Benefit-based expansion cost allocation for large scale remote renewable power integration into the Australian grid

Applied Energy 113 (2014) 836–847

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

Benefit-based expansion cost allocation for large scale remote renewable power integration into the Australian grid Kazi Nazmul Hasan a,b,⇑, Tapan Kumar Saha a,b, Deb Chattopadhyay b, Mehdi Eghbal a a b

Queensland Geothermal Energy Centre of Excellence, University of Queensland, Brisbane, QLD 4072, Australia School of ITEE, University of Queensland, Brisbane, QLD 4072, Australia

h i g h l i g h t s  Shapley Value approach offers a fair and equitable cost allocation.  A case study of the Australian Queensland network is presented.  Cost allocation is contentious for remote renewable power transmission.  Regulatory policies and planning frameworks need to be updated.

a r t i c l e

i n f o

Article history: Received 4 June 2013 Received in revised form 10 August 2013 Accepted 12 August 2013

Keywords: Expansion cost allocation Net market benefit Shapley Value

a b s t r a c t Climate change policies in different jurisdictions enhance the integration of large scale remote renewable power generation into the grid, where the remoteness of the location-constrained generation zone and subsequent high transmission investment appear as a potential barrier. Consequently the justification of investment and equitable cost allocation becomes contentious. This research firstly presents a stateof-the-art review of policy changes in different jurisdictions in this aspect. Then transmission connection and usage cost allocation are presented to address these challenges accordingly, highlighting the implementation of semi-shallow connection cost allocation policy. Afterwards, the benefit-based allocation of network usage cost is reimbursed through Shapley Value approach. Finally, the investigation of the net market benefit and cost allocation are presented by simulating four large scale remote renewable transmission projects to be connected to the Australian Queensland network. This study aims to enhance regulatory policies and associated planning frameworks to be more efficient and justifiable for renewable power integration paradigm. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Climate change policies along with emission pricing strategies worldwide influence power system planning approaches to ascertain a low emission power sector. This trend enhances the integration of large scale renewable power sources into the grid. As potential large scale renewable energy sites are generally located far away from the consumptions centres, transmission infrastructure development is required to reach those location-constrained remote zones. Thereby integration of large scale renewable energy to the grid goes hand in hand with a significant investment in extensive transmission infrastructure [1,2]. Accordingly the share of transmission cost for network users becomes large compared to a conventional generator connection to the grid. This problem

⇑ Corresponding author at: Queensland Geothermal Energy Centre of Excellence, University of Queensland, Brisbane, QLD 4072, Australia. Tel.: +61 733651654. E-mail address: [email protected] (K.N. Hasan). 0306-2619/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2013.08.031

turns out to be noticeable in large countries where a transmission line expands 1000 km to reach a remote generation zone [3–5]. For instance, the Renewable Energy Target (RET) in Australia is targeted to integrate 6000–8000 MW renewable power to the grid by 2020 [6]. To be coherent to the RET, 4000 MW geothermal power is expected to realize step-by-step by 2030 from ‘Cooper Basin’ resources, which is located in the range of 1000 km from the existing Queensland grid [7]. Also, the proposed ‘Kennedy Wind Farm’ (700 MW) is located around 300 km away from the existing grid of North Queensland [8]. The ‘Copper String’ project is expected to connect 400 MW renewable power from the North-West Queensland to the grid through a 700 km long transmission line [9]. Besides 1800 MW hydro power is expected to import to the Australian National Electricity Market (NEM) via North Queensland from Papua New Guinea (PNG), through a 250 km subsea interconnection and 200 km transmission infrastructure. All of the abovementioned large scale remote renewable projects have to go through the traditional Australian Regulatory

K.N. Hasan et al. / Applied Energy 113 (2014) 836–847

Investment Test for Transmission (RIT-T), which is primarily designed for explicit cost benefit assessment of market benefit of transmission for conventional generation. While the RIT-T has a number of attractive features, it also has some shortcomings when it comes to large scale renewable power integration into the grid from a remote location [10]. Under the Australian NEM regime, location-constrained renewable generators have to undergo through the same transmission cost allocation policy which is basically designed for conventional generation plants and generally overlooks the remoteness of renewable sites. It is evident that the existing RIT-T framework does not provide adequate incentive to get some of the efficient transmission investments off the ground, which is eventually shutting off significant green energy opportunities for ever [10]. Keeping this in mind, the current study evaluates the connection cost (investment) and network cost (O&M) allocation policies practiced in different jurisdictions worldwide in the context of large scale remote renewable power integration into the grid. The rest of the paper is organized as follows. Section 2 presents an overview of transmission cost allocation methodologies in the context of large scale renewable energy, followed by Australian transmission cost allocation practices in Section 3. Then the proposed approach of transmission cost allocation in the context of large scale renewable energy is presented in Section 4. Further, Section 5 describes the Australian case studies of large scale remote renewable power integration into the NEM grid. Subsequently, results and analysis are presented in Section 6, followed by conclusions in Section 7. 2. Transmission cost allocation in the context of large scale renewable energy – an overview In a broad sense, there are two parts of the transmission expansion costs needed to be considered – the connection cost and the network cost. These two types of costs are described below in the context of large scale renewable energy integration into the network, followed by the changing trend of cost allocation in different jurisdictions to cope with the renewable energy scale-up. 2.1. Connection costs Transmission connection cost allocation approaches are generally classified as super-shallow, semi-shallow, shallow and deep connection pricing. Market operators’ policies in different jurisdictions allow renewable generators to pay connection costs at different extents. Fig. 1 shows the cost allocation approaches of grid integration, which are adopted from the European RES-E (Renewable Energy Resources for Electricity) policy [11]. Super-shallow charge does not include the cost related to the grid integration.

Connection assets Remote Renewable Generation

Network assets Existing Grid

Super-shallow Semi-Shallow Shallow connection cost Deep connection cost Fig. 1. Connection costs allocation approaches for remote renewable generation connection [11].

