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This paper presents a novel corrective control strategy that can effectively coordinate distributed and bulk energy storage to relieve post-contingency overloads. Immediately following a contingency, distributed batteries are implemented to provide fast corrective actions to reduce power flows below their short-term emergency ratings. During the long-term period, Pumped Hydro Storage units work in pumping or generation mode to aid conventional generating units keep line flows below the normal ratings. This problem is formulated as a multi-stage Corrective Security-constrained OPF (CSCOPF). An algorithm based on Benders decomposition was proposed to find the optimal base case solution and seek feasible corrective actions to handle all contingencies. Case studies based on a modified RTS-96 system demonstrate the performance and effectiveness of the proposed control strategy.

Power systems have been operating in the way that requires the base case operating point can withstand an unexpected loss of components [

Generation redispatch [

Load shedding [

Energy storage (ES) can deliver multiple benefits that enhance grid performance, such as load following, peak shaving, spinning reserve, stability enhancement and power quality improvements [

In this paper, we focus on the use of different types of energy storage as corrective control resources to remove post-contingency overloads. Since the effectiveness of different energy storage technologies for corrective control is constrained not only by their ramp speed, but also the power and energy capacity that they are able to inject or store to cope with a contingency [

A novel control strategy that can effectively coordinate distributed and bulk energy storage to relieve post-contingency overloads is proposed. Under the normal operating condition, this paradigm would reduce generation costs and increase the transmission capability. Following a contingency, control on a short-term and long-term timescale would reallocate the distributed and bulk energy storage to alleviate the violations.

This problem is formulated as a multi-stage CSCOPF, and an algorithm based on Benders decomposition was proposed for the solution of the optimal base case dispatch and feasible corrective actions.

The remainder of this paper is organized as follows: Section 2 explains the coordinate control strategy of distributed and bulk energy storage. Section 3 details the mathematic model formulation. The solution methodology is given in Section 4. Section 5 presents the test results of case studies, and finally, Section 6 states the conclusions.

Every conductor used in transmission networks has associated thermal ratings [

An understanding of the post-contingency timeline will help in choosing which storage technologies are best for a specific corrective control application.

In some severe contingency scenarios, the power flow on an affected line may exceed its normal and STE ratings. Distributed ES (

Batteries are one of the most cost-effective energy storage options available, which normally have limited power and energy capacity but with very fast ramp rate, capable of transitioning from zero to full output in microseconds to several seconds [

PHS is the most widely implemented grid-scale energy storage, which typically provides hundreds to thousands of megawatts of capacity in a single facility [

The operator must deploy the distributed and bulk energy storage in a timely manner to clear the post-contingency short- and long-term violations. To coordinated control these storage units for relief of transmission N-1 congestion, two types of corrective control are proposed:

The detailed process of the proposed coordinated control strategy of distributed batteries and PHS units to relieve the post-contingency overloads is given as follows. To help understand, ^{0} in this figure represents the pre-contingency flow).

_{0}, distributed batteries are controlled instantly to discharge or charge power to bring the post-contingency line flows (_{STE}). Then, the power injections from distributed batteries will remain constant until time _{1} when generators and PHS units start ramping.
_{1} to _{2}, generators and PHS units adjust their output continuously. On the other hand, distributed batteries reduce continuously their injections and extractions, and consequently stop the charging/discharging process at time _{2}. During this time interval, the power flows on the overloaded lines decrease linearly until they reach their normal ratings (^{max}).
_{2}), the output of generators and PHS units keep constant to prevent the power flows exceeding the normal ratings.

By implementing the proposed coordinated control, the post-contingency overloads on a transmission line could be corrected within the perspective of short- and long-term timeframes. To fulfill this control strategy, two key questions are required to be resolved:

How much power should be discharged or charged instantly by batteries after a contingency occurs?

How much power the PHS units should produce or absorb to aid generators removing long-term violations at time _{2} (the operating point at the end of the short-term period)?

Given corrective actions at these two operating points, the post-contingency flows then could be controlled below the STE ratings and decreasing linearly during the ramping period (_{1} to _{2}). Since security during this period is guaranteed, constraints associated with the ramping process are not necessarily considered in the CSCOPF model.

