A Probabilistic Risk Assessment and Control methodology for HVAC electrical grids connected to multiterminal HVDC networks

Proceedings of the 18th World Congress The International Federation of Automatic Control Milano (Italy) August 28 - September 2, 2011

A Probabilistic Risk Assessment and Control methodology for HVAC electrical grids connected to multiterminal HVDC networks E. Ciapessoni* D. Cirio* S. Massucco** A. Pitto* * Ricerca sul Sistema Energetico - RSE S.p.A., Milan, Italy (Tel: +390239921; e-mail: {emanuele.ciapessoni; diego.cirio; andrea.pitto}@rse-web.it) ** Naval and Electrical Engineering Department, University of Genoa, Genoa, Italy (Tel: +39 010 3532718; e-mail: [email protected]) Abstract: Probabilistic techniques are raising more and more interest in power systems operation and planning, due to the fact that they allow to quantitatively consider power system uncertainties and contingency impact, combined into risk indices. This paper presents a probabilistic technique to assess and control the operational risk of High-Voltage Alternating Current (HVAC) power systems connected to Multi-Terminal High-Voltage Direct Current (MT HVDC) networks like those envisaged for the integration of future, large off-shore wind farms. The proposed method minimizes the operational risk by re-dispatching the active power outputs of conventional generators and by shifting the power injections from the HVDC network, taking into account the operating limits of the HVDC network (in particular, cable current limits). Results of the methodology applied to an IEEE test system are presented and discussed, and some conclusions are drawn. Keywords: multi-terminal HVDC grids, optimal control, Power-system control, Quadratic Programming, Risk

1. INTRODUCTION The increasing uncertainties in power system operation, particularly related to the deregulated environment and the penetration of variable renewable generation, raise more and more attention to security issues, as pointed out e.g. by CIGRE C4-WG 601 (2007) and Cirio D. et al. (2008). Interestingly, one of the most urgent research activities, proposed in the EEGI European Electricity Grid Initiative Implementation Plan (2010), concerns “Innovative tools and approaches for the Pan European network reliability assessment”, with the aim to evaluate options to complement or replace the current N-1 preventive security doctrine in the design and operation stages of the transmission network. Within this context probabilistic techniques can provide an important contribution, thus allowing higher operational flexibility without affecting the reliability levels. In recent years some probabilistic techniques have been proposed to support power system operation: in this regard one may consult the works by CIGRE WG C4-601 (2010), Ni M., McCalley J. D., et al. (2003), Uhlen K. et al. (2000), Ciapessoni E. et al. (2009; 2010). At the same time, the integration of larger and larger amounts of renewable power is a major target of the European Commission as recalled in EWEA (2009) and ENTSO-E (2010). To this aim, innovative HVDC technologies may provide cost-effective solutions for the connection of large, remote off-shore wind farms to the continental AC grid. The perspective is indeed to realize multi-terminal (MT), possibly meshed, offshore HVDC networks. The latter would present several advantages over AC solutions and/or point-to-point connections, in terms of higher reliability, support to relieve 978-3-902661-93-7/11/$20.00 © 2011 IFAC

AC system congestions, and greater operational flexibility, as pointed out by Bell K. et al. (2010). Moreover, offshore installations such as DC networks may encounter less public opposition than onshore projects. Much R&D effort is currently ongoing, to cover the existing technology gap (e.g. regarding DC breakers) and make such systems viable (Yao L. et al. (2010)). This paper presents a probabilistic risk-based methodology to assess and control the operational risk of AC grids connected to MT-HVDC networks. In Section 2 the basis of the methodology is illustrated, while Section 3 describes the control strategy to reduce the operational risk. In Section 4 the test system is described, and some simulation results are reported and discussed. At last, in Section 5 conclusions are presented. 2. OUTLINE OF THE METHODOLOGY Risk assessment techniques rely on the concept of risk, which is basically defined as the product of an event probability by its impact on the system. The power system event is called contingency. This section focuses on some aspects concerning contingencies, AC grid and DC network models, and indices to measure the operational risk. 2.1 Contingency modeling The power system risk assessment methodology herein described focuses on the contingencies affecting the AC systems. On-going developments will include the simulation of contingencies affecting the HVDC network components (like converters and DC cables) to evaluate their effects on the overall operational risk of the AC bulk power system.

