The Impact of Distributed Autonomous PV Installations on Critical Infrastructure in Crisis Situations

Ensuring a functioning critical infrastructure system is crucial for many industries, including energy. Due to the transformation of the energy sector, it is necessary to take into account the impact of the induced changes on critical infrastructure. The presented paper focuses on the assessment of the effects of small dispersed photovoltaic power plants (PV), which are gaining in popularity both in households and in other buildings. Specifically, it is an impact assessment within the emergence of island operation in the selected area with the aim of restoring power to the facilities included in the critical infrastructure system as soon as possible. The specificity of the considered small photovoltaic power plants is their autonomy and impossibility of control from the superior control system. The presented case study compares possible approaches to power recovery on the one hand and the impact of different levels of dispersed PV on the other. The results provide important conclusions for the expected development of the critical infrastructure system in connection with the increase in autonomous distributed electricity sources.


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Critical infrastructure (CI) as an element of public space, 20 economy and state security in the Czech Republic also func-21 tions as an important part of the state crisis management 22 system. Amendment to Act 240/2000 Coll. [1] set out firm 23 criteria for the selection, operation and protection of critical 24 infrastructure elements.

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The transformation of the energy sector into a concept using 86 carbon-free technologies brings with it a large number of new 87 challenges that affect a wide range of areas [5]. The municipal 88 challenge of ''decarbonisation'' is bringing ever greater social 89 and economic pressure to accelerate the decommissioning of 90 coal-fired power plants in particular and their replacement by 91 other energy sources. To achieve the required reduction in 92 CO2 production, the installed capacity of photovoltaic, wind 93 and combined cycle power plants is being increased [6]. 94 Regardless of the ongoing changes, we must not forget the 95 basic requirements for the electricity system, which can be 96 summarized in the following points: Our contribution focuses on the safety, reliability and 103 resilience of the power system. The threat of Blackout is 104 becoming more and more common in society. The recent 105 Ernestinovo substation incident in January 2021 is proof of 106 this [7]. A number of new concerns remain in connection with 107 the transformation of the electricity sector, especially with 108 selected risk consequences. 109 A high risk can be identified in the threat to the stability 110 of interconnected power systems. The original concept of the 111 power system based on centralized sources (coal and nuclear 112 power plants) had extensive regulatory options (auxiliary and 113 system services). Many sufficient means were available to 114 control the power supplied to the system, and the most impor-115 tant variable factor was electricity consumption (depending 116 on customer behavior). Due to the small share of renewables, 117 in particular photovoltaic and wind farms in Europe, the 118 factor of variable weather supply dependent on weather did 119 not manifest itself to a significant extent [8]. 120 The growing share of renewable electricity sources unde-121 niably brings new risks associated with the stability of the 122 system. In the area of auxiliary and system services, new solu-123 tions in the form of aggregated blocks [9], [10] and battery 124 storage are being sought and implemented at the same time 125 [11], [12]. Projects of this type are gradually being put into 126 operation [13], [14]. On the other hand, in electricity systems, 127 the factor of variable supply of electricity from renewable 128 sources is becoming more and more important, which results 129 in increased demands on ensuring stable operation of the 130 systems [5]. To increase efficiency in the auxiliary services 131 sector, the MARI and PICASSO projects are currently under-132 way in Europe. The aim of these projects is to create a unified 133 market for selected support services, which will increase the 134 level of cooperation between individual transmission systems 135 and support service providers and will also lead to economic 136 efficiency. However, it is a lengthy and demanding process, 137 as unification is needed across many participants, which show 138 different local specifics [15].

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What is the relationship between these changes and CI? 140 Until now, in the event of major system failures, only tradi-141 tional certified sources have been used, enabling the Black 142 VOLUME 10, 2022 Start function (a certified service provided by power plants in 143 the process of restoring the functionality of generating units 144 or a part of the electric system that allows operation indepen-145 dently of the distribution/transmission system) and the island 146 operation function. These are resources that are fully under 147 the control of the dispatch centre. The increase in the share 148 of renewable dispersed results in an increase in the share 149 of resources operating in the autonomous regime without 150 control by dispatchers. Typically, these are small photovoltaic 151 power plants in a low voltage system (roof installation) [16].