This problem is formulated as a three-stage CSCOPF formulation, explicitly modeling the timeframe to dispatch storage units to comply with the post-contingency security limits. The structure of this problem is shown in

For simplicity sake, the pre- and post-contingency power flows are calculated using the DC power flow method, and generator outages are not considered. It is assumed that the energy stored and spare energy capacity available in each storage unit is large enough to cope with the contingencies. The best location for siting a battery to remove post-contingency short-term overloads is determined through the sensitivity analysis method given in [

The objective function is to minimize the base case generation cost of the generators, which is calculated using the quadratic cost functions:
_{i}^{0} is the power produced by generator _{i}_{i}_{i}_{G} is the set of generators. Superscript 0 represents the base case.

The associated operating costs of storage units under contingency conditions are not included in the objective function [

Energy storage normally have a high investment while relatively low operating cost [

The probability of a contingency would occur is very small (close to zero), therefore, the expected operating costs for storage units to relieve overloads are not necessarily considered.

Since the actual implementation of corrective actions occurs in real time, the proposed model is concerned about the feasibility of distributed and bulk storage units to comply with the post-contingency security, but not the optimal operating costs of such storage units to relieve overloads.

The objective function is subject to the pre-contingency, as well as the post-contingency short- and long-term security constraints.

The total generation should meet the load demand:
_{i}^{0} is the real power output of generator _{l}_{L} is the set of load buses.

At base case, the power flows must not exceed the normal limits:
^{0} is the power transfer distribution factor (PTDF) matrices for the base case. ^{0},

The power output of a generator should be within its limits:
_{i}^{min} and _{i}^{max} are the minimum and maximum real power output of generator

The power output of generators remains the same immediately after the transmission contingency occurs. Thus, the power injections and extractions of the distributed batteries should be balanced to maintain the power balance in the system:
_{m}^{k}_{m}^{k}_{C}). _{B} is the set of distributed batteries.

Immediately following a contingency, distributed batteries respond instantly to bring line flows back within their short-term emergency ratings:
^{k}^{k}^{k}

The battery is capable of transitioning from zero to full output continuously:
_{m}^{max}, _{m}^{max} are the maximum discharging and charging power limits of battery

In the long-term period, the system power balance equation is as follows:
_{i}^{k}_{n}^{k}_{n}^{k}_{S} is the set of PHS units.

The corrective actions from generators and PHS units should reset the power flows on each line be within its normal limit:
^{k}^{k}

The redispatching amount of a generator should be within its ramping limits:

The redispatched generation of a generator should be within its limits:

The online status of a PHS unit is modeled as follows:
_{n}^{k}_{n}^{k}_{n}^{k}_{n}^{k}

The power a PHS unit releases or absorbs should be within its limit:
_{n}^{min}, _{n}^{max} are the minimum and maximum generation power limits of PHS unit _{n}^{min}, _{n}^{max} are the minimum and maximum pumping power limits of the

The proposed model is a large-scale mixed-integer programming (MIP) problem. It is difficult to solve the problem directly due to its high computing dimensionality, especially when the system is large and many N-1 contingencies are considered. Alternatively, Benders decomposition [

The master problem is solved using standard quadratic programming. Sub-problems 1 are linear, and can be solved using linear programming. Sub-problems 2 involve binary variables, the Branch-and-Cut method [

The two sets of sub-problems are solved iteratively with the master problem until all short- and long-term post-contingency violations are removed using corrective actions (zero slack variables achieved). If, after solving a sub-problem, the corresponding slack variables are not equal to 0, then it is labeled an uncontrollable contingency, a feasibility Benders cut must be generated and added to the master problem for mitigating the violations in the next iteration:
^{k}_{i}^{0*} is the base case trial operating point obtained from solving the master problem, λ_{i} is the multiplier associated with the

The controllable contingencies are handled by corrective actions by storage and generating units without requiring any revisions to the existing base case solution.

The flowchart of the Benders decomposition based algorithm for solving the proposed multi-stage CSCOPF formulation is shown in

The proposed coordinated control strategy and algorithm have been tested using a modified RTS-96 system as shown in

The simulation is performed for several load levels, in order to estimate the impact of distributed and bulk energy storage at different load levels. The base case load is 6122.2 MW, and the load level is increased in steps of 2.5%.

The CSCOPF without storage units is relatively costly. As no batteries were installed in the system, preventive actions have to be applied in the pre-contingency state to make sure that no short-term violations would occur, which increases the base case generation cost. On the other hand, since the model relies on generators to guard all the long-term security, under stress conditions (load level > 1.05), the overloads cannot be completely removed, load shedding have to be implemented to handle the contingencies, otherwise the program would be infeasible.