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Renewable generators are responsible for only the generation development in such a cost allocation policy. The costs of network integration and reinforcement are borne by Transmission Network Service Providers (TNSP) and consecutively shared by network users. On the other hand, some jurisdictions adopt a provision to divide the grid-integration cost among generators and consumers. This type of partial connection cost allocation for generators is known as semi-shallow policy. The proportion of connection cost-share for generation depends on its negotiation with the TNSP. Further, shallow connection charge imposes the cost of connecting plants to the grid fully on renewable generators. For a remotely located renewable generator, this cost is significant, even can be in the range of few hundred million to few billion dollars in some cases. Furthermore, the deep charging policy imposes grid connection and reinforcement costs on generators. So generators have to pay the network integration as well as shared network usage cost according to the deep connection charge arrangement. A high transmission connection cost in a deep connection policy may discourage renewable integration into the grid. So, operators cost allocation policies have potential impacts on renewable energy integration and largely influences the viability of renewable investment. 2.1.1. Changing trend of connection cost allocation in the context of remote renewable energy Many regulatory regimes are updating their connection cost allocation policies for remote renewable power integration to the grid [12]. Table 1 presents the changing trend of transmission connection cost allocation policies in different jurisdictions to enhance large scale remote renewable generation into the grid. The GB practices super-shallow cost allocation policy for renewable generators, in case of its shallow approach for conventional generation. Denmark and Germany traditionally practice shallow connection cost policy. Currently these jurisdictions offer super-shallow policy for offshore wind farms. Spain changes its deep connection policy to shallow for renewable integration. Even in some instances semi-shallow approach is adopted in Spain. Also, semi-shallow cost allocation policy is reported in Texas, Panama and the Philippines’ jurisdictions. There was no specified policy for transmission connection of conventional generators in Europe. So, different European countries practiced different transmission policies. Recently, the European Union (EU) recommends the semi-shallow charging policy to be ‘‘preferable and favourable’’ for renewable energy integration [13]. 2.2. Network costs While connection cost allocates the cost of network integration, the network cost allocation recoups the operation and maintenance costs of the shared network. Some of the jurisdictions, e.g. the Australian NEM, refer network cost as the Transmission Use of System (TUOS) charges [14]. Varieties of options to distribute network costs among network users are shown in Fig. 2. The network cost is recovered through either the postage stamp or usage based method. The postage stamp method imposes an average charge based on the amount of energy transmitted through the network, regardless of the location of generators/consumers. Postage stamp charge is allocated based on either the energy consumption or peak demand. In the usage based cost allocation, charges are imposed based on ‘the extent of use’ of the physical network. Usage based method recovers the network cost either through flow-based or distance based charging. Different network cost allocation policies have different impacts on a large scale renewable integration into the grid.

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Table 1 Changing trend of connection cost allocation in different jurisdictions (Madrigal and Stoft, 2011). Jurisdictions

Connection cost allocation Traditional approach

To promote renewable

UK Germany, Denmark Spain

Shallow Shallow Deep

Texas, Panama, Philippines, Egypt EU

Shallow

Super-shallow Super-shallow Shallow, semishallow Semi-shallow

–

Semi-shallow

Network cost

Postage stamp

Energy based

Peak based

Usage based Flow based

Distance based

Fig. 2. Network cost allocation for remote renewable generation connection.

2.2.1. Changing trend of network cost allocation in the context of remote renewable energy Though there are some considerations on connection cost allocations to promote renewable generation integration to the grid, little efforts are reported in network cost allocation. Table 2 presents the change in network cost allocation in different regimes to encourage renewable generation [12]. Panama reduces its network charge from 70% to 0% for renewable generation up to 10 MW. Denmark imposed a 2% network cost on generators, which has been exempted to promote renewable integration into the grid. Also, the Philippines use per-MW h-basis charge for renewable generators in place of its original postage stamp charging. PerMW h-based charging is supportive to intermittent renewable generation as the capacity factors of renewable generators are low. Mexico changes its flow based network pricing policy to a flat rate tariff to encourage renewable energy. In German, Spanish and Texas jurisdictions generators are not responsible at all to share the network cost. So there is a little or no room for further improvement. 3. Australian NEM transmission cost allocation in the context of large scale renewable energy Transmission cost recovery scheme in the Australian NEM is designed to obtain aggregate annual revenue requirement. The revenue consists of the different components of the annual service revenue requirement, which are for entry, exit and common transmission services. Fig. 3 shows different elements of the NEM

Table 2 Change in network cost allocation in different regimes. Jurisdictions

Panama Denmark The Philippines Mexico Germany, Spain Texas

Network cost allocation Generators’ share (%) for conventional generation

Generators’ share (%) to promote renewable

70%, Locational, flow based 2%, Postage stamp 50%, Postage stamp Flow based 0%, Postage stamp 0%, Locational, Postage stamp

0%, For less than 10 MW 0%, Postage stamp 50%, Per-MW h-basis Flat rate 0%, Postage stamp 0%, Locational, postage stamp

Common transmission services

Existing Grid

Remote Renewable Generation

Load

Entry services

TUoS services

Exit services

Fig. 3. Transmission cost allocation practice in the Australian NEM.

transmission cost services [15]. The entry and exit service charges are fixed annual components. These charges are due to the direct cost of substation building, land, infrastructure, switchgear, generator/load transformer, transmission lines to/from the TNSP network, reactive plants for power factor correction, and metering systems. Further, the Transmission Use of System (TUOS) charge composed of two components on a 50–50 basis. One is the locational component, and the other is the non-locational component. The locational component is calculated based on the maximum loading of the network. The non-locational component is calculated based on the contract agreed maximum demand of the network. The non-locational part of the TUOS charge is assigned as a postage stamp basis [14]. To date there is no established or foreseeable change in the transmission policy to scale-up location-constrained remote renewable generation in the Australian NEM. The connection cost of generators which is termed as the ‘entry service’ in the Australian NEM is fully borne by generators. In such a policy, the cost of grid integration for a remote generator may require a significant upfront investment, which may render the renewable project economically unviable. On the other hand, 100% network cost is recovered from consumers. At present, the locational component of the usage cost is recovered through flow-based method. This can be updated with the consideration of net market benefit, as described later in Section 4.4. Hence, network cost allocation is made more justifiable by identifying the beneficiaries of the network augmentation and further by allocating costs based on the proportion of the obtained benefit. The detail of the proposed approach is presented in Section 4. The following section describes a net market benefit framework and presents the Shapley Value approach to identify the beneficiaries. Then the cost allocation is proposed to be proportional to the obtained benefits by system-wide and zonal beneficiaries. 4. Transmission cost allocation in the context of large scale renewable energy – proposed framework Transmission cost allocation for renewable power integration into the grid hold contrasting views from that of the conventional generation in at least two aspects. Firstly, large scale renewable energy sources are site-constrained and generally located far away from the consumption centres. These facilities do not have the flexibility in selecting locations near the existing grid, which conventional generators have. Thus, the rigidity of location-constrained resources has high impact on connection cost allocation policy. Secondly, the transmission investment in remote renewable integration is large compared to the generation investment itself, while the scale of economies for conventional generation is well off to absorb the connection costs. So, transmission cost allocation strategies should be supportive to enhance renewable integration into the grid by offering cost-curtailing policies.