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18th IFAC World Congress (IFAC'11) Milano (Italy) August 28 - September 2, 2011

The current methodology considers the following contingency typologies: single line and transformer faults, simultaneous double circuit line faults, busbar faults, loss of generating units or whole power plants. The first three typologies are assumed to be caused by permanent faults. The simultaneous loss of several generating units can occur owing to problems in the power plant process or for severe faults at the power plant substation. The contingencies consisting of a fault with at most a further malfunctioning (circuit breaker, bus differential protection BDP) are also considered; further multiple contingencies are not taken into account, according to the rare event approximation. The following possible protection system behaviours are identified in case of transmission system faults (short circuits), depending on the assumed intervention of primary or backup protections: • “Correct” operation: protections identify the fault, send a tripping command to the involved circuit breakers which correctly operate. The fault is cleared by tripping the minimum number of components. • Breaker failure (e.g. because it is stuck) with subsequent intervention of backup protections. It is assumed that these protections correctly intervene, tripping the minimum number of components in order to compensate for the failed tripping. • Failed operation of the bus differential protection: due to the malfunctioning it is not possible to isolate the faulty half-busbar, thus the subsequent intervention of backup protections leads to the loss of the entire busbar. The analysis of these typologies of protection systems responses allows to consider a significant set of multiple and extreme faults. The evaluation of the contingency probabilities is carried out by combining the probabilities of the elementary events (i.e. line fault, circuit breaker fault, etc.). 2.2 Cascading modeling Contingency impact can be defined in different ways, allowing to emphasize different aspects relevant for system operation. Typical expressions for the impact are in terms of: (1) functions of the post-fault (possibly violated) operating quantities (e.g. currents, voltages); (2) loss of load associated to the contingency; (3) cost associated to the contingency. In any case, in order to evaluate the impact it is necessary to perform contingency simulation. In the proposed approach, the simulation is carried out by evaluating the sequence of “steady state” conditions which occur at each step of the degradation process of the grid, i.e. while protections progressively trip grid components. The developed “cascading engine”, unlike conventional power flow programs, simulates the steady state response of the main controls (speed control of the generating units), protection and defense systems (line and transformer overcurrent, line minimum impedance, generators and load under- and overvoltage, generators under- and over-frequency, pump and load underfrequency shedding). The methodology allows to associate an “impact” also to the contingencies which lead to situations which cause the loadflow algorithm to diverge. In fact, once recognized the non-convergence, load reduction strategies (based on the

evaluation of the active nodal residuals) are adopted in order to restore loadflow convergence. This demonstrates the possibility of manual load shedding actions, carried out by operators in proximity to collapse conditions, or automatic intervention of defense systems, when instability occurs. The amount of load shedding necessary for convergence restoration is used to quantify the impact of the contingency. 2.3 DC network modeling A static model of meshed DC networks for the connection of off-shore wind farms is considered within the methodology. It must be recalled that within two-terminal Voltage Source Converter HVDC (VSC-HVDC) systems, generally one converter controls the DC voltage, and the other controls the DC power. The obvious extension to n-terminal systems consists of having one terminal performing voltage control, and the other n-1 converters performing power control. However, this solution does not seem adequate for reliability and control performances reasons. Thus, different control strategies (e.g. voltage margin control, droop control, etc.) are being investigated in current research. Voltage-Power droop control, to be applied to several DC/AC converters acting as a “distributed slack” for active power, appears a good solution. The model herein considered provides three different types of nodes: wind generation nodes, transit nodes, HVDC terminal nodes (interface with the mainland AC network). Based on the above discussion, three alternative control types can be assigned to the DC nodes: − V-controlled node (type 1): Voltage is set to a setpoint Vref. It acts as the slack bus of the DC network, in case a “lumped” slack is adopted; − P-controlled node (type 2): power output P is set as a constant. Typically wind generated power nodes belong to this set. Transit nodes are not inverter-controlled, but they obviously belong to the same set; − Voltage-Power (VP) droop node (type 3): voltage Vj and power output Pj are linked via the following control strategy: V j = V ref j + k j × Pj − Pref j . In the following,