As shown in

_{118} (the line connects buses 121 and 325) in the case of the outage of line L_{107} (the line connects buses 316 and 317). The dashed line in the figure represents the pre- and post-contingency flows obtained by the CSCOPF without storage (Case 1), the solid line represents the flows obtained by the CSCOPF with batteries and PHS units (Case 3). In both cases, the load level is 1.05.

As can be seen, if no storage units are placed, the pre-contingency loading level on line L_{118} is 0.91. After the outage of line L_{107}, the immediately flow becomes 1.15, which is lower than the STE rating (1.2). In the long-term period, the flow (0.98) is adjusted to below the continuous rating (1.0).

If both distributed and bulk storage are installed, the pre-contingency loading level on line L_{118} is increased to 0.96. After the outage of line L_{107}, the immediately flow (1.28) on line L_{118} would violate the STE rating. However, this flow is first reduced to the STE rating using the distributed batteries and then the continuous rating through power adjustment of the conventional generating units and PHS units. Hence, the coordinated control of batteries and PHS units provides means for efficient operation in the post-contingency state while still maintaining security in a robust way. It can thus enhance the transmission capability of certain transmission corridors, allowing the lines operating with higher power flows during the pre- and post-contingency state. As can be seen from _{24}, L_{118}, L_{120}) connect those two areas.

The short-term and long-term corrective actions in Case 3 (the load level is 1.05) are given in this part to further explain how the post-contingency overloads were cleared using coordinated control of distributed and bulk storage units.

_{107}. It can be seen that, batteries located in area 3 only need to provide corrective actions in the form of discharge, while batteries placed in area 1 and area 2 need charge or discharge. This is because that, to remove the post-contingency emergency overloads, batteries located downstream from the overloaded lines will inject power in the network, and other batteries located upstream from these lines might extract power from the network.

_{27}, L_{28}, or L_{41}, the PHS unit (PHS_{2}) located at bus 317 in area 3 has to work in generation mode to provide back-up power to aid generators, while under other contingency conditions (L_{66}, L_{67}, L_{72}, L_{86}, L_{103}, L_{104}, L_{105}, L_{106}, L_{117}), the PHS units located in area 2 or area 3 would work in pumping mode to absorb excess power from the network. If the long-term period is set to 1 hour, it would require that 50 × 1 = 50 MWh spare energy capacity be available in PHS_{1}, while 230 MWh energy should be stored and 225 MWh energy margin in PHS_{2}.

The effect of the power capacity of distributed batteries on the generation cost is studied here (Case 3 with load level equals to 1.05).

To analyze the effect of CSCOPF with different number of PHS units, three cases were considered:

Case 4: 1 PHS unit is installed at bus 117 in area 1.

Case 5: 2 PHS units located at buses 117 and 217 (Area 2).

Case 6: 3 PHS units located at buses 117, 217, and 317 (Area 3).

For all the cases, each PHS unit has the same power capacity with PHS_{2} as given in

The power capacity of each PHS unit in case 6 was increased in steps of 40 MW. To help understand the impact of the power rating of PHS units on the generation dispatch, the expected amount of generation redispatch (_{G}), and the expected amount of power adjustment of PHS units (_{S}) are calculated:
^{k}

A novel corrective control strategy that can coordinate distributed and bulk energy storage for relief of post-contingency overloads has been presented in this paper. This problem is formulated as a multi-stage CSCOPF incorporating the distributed batteries and PHS units. Immediately after a contingency, batteries are used to provide fast-response corrective actions to prevent the power flows on affected lines exceeding their short-term emergency ratings. In the long-term period, PHS units work in generation or pumping mode to aid generators bring the flows down within the normal limits.

An algorithm based on Benders decomposition was proposed to solve the proposed CSCOPF model. The primal problem was decomposed into a pre-contingency master problem linked with two sets of post-contingency sub-problems. The master problem determines the optimal base case solution, while the two types of sub-problems seek feasible corrective actions to handle all contingencies.

Test results on a modified RTS-96 system demonstrate that the proposed control strategy offers the following advantages:

Coordinated control of distributed batteries and PHS units following an outage could effectively remove post-contingency overloads and guarantee system operational reliability.

It lightens the requirement of preventive/corrective actions from conventional generators, thus decreases the generation costs.

