On Feasibility and Flexibility Operating Regions of Virtual Power Plants and TSO/DSO interfaces

On Feasibility and Fle xibility Operating Re gions of V irtual Po wer Plants and TSO/DSO Interf aces Shariq Riaz and Pierluigi Mancarella Department of Electrical and Electronic Engineering, The Univ ersity of Melbourne, Australia { shariq.riaz,pierluigi.mancarella } @unimelb .edu.au Abstract —Distributed energy resour ces are an ideal candidate for the pr ovision of additional flexibility required by the power system to support the increasing penetration of renewable energy sources. The integrating large number of resources in the existing market structure, particularly in the light of providing flexibility services, is en visioned through the concept of virtual power plant (VPP). T o this end, it is crucial to establish a clear methodology for VPP flexibility modelling. In this context, this paper first puts forward the need to clarify the difference between feasibility and flexibility potential of a VPP , and then propose a methodology for the evaluation of rele vant operating regions. Similar concepts can also be used to modelling TSO/DSO interface operation. Several case studies are designed to reflect the distinct information conv eyed by feasibility and flexibility operating regions in the presence of slo w and fast r esponding resour ces for a VPP partaking in the provision of energy and grid support services. The results also highlight the impact of flexible load and importantly network topology on the VPP feasibility (FOR) and flexibility (FXOR) operating regions. Index T erms —Active distribution networks, flexibility , fre- quency control ancillary services, TSO/DSO interface, virtual power plant. I . I N T RO D U C T I O N In the pursuit of energy system decarbonisation, replace- ment of con ventional thermal power plants with v ariable and partly unpredictable renewable energy resources (RES) has created new challenges in power systems, primarily due to increased flexibility requirements and simultaneous reduc- tion of av ailable operational flexibility mainly provided by con ventional power plants. On the other hand, increasing electricity prices and decreasing technology cost have boosted in vestments in distributed household photovoltaic (PV) and batteries systems, coupled with the dev elopment of smart grid technologies that allow better control ov er distributed energy resources (DER). This creates new opportunities for flexibility provision from DER, particularly for the purpose of frequency control ancillary services (FCAS) that are essential for stability and security in low-inertia power systems [1]. Currently , aggregated flexibility from DER and various demand-side resources that can pro vide Demand Response (DR) is still vastly an untapped resource, restrained by the traditional po wer system management approach of treating distribution networks as passiv e entities [1]–[4]. Nonetheless, significant benefits can be derived through proper management The authors gratefully acknowledge the partial support for this research receiv ed from the V ictorian Governments veski programme. and control of DER as they have the potential to assume a central role in the future grid stability and security [5]. Recently , in the Open Energy Network (OEN) consultation paper [6], Energy Network Australia and the Australian Elec- tricity Market Operator expressed the need and urgency to integrate DER and tap their flexibility potential in order to reduce electricity costs. The OEN paper also explores the impact from collectiv e orchestration of DER to deliver a significant amount of operational flexibility and potential to influence grid management strategies. Howe ver , the key challenge lies in finding ways to facilitate effecti ve integration of a vast number of devices in the existing market structures. Central to the aggregation and coordinated control of DER is the concept of virtual po wer plant (VPP) [2], which seeks to address the challenge by clustering a large number of devices, backed by appropriate control policies, to efficiently deliv er some of the ke y grid support services in an economically viable manner . A VPP takes into account operational constraints of DER coupled with network restric- tions and offers the aggregated capacity in various markets for the provision of energy and grid support services, thus boosting business case of grid participation of DER [2]. In line with VPP flexibility modelling, recently there is rising interest in the ideas of flexibility estimation at transmission system operator (TSO) and distrib ution system operator (DSO) interface boundary of an acti ve distribution network [1]–[3], [7]–[11] 1 . The concept and high-le vel structure of a VPP were compre- hensiv ely discussed [2] in the FENIX project, classifying VPPs into commercial and technical roles and establishes simple algorithms for estimating their static characteristics. A general framew ork for quantifying and techniques for visualizing the operational flexibility