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4.1. Connection cost and network cost allocation Considering location-constrained remote renewable connections to the grid, a high investment cost imposes a large payment on network users. As the investment cost is very high and the proportional share is large, the cost allocation policy should be favourable to large scale renewable generators and justified from an economic point of view. The transmission cost allocation strategy should acknowledge that ‘one unit’ of power flow from a renewable generator is not equivalent to that of a conventional generator. Because the environmental benefit is missing here. It is also worth mentioning that the benefit of generating renewable energy is not only come from the generation itself but also from the conventional power it replaces. For remotely located large scale renewable generators, semishallow cost allocation policy is proposed to be implemented, as shown in Table 3, in place of shallow approach which is practiced in the Australian NEM. Further, beneficiaries are identified among the market participants due to the integration of large scale renewable generators. Then the proportions of benefit obtained by different market participants (i.e. producers and consumers) are calculated by a game theoretic approach, namely Shapley Value, which is described in Section 4.4. Finally, network cost is allocated among different market participants, so that the network users pay a fair and reasonable share of the network cost. 4.2. Net market benefit Firstly, an hourly OPF calculates the power flow and market dispatch. Then by aggregating the benefits underlying this flows/dispatch – the net market benefit has been obtained. The detail of the net market benefit framework is found in [16,17]. The objective function of a yearly net market benefit is formulated as below:

2

Max 0 þ@

8760 X

4

 X 0  X pig  kig  pig  /ig þ CSid  CSid

t¼0

i2ng

i2nd

1

i2ng

3 ð1Þ

i2ng

where t is the time span (h), ng the number of generator set, nd the number of load set, pig is power produced by generator i ðMWÞ, kig is Locational Marginal Price (LMP) at generator bus i ð$=MW hÞ, /ig is generation cost of generator i ð$=MW hÞ, CSid is consumer surplus 0 earned by consumer i ð$Þ, CSid is consumer surplus before augmentation ($), pid is the power consumed by load i ðMWÞ; kid is LMP at load bus i ð$=MW hÞ, Ei is the amount of CO2 produced by generator i ðtonÞ, pCO2 is emission cost ($/ton CO2), wi is renewable generation from generator i ðMWÞ, r is LRET payment ($/MW h). In Eq. (1), five mathematical terms represents the producer surplus, consumer surplus, merchandizing surplus, carbon emission tax and Large-scale Renewable Energy Target (LRET) payment, respectively. Accumulation of all of these benefits composes the net market benefit. Fig. 4 presents the block diagram of the meth-

Table 3 Existing and proposed transmission cost allocation scheme in the NEM.

Connection cost Network cost

4.2.1. Locational marginal price calculation The Locational Marginal Prices (LMP) have been calculated from the generators cost function. Cost functions are derived from the cost coefficients, which are obtained from Queensland generators. Cost coefficients vary for unit size, fuel type, location, etc. [18]. The cost function of a generator is formulated in the simulation as presented below [17,19]:

C¼

X fPi ðPig Þ

ð2Þ

i2ng 2

fPi ðPig Þ ¼ ai Pig þ bi Pig þ ci

ð3Þ Pig

where C is the total cost of generation, the power produced by generator i, and ai, bi, ci are cost coefficients of generators. 4.2.2. OPF formulation The AC Optimal Power Flow (OPF) solution is executed through the MATLAB Interior Point Solver (MIPS) algorithm which is implemented in the MATPOWER 4 version. Single-sided auction-based market clearing mechanism has been implemented in this study [20]. More details about the market clearing mechanism and LMP calculation can be found in our previous works [16,17,21]. The objective function of the OPF considers the polynomial cost function of real power injections for each generator as follows [22]:

min

X X 2 fPi ðpig Þ ¼ min ðai pig þ bi pig þ ci Þ i2ng

ð4Þ

i2ng

In the OPF algorithm, the power balance equality constraints are,

X X X  X pid  kid  pig  kig A  Ei  pCO2 þ ðwi  rÞ5 i2nd

odology to obtain net market benefit from Eq. (1). As the surpluses of producers, consumers and merchandisers are almost same in different market structures, emission pricing arises as a significant factor. The calculated net benefit considerably changes with the inclusion of emission benefit adopted by the carbon tax and LRET benefit.

Existing NEM approach for conventional generation

Proposed approach for large scale remote renewable generation

Shallow

Semi-shallow

Postage stamp, CRNP

Benefit based Shapley Value

g P ðh; V; Pg Þ ¼ Pbus ðh; VÞ þ P d  Pg ¼ 0

ð5Þ

g Q ðh; V; Q g Þ ¼ Q bus ðh; VÞ þ Q d  Q g ¼ 0

ð6Þ

Branch flow limit inequality constraints are,

hf ðh; VÞ ¼ jF f ðh; VÞj  F max 6 0

ð7Þ

ht ðh; VÞ ¼ jF t ðh; VÞj  F max 6 0

ð8Þ

variable limits are,

himin;ref 6 hi 6 himax;ref

ð9Þ

V im;min 6 V im 6 V im;max

ð10Þ

Pig;min 6 Pim 6 Pig;max

ð11Þ

Different components of the net benefit framework have been described and formulated below [16,23,24]. 4.2.3. Producer, consumer and merchandizing surplus The producer surplus is the benefit obtained by producers through the selling of power. This comes from the difference of their revenue less the production cost, which can be expressed as below [17]: 8 760X X

8 760X X

t¼0 i2ng

t¼0 i2ng

ðPRig  PC ig Þ ¼

ðpig  kig  pig  /ig Þ

ð12Þ

where PRig and PC ig are revenue earned and cost incurred by the producer, respectively, kig is the LMP at generator bus and /ig is the cost of generation. The consumer surplus arises from the incremental benefit of consumers due to the augmentation [17].

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Generation Generation information information and and cost cost functions functions

considered as 25 and 40 years respectively [26,27]. The discount rate is assumed to be 10%. The Annual Required Revenue (ARR) is calculated using the following formula [26]:

Queensland Queensland network network data data

ARR ¼

Optimal Optimal Power Power Flow Flow (OPF) (OPF)

LMP LMP

Loss Loss

rð1 þ rÞy ð1 þ rÞy  1

ð17Þ

where r is the discount rate and y is the number of year. This formula gives an ARR of 0.11 and 0.10 for generation and transmission projects, correspondingly. So, the annualized cost is 11% and 10% of the total capital investment for generation and transmission respectively.