It allows the system operates with higher pre- and post-contingency power flows, therefore reinforces the available transfer capabilities. It would thus reduce the need for investments in additional or upgraded transmission lines.

Although this paper is focused on the intraday single operating point, the proposed coordinated control method can also be extended to the multi-period CSCOPF or day-ahead Security-constrained Unit Commitment problems. These topics are left for future work.

The work of Yunfeng Wen, Shufeng Dong and Chuangxin Guo was supported in part by the State Key Development Program for Basic Research of China (2013CB228206), in part by the National Natural Science Foundation of China (51177143), and in part by the Zhejiang Province Natural Science Key Foundation (LZ12E07002).

The authors declare no conflict of interest.

The optimization models of the master and sub-problems are given as below:

The master problem corresponds to

Slack variables ^{k}_{T} is the set of transmission lines.

Slack variables ^{k}

The modified line parameters of the RTS-96 system are given in the following table:

Line parameters of the RTS-96 system.

^{max} (MW) |
^{max} (MW) | ||||||||
---|---|---|---|---|---|---|---|---|---|

L_{1} |
101 | 102 | 0.014 | 150 | L_{61} |
212 | 213 | 0.048 | 250 |

L_{2} |
101 | 103 | 0.211 | 150 | L_{62} |
212 | 223 | 0.097 | 300 |

L_{3} |
101 | 105 | 0.085 | 150 | L_{63} |
213 | 223 | 0.087 | 300 |

L_{4} |
102 | 104 | 0.127 | 150 | L_{64} |
214 | 216 | 0.059 | 300 |

L_{5} |
102 | 106 | 0.192 | 150 | L_{65} |
215 | 216 | 0.017 | 300 |

L_{6} |
103 | 109 | 0.119 | 150 | L_{66} |
215 | 221 | 0.049 | 300 |

L_{7} |
103 | 124 | 0.084 | 300 | L_{67} |
215 | 221 | 0.049 | 300 |

L_{8} |
104 | 109 | 0.104 | 150 | L_{68} |
215 | 224 | 0.052 | 300 |

L_{9} |
105 | 110 | 0.088 | 150 | L_{69} |
216 | 217 | 0.026 | 300 |

L_{10} |
106 | 110 | 0.061 | 150 | L_{70} |
216 | 219 | 0.023 | 300 |

L_{11} |
107 | 108 | 0.061 | 150 | L_{71} |
217 | 218 | 0.014 | 300 |

L_{12} |
107 | 203 | 0.161 | 150 | L_{72} |
217 | 222 | 0.105 | 250 |

L_{13} |
108 | 109 | 0.165 | 150 | L_{73} |
218 | 221 | 0.026 | 250 |

L_{14} |
108 | 110 | 0.165 | 150 | L_{74} |
218 | 221 | 0.026 | 250 |

L_{15} |
109 | 111 | 0.084 | 300 | L_{75} |
219 | 220 | 0.04 | 250 |

L_{16} |
109 | 112 | 0.084 | 300 | L_{76} |
219 | 220 | 0.04 | 250 |

L_{17} |
110 | 111 | 0.084 | 300 | L_{77} |
220 | 223 | 0.022 | 250 |

L_{18} |
110 | 112 | 0.084 | 300 | L_{78} |
220 | 223 | 0.022 | 250 |

L_{19} |
111 | 113 | 0.048 | 300 | L_{79} |
221 | 222 | 0.068 | 250 |

L_{20} |
111 | 114 | 0.042 | 300 | L_{80} |
301 | 302 | 0.014 | 150 |

L_{21} |
112 | 113 | 0.048 | 300 | L_{81} |
301 | 303 | 0.211 | 150 |

L_{22} |
112 | 123 | 0.097 | 300 | L_{82} |
301 | 305 | 0.085 | 150 |

L_{23} |
113 | 123 | 0.087 | 300 | L_{83} |
