International Journal of Power and Renewable Energy Systems (IJPRES) Volume 2 Issue 2, 2015 www.as‐se.org/ijpres
Simple nodal‐PTDF Deviation Method: Solution for the Assessment of Unallocated and Unexpected Flows and Redispatch Cost‐ Sharing among TSOs in the European Zonal Market Design? Milan Vukasovic Operations Department, Austrian Power Grid AG Milan.Vukasovic@apg.at Abstract Due to the European zonal electricity market design and bidding zone structure which corresponds to national borders of the EU countries, flow‐based market coupling could lead to a non‐optimal allocation of costs and benefits among EU member states. This problem is especially visible during the process of cross‐border market coupling capacity calculation as well as during the application of cross‐border firmness regime (over redispatching) in post‐coupling phase. In this paper, an efficient method for the allocation of benefits and costs among different EU members states has been explained and analysed. The method is applicable for cross‐zonal cost‐sharing of remedial actions as well as for identification of a causer (or causers) of remedial action deployment. Key words Congestion, Redispatch, Remedials, Electricity, Market, Design
Introduction With the very fast increase of pan‐European electricity trading volume in recent years, priority dispatch of certain types of generation units (mainly renewables) and market‐based European‐wide self‐dispatch of conventional generation units have made the operational planning process and identification of congested elements by Transmission System Operators (TSOs) more complex. Consequently, the frequency of situations where a TSO has to relieve an overload over a network element in its own grid, due to conditions in its own control area or due to conditions within the control area of another TSO, has been constantly increasing. Therefore, the use of a method, when the TSO(s) requesting the activation of remedial action pay(s) the costs, may have to be replaced with a mechanism for redispatch cost‐sharing in which the TSO (or the Bidding Zone) is responsible for the cause of congestion pays some or all of the redispatch cost, proportionally to its influence on the overloaded network element. In this paper, different types of basic load flows, which are in line with the officially agreed ENTSO‐E (The European Network of Transmission System Operators for Electricity) definition, are described. Out of them, derived flows used for the development of methodology are presented. In the final chapter of the paper, the simple nodal‐PTDF deviation method has been introduced and its application has been tested on the redispatch concept. Definition of Different Type of Flows ENTSO‐E has worked on definition of the different type of flows. Some of these agreed terms are cornerstone for the developed simple nodal‐PTDF deviation method. It is worth to mention that, according to these definitions, it will not be possible to calculate uniquely physical loop flows in the European zonal market design but only
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unallocated flows which are (along with the unexpected flows) relevant for development of the method described in this paper. Nevertheless, ENTSO‐E has defined different types of flows and some of them – such as import/export flows – would eventually diminish once the flow‐based market coupling methodology is in place. The list of the basic flows and their definitions is as follows:
An Internal Flow is defined as the physical flow on a network element where the source and sink and the complete element are located in the same zone (Figure 1 – Flow 1).
An Import/Export Flow is defined as the physical flow on a line or part of a tie line that belongs either to the zone with the source or the sink (Figure 1 – Flow 2).
A Loop Flow is defined as the physical flow on a line where the source and sink are located in the same zone and the network element is located in a different zone including tie lines (Figure 1 – Flow 3).
A Transit Flow is defined as the physical flow on a network element where the source, sink and the line or part of the tie line are all located in different zones (Figure 1 – Flow 4).
FIGURE 1: FLOW DEFINITIONS ACCORDING TO ENTSO‐E
Out of these basic flows, so‐called derived flows can be defined:
An Unallocated Flow is the difference between expected physical flow from the operational planning models and flow which would occur if the assumptions which are used for capacity allocations are correct. Expected physical flows are derived out of load flow calculations performed always on the latest operational planning model, usually IDCF (Intraday Congestion Forecast).
An Unexpected Flow is the difference between measured and expected physical flow.
Simple Nodal-PTDF Deviation Method The approach to trace the flow of electricity developed in [1] is a proportional sharing which works under the assumption that the network node is a perfect “mixer” of incoming flows so that it is impossible to tell which particular inflowing electron goes into which particular outgoing line. Recently, a novel power flow decomposition model has been proposed [2] which assumes that each load is fed by each generator of the system and each generator of the system is feeding all loads of the system proportionally to their individual repartition to the power balance of the entire system. Both of the mentioned methods are linked only to the physical network model and do not take into account the way how the cross‐border capacity has been allocated on the congested interfaces and how the commercial dispatch of units in the different nodes of a system has been changed during the different timeframes of system operation (from capacity calculation, over operational planning to the real‐time). With that, it is not possible to identify actual causer(s) of an element overload and assign clearly a responsibility for the reason of redispatch activation. Above‐mentioned methods [1,2] could be considered in the nodal market design where all nodes are treated equal and imposed network limitations have an influence on all nodes of the system in the same manner and where each node belongs to one area as there are no bidding zone borders such as in Europe.