Power Power flow flow

Producer Producer surplus surplus

4.4. Pay-back scheme [17]

Consumer Consumer surplus surplus

Fig. 4. Assessment of net market benefit from network data and relevant information.

In regard to the payback to investors, two schemes have been discussed in one of our previous publication [17]. One scheme considers that the costs will be permanently funded by the electricity consumers. Another scheme demonstrates that the costs will be primarily funded by consumers, but later they will get back rebate from consecutive generators. Implementation of these schemes will be dependent on the policy of the market operators. To limit the size of the paper, we exclude detail explanation. Details of the payback scheme can be found in [17].

8760 XX

4.5. Economic rationale behind cost allocation

Emission Emission pricing scheme

Merchandizing Merchandizing surplus surplus

LRET model

LRET LRET benefit benefit

Emission Emission tax tax

Net market benefit

0

ðCSid  CSid Þ

ð13Þ

t¼0 j2nd 0

where CSid and CSid are consumer surplus after and before the transmission augmentation, respectively. Merchandizing surplus is calculated from the total consumer payments less the generation income.

0 1 X j j X i i @ p k  pg  kg A d d

8760 X t¼0

j2nd

ð14Þ

i2ng

where pjd and pig are power consumed and power produced by load and generator, respectively, and kjd and kig are LMP at load and generator bus, respectively. 4.2.4. Emission tax and LRET benefit The emission tax is calculated based on the carbon emission and the emission price. The carbon price scheme has been considered from the Australian NEM as presented in Table A4 (in Appendix A). Emission tax is evaluated in monetary terms as follows [17]:

0 1 X @ Ei  pCO A

8 760 X

2

t¼0

ð15Þ

i2ng

where Ei is the amount of CO2 produced by generators and pCO2 is the carbon emission cost ð$=tonCO2 Þ. The Large Scale Renewable Energy Target (LRET) is a financial incentive scheme to promote renewable energy generation. One MW h renewable generation is equivalent to one LGC (Large-scale Generation Certificates), which can be sold or traded. The LRET model used in this study is adopted from the Australian NEM [25]. The numbers of LGCs to be purchased are calculated using the renewable power percentage (RPP). RPP is determined using the following formula, which is targeted to meet the Renewable Energy Target (RET) of Australia, i.e. 20% renewable energy by 2020 [25]: RPP ¼ RPP for the previous year 

Required GW h of renewable source electricity for the year Required GW h of renewable source electricity for the previous year ð16Þ

4.3. Cost considerations [17] The capital costs of generation and transmission are annualized. The lifespan for generation and transmission projects are

The transmission cost allocation is targeted to enhance the efficient use and investment in the network. The transmission charges provide short-term and long-term signals to network users, reflecting the network assets engaged in service, and location and amount of energy injected or withdrawn. It also captures the differences in spot prices, intra-regional loss factors and intra-regional congestion. Transmission charges are imposed to attract renewable generators for long-term investment without fearing transmission charge volatility. Also, the proposed remuneration scheme captures the marginal pricing signal of the electricity market. On the demand side, transmission charges provide signals for efficient utilization and investment decisions. On the supply side, transmission charges provide signals for timing, location and amount of generation by generators [14]. Semi-shallow connection cost policy opens a platform of negotiation for large scale remote renewable generators, which may bring long term benefits to the market with some flexibility in the initial investment barrier. Regarding the network cost allocation policy, benefit-based Shapley Value will identify the beneficiaries, and allocate cost according to the proportion of the benefit they obtained. This will truly reflect the market signal for producers and consumers to induce market investment. 4.6. Shapley Value approach for network cost allocation Shapley Value is a payoff allocation approach in cooperative game theory. This approach allocates the cost among market participants based on their contribution to the augmentation. Shapley Value recognizes the marginal contribution of each participant in the coalition and accumulates the weighted average to obtain the payoff distribution [28–31]. Mathematically, the allocation /i to the player i can be expressed through Shapley Value as:

/i ¼

Xðn  SÞ!ðS  1Þ! S

n!

  ½cðSÞ  cðS  figÞ

ð18Þ

where S is the number of players in the coalition, n is the total number of players in the game, c is the characteristic function representing the net market benefit of different combination of the players, i is any player in the game. The term [c(S)  c(S  {i})] represents the incremental contribution which the player i can make in the coalition. Further the

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expression ðnSÞ!ðS1Þ! depicts the weighing factor which allocates n! proportional share of marginal contribution of each player in the coalition. Fig. 6 shows the block diagram of the methodology to calculate benefit/cost allocation through Shapley Value. In the case studies of this research, a player is a specific project like Copper String, Cooper Basin, Kennedy Wind Farm and PNG hydro. By Shapley Value approach, their contributions in each of the coalition are calculated. The advantages of this method are that this approach is easily understandable and identifies specific beneficiaries. The challenge of this approach is that beneficiaries may easily change over time which may require high computational flexibility to capture changes of the market dispatch. 4.7. System-wide and zonal cost allocation The net market benefit can identify system wide and zonal beneficiaries from the transmission augmentation. Further, the Shapley Value approach assigns the proportion of benefit and/or cost to different market participants located at the different zones of the network. Hence, this approach is able to allocate the cost among market participants specifically by system wide or on zonal-basis. The following Section illustrates the case study of the Queensland network. 5. Queensland case study – large scale remote renewable power integration into the grid 5.1. Network data Queensland network of the Australian NEM is simulated in this study. This network is 1700 km long, having a generation capacity of 12,788 MW in 2010, which is forecasted to be 15,500 MW in 2015 [32]. Fig. 6 shows the Queensland area of the Australian NEM. This area is divided into four zones considering the national flow path – North Queensland (NQ), Central Queensland (CQ), South-East Queensland (SEQ), and South-West Queensland (SWQ). The percentages of existing generation and load in different zones are shown in Fig. 5. The CQ zone has the highest (43%) percentages of generation, followed by SWQ (42%), SEQ (9%), and NQ (6%). On the other hand, SEQ zone has the highest (61%) proportion of load. The share of load for CQ, NQ, and SWQ are 22%, 14% and 3%, respectively. Existing Queensland generators with their fuel type, capacity, heat-rate, and emission factor are shown in the Appendix in Table A1. To meet the increasing future demand and to align with the ‘energy market frameworks in light of climate change policies’ [33], connection of large scale renewable generation clusters are greatly required in the Australian NEM. Connections of such renewable enhancing projects to the Queensland network are described in the next.

