Risk Assessment of Power System based on a Three Level Capability from Active Distribution Network

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Energy Procedia 158 Energy Procedia 00(2019) (2017)3184–3190 000–000 www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Risk Assessment of Power System based on a Three Level The 15th International Symposium on District Heating Cooling Risk Assessment of Power System based on aand Three Level Capability from Active Distribution Network Capability from Active Distribution Network Assessing the feasibility of using the heat demand-outdoor a Gongxin Lia, Zili Yinaa, Gonglin Zhangbb, Wenying Huangaa, Lin Licc, Lijia Ducc, Wanqing c Gongxin Li , Zili function Yin , Gonglin ,c,Wenying Huang , Lin Li demand , Lijia Du ,forecast Wanqing temperature forZhang aChen long-term district heat Shiqi Guo c c Chen , Shiqi Guo *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

State a Grid Fujian Electric a Power Co., Ltd.bFuzhou, China c c b State Grid FujianaState Electric Power Co., Ltd. Electric Power Grid Fujian Electric Power Co., Ltd.Research Fuzhou, Institute, China Fuzhou, China b cKey Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China State Grid Fujian Electric Power Co., Ltd. Electric Power Research Institute, Fuzhou, China a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal c KeybLaboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin, China 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

a,b,c

I. Andrić

a

Abstract Abstract The loads of traditional distribution network were usually aggregated as an equivalent rigid load in risk analysis. However, Abstract The loadsnetwork of traditional distribution network an equivalent rigid load in risk analysis. However, distribution is becoming more and more were activeusually due to aggregated the flexible as topology and responsive resources, such as distributed distribution network is becoming more and more active due to the flexible topology and responsive resources, such as distributed generations (DGs) and flexible loads. In this paper, a three-level risk relief strategy for power system is proposed, which includes District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the generations (DGs) and flexible loads. In this islanding paper, risk relief strategy for power system is proposed, which out includes angreenhouse active reconfiguration scheme, an building active scheme and a require demand response scheme. Case are studies are carried a gas emissions from the sector.a three-level These systems high investments which returned through theonheat ansales. active reconfiguration scheme, an active islanding scheme a demand responseheat scheme. are carried out on a Due to the changed climate conditions andnetworks buildingand demand instudies the future couldrisk decrease, typical IEEE 30-bus system with active distribution torenovation verify thepolicies, effectiveness of theCase proposed multi-level relief prolonging the investment return period. typical IEEE 30-bus systemshow with that active totoverify the effectiveness of theto proposed multi-level risk relief strategy. Simulation results thedistribution strategy cannetworks contribute risk relief of power system some extent, and guarantee the The main scopeofofthe this paper is tothat assess the feasibility of using to therisk heatrelief demand – outdoor temperature function heat demand strategy. Simulation results show the strategy can contribute of power system to some extent, andfor guarantee the reliability index distribution network at the same time. forecast. index The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 reliability of the distribution network at the same time. buildings ©that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright 2018 Elsevier Ltd. All rights reserved. ©renovation 2019 The Authors. Published by Elsevier Ltd. intermediate, deep). To estimate the error, scenarios were developed (shallow, obtained heat demand on values were Copyright © 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the 10th International Conference Applied This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) th International compared with results from a dynamic heat demand model, previously developed and validated by the authors. Selection and peer-review under responsibility of the scientific committee of the 10 Conference on Applied Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – The of 10th International Conferencefor onsome Applied Energy. The results showed that when only weather change is considered, the margin error could be acceptable applications Energy (ICAE2018). (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: Risk; Active distribution network; Responsive resources; Distributed generation scenarios,Risk; the Active error value increased up Responsive to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: distribution network; resources; Distributed generation The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and 1.decrease Introduction renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the 1. Introduction coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and Severe attacks on power line often lead to high risk level of power system[1]. Power system under high risk improve the accuracy of heat demand estimations. [1]