from generic resources is described in [19]: this work deplo ys Minko wski summation technique that yields a good estimation of the aggregated flexibility in the absence of network constraints. The relationship between power consumption and voltage is exploited in [3] through the control of on-load tap changer at the interface of DSO and TSO. In [7], [8] cost maps are proposed as an instrument to provide fle xibility information to TSO. T ime dependency 1 Also to note is various work that deals with different integration approach for VPP into various markets, tackling uncertainty from DER, incorporating multi-energy storage systems, and proposing real-time control, uncertainty management, active monitoring and risk minimization algorithms [12]–[18]. Fig. 1. Conceptual visualisation of a virtual power plants feasibility operating region. and the uncertainty of feasible operating space are detailed in [1], [9], while techno-economic modelling of flexibility from multi-energy vectors is introduced in [10] and a lin- earisation approach to reduce the computation time of VPP non-linear framew orks is elaborated in [11]. Almost all of these works are primarily focused on the estimation of operational feasibility and uses the concept of flexibility interchangeably with feasibility . Ho we ver , this is only v alid if all of the considered resources hav e a sub-second response time, for example, conv erter base systems and some demand response (DR) technologies. In contrast, in the context of the provision of fast and slow responding resources and relev ant market services, feasibility is not sufficient to model the actual flexibility characteristics of a VPP . Systematic ev al- uation of the temporal ability to move between two set-points (flexibility) that are located in a feasible operating region (feasibility) is therefore needed to assess the VPP potential to optimise its participation in energy and grid service provision. The conceptual visualisation of the feasibility operating region (FOR) of a VPP corresponding to a giv en operation set-point is represented in Fig. 1, while flexibility operating regions (FXOR) would be a subset, as elaborated on belo w . In light of the above, as v arious works treat flexibility synonymously to feasibility , there is a need for precise defini- tions and identifying the distinct role of flexibility apart from feasibility . Thus, the key objectiv es and contributions of this paper are as follows: • Acknowledge the distinct information provided by feasi- bility and flexibility in the context of a VPP; • Methodology for the assessment of VPPs flexibility po- tential, as a subset of its feasible operating set; • Identify the role of the information pro vided by feasibility and flexibility for a VPP providing different grid services. The proposed methodology is demonstrated on the IEEE 33 bus network. The case studies are designed to bring out the usefulness of clearly distinguishing feasibility and flexibility , with application in the Australian context. Furthermore, case studies also explore the impacts of flexible loads and network topology on the FOR and FXOR of a VPP . The remainder of the paper is structured as follo ws: Sec- tion II discusses the state of art on the topic under analysis and outlines the methodology adopted in this work. Section III introduces the test system, cases and assumptions used in the simulations, while the results from the case studies are then discussed in Section IV. Section V concludes the paper . I I . M E T H O D O L O G Y This Section provides key definitions, discusses the state of the art, and outlines the methodology adopted in this paper . A. Ke y Definitions As acknowledged by [1], there is a need for dev eloping a consistent terminology . Some definitions in the context of presented work are therefore described as follo ws: 1) Dispatch P ower ( S λ ) : is defined as the complex power (pair of acti ve and reacti ve powers) exchange between VPP and the grid, resulting from the VPP participation in the energy market alone. 2) F easible operating Region (FOR) : represents the set of all feasible dispatch po wer points of a VPP , mathemati- cally represented as ∪ S λ . In general, this is time-v arying and depends on the current dispatch point too. 3) Ancillary P ower ( ∆ S a ) : is the complex power deviation of a VPP from a gi ven dispatch power point to partici- pate in grid support services. 4) Flexibility : is the measure of VPPs responsiv eness to deviated from its dispatch power point; in other words, it represents how fast a combination of resources within the VPP can be deployed to provide grid support ser- vices. 