of scientificThree committee the 10 (low, buildings that access vary inarticle both under construction andthetypology. weatherofscenarios medium,Conference high) and on three district This is an open the CC period BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). Peer-review responsibility of the scientific of ICAE2018 The 10ththe International Conference on Applied Energy. renovation under scenarios were developed (shallow,committee intermediate, deep). To– estimate error, obtained heat demand values were Energy (ICAE2018). compared with results from a dynamic heat demand model, previously developed and validated by the authors. Keywords: energy storage system, consumer-centric, reliability metrics, reliabilty pricing The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Keywords: energy storage system, consumer-centric, reliability metrics, reliabilty pricing (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1.The Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1. Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and With thescenarios integration of energyOnstorage system into distribution network, the power renovation considered). the other hand,(ESS) function intercept increased for 7.8-12.7% persupply decade reliability (dependingof onthe the With the integration energy storagecould system (ESS) into distribution network, thetopower reliability of the distribution network hasof been increasingly improved, makes it also possible meet the different reliability coupled scenarios). The values suggested be used towhich modify the function parameters for the supply scenarios considered, and distribution hasheat been improved, which makes itpricing also possible to meet different reliability reliability demands of network consumers. In thisincreasingly context, through the corresponding mechanism, thethe reasonable improve the accuracy of demand estimations.
demands of consumers. In this context, through the corresponding pricing mechanism, the reasonable reliability © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +86-22-27403740; fax: +86-22-27403740. * E-mail Corresponding Tel.: +86-22-27403740; fax: +86-22-27403740. address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.815
2
Shouxiang Wang et al. / Energy Procedia 158 (2019) 4160–4165 Shouxiang Wang/ Energy Procedia 00 (2018) 000–000
4161
pricing can help to effectively reflect the influence of ESS and share the cost of ESS among consumers with different demands for power supply reliability. The existing reliability pricings mainly focus on the pricing of system reliability considering customers’ different reliability demands and evaluate the cost of reliability enhancement with the application of reserve capacity from the power supply side. Reference [1] presents a synthetically method of deciding on power price based on customers’ different reliability demands, and gives the function of reliability compensation with the technique of value analysis and distribution system’s reliability assessment, and also establishes the reliability power price and compensation mechanism which are seasoned with electricity market operation. Based on quantitative analysis to price elasticity, a new algorithm is presented in [2] to coordinate spot price and reliability. Authors in [3] study demand-price elasticity and its effect on nodal spot price and reliability of deregulated power systems using optimal power flow (OPF) and reliability evaluation techniques. Reference [4] evaluates the efficiency of power supply enterprise and builds an approach to regulate power supply price correlated to efficiency evaluation results to determine the biggest margin of raising price and customer interruption costs. A reliability price and compensation model for distribution network with distributed generation(DG) is proposed in [5], where the interests of both the power supplier and the customer is fully considered, and the investment cost is apportioned according to the customers’ reliability requirements to obtain the reliability price. However, the reliability pricing based on traditional reliability metrics can’t fully reflect the power supply reliability demands that individual consumers possess. In this paper, consumer-centric reliability metrics are used to evaluate the influence of ESS on the reliability of distribution network. Besides, considering the influence of ESS on different consumer’s reliability demands, a reliability pricing method based on the consumer-centric metrics is proposed. The IEEE RBTS bus-2 distribution system is studied to verify the feasibility and effectiveness of the proposed reliability pricing method. 2. Consumer-centric reliability metrics The existing reliability metrics mostly focus on the system side, which can’t fully reflect the power supply reliability that the consumers have perceived. In order to make up for the shortcomings of the traditional reliability evaluation metrics, this paper adopts the consumer-centric reliability metrics proposed by reference [6] to evaluate the improvement of reliability after the installation of ESS. i , x and Sm,i , x are firstly proposed in Reference [6], which represent the severity of an outage happened at interval i to consumer x and consumer-centric severity respectively. Ti ,0 +Ti ,x i , x Pi , x t Ti , x Px ,total Ti ,0
mean, x = i , x Ti , x
i
T i
i, x
T i
i, x
8760h
Sm,i , x i , x mean, x
(1) (2) (3)
The larger value of i , x represents that a severer outage happened at time interval I to consumer x and the larger value of Sm,i , x means an outage occurs when the specific consumer requires more power. Based on these two metrics, consumer-aware failure rate f x , aware , consumer-aware Customer Annual Interruption Duration Index
CAIDI x, aware and consumer-aware interruption average duration rx , aware for consumer x are defined as follows. Nx f x , aware Sm,i , x f m 1 Nx
CAIDI x , aware Tm Sm,i , x
(4) (5)
m 1
rx , aware
CAIDI x , aware f x , aware
(6)
Shouxiang Wang et al. / Energy Procedia 158 (2019) 4160–4165 Shouxiang Wang/ Energy Procedia 00 (2018) 000–000
4162
3
f x , aware refers to the failure rate that consumers are aware of in a year. Consumer-aware Customer Annual Interruption Duration Index CAIDI x, aware is proposed to quantitatively describe the annual interruption hours from the consumers’ side. A larger value of rx , aware represents a larger interruption average duration. The consumer-aware Expected Energy Not Served ( EENSaware, x ) for x which describes the amount of power not served expected by the consumers is defined as follows.