. Power system under high As riska Severe conditions attacks onmay power lead of tonetwork high risk level of power system operation leadline to aoften violation security limits, such as thermal and reliability margins. operation conditions may lead to a becomes violation more of network security limits, such as thermal and reliability margins. As a result, effective risk relief strategy and more important. © 2017 The Authors. Published by Elsevier Ltd. result, risk relief strategy becomes and more important. Theeffective determination of sensitive generators to reschedule for15th riskInternational relief was proposed inon [2]. In [3], a dispatching Peer-review under responsibility of the Scientificmore Committee of The Symposium District Heating and The determination of sensitive generators to reschedule for risk relief was proposed in [2]. In [3], a dispatching strategy was developed by optimizing the generators for minimizing the cost. The load shedding methods were Cooling. strategy developed by optimizing generators for minimizing cost. system The load methodsinwere proposedwas in [4-6]. A demand response the method was proposed to reducethepower riskshedding was developed [7], proposed [4-6]. A Forecast; demandClimate response method was proposed to reduce power system risk was developed in [7], Keywords: in Heat demand; change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10 th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility of the scientific committee of the 10 th 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.2019.01.1014

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where the capability from responsive loads was utilized. A multi-objective expansion planning approach was proposed in [8] to enhance the power transfer capacity. Power loss risk relief methods using energy storage were carried out in [9-11]. However, few studies have been carried out to investigate the risk relief capability from active distribution network with flexible topology, distribution generations (DG) and responsive loads. However, with the integration of DGs, responsive loads, the flexible distribution network is transformed from passive status to an active distribution network (ADN). Thus, the distribution network was not supposed to be traditionally treated as a simple P Q-load bus in risk assessment[12-14]. A hierarchical risk assessment approach for power system was proposed in [15], where the DGs’ capacities were used. In this paper, a three-level risk relief strategy for power system is proposed, which can make full use of the capability from multi-source ADN for risk relief of power system, and guarantee the reliability index of the distribution network. 1.1. Framework of the three-level risk relief strategy A multi-source ADN has the capability to adjust its topology and flexible resources. The MG control center (MGCC) can coordinate the DGs and controllable loads in MG. Also, MGCC can communicate with up-stream active distribution management system (ADMS) and power system dispatching center (PSDC) to adjust its operation status to support operation of power system. At the demand side, the controlling of the EV charging process can reduce the load level of distribution network. The state of charge (SOC) is used as an indicator to represent the available capacity of EV battery. The framework of the three-level risk relief strategy is shown in Fig.1 Plug-in Electric Vehicles

Roofto p Solar

EV

...

Plug-in Electric Vehicles

Roofto p Solar Plug-in Electric Vehicles

..

.

Roofto p Solar

…

EV

Operation data acquisition

Operation data acquisition

PSDC

EV

The actual operation demand from the transmission network

Operation data acquisition

ADMS

The capability from multi-source active distribution network

:Power flow :Control flow PSDC: Power system dispatching center ADMS: Active distribution management system MGCC: MG control center EVCS: EV charging station control center ADN：Active distribution network

MGCC

EVSCC

Centralized

Selection of multi-level control strategy for transmission congestion

Adjustment the operation of the ADN

Active reconfiguration scheme Active islanding scheme Demand response scheme

Fig. 1. Framework of multi-level risk relief strategy

1.2. Risk index The expected energy not supplied (EENS, MWh/y) is used in this paper to assess the risk of power system.  EENS

NL

( p

i 1

sQi

T

( s)  C0 ( s))Ti

(1)

where NL is the total number of load levels; Ti is the duration of load level; Qi is system state set for load level i; pT(s) is occurrence probability of system state s; C0(s) is total load curtailment in system state s. 2. The multi-level risk relief strategy 2.1. Active reconfiguration scheme The active reconfiguration scheme is selected when (2) is satisfied.

PFR  PLD

(2)

where PFR is the reduction of tie line power; PLD is the power of exceeding the upper power limit that leads to the risk of i-th line, which is obtained by (3).