Net Net market market benefit benefit or or estimated estimated cost cost of of individual individual projects, projects, e.g. e.g. Copper Copper String, String, Cooper Cooper Basin Basin etc. etc. Coalition Coalition formation, formation, such such as, as, [{CS}, CB}, . . . ,{CS,CB}, . ..... [{CS}, {{CB}, ,{CS,CB,KWF,PNG}] ,{CS,CB,KWF,PNG}] Shapley Shapley Value Value approach approach for for assessing assessing marginal marginal contribution contribution of of aa project project in in the the coalition, as in Eq.2 coalition,as Fig. 5. Assessment of network benefit/cost allocation through Shapley Value.

5.2. Projects to be connected Four candidate projects of the Queensland network are evaluated to assess their net market benefits. These projects considered large scale renewable power penetration to the existing Queensland grid. Table 4 presents the amount of generation, distance and zone/area of the projects. A brief overview of the projects is given below: i. Copper String project (400 MW, 720 km) [9]: The Copper String transmission project connects the Mount Isa region of North West Queensland to Townsville. It is designed for a capacity of 400 MW with a length of 720 km. Construction is scheduled to start in late 2012 and the project completion date is early 2015. ii. Cooper Basin Geothermal (2000 MW, 1000 km) [7]: The Cooper Basin transmission line connects hot fractured rock based geothermal power generators located 1000 km away from the existing Queensland grid. It is reported that there is a potential of 4000 MW of geothermal power to be connected to the grid by 2030. In this case study, transmission connection is designed for a capacity of 2000 MW. As reported by the geodynamics, construction and commissioning of a 25 MW commercial demonstration plant is scheduled for 2015. iii. Kennedy Wind Farm (700 MW, 290 km) [8]: The Kennedy Wind Farm Project connects Hughenden, located approximately 290 km South-West of Townsville to the existing Queensland grid. The 290 km transmission line is designed for a capacity of 700 MW. Approval was sought for this project in late 2011, construction is scheduled from 2012 and commercial operation is expected from 2014. iv. PNG Hydro (1800 MW, 250 km cable + 200 km): The PNG Hydro transmission line connects Papua New Guinea (PNG) to the existing NEM grid to provide 1,800 MW of

1800 MW Hydro

400 MW Wind

Far North Ross

700 MW Wind

NQ

QUEENSLAND

6% Gen 14% Load

CQ

42% Gen 2000 MW 3% Load Geothermal Cooper Basin SOUTH AUSTRALIA

SWQ Bulli

43% Gen 22% Load

9% Gen 61% Load SEQ

NEW SOUTH WALES

Fig. 6. Different zones of the Queensland electricity network.

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hydro power. A 250 km subsea cable along with 200 km overhead transmission line is designed to transfer baseload hydro power.

5.3. Assumptions Some realistic assumptions are considered in this study based on the available references, as shown below: i. As the Queensland network is connected to the other states (i.e. NSW, VIC, SA and TAS) of the NEM, the benefit of the generation and transmission expands to the whole NEM network. However, this study only investigates the benefits obtained by the Queensland network.  Queensland (QLD) network is connected with the New South Wales (NSW) network with two interconnections. One connection is AC with transfer capability of QLD– NSW is 1078 MW and NSW to QLD is 700 MW, which has been modelled as a generator of 800 MW capacity. Another interconnection is HVDC with capacity of 200 MW for QLD–NSW and 100 MW for NSW to QLD, which has been modelled as a switched shunt of 120 Mvar capacity.  Queensland network data used in this study has been obtained from Powerlink Queensland (transmission network service provider). Modelling of these interconnections in PSS/E format has been supplied by the Powerlink Queensland. Some key features of those buses are presented in Table A5. ii. As the intermittent nature of wind power makes it nonscheduled generation, there is an impact of wind capacity factor which differentiates the cost of ‘installed-capacity’ with that of ‘generated-capacity’. In this study, cost per unit of generated capacity, using capacity factor of South Australian wind farms are considered [34]. iii. Peak loading conditions are considered here. Loading patterns in different regions of the network and forecasted load are obtained from the Powerlink Annual Planning Report [32]. iv. Generation commencement and retirement are obtained from the AEMO’s Electricity Statement of Opportunities (ESOO) for the National Electricity Market [35]. v. All simulation results presented here are for the year 2015. This is a random selection, and result in every year shows the same trend. vi. Calculation of the carbon tax is taken from the Clean Energy Bill 2011 and ACIL Tasman report [18].

6.1. Connection costs Connection costs for different projects according to different connection cost allocation schemes are shown in Table 5. The super-shallow cost allocation policy imposes no cost burden on renewable generators. In this case, the Copper String, Cooper Basin, Kennedy Wind Farm and PNG hydro generation developers do not need to pay for transmission. The transmission infrastructure is built by the TNSP and further socialized among the consumers. Super-shallow policy resembles the SENE proposition of the Australian NEM, considering the initial investment arrangement, as discussed in Section 2.1.iii. However, the SENE differs from the super-shallow policy regarding cost recovery mechanism. SENE recovers proportional cost from connecting generators where super-shallow socialized the cost. Whether it is the super-shallow or SENE, this is the most favourable scheme to enhance renewable integration into the grid. The driving force for this scheme is meeting climate change policies in different jurisdictions, e.g. in the form of Renewable Energy Target (RET) or Renewable Portfolio Standard (RPS) or so on. The semi-shallow policy imposes the connection cost burden both on producers and consumers based on the negotiation between the generation developers and TNSPs. A 50–50 cost sharing scheme is implemented in this study. Though there is a similarity between the semi-shallow and SENE hub approach regarding the initial investment method, the discrepancy remains the same as the super-shallow and SENE in case of cost recovery mechanism. Further the shallow cost allocation policy imposes the connection cost burden solely on generators which is practiced in the Australian NEM. As can be seen from Table 5 that under the current scheme, the remote renewable generation developers of the Copper String, Cooper Basin, Kennedy Wind Farm and PNG hydro is responsible to pay few hundred million to few billion dollars for network integration. This arrangement of high connection cost eventually can delay or defer large remote renewable power integration to the grid. Total generation and transmission investment for a large scale remote renewable generator is shown in Table 6. This represents the connection cost burden, according to the current shallow approach, and proposed semi-shallow approach. The costs considered here are the cost of generation development and the cost of network integration. The costs of generation development are adopted from the Appendix as shown in Table A2. As high connection cost of remote renewable generator is identified as a potential burden to connect to the network, semi-shallow cost policy is implemented in this study. There is flexibility in semi-shallow policy that both the producers and consumers can negotiate the proportion of connection cost. A 50% connection cost for renewable generators is considered in this study. Reasonably the shallower the cost policy would be, the more supportive it would be for remote renewable generation development.