EENSaware, x
Nx
S m 1
m,i , x
Px ,total Tm
(7)
3. Reliability pricing method considering the application of ESS 3.1.Cost of ESS The capital recovery factor Cc(r,n) which represents the present value of an annuity is expressed as: r (1 r ) n (8) Cc (r , n) (1 r ) n 1 where r is the interest rate and n represents the operating life of ESS. The capital recovery factor Cc(r,n) is converted into daily cost, replacement cost, operating and maintenance cost, as shown below: 24 BAT (9) Ccap Cc (r , n)(CP Pmax CW Amax ) kiTa BAT Ccap
BAT Com
24 W W BAT (Crep Amax kSFF (r , LBAT ) Crep S kSFF (r , LBAT )) kiTa
C P(t ) C
P (t ) 0
o
W
M hourly P (t ) 0
(10) (11)
where Pmax and Amax are ESS power and capacity respectively. CP and Cw are their unit cost. ki is the capacity factor which represents that the unit cost of ESS varies slightly with its capacity. Ta is the operation hours of ESS per year. SBAT is the salvage value of ESS, kSFF refers to the sinking fund factor of ESS. LBAT is the life of ESS. Co and CM are the operation and maintenance cost respectively. Whourly is the empirical value of the self-discharge of ESS in the unit time which is 0.02% Amax normally. P(t)(t=1,2,3…,24) represents the charge-discharge power . 3.2. Reliability pricing with the integration of ESS According to the value engineering theory, the price function elasticity coefficient Epf is used to express the sensitivity of price P to function F. P / P0 E pf (12) F / F0 where P0 is the original base price of the system and F0 is a certain index representing the reliability . When the function changes from F0 to F0 F , the price changes from P0 to P0 P . Only when Epf is identical to 1, can we determine the orientation of function adjustment so as to taking into account the interests of both enterprises and consumers to achieve social equity. Hence it is derived that: F P P0 (13) F0 Epf is used to calculate the price of load point in distribution network, and the consumer-centric reliability metrics are included in the function. In this paper, CAIDI 0,aware is used to represent function F0. The formula of reliability price is shown as below:
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CAIDI x , aware CAIDI 0, x , aware P0 max P P (2 CAIDI x , aware ) P CAIDI x , aware CAIDI 0, x , aware x 0 0 CAIDI 0, x , aware (14) Px (CAIDI x , aware ) P P (2 CAIDI x , aware ) P CAIDI 0, x , aware
CAIDI x, aware is less than or equal to CAIDI 0, x, aware , which means that consumers enjoy power supply with higher reliability, they should be charged more for the better service. When the actual value of CAIDI x, aware is less than
CAIDI 0, x, aware but larger than CAIDI 0, x, aware , which indicates the actual reliability can’t satisfy the demands of consumers, the reliability price is set to be lower than normal situation. 4. Case study The IEEE-RBTS feeder F4 at bus-2 shown in Fig.1 is used to demonstrate the proposed reliability pricing method.
Fig.1.IEEE-RBTS feeder F4
In Fig.1, a microgrid with several types of DG including Photovoltaic (PVs), Wind Turbines (WTs), diesel generators and ESSs is connected to the grid through a PCC node. LP16-LP22 are load points. This paper assumes that the loads are of the same type with different reliability demands, and mainly focuses on the influence of ESS on the reliability pricing of LP21and LP22. The parameters of the loads including rate power and CAIDI 0, x, aware are presented in Table 1. Table 1.Load parameters Load number
P(kW)
CAIDI0,x,aware(h)
LP16
18000
1.20
LP21
3000
2.70
LP17
24000
3.10
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LP22
2500
3.45
LP18-19
8000
3.55
LP20
800
3.68
5
In order to demonstrate the impact of ESS on reliability pricing, three scenarios are discussed in this paper. Scenario 1 (S1) assumes no ESS is connected to the system. Scenario 2 (S2) assumes only 2 ESSs are connected to the system. And in scenario 3 (S3), 4 ESSs are considered. Parameters and reliability metrics of DG are presented in Table 2[9], in which MTBF represents mean time between failures, and MTTR is mean time to repair. Table 2.DG parameters DG
PV
WG
Diesel
ESS
Number
4
2
2
——
Rate power(kW)
92
100
227
120
MTBF(h)
900
1000
600
970
MTTR(h)
20
50
50
30
Cut-in/ 20 groups of 20 Specification
series 230Wp; CNPV-230M
FOR=0.03;
Failure rate
rated/
50
to start is
cut-out wind speeds (m/s):
12V/200Ah
0.2;
in series;
Time to
Min SOC is
start is 0.1h
4/12/25
4%
The results of CAIDI0,x,aware are shown in Table 3. The original CAIDIx,aware of LP21 and LP22 in S1 is lower than CAIDI0,x,aware shown in Table 1 which indicates that the actual reliability in S1 can’t meet their demand. It is obvious that the reliability increases in S2, S3 compared with S1, which indicates that the integration of ESS to distributed network has improved the reliability of LP21and LP22. Table 3. CAIDIx,aware in different scenarios Load number
S1
S2
S3
LP21
3.567
3.423
3.198
LP22
3.467
3.389
3.176
The results of reliability price with the integration of ESS in different scenarios are presented in Fig.2. Due to the application of ESS, the reliability of LP21 and LP22 improves which makes the reliability prices of LP21 and LP22 increase as well. Since more ESSs are integrated into the system in S3 which makes reliability of LP21 and LP22 improve closer to what they demand, the reliability price of each consumer is the highest among 3 scenarios. Meanwhile in 3 scenarios, the reliability of LP22 is higher than that of LP21, so LP22 should bear more expenses, which indicates that consumers who enjoy higher reliability should pay more.
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Fig.2.Reliability price in 3 scenarios
5. Conclusions In this paper, a reliability pricing method based on consumer-centric metrics with the integration of ESS is proposed. By sharing the investment and operation cost of ESS and power supply cost under different consumer-centric reliability level, the power supply requirements of consumers with different reliability demands can be satisfied. The case study indicates the effect of different amount of ESSs in the microgrid on the reliability pricing, and it also shows the effectiveness and practicability of proposed pricing method in realizing the differential pricing of different reliability services.
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