302 | 304 | 0.127 | 150 |

L_{24} |
113 | 215 | 0.075 | 300 | L_{84} |
302 | 306 | 0.192 | 150 |

L_{25} |
114 | 116 | 0.059 | 300 | L_{85} |
303 | 309 | 0.119 | 150 |

L_{26} |
115 | 116 | 0.017 | 300 | L_{86} |
303 | 324 | 0.084 | 220 |

L_{27} |
115 | 121 | 0.049 | 300 | L_{87} |
304 | 309 | 0.104 | 150 |

L_{28} |
115 | 121 | 0.049 | 300 | L_{88} |
305 | 310 | 0.088 | 150 |

L_{29} |
115 | 124 | 0.052 | 300 | L_{89} |
306 | 310 | 0.061 | 125 |

L_{30} |
116 | 117 | 0.026 | 300 | L_{90} |
307 | 308 | 0.061 | 150 |

L_{31} |
116 | 119 | 0.023 | 300 | L_{91} |
308 | 309 | 0.165 | 125 |

L_{32} |
117 | 118 | 0.014 | 300 | L_{92} |
308 | 310 | 0.165 | 125 |

L_{33} |
117 | 122 | 0.105 | 300 | L_{93} |
309 | 311 | 0.084 | 250 |

L_{34} |
118 | 121 | 0.026 | 300 | L_{94} |
309 | 312 | 0.084 | 250 |

L_{35} |
118 | 121 | 0.026 | 300 | L_{95} |
310 | 311 | 0.084 | 250 |

L_{36} |
119 | 120 | 0.04 | 300 | L_{96} |
310 | 312 | 0.084 | 250 |

L_{37} |
119 | 120 | 0.04 | 300 | L_{97} |
311 | 313 | 0.048 | 250 |

L_{38} |
120 | 123 | 0.022 | 300 | L_{98} |
311 | 314 | 0.042 | 250 |

L_{39} |
120 | 123 | 0.022 | 300 | L_{99} |
312 | 313 | 0.048 | 250 |

L_{40} |
121 | 122 | 0.068 | 300 | L_{100} |
312 | 323 | 0.097 | 300 |

L_{41} |
123 | 217 | 0.074 | 300 | L_{101} |
313 | 323 | 0.087 | 300 |

L_{42} |
201 | 202 | 0.014 | 150 | L_{102} |
314 | 316 | 0.059 | 270 |

L_{43} |
201 | 203 | 0.211 | 150 | L_{103} |
315 | 316 | 0.017 | 300 |

L_{44} |
201 | 205 | 0.085 | 150 | L_{104} |
315 | 321 | 0.049 | 270 |

L_{45} |
202 | 204 | 0.127 | 150 | L_{105} |
315 | 321 | 0.049 | 270 |

L_{46} |
202 | 206 | 0.192 | 150 | L_{106} |
315 | 324 | 0.052 | 270 |

L_{47} |
203 | 209 | 0.119 | 150 | L_{107} |
316 | 317 | 0.026 | 250 |

L_{48} |
203 | 224 | 0.084 | 220 | L_{108} |
316 | 319 | 0.023 | 270 |

L_{49} |
204 | 209 | 0.104 | 150 | L_{109} |
317 | 318 | 0.014 | 300 |

L_{50} |
205 | 210 | 0.088 | 150 | L_{110} |
317 | 322 | 0.105 | 250 |

L_{51} |
206 | 210 | 0.061 | 125 | L_{111} |
318 | 321 | 0.026 | 250 |

L_{52} |
207 | 208 | 0.061 | 150 | L_{112} |
318 | 321 | 0.026 | 250 |

L_{53} |
208 | 209 | 0.165 | 125 | L_{113} |
319 | 320 | 0.04 | 250 |

L_{54} |
208 | 210 | 0.165 | 125 | L_{114} |
319 | 320 | 0.04 | 250 |

L_{55} |
209 | 211 | 0.084 | 250 | L_{115} |
320 | 323 | 0.022 | 250 |

L_{56} |
209 | 212 | 0.084 | 250 | L_{116} |
320 | 323 | 0.022 | 250 |

L_{57} |
210 | 211 | 0.084 | 250 | L_{117} |
321 | 322 | 0.068 | 250 |

L_{58} |
210 | 212 | 0.084 | 250 | L_{118} |
325 | 121 | 0.097 | 400 |

L_{59} |
211 | 213 | 0.048 | 250 | L_{119} |
318 | 223 | 0.104 | 400 |

L_{60} |
211 | 214 | 0.042 | 250 | L_{120} |
323 | 325 | 0.009 | 400 |

Power flow on an overloaded line.

Structure of the multi-stage CSCOPF.

Flowchart of the proposed algorithm.

RTS-96 system.

Evolution of the power flow on line L_{118}.