(

)

this control strategy is the typical solution adopted by the HVDC terminal nodes. The default settings of the implemented approach provide that all transit and wind generation nodes are type 2 nodes, while the HVDC mainland terminal nodes are type 3 nodes. Once the control options have been set for all the nodes, the loadflow on the DC network can be performed by means of classical Newton-Raphson algorithm, in order to find the unknown quantities (V at type 2 nodes, V and P at type 3 nodes) and other derived quantities (like the current and power flows along the DC cables of the MT-HVDC network). Grid losses are taken into account in the solution procedure. 2.4 Loss-of-load risk indices According to the adopted definition of contingency impact (see Section 2.2), different risk indices result, highlighting different security aspects. By expressing the contingency impact as the loss of load at the end of the cascading, the risk index associated with that contingency is the expected loss of load, defined in (1).

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ρ ctg = pctg ⋅ Lctg [MW ]

Ng

(1)

pctg is the probability of occurrence of the contingency in a specified time interval and Lctg is the estimated lost load for the contingency (MW, result of the cascading analysis). Analogously, the risk associated with a set of independent contingencies is defined as the sum of the individual risk indices related to each contingency of the set. The approach can be extended to estimate, by means of correlation models, the unsupplied energy (MWh) associated with a contingency, and eventually its cost. 2.5 Risk indices based on post-fault operative quantities Other risk indices linked to operative quantities like line/transformer currents or node voltages are calculated, similar to the ones proposed by Ni M., McCalley J. D., et al. (2003). In particular, these indicators are used to assess the power system conditions (in terms of currents and voltage profiles) just after the application of the initiating contingencies. This information is relevant to identify the possible inception of a cascading process. The proposed methodology adopts a parametrized formula which allows to introduce different severity functions by adjusting suitable parameters. The severity function herein adopted is referred to as “proximity” model and shown in (2).

Sev j ,k (ik ) = 2 ⋅

ikN 1 + ikM

(2)

M and N in (2) are parameters: in order to calculate the total “high current risk” due to a contingency j (a function of vector i containing branch currents ik), the individual severities of the branches are combined together through a weighted average, where the weights are the capacities of the individual branches (see (3)). Nbranches

Sev j , tot (i ) =

∑ Sev

j ,k

× Anom k

k =1 Nbranches

O.F . = α ⋅ ∑ ∆Ph2 + β ⋅

R − ∆R = R 0

j

ctg N br

j

j

Objective Function The objective function to be minimised is:

j =1

k =1 ctg N br

Nctg

j

∑ pr × ∑ j

j =1

k =1 ctg N br

Nctg

j

× ∆Sev j ,tot =

j

∑ pr × ∑ j

k =1

dSev j , k dik dSev j ,k di k

Anom k

j

j =1

Anom k dSev j ,k ⋅ ⋅ ∆i k di k ATOT

j

∑ pr × ∑

j =1

∑ pr × ∑

ctg N br

Nctg

Nctg

∑ ∆R = ∑ pr

Nctg

j =1

(5)

∆R indicated in (6). j =1

3. RISK CONTROL TECHNIQUE The next stage, after assessment, is the identification and evaluation of control actions. A preventive control strategy is proposed, aimed to minimize the risk of high currents on lines/transformers after the first step of cascading, by means of generation redispatch. The goal of the control is to minimize the power shift of conventional generators and HVDC terminal power injections while guaranteeing the minimum risk of high currents on longitudinal branches at the end of the first step of cascading. The optimal problem formulation takes advantage of the definition of risk indices expressed in terms of analytical functions of operative quantities like voltages or currents. This feature allows to develop a risk minimization function, which exploits the sensitivity of generator nodes with respect to the longitudinal branches of the grid model, i.e. lines and transformers. The mathematical formulation is based on quadratic programming described in Zhu J. (2009) and it is illustrated in the sequel.