6. Results and analysis 6.2. Network costs Results of the case studies of Queensland electricity network with four potential expected projects are presented in this section. Connection cost and network cost allocation for these projects are presented below.

The network cost allocation policy allocates the cost among market participants derived from system-wide and zonal basis. Beneficiaries of those projects are identified and costs are allocated

Table 4 Overview of the large scale renewable projects to be connected to the Queensland network. Project

Generation (MW)

Transmission line (km)

Connection area

Connection zone

Copper String Cooper Basin Kennedy Wind Farm PNG Hydro

400 2000 700 1800

720 OH 1000 OH 300 OH 250 cable + 200 OH

Ross Bulli Ross Far North

NQ SWQ NQ NQ

Copper String Cooper Basin Kennedy Wind Farm PNG Hydro

Supershallow/ SENE

Semi-shallow/SENE HUB (50/50 cost sharing)

Shallow/ current NEM policy

Deep

0 0 0

550 1350 400

1100 2700 800

1200 3000 900

0

1600

3200

3600

1800 1600 1400 1200 1000 800 600 400 200 0

Producer Surplus Consumer Surplus Base CS KWF CS, KWF CS, PNG KWF, PNG PNG CS, KWF, PNG CB CB, KWF CS, CB CS, CB, KWF CB, PNG CB, KWF, PNG CS, CB, PNG CS, CB, KWF,…

Transmission investment (m$)

Producer Surplus (m$/yr)

Table 5 Connection cost allocation for different projects through different schemes.

8000 7000 6000 5000 4000 3000 2000 1000 0

Consumer Surplus (m$/yr)

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Different combinations of the projects Table 6 Large scale renewable generators’ investment according to the existing and proposed method.

Fig. 8. Producer and consumer surplus of Queensland electricity network after adding combinations of CS, CB, KWF and PNG projects.

1900 9700 2200 7700

1350 8350 1800 6100

50 45 40 35 30 25 20 15 10 5 0

2500 2000 1500 1000 500 0

Emission tax LRET payment

2000 1800 1600 1400 1200 1000 800 600 400 200 0

LRET payment (m$/yr)

Proposed approach (50/50 cost sharing)

CS Base KWF CS, KWF KWF, PNG PNG CS, PNG CS, KWF, PNG CB CB, KWF CS, CB CS, CB, KWF CB, PNG CB, KWF, PNG CS, CB, KWF, PNG CS, CB, PNG

LMP ($/MWh)

CS CB KWF PNG

Current approach

Emission Tax (m$/yr)

Generator investment to develop generation and transmission (m$)

Different combinations of the projects Fig. 9. Emission tax and LRET payment of Queensland electricity network after adding combinations of CS, CB, KWF and PNG projects.

Different combinations of the projects Fig. 7. Average LMPs of Queensland electricity network after adding combinations of CS, CB, KWF and PNG projects.

proportional to the obtained benefit through a game theoretic approach, Shapley Value. 6.2.1. System-wide benefit/cost allocation The impact of large scale renewable power integration on locational marginal price is shown in Fig. 7. It is expected that the addition of new generation will reduce the LMP. Fig. 7 highlights the proportional impact of different projects. The base case scenario slightly improves with the addition of Copper String (CS) and Kennedy Wind Farm (KWF) projects to the existing network. The impact of PNG hydro project is rather noticeable. It brings down the LMP from around 46–40 $/MW h. This is because of the cheap hydro power injection. Also, the power injection of PNG hydro arrives in the region of NQ, where load (14%) is comparatively higher than the generation (6%). Further, the introduction of Cooper Basin (CB) projects with any combination of other projects bring down the LMPs significantly. This is because of the large amount of cheap geothermal power penetration to the network. Moreover, this power can be transferred to the load centre of South East Queensland, where load (61%) is significantly higher than the generation (9%). As can be seen from Fig. 7, the impact of Cooper Basin integration into the network is even influential than the integration of combined CS, KWF and PNG projects.

The change in producer and consumer surplus due to the integration of Copper String (CS), Cooper Basin (CB), Kennedy Wind Farm (KWF) and Papua New Guinea (PNG) projects are shown in Fig. 8. Analogous to the change in LMPs, consumer surplus of base case scenario is not changed with the addition of CS and KWF projects. The consumer surplus is slightly improved with the integration of PNG hydro to the network. On the other hand, integration of the CB significantly increases the consumer surplus. Any combination of the projects with CB has the similar impact on the consumer surplus of the network. The change in producer surplus is in opposite to that of the consumer surplus. It is clear that the availability of cheaper generation brings the marginal price lower and reduces the premium for existing generators. So, a decline in market price lessens the producer surplus. This is preferable to consumers is not profitable for the producers. However, the market operates with the objective of maximizing the net benefit. In the Australian NEM context, the emission tax and LRET payment has also impact on the market benefit, which are described below. The amount of emission tax and LRET payment for different scenarios with the integration of CS, CB, KWF and PNG projects are shown in Fig. 9. As the renewable power integration to the grid replaces some fossil-fuel based generators, the emission tax is reduced. As can be seen from Fig. 9, the power injection from CB and PNG projects significantly lessens the emission payment. The LRET payments also depend on renewable power injection to the grid. As the CB and PNG projects have higher power penetration to the grid, these projects largely affects the LRET payments. A combination of CB and PNG projects highly increases the LRET payments in the market.