PLD  Pini ,i  Plim,i

(3)

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where Pini,i is the initial power on i-th line; Plim,i is the upper power limit of line i. A modified PSO algorithm in [16] is used to solve the active reconfiguration problem, and the specific optimization model can be obtained in [17]. 2.2. Active islanding scheme The active islanding scheme is selected when (4) is satisfied. PFR  PLD   PL,i

(4)

iV

where PL,i is the power at bus i. V is the set of the largest possible size of autonomous island. 2.3. Demand response scheme The demand response scheme is selected when (5) is satisfied. p

P iV

 PLD   PL,i   EVk

L ,i

iV

(5)

k 1

where p is the number of EVCS outside the scope of the island obtained in the active islanding scheme; EVk is the total available EV charging load obtained in the active islanding scheme. The demand response scheme uses the available EV charging loads to extend the range of active islanding, and reduce the power flow of the tie line between the up and downstream distribution networks. The demand response scheme is divided into two sub-optimization models. determine the amount of EVs load shedding of the EVCS. The optimization model can determine the load transfer amount from the distribution network to the active island to satisfy the demand of upstream network or determine the amount of EVs load shedding of the EVCS. 3. Risk assessment 3.1. System state selection A state of the power system is defined as the combination of the available and failed states of each component, which can be represented by a vector as follows:

s  ( x1 , x2 ,..., xi ..., xn )

(6)

where xi is the state of the i-th component. Enumeration and Monte Carlo simulation method are extensively applied in system state selection. In enumeration method the probability of system state s, pT(s), can be expressed as follows:

 pT ( s)

Nf

Nn

U  (1  U

i i 1 j 1

Ui 

j

)

i i  i

(7) (8)

where Ui is the average unavailability of component i; Nf and Nn are the numbers of failure and normal components, respectively; i and i are the outage rate and repair rate of component i. In Monte Carlo simulation method, pT(s) can be calculated as follows:

pT (s)  m / M where M is the sampling number in Monte Carlo simulation; m is the occurrence number of system state.

(9)

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3.2. Risk Indices Calculation The three- level risk relief strategy is intensively used in this paper to convert the system for risk assessment. Then the total load curtailment, C0 assigned to evaluate consequence of fault can be calculated as follows: C0 

l

 (P -P d

h 1

Lh

(10)

)

where l is the total number of load buses in the upstream network; PLh is the equivalent load curtailment with the three- level risk relief strategy at the downstream networks; Pd is the absolute load curtailment determined by the optimal power flow model at the upstream network [15]. Then the expected load curtailment is determined as (11). N

E (C0 ) 

 C ( j) j 1

e

(11)

N

where N is sampling number in Monte Carol Simulation. G

1

G

2

5

L1-2 L1-3

L2-5 L2-4

L2-6

L3-4

L8-28

6

4

8

L6-9

13

14

L9-11

12

L12-13 L12-14

L12-16

10

L16-17

L10-17 17

18

L14-15

L18-19

15

20 19

L15-18 Distribution network

L15-23

G

L25-27

L22-24

G L23-24

24

12

29

15

11

19

14

30

6

L29-30

L25-26 26

5

13

L27-29

25

L24-25

4

L27-30

21

22

L19-20

23

27

L10-22 L21-22

2

F3

3

22

7

18 9

20

17

27 12 26

Sectionalizing switches

Fig. 2. The IEEE 30-bus system

EVCS 1

16 8

G

L10-21

L10-20

F2

EVCS 2

1

11

L9-10

16

L12-15

L27-28 11

9

G

F1

L6-8

L6-10

L4-12

28

L6-7 L6-28

L4-6

3

7

L5-7

10 17

21 14 28 18

24

13

29 23

MG 16

25

15

Tie switches

Fig. 3. A three-feeder distribution network

The proposed three-level risk relief strategy and risk assessment is verified using a typical IEEE 30-bus system shown in Fig.3 with ADNs (a three-feeder distribution network with MGs and EV charging stations shown in Fig.4). The data of the three systems can be obtained in [18-20]. 3.3. Simulation results of the three level risk relief strategy It is assumed that a contingency occurs at line L18-19 leading to a violation of the thermal limit of line L19-20. The three-feeder distribution network with MGs and EV charging stations is connected to bus 19 of the upstream network. The MG locates at bus 17 and two EVCSs locate at bus 8, 12 respectively. 1) Simulation results of the active reconfiguration The contingency at L18-19, the power flow of line L19-20 is 32.02MW, which leads to a violation of thermal limit (32MW). The active reconfiguration scheme is selected. The reconfiguration results of the distribution network are shown in Table 1. Table 1. Reconfiguration results. Open switches