(4)

+µ×R

Constraints The constraints which bound the search space are: 1) Expression of the overall overload risk R

∆R =

k =1

2 q

q =1

where Ph with h = 1, …, Ng are the power injections of Ng conventional generators, Pq with q = 1, …, NHVDCterm are the power injections of NHVDCterm HVDC terminals and R is the overall operational risk for overloads. Factors α and β allow to weight the contributions of conventional generation and of HVDC power injections. Parameter µ is a penalty factor which weights the importance of R in the objective function. The rationale for this definition of the OF is to minimize the risk, while minimizing the power redispatch. In fact, particularly in the operational stage, it is desired to change the generation profile as little as possible, mainly because of economic reasons (resorting to the ancillary services market is costly). The algorithm seeks a trade-off between risk reduction and generation redispatch. Moreover, the introduction of redispatching unitary costs into the OF may allow to formulate the problem in terms of minimization of the total redispatching cost. Conventional generators considered in the optimization process are selected as the most “effective” in risk control, identified on the basis of the absolute value of parameter Kh defined for each redispatchable power injection h (conventional generator or HVDC injection) as in (7) below. Parameters Kh indicate the post-fault “sensitivity” of injection h towards the grid lines over the contingency set under study.

Nctg

∑ Anom k

∑ ∆P

h =1

with (3)

N HVDCterm

§§

=

ATOT

k =1

ctg N br

Nctg

j

∑ pr × ∑ j

j =1

k =1

⋅∆Sev j ,k =

Anom k dSev j , k ∆Tk ⋅ ⋅ = di k Anom k ATOT

∆T ⋅ TOTk = A ⋅

1 A

TOT

N injTOT

N injTOT

⋅

∑S

hk

× ∆Ph =

h =1

∑K

h

× ∆Ph

h =1

(6) where prj is the probability of contingency j, ik the power flow through branch k, Sevj,k the severity for branch k in case of contingency j, Shk the sensitivity factor of injection h towards power flow in branch k [MW/MW], ATOT the sum of all the branch capacities [MVA], Nctg the number of contingencies considered in the list, ∆R is the risk variation, Tk the active power flow along branch k, NinjTOT = Ng + NHVDCterm the number of redispatchable active power injections (conventional generators, HVDC injections). The inner summation (i = 1, …,

N brctg j ) includes all of the

grid branches not affected by contingency j. Passage §§ in (6) is justified by the DC loadflow approximation: I T ik = k MAX ≅ k Anom k Ik

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18th IFAC World Congress (IFAC'11) Milano (Italy) August 28 - September 2, 2011

IEEE Reliability Test System, a well-known test system widely used for comparing risk methodologies and techniques, shown in Fig. 1. System data from RTS TF (1999) have been adapted for the purposes of the work. The base case scenario used in the present analysis is characterized by the peak load. In this condition the North area (which has a generation surplus) exports large amounts of power towards the South characterized by a power deficit.

The parameters Kh have been defined in (7). ctg N br

Nctg

Kh =

j

∑ pr × ∑

dSev j ,k

j

j =1

k =1

dik

⋅

1 ⋅ S hk ATOT

(7)

2) Invariance of the total generation NinjTOT

∑ ∆P

h

=0

(8)

h =1

3) Invariance of the total HVDC terminal power injection for each MTDC network w N term , w ( w) z

∑ ∆P

(9)

tot ∀ w = 1,..., N OW

=0

z =1

tot

where N OW is the total number of (independent) MT-HVDC networks, N term,w the total number of connections of MTHVDC network w to AC mainland system, while

∆Pz(w) represents the power injection at HVDC terminal z of MT-HVDC network w. 4) Operational limits of the generating units

Phmin ≤ Ph ≤ PhMAX

(10)

5) Range of overload risk R

0 ≤ R ≤ R0

(11)

6) Current limits on the DC cables of MT-HVDC networks DC DC (12) − I MAXp ≤ ∆I pDC + I 0DCp ≤ I MAXp where p,

I

DC 0p

DC I MAXp is the maximum current limit for HVDC cable

is the initial current flowing in cable p, before control

deployment, while

∆I pDC is given by (13) where Λ qp is the

DC sensitivity between HVDC terminal injection q and DC cable p, and

∆I qHVDC and ∆PqHVDC are respectively the

current and power injection of HVDC terminal q.