5) 5) Flexibility Operating Region (FXOR) : ): is a quan- titativ e representation of the VPP flexibility; it consists on the set of achiev able ancillary power points in a giv en amount of time ( τ ) and mathematically can be represented as S λ + ∪ ∆ S a ( t ) | t ≤ τ . B. State of the art There are two fundamental methodologies described in the literature to estimate the feasibility potential of a VPP , namely , i) Monte Carlo estimation [1], [7] and ii) optimization approach [3], [8], [11], [19]. Both methodological approaches hav e the potential to be extended in the context of estimating flexibility potential. In Monte Carlo based simulations, first, a large number of power flow instances are ex ecuted for randomly generated operating points; then, non-con ver gent points are discarded, and the remaining points provides the feasibility region of a VPP . While this method is simple and can generally be applied to various network topologies, it is inherently limited by the fact that a large number of operating points are required to get an accurate estimate. In contrast to the Monte Carlo approach, the optimization approach directly aims to find the boundary of the feasibility region by considering network and DER operation constraints. In this method, first, four optimization problems are setup aiming to find the minimum and maximum acti ve and reactive power requirement of the VPP [3], [8], [11]. This pro vides with four e xtreme points of the PQ capability curve of a VPP . Then, the boundary is further refined by solving the Algorithm 1: Algorithm for determining FOR Input: Network constraint, loads and DER operational constraints 1 Randomly generate correlated DER operating points n ∈ N , within each DER operational constraints; 2 for n ← 1 , |N | do 3 Solve power flo w; 4 if Network constraint are violated then 5 Discard n ; 6 else 7 Add n to O ; 8 Add corresponding VPP dispatched point to FOR; 9 end 10 end Output: FOR and O optimization problem for intermediate points. This approach is more suitable for small to medium scale networks; howe ver , the size and computation complexity grows exponentially with network size and number of DER. Also, due to network constraints, the optimization problem is non-linear in nature, though promising research is ongoing to propose linearisation of network and DER constraints which might be suitable to re- duce the computational complexity of this method (e.g., [11]). Since the main aim of this paper is to distinguish between the feasibility and flexibility potential of a VPP , a Monte Carlo approach, which is simple and straightforward, is deployed for both feasibility and flexibility estimations. C. F easibility Estimation The procedure for assessment of FOR is summarised in Algorithm 1, deri ved from the method provided in [1], [7]. The inputs to the algorithm are network constraints, load require- ment of each b us and operational limitations of participating resources in the VPP arrangement. The algorithm starts by generating a large number of vectors ( n 1 × 2 R ) consisting of operational points for the resources: n = { P 1 , P 2 , . . . , P R , Q 1 , Q 2 , . . . , Q R } , where, R is the total number of resources in the VPP . Then, an AC po wer flow is solved for each combination of oper- ating points n and the solution is checked against network constraints, that is b us v oltage and component thermal limits. The points satisfying network limits are then included in a set of feasible operating points O and the resulting dispatched point of VPP S λ is added to the FOR, otherwise, the point is discarded. In the end, the algorithm returns the FOR along with the associated set of DER operating points. The FOR returned at the end of Algorithm 1 is basically the aggreg ated PQ capability curve of a VPP and contains sufficient information the VPP to participate in the energy market. D. Flexibility Estimation Flexibility reflects the time required by a VPP to de viate from a particular dispatched point to another point within the Algorithm 2: Algorithm for determining the FXOR Input: Outputs of Algorithm 1, VPP market dispatched point ( S λ ), resources activ ation time, ramp rates and target time intervals ( τ ) 1 for n ← 1 , |O | do 2 Calculate time ( t ) required by resources to change their output to n from set-points corresponding to market dispatch; 3 if t ≤ τ then 4 Add corresponding deviated dispatched point to flexibility curve X τ ; 5 end 6 end Output: X τ FOR. It depends upon the acti vation time, ramp rates and status of the participating DER. The process to identify the achiev able operating points from the dispatch point in a giv en amount of time is outlined in Algorithm 2. The activ ation time, ramp rates, status and operating points of resources corresponding to FOR, dispatch point of the VPP , and target time interv als ( τ ) serve as the inputs for the algorithm. The algorithms calculate the time required to change the output of VPP from the current dispatch point to all other points within the FOR. Feasible points that can be achie ved within the specified time interv al are included in the fle xibility curve ( X τ ) that bounds the FXOR, which is returned by the algorithm. The FXOR is a reflection of VPPs ability to participate in FCAS markets and providing reactive power services after being dispatched at a particular dispatch point in the energy market. I I I . S I M U L A T I O N S E T U P