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The main question is to what extent the rapid recovery of 153 power to the critical infrastructure facility will be endangered.  The main question is how its facilities will behave in condi-201 tions of island operation, where there are more significant and 202 more frequent changes in operating parameters, especially 203 the frequency, mainly in relation to the functionalities spec-204 ified in the Regulation [17] for non-synchronous production 205 modules of category A. The issue is crucial for the reliable 206 operation of a critical system, especially in island power 207 mode during emergencies. The mathematical model is created in the Matlab-Simulink 211 software and its aim is to investigate changes in voltage and 212 frequency when connecting individual loads. It is a model 213 of a gas turbine and generator, including the excitation and 214 control system. The unit's own consumption and output to 215 the required distribution substations for powering critical 216 infrastructure are modeled.   small PV installations -for example, roofs of family houses 282 and other buildings. This category is also characterized by the 283 fact that they work in autonomous mode without the reach 284 of the relevant dispatching workplace. For each category of 285 production modules mentioned regulation specifies a number 286 of functionalities. For Category A production modules, the 287 most important requirements are the operating time with 288 system frequency deviations and changes in operating modes 289 from certain system frequency thresholds.

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At over-frequency, the output power of the production 291 module should be gradually reduced. The activation threshold 292 for this functionality can range from 50.2 -50.5 Hz [17]. 293 Activation must take place with a maximum delay of 2 sec-294 onds [17]. The limitation of the output power should take 295 place with droop in the range from 2% to 12% [17]. Droop 296 can be expressed according to (1).
where s 2 is the droop in %, f is the current frequency 299 deviation in Hz, f nad is the frequency deviation when the 300 threshold value is reached in Hz, f n is the nominal system fre-301 quency, P ref is the current reference active power in kW and 302 P is the required output change power in kW. Based on the 303 current frequency value and the reference power, it is possible 304 to define the required change (in this case power reduction) 305 for the entered droop value. The described functionality was 306 integrated into the model described in previous section A. 307 At under-frequency, the output power of the production 308 module should also be limited. However, the regulation spec-309 ifies 2 thresholds for this case. Here, the definition of the 310 required power change is somewhat different from the over-311 frequency functionality. When the frequency drops below 312 49.5 Hz, the output power of the production module should 313 decrease by 10% of its maximum power for every 1 Hz 314 deviation from the threshold value. When the frequency drops 315 below 49 Hz, the output power of the production module 316 should decrease by 2% of its maximum power for every 1 Hz 317 deviation from the threshold value, so a smaller decrease is 318 desirable for more significant sub-frequencies.

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The aim of the case study is to demonstrate the behavior 321 of independently created island operation with the gradual 322 renewal of power supply to critical infrastructure facilities.

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The case study looks at two crucial factors. 324 The first factor is the method of restoring power to individ-325 ual objects of CI within the island operation. For this factor, 326 it is necessary to consider 3 options for power recovery.

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The first option is to restore the power supply at the level  The electrical diagram of the studied area is shown in Fig. 6.

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The key technological unit for crisis situations is the unit in the given area. The MV distribution system also consists 375 of switching stations and individual feeders, which can be 376 controlled automatically and remotely.

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The nature of the load in the selected area needs to be 378 viewed with a different approach in an emergency. In standard 379 operation, the HV system is connected to other HV nodes 380 (and EHV/HV nodes with a link to the transmission system) 381 and power is fed into it from other power plant units in the 382 given area. These conditions make it possible to safely and 383 reliably cover the needs of all consumption points in the 384 area. In the event of an emergency, assuming the loss of a 385 synchronous connection with the HV system, HV/HV nodes 386 and other generation units, limited power is available for the 387 given area, limited by the turbine power. The main goal is 388 therefore the fastest possible resumption of electricity supply, 389 preferably for CI facilities.

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For the given area, we performed an analysis and selected 391 feeders providing power supply for objects included in the CI 392 with three levels of priority (key, important, other). The case 393 study assumes the smallest unit of the output due to network 394 characteristics. The fact that the possibility of automatic and 395 remote control is available at the feeders led us to this step. 396 However, the optimal situation would be that it is possible to 397 switch on only the consumption points supplying CI objects 398 during an emergency. However, it would be necessaryto 399 automate each distribution substation within the distribution 400 system. In the analyzed case, in addition to the restoration of 401 power supply to the supply points supplying the CI objects, 402 the power supply will also be restored at other supply points 403 falling under the given feeder. This is a compromise solution, 404 in which the compromise is between the degree of automation 405 of the MV distribution system and the available power during 406 an emergency. 407 Table 1 summarizes the parameters of individual feeders -408 maximum power consumption, priority and affiliation under 409 a specific HV / MV substation and MV switching station.