∆I pDC =

N HVDCterm

N HVDCterm

∆PqHVDC

q =1

q =1

VqHVDC

∑ Λ qp ⋅∆I qHVDC =

∑ Λ qp ⋅

(13)

The choice of penalty factor µ affects the results of the optimal control. The most suitable parameter value depends, for example, on the dimension of the grid under study. From the OF structure it is straightforward that the larger the µ value, the more important the weight given to risk minimization goal with respect to the limitation of generation shift. On the other hand, a too small µ value determines very limited generated power shifts, which makes redispatching not convenient due to the modest risk reduction achieved. Thus, in order to identify a suitable µ value, the control module initializes the factor at a value µ0 and iterates the optimization algorithm by multiplying the current µ value by 10 until at least one power shift at a generating unit overcomes a MW threshold (defined by the user) or an upper limit for µ is reached. In this way it is not necessary to know a priori the setting of µ. 4. SIMULATION RESULTS This section presents some results of the application of the proposed methodology to an IEEE test system, namely the

Fig. 1. IEEE Reliability Test System The RTS has been extended by adding injections into two nodes, thus simulating the HVDC contribution. The GridSide VSC (GSVSC) converters are connected to nodes 17 and 20 in all the performed simulations. The power injections simulating the HVDC terminals are compensated by reducing conventional generation of the area hosting the terminals in proportion to their initial active power outputs. The time interval for risk assessment, used to set the contingency probability, is 10 minutes, a typical time frame for the operator in the control room. The topology chosen for the DC network adopted in the current study case is reported in Fig. 2 together with few significant loadflow results. WF

153.25 kV

∼

=

Wind farms Wind Plant VSC

WF

∼

=

1

2 DC system

0A

3

4

152.35 kV 1305 A

1305 A 5

= 151.44 kV AC bus 17

Grid Side VSC

∼ AC system

6

=

∼ AC bus 20

Fig. 2. Base case loadflow and topology of the DC network An interesting research topic is dedicated to shape the most suitable topology for the MT-HVDC network of the future, taking into account technical constraints (like actual DC

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breakers technology, length of DC cables in the grid, grid and converters losses, protection selectivity). The considered DC network consists of two wind farm connection nodes (nodes 1 and 2), two transit nodes (nodes 3 and 4), and two VP droop-controlled HVDC terminal nodes (nodes 5 and 6). Loadflow has been carried out under the assumption that the voltage setpoints Vref at nodes 5 and 6 are set to 1.01 p.u. Moreover, the two wind farms connected to the DC network are producing 200 MW both, and the two HVDC terminals equally share the amount of power injected into the AC grid. Each DC cable is 50 km long and has a resistance equal to 0.0139 Ω/km. The resulting total DC grid losses (excluding converter losses) are equal to 4.73 MW (about 1% of total power injected into the AC mainland grid). 4.1 Risk Assessment Results The risk assessment phase of the methodology is used to evaluate the operational risk associated with a comprehensive set of single and multiple (dependent) contingencies affecting buses 10 and 11. The time interval of analysis is 10 minutes. Moreover the “proximity” severity function is adopted. Fig. 3 shows the ranking list of the contingencies sorted according to the risk indices calculated by the methodology.

set for conventional generation and HVDC terminal contributions. Given these assumptions, the prototype is run over an exhaustive set of single and multiple (also dependent) contingencies applied to AC buses 3, 11 and 12. The analysis in this subsection is focused on high current risk indices. Fig. 4 compares the overload impact of all the contingencies before and after the control strategy deployment. The overall overload risk index passes from 3.94*10-8 to 2.00*10-8, providing a 49% reduction of the overall risk index. The deployed control strategy has also a beneficial effect on the low voltage risk which undergoes a slight decrease for all the analysed contingencies. Current-based Severity Index (time interval = 10 minutes) TR_B24YT2 SB_B03I303 SB_B11Q211 SB_B12Q212 SSB1_B11Q211 SSB1_B12Q212 LIN_B11Q211_B14Y214 TR_B11QT4 LIN_B12Q212_B23X223 TR_B12QT5 SSB2+DT_B12Q212_L_QU21 SSB2+DT_B11Q211_L_QU23 DT_QU21_QU23 TR_B12QT3 SSB2_B12Q212 LIN_B11Q211_B13U213 SSB2_B11Q211 TR_B11QT1 LIN_B12Q212_B13U213 LIN_B03I303_B09Q309 LIN_B01I301_B03I303 0