Tie line power (MW)

Before reconfiguration

15, 21, 26, 28

32.02

After reconfiguration

19, 21, 26, 28

31.98

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2) Simulation results of the active islanding It is assumed that the thermal limit of line L19-20 is 85% of its initial value. If the active reconfiguration scheme is used, the power flow of critical L19-20 is 31.98MW, which is higher than the thermal limit (27.2MW). At this scenario, the PLD is 4.82MW and the PFR is 0.0242MW, which satisfies (4) and the active islanding scheme is selected. The result of the final active islanding scheme is shown in Fig.5. The active islanding results are shown in Table 2. F1

F2

EVCS 2

1

11

EVCS 1

12 5

13

15

11

19

14 6

7

F1

F3

3

22

16 8

4

2

18 9

20

17

27 12

21 14

10

28 18

17

11

13

4

16

12

29 23

5

13 25

15

11

19

18 9

7

17

27 12

F3

3

21 14

10

28 18

17

24

13

29 23

MG

26

Sectionalizing switches

Tie switches

Fig. 4. The result of active islanding scheme

EVCS 1

22

20

14 6

15

2

16 8

MG

26

Sectionalizing switches

24

F2

EVCS 2

1

16

25

15

Tie switches

Fig. 5. The optimization active islanding in demand response scheme

Table 2. Active islanding of the distribution network. Power output from MGs (MVA)

Load shedding from EVCS1 (MW)

Load shedding from EVCS2 (MW)

Line loss (MW)

5.511-j1.189

0

0

0.011

3) Simulation results of the demand response It is assumed that the thermal limit of the line L19-20 is 60% of its initial value. If the active islanding scheme is selected, the power flow of line L19-20 is 26.06MW (obtained in section 2.3), which is higher than its thermal limit (19.2MW). The maximum available EV charging load of EVCS1 and EVCS2 are set to be 4 MW and 4.5MW respectively in this case. The PLD based on the island obtained in the active islanding scheme is 6.86MW; the ∑PLi is 5.5MW; and the EVk is 4MW, which satisfies the equation (5), and the demand response scheme is selected. The results of the active islanding scheme is shown in Fig.6. The results of the model II are shown in Table 3. Table 3. The results of the optimization model II. Power output from MGs (MVA)

Load shedding from EVCS1 (MW)

Load shedding from EVCS2 (MW)

Line loss (MW)

9.79+j2.04

4.0

0.265

0.0988

By using the demand response scheme, the power flow of line L19-20 is reduced to 18.53MW, which is lower than its thermal limit. 3.4. Risk Assessment The comparison results of EENS with and without the three- level risk relief strategy are shown in Fig7. The results show that with the strategy, the EENS is reduced by 24.9%, which means the risk relief capability from ADN can significantly reduce the risk of power system. 3500 3000

EENS, MWh/y

2500 2000 1500 1000 500 0

Without str ategy

With Str ategy

Fig. 6. Comparison results of EENS

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4. Conclusions In this paper, a three-level risk relief strategy for power system is proposed based on the capability from multisource ADN. The EENS is used for risk assessment with and without the three-level risk relief strategy. The results show that the proposed three-level risk relief strategy can make full use of the capability from multi-source ADN to alleviate the risk of upstream network in an active way. The advantages are summarized as follows: 1) The three-level risk relief strategy can not only guarantee the reliability of distribution network, but also can alleviate the risk of upstream network, effectively. 2) Compared with traditional method, the three-level risk relief strategy has significant economic efficiency compared with the traditional methods. Acknowledgements This work is financially supported by the Research and demonstration of management and control verification for distribution network dispatching based on battalion coordination information(State Grid Corporation Science and Technology Project). References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]

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