Description of the test system, study cases and assumptions are presented in this section. A. T est System The presented studies use a modified IEEE 33 bus system as a test bench to distinguish between feasibility and flexibility , as shown in Fig. 2. Network characteristics such as line impedances and load requirements are taken from the original model first presented in [20]. The network is then populated with four diesel generators, fi ve b uses with aggregated rooftop- PV systems ranging from 28 kW to 38 kW summing to a total installed capacity of 162 kW ; and fi ve buses with household PV -battery systems with the average capacity of 38 kW of PV backed by 20 kW h of storage. Therefore, the total installed capacity of diesel generators, PVs and batteries in the system is 1 MW , 352 kW and 100 kW , respectively . The net active and reactive power demand of the system is 3 . 71 MW and 1 . 76 Mv ar , respectiv ely . Specification of installed diesel gen- erators are gi ven in T able I, where as the models of deployed resources are shown in Fig. 3. PV , battery and flexible loads are subjected to acti vation and ramp times between 0 . 1 a nd 0 . 3 s econds, so these resources possess the capability to mov e Fig. 2. Modified IEEE 33 bus network. T ABLE I D I ES E L G E N ER ATO R ’ S S PE C I FI CAT IO N S . Bus Power limits T ime P min P max Q min Q max Activ ation Ramp ( kW ) ( kW ) ( kv ar ) ( kvar ) ( sec ) ( sec ) 8,30 15 100 -40 60 15 20 18 150 500 -200 300 40 60 25 60 300 -120 180 25 45 Fig. 3. Actual (red) and assumed (grey) PQ capabilities of resources considered in this study . from minimum to maximum values of power within a second and vice versa . Furthermore, PV inv erters are subjected to a power factor limit of 0 . 9 a nd the minimum output power is set to 10 % of the in verter rated value. The FOR and FXOR are calculated for one instant of time and do not consider the inter- temporal constraints. Moreover , it is assumed that the VPP is equipped with proper communication, control and incentiv e mechanism and is authorised to operate resources within their PQ capabilities while acknowledging their operational requirements. B. T est Cases Three different study cases are designed to discriminate between feasibility and flexibility potential of a VPP , along Fig. 4. FOR of the VPP in Case I. with the impact of flexible demand and network restrictions. More specifically , Case I first deploys test system explained in Section III-A and highlights the usefulness of flexibility in the context of a VPP partaking in grid support services. Second, Case II considers 5 % of the load at each bus to be flexible, in order to study its impact on the VPP’ s flexibility . Finally , in Case III tie switches connecting b us 8 to 21 and 12 to 22 are open to study the impact of network reconfiguration. I V . R E S U LT S A N D D I S C U S S I O N The results of the feasibility and flexibility potential of DER aggregation in the proposed study cases are presented as follows. A. F easibility The FOR of the VPP in Case I is sho wn in Fig. 4, along with the con vex hull of FOR approximating its boundary . The figure represents the PQ capability of the VPP and identifies the dispatchable activ e and reactiv e power levels for a specific instant of time. Due to the time-varying nature of demand and resources (e.g. flexible loads, PV and battery systems) the FOR of the VPP changes continuously and thus needs to be ev aluated at re gular interv als. For example, in an Australian context, with 30 min commitment and 5 min dispatch market, FOR needs to be ev aluated with the time resolution of 5 min. FOR is particularly effecti ve to e valuate the VPP capacity to bid in the energy market. After being dispatched at a specific dispatch point, the maximum feasible deviation represents the VPP potential to participate in grid support services. Example: Lets assume that the VPP is arbitrarily dispatched by the ener gy market at point S λ , and the comple x power difference between any other point in the FOR and the dispatch point ( S λ ) is called ancillary power ( ∆ S a ). The ancillary power points with positi ve activ e power can provide FCAS raise 2 services and points with negati ve acti ve power can provide FCAS lower 3 services, as illustrated in Fig. 5. Howe ver , in order to participate in various FCAS services (e.g. 6 sec , 60 sec or 5 min [21]) it is important to understand the time requirement associated with each ancillary power point. 2 Raise FCAS are deployed to regulate, arrest, stabilise and recov er drop in frequency . 