before control after control

0.5

1

1.5

2

Current-based Severity Value

2.5

3 -3

x 10

Fig. 4. Overload impacts for all the contingencies analyzed before (blue bars) and after (red bars) the control deployment Table 1 shows the power shifts suggested by the risk-based control. The power shifts determine an overall reduction of the injected power from conventional generation into the Northern part of the RTS equal to 40 MW and a corresponding increase of generation in the Southern part. After the control application, the current flows along the DC cables of the MT-HVDC network have changed: the cable from node 4 to node 6 undergoes a 66 A current increase from 1305 A to 1371 A, while the current on the cable from node 3 to node 5 decreases from 1305 A to 1238 A. Table 1. Proposed generators redispatching for the analyzed cntg set (Imax for DC cables = 1500 A) Generator (G)/ HVDCTerminal (H) B15Y215 (G) B16Y216 (G) B22W222 (G) B01I301 (G) B02K302 (G) B17W217 (H) B20X220 (H)

Fig. 3. Ranking list of the contingencies according to the technical risk indices calculated by the methodology It can be noticed that all the three indices show that the highest contribution to the operational risk comes from the N-1 line contingency applied to the AC cable between buses 6 and 10. In particular the first two contingencies at the top of the loss-of-load risk based ranking list affect the aforementioned cable and determine the loss of the load at bus 6 for undervoltage problems. 4.2 Risk Control Results Initially, the current limits for all the DC cables are set to 1500 A, far beyond their initial currents: in fact the most loaded DC cable has an initial current equal to 1305 A. Wind power generation fluctuations in the time interval of study (i.e. 10 minutes) are considered negligible. Equal weights are

P0 [MW] 215.0 155.0 180.1 172.0 172.0 197.6 197.6

Pfinal [MW] 201.2 146.6 162.3 192.0 192.0 184.8 210.4

∆P [MW] -13.8 -8.4 -17.8 20.0 20.0 -12.8 12.8

Due to the different active power injections at the HVDC terminals the DC cable between nodes 3 and 4 is characterized by a current equal to 66 A, while in the initial state its current flow is negligible. Table 2 compares the risk indices relevant to all the examined contingencies before and after the control strategy deployment. A general reduction of the high current risk index is detected for both high risk contingencies (typically N-1 line and transformer outages, see for example a 61% reduction for N-1 contingency applied to line 11-14) and to

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18th IFAC World Congress (IFAC'11) Milano (Italy) August 28 - September 2, 2011

low risk contingencies (like, contingency applied to halfbusbar SSB1 at node 11). The drastic reduction (around 50%) of the risk indices for the most risky contingencies causes a significant decrease of the overall risk index. In order to test the effect of DC cable current limits on the effectiveness of the risk-based proposed control, the simulations have been repeated under the same assumptions except for the current limits of the DC cables which have been set to 1320 A, which is close to the initial currents in the most loaded DC cables. Table 2. Comparison of the risk indices before and after the preventive control implementation Contingencies description

Imax risk indices Variation % After control 1.10*10-8 5.94*10-9 -46.0 1.03*10-8 5.22*10-9 -49.3 7.32*10-9 2.85*10-9 -61.1 1.86*10-9 1.24*10-9 -33.3 2.18*10-9 1.01*10-9 -53.7 2.14*10-9 9.75*10-10 -54.4 1.17*10-9 7.79*10-10 -33.4 1.35*10-9 7.39*10-10 -45.3 5.72*10-10 3.61*10-10 -36.9 5.12*10-10 3.27*10-10 -36.1 5.22*10-10 3.22*10-10 -38.3 4.12*10-10 2.54*10-10 -38.3 2.85*10-11 1.44*10-11 -49.5 5.53*10-12 2.70*10-12 -51.2 4.40*10-12 1.77*10-12 -59.8 3.81*10-13 2.34*10-13 -38.6 3.47*10-13 2.15*10-13 -38.0 3.06*10-13 1.73*10-13 -43.5 1.75*10-13 9.16*10-14 -47.7 3.94*10-8 2.00*10-8 -49.1 Before control

LIN_B12Q212_B23X223 TR_B24YT2 LIN_B11Q211_B14Y214 LIN_B01I301_B03I303 TR_B11QT4 TR_B12QT5 LIN_B03I303_B09Q309 DT_QU21_QU23 LIN_B11Q211_B13U213 LIN_B12Q212_B13U213 TR_B12QT3 TR_B11QT1 SB_B03I303 SSB1_B12Q212 SSB1_B11Q211 SSB2_B12Q212 SSB2_B11Q211 SSB2+DT_B12Q212_L_QU21 SSB2+DT_B11Q211_L_QU23