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International Journal of Power and Renewable Energy Systems (IJPRES) Volume 2 Issue 2, 2015 www.as‐se.org/ijpres
FIGURE 2: IDENTIFICATION OF REDISPATCH ACTIVATION CAUSERS
Simple nodal‐PTDF deviation method, as its name suggests, uses nodal power‐flow distribution matrix for calculating the influence of one node to the particular element of the system. Methodology is additionally based on deviation which could occur in the three different phases of system operation: a) Immediately after MC clearing and operational planning process (models: Outcome MC vs. DACF) b) Between day‐ahead operational planning and intraday operational planning (models: DACF vs. IDCF) c)
Between intraday operational planning and real‐time (models: IDCF vs. SN)
Cases a) and c) are depicted in the FIGURE 2 (as #1 and #2, respectively), while case b) is rather trivial as cross‐ border intraday market will be organized as a continuous trading process. For case 1, unallocated flows are identified as the cause of remedial actions activation while for case 2 unexpected flows along with loop flows are punished. Only those flows which are violating the (n‐1) security limit imposed by TSOs, denoted as Permanently Admissible Transmission Loading (PATL), will be considered for the sharing of the redispatch cost. The critical loading of a network element in the planning phase may appear to be higher than its allowed security limit (PATL). This value represents the acceptable loading of a network element in Amps or MW for unlimited time duration without any risk for the material. Depending on the risk level agreed and taken by a particular TSO in the different operational planning phases, it can be decided anytime to prepare remedial actions based on their technical and economic efficiency. As long as net positions of the two models, whose nodal deviations are to be compared, are equal, selection of a slack for nodal‐PTDF calculation has no influence on the results. Burdening flows, which are the component of physical flows and flow in the same direction as the measured/forecasted physical flows, and relieving flows, which flow in the opposite direction, are separately calculated on a nodal level but are netted on a zonal level as the cost‐sharing is to be performed on the basis of the European zonal market model. Negative zonal contribution, considered as an unintentional relieving effect, is not taken into account for redispatch cost‐sharing calculation.
FIGURE 3: CALCULATION ALGORITHM FOR SIMPLE NODAL‐PTDF DEVIATION METHOD
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In the FIGURE 3, the algorithm of simple nodal‐PTDF deviation method is shown. One should note that the activation of remedial actions due to unexpected flows (IDCF vs. SN) could be reduced by introducing a more frequent intraday capacity calculation process, based on a more frequent creation of high‐quality forecast models closer to the real‐time (one hour before energy delivery: H‐1) with the provision of more accurate information (individual generation dispatch) from market participants to the TSOs. Remedial actions are much more frequently activated due to the unallocated flows (cause 1) and therefore this phenomenon will be explained in the following chapters with a small calculation test example. Generation Shift Keys (GSKs) According to the flow‐based capacity calculation methodology developed in Europe, Generation Shift Keys (GSKs) are a method of translating a net position change of a given bidding zone into estimated specific nodal injections or withdrawals in the Common Grid Model (CGM). These technical parameters are needed to describe a zonal distribution of generation and consumption and are used as an input for the implicit capacity allocation ‐Market Coupling (MC). On the basis of GSKs, zonal PTDFs could be calculated and these data are used for the MC clearing process [3]. Nodal Power Transfer Distribution Factors (PTDFs) Power Transfer Distribution Factors (PTDFs) represents a variation in the active power flows on the observed lines, which are the consequence of change of generation or load in a specific node of the system. The calculation of PTDF matrix can be performed, for example, by increasing of the generation in a node of system A for 100 MW, and simultaneous decreasing of the generation in the reference node of the system B for the same amount [4]. On that way, additional power transferred from system A to system B is simulated. It is also possible to use different value than 100 MW, because only relative change in active power flow over transmission system elements (lines or transformers) is important. The ratio between the active power flow change at the respective line ( Pi ) and amount of power change ( P ) is the PTDF for the line:
PTDFi A B
Pi [ MW ] 100 Pi [%] , (1) P[ MW ]
where: A – node that is source of transaction, B – node that is sink of transaction, P ‐ delta power change in sink node (MW), i – system element (line or transformer), Pi ‐ change in active power flow at the line i. For the calculation of PTDFs, linear DC load flow without losses is used:
Pij
1 ( i j ) , (2) xij
Where: i and j – nodes of the transmission system, Pij ‐ active power flow over an element of transmission system, ( i j ) ‐ phase angle between voltages Vi and V j , xij ‐ reactance of a transmission element (between nodes i and
j ). Additionally, next equations are fully satisfied (for energy transaction from node A to node B): PTDF ( A B ) PTDF ( A C ) PTDF (C B ) (3) PTDF ( A B ) PTDF ( B A) . (4)
An important feature of DC load flow method is a possibility of “associative” property implementation, because it allows creation of complete PTDF matrix after the calculation of only rows that represent transactions from each node towards the single arbitrary chosen node (so called “slack”). Network topology and physical network parameters (resistance and reactance) have great influence on PTDFs, much more than production/ load pattern.