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Table 7 Coalition of projects and corresponding power injection, net market benefits and estimated costs. Injected power (MW)

Net market benefit (m$/yr)

CS CB KWF PNG {CS, CB} {CS, KWF} {CS, PNG} {CB, KWF} {CB, PNG} {KWF, PNG} {CS, CB, KWF} {CS, CB, PNG} {CS, KWF, PNG} {CB, KWF, PNG} {CS, CB, KWF, PNG}

85 1276 0 708 1365 169 793 1364 1984 793 1451 2064 793 2064 2142

1823 2140 351 1568 1671 523 1390 2005 2717 1306 2102 2836 1380 2693 2766

Table 8 Normalized Shapley Value for different projects. Projects

CS CB KWF PNG

Normalized Shapley Value Power Injection

Net Market Benefit

Estimated Costs Semishallow

Shallow

Deep

0.21 0.65 0.08 0.39

1.13 0.89 0.38 0.66

1.23 0.65 0.58 0.95

2.25 1.25 1.29 1.94

3.00 1.50 1.29 2.00

Estimated transmission investment (m$) Shallow

Deep

Semi-shallow

Shallow

Deep

550 1350 400 1600 1900 950 2150 1750 2950 2000 1800 3300 2550 3350 3900

1100 2700 800 3200 2800 1900 4300 3500 5900 4000 3600 6600 5100 6700 7800

1200 3000 900 3600 4200 2100 4800 3900 6600 4500 5100 7800 5700 7500 8700

3.31 1.59 0.88 0.98 0.88 0.55 0.65 1.15 0.92 0.65 1.17 0.86 0.54 0.80 0.71

1.65 0.80 0.40 0.50 0.60 0.28 0.32 0.57 0.46 0.33 0.58 0.42 0.27 0.40 0.36

1.52 0.71 0.39 0.44 0.40 0.25 0.29 0.51 0.41 0.29 0.41 0.36 0.24 0.36 0.32

1.1 1.05

QLD Load QLD Generator

1 0.95 0.9 0.85

Average zonal LMP ($/MWh)

SWQ CQ NQ SWQ SEQ

60 50

Benefit-to cost ratio

Semi-shallow

Marginal Loss Factor (MLF)

Coalition of projects

CQ

SEQ

NQ

Different regions of Queensland network Fig. 12. Marginal loss factor (MLF) of QLD generators and loads at different regions reflecting the marginal price of electricity (AEMO, July 2011).

40 30 20 10

Base Case

CS

KWF

PNG

CS, KWF, PNG

CB

CS, CB, KWF, CS, CB PNG

Different combinations of the projects Fig. 10. LMPs of different zones of the Queensland electricity network.

3

Normalized Shapley Value

Producer Surplus 2

Consumer Surplus

1 0 -1 NQ

CQ

SEQ

SWQ

-2 -3

Different regions of Queensland network

Fig. 11. Normalized Shapley Value of surpluses obtained by producers and consumers of the QLD electricity market for CS, CB, KWF and PNG projects.

The impact of the considered four projects on the network in terms of power injections, net market benefits and estimated costs

are shown in Table 7. The coalition of players and their corresponding injected power and the net market benefits are obtained from the Queensland network optimal power flow solution. The benefits are calculated based on Eq. (1). The estimated cost figures are obtained from Appendix, Table A3. The benefit-to-cost ratio shows the potential of Cooper Basin project compared to others. Combinations of all projects which have higher than 0.5 benefit-to-cost ratios are contained by the CB project. Two notable intimation to consider in Table 7 is that the benefit is calculated in m$/yr where the cost is in m$. Also the OPF considers pick loading conditions that may exaggerate the market benefit. Table 7 reveals that the semi-shallow cost allocation policy takes the benefit-to-cost ratio of CB, {CB, KWF}, and {CS, CB, KWF} to higher than 1, which makes these projects feasible in the market environment. This is not possible for these projects in shallow cost allocation policy. Also, semi shallow policy makes some other renewable projects very competitive in the market considering the benefit-to-cost ratio, such as PNG and {CB, PNG} scenarios. The Shapley Value considers the marginal contribution of each project on market participants, and then accumulates all marginal contribution to obtain total contribution of that project. Normalized Shapley Value represents the quantity for per unit of generation capacity from different projects. The normalized Shapley Value for different projects considering the power injection, net market benefits and estimated costs are shown in Table 8. As can be seen from Table 8 that Cooper Basin project attains the highest amount of power injection, and net market benefit with lower amount of estimated costs. Then PNG hydro is another potential candidate in terms of power injection and net market benefit.

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K.N. Hasan et al. / Applied Energy 113 (2014) 836–847 Table A1 Queensland power plants with fuel-type, capacity, heat-rate and emission factor [18,32]. Name

Fuel

Capacity (MW)

Heat-rate (GJ/MW h)

Emission factor (t CO2/MW h)

Callide B Callide C Collinsville Gladstone Kogan Creek Millmerran Stanwell SwanbankB Tarong Tarong North Barcaldine Condamine Darling Downs SwanbankE Townsville Braemer Braemer 2 Oakey Roma Mackay MT Stuart Yarwun Barron George Kareeya Wivenhoe Windy Hill Total (MW) 12,788

Coal Coal Coal Coal Coal Coal Coal Coal Coal Coal CCGT CCGT CCGT CCGT CCGT OCGT OCGT OCGT OCGT Oil Oil Cogen Hydro Hydro Hydro Wind

700 840 187 1680 744 852 1400 500 1400 443 36 135 630 370 242 504 504 282 68 30 415 160 60 94 500 12

9.97 9.47 13.00 10.23 9.60 9.60 9.89 11.80 9.94 9.18 9.00 7.50 7.83 7.66 7.83 12.00 12.00 11.04 12.00 12.86 12.00 10.59 – – – – Avg. 10.22

0.95 0.92 1.19 0.96 0.92 0.90 0.91 1.09 0.94 0.86 0.51 0.40 0.42 0.43 0.44 0.68 0.68 0.63 0.68 0.96 0.90 0.60 0 0 0 0 Avg. 0.65

Table A2 Capital cost of large scale renewable generators (NREL, July, 2012).

Capital cost ($/installed kW) Fixed O&M ($/installed kW)

Wind

Hydro

Geothermal

2000 35

2500 20

3500 120

Table A3 Indicative transmission cost in Queensland electricity market (Intelligent-EnergySystems, 2010). NEM zone

Generation type

Transmission cost (m$/MW)

NQ CQ

OCGT, CCGT OCGT, CCGT Geothermal

1.03 1.43 1.56

SEQ SWQ

OCGT, CCGT OCGT, CCGT Geothermal

0.23 0.63 1.20

Copper String gives a negative expected benefit in the four-project coalition. Relative cost of CS project is also very high. KWF, on the other hand, produces a positive benefit with moderate cost, though with minimal power injection.