Short-term corrective actions of distributed batteries to cope with the outage of line L_{107}.

Minimum cost achieved by the CSCOPF as a function of the power capacity of batteries.

Key technical characteristics of batteries and PHS.

Battery | less than 50 MW | seconds to hours | microseconds to seconds |

PHS | 100 MW to 5000 MW | 1 h to more than 24 h | seconds to minitues |

Location and power limits of the PHS plants.

^{min} (MW) |
^{max} (MW) |
^{min} (MW) |
^{max} (MW) | ||
---|---|---|---|---|---|

PHS_{1} |
217 | 16 | 180 | 20 | 225 |

PHS_{2} |
317 | 10 | 200 | 15 | 250 |

Results obtained in Case 1.

_{G} ($) |
||||
---|---|---|---|---|

1.0 | 139,987 | 0 | 25 | 4.0 |

1.025 | 140,517 | 0 | 26 | 4.1 |

1.075 | Infeasible |

Results obtained in Case 2.

_{G} ($) |
|||||
---|---|---|---|---|---|

1.0 | 139,610 | 4 | 29 | 0.3 | 6.1 |

1.025 | 140,240 | 8 | 33 | 0.2 | 6.9 |

1.075 | Infeasible |

Results obtained in Case 3.

_{G} ($) |
|||||
---|---|---|---|---|---|

1.0 | 139,610 | 4 | 29 | 0.3 | 6.2 |

1.025 | 140,240 | 8 | 33 | 0.2 | 7.4 |

1.05 | 141,378 | 9 | 34 | 0.3 | 7.5 |

1.075 | 142,766 | 9 | 37 | — | 8.1 |

1.1 | 144,251 | 9 | 38 | — | 8.2 |

1.125 | 145,840 | 11 | 39 | — | 9.1 |

1.15 | 147,458 | 11 | 43 | — | 9.6 |

— | |||||

1.2 | Infeasible |

List of lines those with an increase in their loading level.

Area 1 | L_{1} to L_{15}, L_{17}, L_{20}, L_{22}, L_{23}, L_{25}, L_{27} to L_{32}, L_{40} |
27 |

Area 2 | L_{42} to L_{48}, L_{52}, L_{53}, L_{57}, L_{60}, L_{64} to L_{71}, L_{79} |
20 |

Area 3 | L_{87} to L_{90}, L_{92}, L_{94}, L_{96}, L_{97}, L_{100}, L_{101}, L_{103} to L_{105}, L_{107}, L_{109}, L_{110}, L_{113} to L_{116} |
21 |

Tie-Lines | L_{24}, L_{118}, L_{120} |
3 |

Long-term corrective actions of PHS units to cope with all contingencies.

_{1} (MW) |
_{2} (MW) | ||
---|---|---|---|

L_{27} |
115, 121 | 0 | 230 |

L_{28} |
115, 121 | 0 | 230 |

L_{41} |
123, 217 | 0 | 54 |

L_{66} |
215, 221 | −50.0 | 0 |

L_{67} |
215, 221 | −50.0 | 0 |

L_{72} |
217, 222 | 0 | −225 |

L_{86} |
303, 324 | 0 | −88.7 |

L_{103} |
315, 316 | 0 | −40.6 |

L_{104} |
315, 321 | 0 | −156.6 |

L_{105} |
315, 321 | 0 | −156.6 |

L_{106} |
315, 324 | 0 | −88.7 |

L_{117} |
321, 322 | 0 | −53.4 |

Results obtained in three cases with different number of PHS units.

_{G} ($) |
|||||
---|---|---|---|---|---|

Case 4 | 1.07 | 142,488 | 9 | 35 | 6.7 |

Case 5 | 1.15 | 147,001 | 11 | 37 | 8.5 |

Case 6 | 1.2 | 150,266 | 11 | 48 | 12.8 |

Results obtained in case 6 with different power capacity of PHS units.

^{max} (MW) |
||||
---|---|---|---|---|

| ||||

_{G} (MW) |
_{S} (MW) | |||

−80 | 150,217 | 90.2 | 1.42 | 1.1195 |

−40 | 150,266 | 89.9 | 1.53 | 1.2 |

0 | 150,266 | 89.8 | 3.0 | 1.2 |

+40 | 150,340.3 | 89.4 | 3.2 | 1.201 |

+80 | 150,340.3 | 88.8 | 4.8 | 1.201 |