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The main advantage of DC load flow over AC calculation is that change in losses in every national power system, when simulating additional energy trade between them, doesn’t have an influence on calculated results. Natural Flow (NF) Model For the calculation of the unallocated flows, a so‐called Natural Flow (NF) model needs to be prepared ‐ a grid model in which the net positions of all zones are set to zero [5]. As such, this model shows how the electricity needs of a particular zone are covered with its own, and usually the cheapest, generation sources. From this calculation, it is possible to derive the so‐called natural flows that are loading elements of internal and external zones in the case that there is no cross‐zonal commercial energy exchange among them. In order to arrive at a grid model which is an outcome of the market coupling, and, at the same time starting from a NF model, GSKs are used. For the calculation, a GSK that is based on the merit‐order list (if available) should be used, to ensure that the consumption in zones is always covered with their own cheapest production units. If the actual available generation capacity within a zone is lower than the consumption which is to be covered, the demand has to be scaled down. This situation should not happen in practice as each European bidding zone (which usually corresponds at least to a country1) needs to have enough reserve capacity to cope with peak demand (system adequacy [6]) as well as with an unexpected outage of the largest unit for at least 15 minutes. Natural flows should not be considered as something negative as these are just the outcome of different sources and sink feed‐ins and off‐takes, the interconnected meshed network, and Kirchhoff’s laws. Nevertheless, due to the European zonal market design, different sizes and shapes of bidding zones and different energy renewables subsidies, the inner‐bidding zone energy exchanges influence the neighboring systems and have an impact on the possibilities for cross‐zonal trade. Redispatch Cost-Sharing: Calculation Concept TABLE 1: SMALL 3‐ZONES EXAMPLE ‐ NODAL GENERATION AND CONSUMPTION PER BIDDING ZONE
Bidding Zone
Node
NF Model [MW]
Outcome MC [MW]
DACF [MW]
1
PGABKO1
54
65
79
1
PHCILE5
54
65
79
1
PHPIVA2
215
262
315
1
PHPIVA2
‐30
‐30
‐30
1
PIVANG1
‐70
‐70
‐70
1
PIVANG2
377
458
377
1
PLOLAN1
‐20
‐20
‐20
1
PMIJAI1
‐400
‐400
‐400
1
PMIJAI2
‐200
‐200
‐200
1
PTPLJE2
100
100
100
1
PZAGLA1
‐80
‐80
‐80
3
OVOJN21
‐300
‐300
‐300
3
OVOJN25
‐150
‐100
‐150
3
OZENIO5
‐100
‐100
‐100
3
OVOJN15
550
400
450
2
GCVOR25
‐50
‐50
‐50
2
GCVOR35
‐100
‐100
‐100
2
GRIBPG1
‐100
‐100
‐100
2
GRIBPG2
‐150
‐150
‐150
2
GRPRIB1
400
350
350
In order to demonstrate how the calculation with the simple nodal‐PTDF deviation method for redispatch cost‐ sharing due to cause 1 (unallocated flows) works in practice, a small 3‐zones example has been created (Figure 4). It With the exception of common DE‐AT bidding zone
1
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corresponds to the European market design where the shape and configuration of bidding zone 1 (non‐disperse location of installed generation and load capacity) could lead to the higher level of unallocated flows. The transmission network consists of different voltage levels (110 kV – black; 220 kV – green; 380 kV – red). After the submission of energy buy/ sell bids and offers by the market participants to power exchanges and clearing process, the results of Market Coupling (MC) show that the cheaper area, bidding zone 1, exports 150 MW of electricity towards more expensive zones – bidding zone 2 (‐50 MW) and bidding zone 3 (‐100 MW). As in all zones a self‐dispatch market model is in place, actual dispatch schedules are determined by market participants based on market clearing prices per zone and actual short‐term (marginal) costs of the power plants. These final unit commitment schedules are not known in the moment of MC clearing, but only in the late afternoon hours on a day‐ahead level during the operational planning phase (DACF: day‐ahead congestion forecast). All TSOs, based on their best knowledge and available information, are trying to estimate the merit‐orders of different power producers according to their plants portfolio. This information is used as an additional input for the MC clearing process. In case that TSOs would have perfectly forecasted GSKs (actual merit‐order list) per zone, it would lead to a situation in which both models, DACF and model based on the outcome of MC, do not mutually deviate and there should be no overload in the system2. One additional precondition is that more volatile renewable energy generation has been accurately forecasted, as otherwise it would influence merit‐order list per zone. As a reference (slack) node for the calculation of PTDF matrix, one bus in the bidding zone 3 without feed‐in and off‐take has been selected. The selection of slack node doesn’t have an influence on the final results of calculation as the difference in a zonal net position between two models (Outcome MC and DACF model) is zero. The only difference among them would be caused by deviation of estimated GSKs.
FIGURE 4: SMALL 3‐ZONES EXAMPLE USED FOR THE DEMONSTRATION OF SIMPLE NODAL‐PTDF DEVIATION METHOD
From the table 2, one can see that TSO 1 has expected proportional dispatch of power plants (according to their engagement in the base case) located in nodes PGABKO1, PHCILE5, PHPIVA2 and PIVANG2 in case of energy export from Zone 1. But, based on unit commitment scheduled received from power plants operator, only first three generators deliver additional 150 MW, proportionally to their production in base case. Generator PIVANG2 doesn’t change its schedule after the zonal day‐ahead energy prices are known. This leads to nodal deviation in Zone 1: in a particular hour of question for which the calculation is done, Zone 1 loads additionally the critical line
Statement valid under the assumption that TSOs selected proper critical branch/critical outage (cb/co) pairs to constrain cross‐ border dispatch of units during the market coupling process 2
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(Ln: OVOJN25 – OVOJN15) on the same way like in a case that there is internal transaction within Zone 1 between nodes PGABKO1, PHCILE5, PHPIVA2 on one side as a source and PIVANG2 on the other one as a sink. Situation is similar in Zone 3. Instead of 75%/25% sharing key assumed by TSO 3 between nodes OVOJN15 and OVOJN25, production decrease took place only in node OVOJN15. Nevertheless, the effect of GSK forecast error on critical line is quite different for both zones. While Zone 1 contributed with 1.3 MWh to the additional loading over the critical line, Zone 3 loaded it with additional 23.3 MWh (table 3). It could also happen that, due to error in GSK estimation, one zone relieves the critical line and with that unintentionally allows additional energy trade between a cheaper and a more expensive zone. TABLE 2: SMALL 3‐ZONES EXAMPLE ‐ GSKS ESTIMATED BY TSO 1/TSO 3 IN ZONE 1/ZONE 3
Zone
Node
GSK (TSO 1)
GSK (TSO 3)
1
PGABKO1
7,7%
‐
1
PHCILE5
7,7%
‐
1
PHPIVA2
30,8%
‐
1
PIVANG2
53,8%
‐
3
OVOJN15
‐
75%
3
OVOJN25
‐
25%
In the particular example and according to the redispatch cost‐sharing principle proportionally to the level of unallocated flows, Zone 1 would bear only 5.3% of the total redispatch costs and Zone 3 94.7%. The calculation is performed under the assumption that nodal consumption in L1 and L2 has been perfectly forecasted. TABLE 3: SMALL 3‐ZONES EXAMPLE ‐ CONTRIBUTION OF EACH ZONE TO OVERLOADED ELEMENT (LINE 1)
Zone
Node
NF Model
Outcome MC
DACF
DELTA Node
Node to slack PTDF on line 1
Unallocated Flow caused by a Node
1
PGABKO1
54
65
79
13,46
‐0,137
‐1,85
1
PHCILE5
54
65
79
13,46
‐0,001
‐0,01
1
PHPIVA2
215
262
315
53,85
‐0,091
‐4,92
1
PIVANG2
377
458
377
‐80,77
‐0,100
8,09
3
OVOJN25
‐150
‐100
‐150
‐50,00
‐0,256
12,84
3
OVOJN15
550
400
450
50,00
0,209
10,46
Contribution per zone
1,3
23,3