3 Lower FCAS are deployed to regulate, arrest, stabilise and recover rise in frequency . Fig. 5. VPP feasibility regions (FOR) in Case I, along with the resulting potential regions for FCAS raise (green) and lower (red) services. Fig. 6. VPP flexibility regions (FXOR) in Case I, for four dispatch points. B. Flexibility Flexibility is the quantitative measure of the time required to achiev e ancillary po wer point from the dispatch power point. It depends upon f actors such as status, acti vation and ramp time of the resources. It is particularly important in the presence of slow resources (e.g., diesel generators), as they could restrict the participation of the VPP in grid support services. The flexibility operating regions (FXOR) of the VPP in Case I are sho wn in Fig. 6, for four dif ferent dispatch points, characterising the strong interdependence between dispatch and flexibility . The dispatch point in Fig. 6-B and Fig. 5 is the same, reflecting the additional information provided by the FXOR. While the results are mostly self-explanatory , Fig. 6-A is particularly interesting due to the lar ge jump in the FXOR. The figure, in fact, shows that the ancillary power points in the blue area can be achiev ed within one second; howe ver , no other point in the map is achie vable under 20 sec , attributed to the fact that at this dispatch point all diesel generators are offline and the jump results due to the activ ation time requirement 4 . In summary , a FXOR representation is a very useful tool for understanding VPPs restriction to participate in FCAS markets because of the activ ation and ramp time constraints of the resources. 1) Australian Conte xt: The Australian National Electricity Market (NEM) operates with six FCAS contingency services, 4 Similar discontinuities in the operation of devices for the provision of ancillary services are also presented in [22] Fig. 7. Case I VPP’ s potential for FCAS participation resulting from the dispatch point S λ . Fig. 8. FORs for all three study cases. namely , Fast ( 6 sec ) Raise and Lower , Slow ( 60 sec ) Raise and Lower , and Delayed ( 5 min ) Raise and Lo wer . Let us assume that the VPP is dispatched at S λ (same as depicted in Fig. 5 and Fig. 6-B). Then, utilising the FXOR representation, the potential of VPP to participate in various FCAS markets can be identified, as shown in Fig. 7. C. Impact of Flexible Demand and Network T opology Additional flexible resources in a VPP will increase its flexibility and results in lar ger FOR, along with a slight change in shape depending upon the PQ capabilities of the resources. If network constraints are not binding, then the resulting FOR can be accurately calculated using the VPP FOR and PQ charts of ne w resources. Howe ver , the impact of altering network topology is complicated and mainly depends upon the line limits and structure of the network. The FORs for all three study cases are sho wn in Fig. 8, representing that addition of flexible demand in Case II in- creases the FOR of the VPP , because of the direct relationship between FOR area and quantity of flexible resources and reconfiguration of the network by opening tie switches in Case III resulting into voltage limit violations for higher demand requirement of VPP thus eliminating corresponding points in FOR and FXOR.. In other words, network reconfiguration can reduce or enhance the FOR of VPP by affecting the operation of its resources due to underlying network constraints. In terms of flexibility , the addition of flexible demand which can be turned on or off rapidly increases the sub-second Fig. 9. FCAS participation potential of VPP for Case II. Fig. 10. FCAS participation potential of VPP for Case III. flexibility for FCAS participation, as represented by Fig. 9. On the other hand, Fig. 10 shows that VPP participation in all FCAS markets is affected by the network reconfiguration, which adds binding constraints mainly based on resource location and system demand. Whereas, Fig. 10 represents the that the VPP participation factor in all contingency FCAS markets is effected due to the network restriction, which add binding constraints mainly based on resource location and system power demand. V . C O N C L U S I O N A N D F U T U R E W O R K S This paper discusses how feasibility and flexibility for aggregation of DER in a VPP (or similarly for the purpose of modelling TSO/DSO interface operation) are not the same, with each concept bringing distinct useful information for the operation of VPP partaking in energy and grid support services. The feasibility space is characterized by the PQ capability chart of a VPP , whereas flexibility quantifies the FCAS (along with associated reactiv e po wer response capabil- ity) potential giv en a specific dispatch point. T wo algorithms hav e been introduced to quantify the feasibility (FOR) and flexibility (FXOR) operating regions of a VPP . The case studies demonstrate the efficacy of the proposed methodology and algorithms and illustrate the impact of VPPs dispatch on its FCAS participation. The case studies also rev eal that the greater is the pool of resources (for example, adding DR), the larger will be the feasibility and flexibility potential of a VPP . Howe ver , the topology of the network is also an important parameter that introduces location-based constraints, which demands a further in vestigation to optimise the location of resources within a VPP . R E F E R E N C E S [1] D. M. 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