The merit of semi-shallow approach to enhance large scale renewable projects can be seen from the results of Table 8. The Shapley Value of estimated costs in semi-shallow approach for the Cooper Basin project is less than that of the net market benefit. It can be seen that the shallow approach, which is currently practiced in the Australian NEM, cannot get the market signal for Cooper Basin investment. But the semi-shallow approach can bring large scale renewable energy projects into the market. 6.2.2. Zone-wide benefit/cost allocation The base case LMPs for different regions of Quensland network is shown in Fig. 10. The lowest LMP is observed in SWQ region (avg. 42.81 $/MW h) as this is predominantly a generation zone. Then comes the CQ region (avg. 46.10 $/MW h), which is also a generation dominated area. The average LMP of NQ zone (avg. 47.92 $/ MW h) is a little bit higher than the CQ zone. The highest LMP is experienced in the SEQ region (avg 58.57 $/MW h), which has the highest proportion of load. The integration of CS, CB, KWF and PNG projects lessens the LMPs significantly. The decline in LMPs compared with the base case for SWQ, CQ, NQ and SEQ are 38%, 39%, 45% and 42% respectively. As SWQ is already a generation-dominant region, the decline in LMP is less in this region. The power injection from PNG

Table A4 Emission cost mark-up of Queensland electricity market (ACIL-Tasman, 2009).

Emission cost ($/ton CO2)

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

–

23.39

24.19

26.14

28.09

30.04

31.99

33.82

35.43

37.03

38.75

Table A5 Interconnection modelling between Queensland and New South Wales. Bus

Base kV

Area

Code

Voltage (pu)

Angle (°)

Specifications

22,670 10,860

330 330

NSW NSW

1 2

1.0735 1.0700

7.70 5.96

Generator having Pmax 800 MW, Q [400, 400] Mvar Switched shunt, discrete control voltage, initial switched shunt admittance 120 Mvar

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hydro in the NQ region makes the highest decline in LMPs in this region. Then the power injection from the Cooper Basin geothermal brings down the LMPs of SEQ region. The trend in LMPs (both for the base case and with four projects) of different regions can be explained from the generationto-load ratio of the corresponding regions. The movement of LMPs for these regions respectively follow the generation-to-load ratio 0.07, 0.51, 2.33 and 6.8 for SWQ, CQ, NQ and SEQ respectively. The normalized Shapley Value for Queensland regions is shown in Fig. 11. It can be seen that the consumers located in each region will get surplus from those projects. It is understandable that the decline in the market price (LMP) brings benefit for the consumers. Conversely, the producers in all regions, except NQ, will see a negative surplus. This is because of the availability of cheaper generation and a decline of the LMPs. The consumers located at the SWQ and SEQ region will get the maximum surplus due to the CB power injection. This is also a clear indication for consumer investment in SWQ region, where they can obtain maximum surplus. The least amount of consumer surplus in the NQ region is due to distance from the major generation resources and the weak–sparsely network. The LMPs values and consumer/producer surpluses in different regions also align with the Australian NEM policy of locational marginal pricing. The NEM pricing policy recognizes the losses in the network and allocates marginal loss factor (MLF) for different points of the network. The MLF calculates ‘‘the marginal loss in adition to the total loss for each additional unit of electricity (MW) delivered’’ [36]. The assigned MLF (with respect to the regional reference node, RRN) at different network location is used to calculate the marginal price of electricity. Fig. 12 shows the Australian NEM allocated MLF for different regions of the Queensland network. The lower MLF at SWQ provides an indication for the load to be located at this region of the network. The lower LMP results in Fig. 7 and the higher consumer surplus in Fig. 8 also provides the same signal. On the other hand, the highest MLF in NQ is due to its distance from the RRN, which is located in South Pine of SEQ region. Hence, the consumers are discouraged to invest in SEQ region, due to its load-dominance and in NQ region, due to the generation shortfall and weak–sparsely network. Overall, the simulation results of LMPs and consumer/producer surpluses align agreeably with the Australian NEM marginal pricing policy. One limitation of the MLF-based calculation is the location of regional reference node (RRN). Another aspect is that there can be a significant change in MLF due to the large scale renewable power integration and subsequent transmission augmentation.

Subsequently this connection provides a very high consumer surplus. The net market benefit and the benefit-to-cost ratio of this project are also noticeable from the simulation studies. Comparatively, Copper String and Kennedy Wind Farm are less attractive compared to the Cooper Basin due to the intermittency and low capacity factor of the wind. On the other hand, the PNG hydro needs a very high capital investment for submarine cable and suffers from the inconvenience of injecting power through the weak network of North Queensland to the load centre of South East Queensland. Integration of these renewable projects largely provides benefits to the consumers of all regions, specifically to the higher load dominated areas, such as South East Queensland. These projects also bring investment opportunities for generation developers and consumers. The high connection (i.e. investment) cost appears as a potential barrier for large scale renewable energy development in remote locations. The super-shallow policy is the most favourable to large scale remote renewable generators. Also, the semi-shallow policy allows the generators to negotiate with the TNSPs and to avoid high investment barrier. Traditional shallow cost allocation policy practiced in different regimes, however, hinders the development of remote renewable generation. The network (O&M) cost allocation policy is proposed to be implemented based on net market benefit. This framework takes care of the network constraints and economic aspects of the market, as this methodology is based on the OPF and net market benefit. Further the Shapley Value approach is adopted to allocate the costs among market participants. This is an attractive solution because of its economic basis. An economic approach of cost allocation should be resolved in an economic way of benefit evaluation. This approach reflects the benefit of each market participants obtain from the network augmentation. The usage based method depends mainly on the network power flow. The benefit-based method is more appropriate than the usage based method because of its consideration of power flow, economic dispatch and net market benefit evaluation. The challenge of this method lies in the complexity of the benefit calculation. Also, two or more simultaneous network expansion issues can also increase complexity, which will be addressed in detail in future endeavours.

Appendix A. See Tables A1–A5.

7. Conclusions Provided that climate change policies require renewable power integration into the grid, this brings along the challenges to justify the traditional approaches of cost benefit analysis and cost allocation. This research investigates the changing trend of transmission cost allocation policies in different jurisdictions to accommodate large scale remote renewable power generation into the network. Network integration and usage cost allocation principles are reported to have high impact on renewable energy scale-up. In this aspect, connection cost allocation and network infrastructure pricing methodologies are changing to be more favourable to location-constrained renewable development in different electricity markets. Four prospective large scale remote renewable power projects in Queensland and the Australian NEM practice are scrutinized against renewable enhancing cost allocation policies. Case study of the Australian Queensland grid is provided considering the existing and proposed renewable intensive approach. The Cooper Basin geothermal connection significantly lessens the LMPs, as this project injects power to a load dominated area.

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