The same approach for the identification of causer could be applied when the decision for redispatch activation has been made close to the real‐time and after IDCF model is created. In this case, the comparison of IDCF model and snapshot (SN) is necessary. The activation of redispatch is caused by additional loop flows due to intraday trade within a zone (cross‐zonal ID trade is usually allowed up to 60 minutes before the energy delivery) or due to activation of replacement reserve/frequency restoration reserve and/or unintended deviations. Nevertheless, these cases of redispatch activation should be applied rather in exceptional cases, as it would mean that the system is operated close to its limits (line loading in n‐1 case is very close to PATL [7]). Conclusions In this paper, description of simple nodal‐PTDF deviation methodology is given which is helpful for the assessment of unallocated and unexpected flows and which could be deployed as a solution for the redispatch cost‐sharing methodology among TSOs in the European zonal market design. Developed redispatch cost‐sharing methodology ensures a fair distribution of costs and benefits between the TSOs that is in line with the European target model for capacity calculation and allocation (flow‐based market coupling). Proposed key incentives TSOs to better estimate zonal merit order list (GSKs) within their zones of responsibility. Contribution from the different zones to a particular element decreases rapidly with electrical distance in case that the decision for redispatch has been made based on the planning models. It means that the impact of zones close to the overloaded line(s) is higher than the impact of remote zones. In case of redispatch identification and activation close to the real‐time, high regional imbalance of zones plays an important role for the costs allocation.
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ACKNOWLEDGEMENT
The author would like to thank Cherry Yuen Yee Shan and Christophe Dunand (swissgrid) for valuable discussions on the topic. REFERENCES
[1]
J. Bialek, “Tracing the flow of electricity”, IEE Proceedings of Generation, Transmission and Distribution, Volume 143, Issue 4, July 1996
[2]
B. Klöckl, “Cost and usage based cross‐border TSO tarification with power flow decomposition models”, IEEE Power & Energy Society (PES) General Meeting, Calgary, AB, 2009
[3]
A. Marien, P. Luickx, A. Tirez and D. Woirin, “Importance of design parameters of flow based market coupling implementation”, IEEE 10th International Conference on the European Energy Market (EEM), Stockholm, Sweden, May 2013
[4]
R. Audouin, D. Chaniotis, P. Tsamasphyrou, J‐M. Coulondre, “Coordinated auctioning of cross‐border capacity: an implementation”, Proc.IEE 5th International Conference on Power System Management and Control, London, 2002
[5]
M. Vukasovic, P. Schavemaker, “Towards the European internal electricity market: An efficient cross‐zonal redispatch cost‐sharing methodology among European transmission system operators”, IEEE 11th International Conference on the European Energy Market (EEM), Krakow, Poland, May 2014
[6]
ENTSO‐E Target Methodology for Adequacy Assessment, July 2014
[7]
UCTE Operational Handbook – Appendix 3, “Operational Security”, Final Version (approved by SC on 19 March 2009)
BIOGRAPHY
Milan Vukasovic (born 1981 in Sarajevo, BiH) received his MSc. in Electrical Engineering from the Faculty of Electrical Engineering Podgorica, Montenegro. After gaining valuable experience in real‐time transmission system operation in CGES (Cronogorski Elektroenergetski Sistem), he moves to Austria at the end of 2007 where he starts to work in the Market Management Department of the Austrian Power Grid AG (APG). In the last five years, Milan has been engaged in the most important European energy market projects. His main interests are in demand‐side management, load‐flow analysis, electricity markets simulation, congestion management, and computer applications in power systems. He is co‐convener of the regional ENTSO‐E market Task Force (TF) in South‐East Europe as well as being the Chairman of the Economic Group within the TSO TSC Security Co‐operation (http://www.tscnet.eu).
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