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Four scenarios were identified for the case study, taking into 412 account the different approach to power recovery for selected 413    Table 2.  Table 3.  recovery process is estimated to be 30 minutes from the start 443 of the gas turbine production unit. As far as power changes 444 are concerned, the maximum power change is similar to 445 scenario 3, i.e. a maximum of 3.75 MVA, resp. 2.96 MW. 446 The risks of this scenario are similar or rather smaller than in 447 scenario 3. The switching times for scenario 4, including the 448 power consumption falling under the individual MV feeders, 449 are summarized in Table 4. 450 For the purposes of assessing the impact of small PV plants 451 within the study area, the case study considers different levels 452 of representation for each scenario. The power of the PV 453 plant within a certain unit (depending on the scenario -HV 454 stations (scenarios 1 and 2), MV switching stations (sce-455 nario 3) and MV terminals (scenario 4)) is defined on the 456 basis of multiplying the active power of the selected unit. 457 An example is the total output of PV within the MV output 458 A_1_1 at a representation rate of 10%. The active power 459 for a given feeder is 1204 kW according to Table 1

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These results indicate the potential use of a given production 506 unit for island operations supplying CI in a much larger area.

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However, the risk lies in the variable nature of the power 508 supplied by distributed PV plants. does not apply to them. Each feeder consumes reactive power 520 FIGURE 7. Courses of active power at the output of the gas turbine production unit (output to the HV system) for the determined scenarios of island operation for the supply of CI facilities and for different shares of small PV plants.
which must be supplied to the network. In this case, there 521 is just one option -supplying reactive power by gas turbine 522 generator. This is the main reason why the power factor is 523 less than 0.8 in all cases.

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Excessive consumption of reactive power in a defined part 525 of the network can lead to other potential problems threaten-526 ing the stable operation of the island system.  Table 5.

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Based on the results summarized in Table 5, it can be stated 533 that in scenarios 1 and 2, at certain moments the network 534 frequency will fall below the permissible threshold value for 535 the frequency protection of the production unit -48.5 Hz. 536 In practice, this phenomenon would lead to the failure of 537 island operations and thus to the resumption of power to CI 538 facilities. In the case of the effects of small PV plants, it can 539 FIGURE 8. Power factor waveforms at the output of the gas turbine production unit (output to the HV system) for specified scenarios of island operation for powering critical infrastructure facilities and for various proportions of small PV plants.    PV plants, which, however, is not so significant. Fig. 11 and  3 requires 15 minutes to restore power to the production 569 unit operating in island mode. Scenario 4 requires twice the 570 time -30 minutes. However, in Scenario 4, there is a greater 571 risk of a longer delay because the recovery process is more 572 complex, requiring a larger number of operations involving a 573 significant number of components that can potentially fail.

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Distributed small PV plants characterized by an 575 autonomous operating mode (without the possibility of inter-576 vention by the dispatching workplace) and after a mains 577 power failure and its resumption, they are automatically 578 synchronized with the grid and electricity supply is restored. 579 The results of the case study show that they do not have 580 significant negative effects in the gradual renewal of power 581 supply to CI facilities and other facilities dispersed by the PV 582 power plant, even at higher levels of representation. Due to 583 the short duration of frequency deviations, the functionalities 584 described in subsection III.B do not even apply within the 585 analyzed process. On the contrary, these functionalities would 586 be fully reflected in the beginnings of the emergency -587 significant and persistent frequency deviations, which in an 588 unfavorable case precedes the Blackout state.

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The results of the case study provide further findings 590 regarding reactive power in the emerging island operation. 591 Due to the fact that the voltages are maintained within the 592 required limits during the emerging island operation, the 593 inverters of the dispersed PV plants do not activate the Q-U 594 control mode. Thus, dispersed PV plants only act as sources 595 of active power. By reconnecting these sources to the emerg-596 ing island operation, the active power of the production unit 597 controlling the island operation is reduced, but there are no 598 changes on the reactive power side. Here it is possible to iden-599 tify a potential risk in the event that the synchronous machine 600 would be more heavily loaded, the operating state could pass 601 outside the permitted operating area of the machine and the 602 system would collapse.

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The paper aims to address the issue of the functional system 605 of critical infrastructure in the context of the transformation 606 of the energy sector. More specifically, the paper focuses 607 on analyzing the impacts of distributed generation (small 608 PV systems) through the use of a mid-term islanded grid 609 dynamics model with a focus on critical infrastructure assets. 610 For the analysis, a mathematical model of mid-term 611 dynamics implemented in MATLAB Simulink was used. The 612 developed model consists of sub-models of the synchronous 613 generator, the excitation system model including control, the 614 gas turbine model including control, the model of a defined 615 part of the grid under islanded operation and the dispersed 616 generation model, which takes into account the requirements 617 of the regulation [17]. Simulations were performed for several 618 different scenarios and the results were then evaluated.

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The evaluation of the results of the case study brings the 620 following findings:  Table 6.