Sum and related variation

The implemented control allows again to reduce the overload risk index for the contingency set under test, which decreases from 3.94*10-8 to 2.11 *10-8, undergoing a 46% reduction (less significant than 49% with larger current limits). The lower current limits on the DC cables allow a lower shift of power between the two HVDC terminals: in particular, the shift of injected powers from bus 17 to bus 20 passes from 13 MW to 2.3 MW. This allows to keep the currents on all the DC cables within the maximum limit of 1320 A, which partially reduces the effectiveness of the risk-based control. 5. CONCLUSIONS The paper presented a methodology for risk assessment and control for AC systems also connected to multiterminal HVDC networks. The subject is raising interest in view of the integration of large off-shore wind farms into AC grids. The proposed methodology takes into account the topology of the MT-HVDC network and the controls (droop control, P constant control) typically adopted at the HVDC terminals. Risk is measured via both loss of load risk indices and other technical indices based on operative quantities (current and voltages), thus identifying the contingencies which most contribute to the overall risk index of the contingency set.

The control algorithm effectively reduces the operational risk of high currents in the power systems, by exploiting the generator-branch sensitivities weighted with the probability of occurrence of the contingencies under study. It also takes into account the current limits of the DC cables on the MTHVDC network, as indicated in the described simulations. ACKNOWLEDGEMENT This work has been financed by the Research Fund for the Italian Electrical System under the Contract Agreement between ERSE and the Ministry of Economic Development General Directorate for Energy and Mining Resources stipulated on July 29, 2009 in compliance with the Decree of March 19, 2009. REFERENCES Bell, K., Cirio, D., Denis, A.M., He, L., Liu, C. C., Migliavacca, G., Moreira C., Panciatici P. (2010). Economic and technical criteria for designing future off-shore HVDC grids. Innovative Smart Grid Technologies Europe Conference, Gothenburg, Sweden Ciapessoni, E., Cirio, D., Massucco, S., Pitto, A. (2009). A Probabilistic Risk Assessment Approach to support the operation of large electric power systems. Power System Conference and Exposition (PSCE) 2009, Seattle, WA, USA Ciapessoni, E., Cirio D., Gaglioti E., Massucco, S., Pitto, A., Silvestro, F. (2010). Risk evaluation in power system contingency analyses. CIGRE Session, Paris CIGRE WG C4-601 (2007). Technical Brochure “Review of On-line Dynamic Security Assessment Tools and Techniques” CIGRE WG C4-601 (2010). Technical Brochure “Review of the Current Status of Tools and Techniques for Risk-Based and Probabilistic Planning in Power Systems” Cirio, D., Lucarella, D., Massucco, S. (2008). On-line Dynamic Security Assessment to Mitigate the Risk of Blackout in the Italian Power System, European Transactions on Electrical Power (ETEP), Vol. 18, No. 8, pp. 784-801 EEGI European Electricity Grid Initiative (2010). Roadmap 2010-18 and Detailed Implementation Plan 2010-12 ENTSO-E, European Network of Transmission System Operators for Electricity (2010). Research & Development Plan EWEA, European Wind Energy Association (2009). Report “Oceans of Opportunity: Harnessing Europe’s largest domestic energy resource” Yao, L., Xu, L., Bazargan, M., Critchley, R. (2010). Multiterminal HVDC grid for network interconnection and renewable energy integration. CIGRE Session, Paris Ni, M., McCalley, J. D., Vittal, V., Tayyib, T. (2003). Online Risk-Based Security Assessment. IEEE Transactions on Power Systems, Vol. 18, No. 1 RTS TF (Reliability Test System Task Force) of the Application of Probability Methods Subcommittee (1999). The IEEE Reliability Test System - 1996. IEEE Transactions on Power Systems, Vol. 14, No. 3 Uhlen, K., Kjølle, G. H., Løvås, G. G., Breidablik, Ø. (2000). A Probabilistic Security Criterion for Determination of Power Transfer Limits in a Deregulated Environment. CIGRE Session, Paris Zhu J. (2009). Optimization of Power System Operation. Wiley-IEEE Press Series on Power Engineering

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