Delft University of Technology
MATERIALS ENGINEERING
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This report consists of 156 pages and 9 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning
Specialization:
Transport Engineering and Logistics
Report number: 2014.TEL.7912
Title:
Storage sharing at import
dry-bulk terminals: a case study at
Gadani Energy Park
Author:
S. van den Brand
Title (in Dutch) Opslag delen in import droge bulk terminals: een casus in Gadany Energy
Park
Assignment: Masters thesis
Confidential: yes (until Jan. 29, 2017)
Initiator (university): prof.dr.ir. G. Lodewijks
Initiator (company): ir. T. van de Sande (Royal HaskoningDHV)
Supervisor: ir. M.B. Duinkerken
Preface
This report presents my research conducted for my graduate assignment as a part of the master Mechanical Engineering, track Transport Engineering, at the faculty Mechanical, Material and Maritime Engineering of Delft University of Technology. The research is performed in collaboration with Royal HaskoningDHV.
During my Bachelor study Mechanical Engineering at Delft University of Technology, I developed an interest in large and complex logistic problems. Previous assignments in my master studies amplified these interests and extended them towards Port operations and logistics. Royal Haskoning DHV provided a setting in which I can engage my interest and supplied a case at Gadani Energy Park. The past eight months have been a demanding period in which I’ve learned, worked and stressed more intensive than ever before. However, due to the interesting subject, colleagues, help of fellow-students and friends, I look back at a pleasant time.
I would like to thank Ir. M.B. Duinkerken, my supervisor at Delft University of Technology, for always taking time for me on a short notice to evaluate my work and deliver extensive feedback on content as well as presentation. Also, I would like to thank Prof. Dr. ir. G. Lodewijks for his support and feed-back during the whole process. Furthermore, I would like to thank Ir T. van Vianen for his supporting me on bulk terminal specific simulation, even though his phd research at the university was already ended.
In addition, I would like to thank Royal HaskoningDHV for providing me with the opportunity to conduct this research. Specific thanks go to my intern ship supervisor of Royal HaskoningDHV, ir. T. van de Sande, for guidance on the project as well as finding my way around at the company.
Finally, I would like to thank my family, friends and girlfriend for their unconditional support during my research.
Summary
In order to face Pakistan‘s chronic power shortage problems, its government announced the ambitious project of Gadani Energy Park in 2013. The project aims to establish ten coal-fired power plants at one site, each with a capacity of 660 MW. It is expected that the Energy Park will require 17 M tonnes of coal per year. In the current concept, all the power plants will be supplied via deepsea vessels, which are unloaded at the shared unloading facility. At the moment the coal arrives at the shore, individual operations start as the coal is transferred to the conveyor which is dedicated for the stockyard of one plant.
The case of Gadani Energy Park can be seen as an import dry bulk terminal due to its unloading operations and storage capacity. Literature provides extensive research on the control of container ter-minals, however less on bulk terminals. Facility sharing has been researched for many different fields. Although in numerous real-life situations multiple bulk-handling operators collaborate in storage yards, no research is found on yard collaboration. The objective of this study therefore is to gain insight in the effects of yard collaboration in dry bulk environments, by using the Gadani Energy Park as a case study. This leads to the following research question:
What are the advantages and disadvantages of collaboration in storage facilities within an import coal terminal?
To answer this question, an extensive literature survey on dry bulk terminals, power plants and facility sharing was made. Little is known at this stage of the Gadani Energy Park and therefore its conceptual design is mostly based on rules-of-thumb and experience. These rules of design have lead to a generic deterministic model. It is found that blending operations effect the internal logistics of the terminal since an additional stockpile with specific stacking and reclaiming operations is required. Since little literature exists on logistics concerning blending, this report presents its own research on blending bed logistics. It is found that collaboration is possible in blending operations, as a similar type of collaboration is found at the coal-fired power plants of E.ON at Maasvlakte II in Rotterdam. Although the three plants are operated by the same company, the plants are constructed in different years, with different specifications. Still, they are supplied by the same blending beds. Also, a rule-of-thumb is developed to define feasible blending bed capacity related to the size of a power plant it feeds.
In order to take stochastic influences into account and be able to make proper conclusions on the effect of redundancy, a simulation model is made. First, a model without any collaboration and a model with full collaboration is made and compared. If a financial benefit exists in collaboration, a cost allocation structure must be found which is feasible for all the owners. This is done with the Shapley value, a method based on the determination of the average costs of adding players to (sub)collaborations.
To find the Shapley Value, all the possible collaborations must be evaluated. In order to reduce the amount of possible collaborations to a manageable amount, an assumption on ownership is made. An owner A of four, an owner B of three, an owner C of two and an owner D of one power plant is defined for Gadani Energy Park. Since collaboration between owners requires adjacent positioning in the terminal, a limited amount of combinations can be made. The required capacities to guarantee an uninterrupted feed of coal to the power plants has been determined and translated to capital expenditures (CAPEX) and operational expenditures (OPEX) of the equipment, coal and demurrage.
The concept without collaboration shows a very rigid control with low occupancies of equipment, but a lot of blockages throughout the terminal. This indicates overcapacity, but little redundancy. As the size of the collaboration increases, generally the capacity calculations become more and more based on
capacity constraints than on constraints imposed by lay-out. Therefore the terminal configuration can be optimized and occupancy of equipment increases, while waiting times decrease. The number of stacker and reclaimers is reduced from 20 stackers and reclaimers at a terminal without collaboration to 5 stacker reclaimers, 3 stackers and 3 reclaimers with full collaboration.
The first results show that the difference between no collaboration and full collaboration is $ 160 M CAPEX, which is a cost reduction of 47%. The OPEX is reduced by 86% to $ 7.99 M and the yearly costs, in which the CAPEX and OPEX is combined through the depreciation time, declines 74% to $ 19.1M for all the ten power plants combined. Since the advantages of collaboration have been proven, the follow-up is to determine how the advantages should be distributed amongst the owners at the Energy Park.The cost allocation structure calculated with the Shapley Value for the most beneficial situation, in which all owners collaborate, can be found in the table below. In this table, only the determination of the yearly costs is incorporated because it gives an addition of both CAPEX and OPEX. However, it is found that the percentages of benefit per player differ among the CAPEX and OPEX. This indicates that players have a different share in these costs.
Owner Yearly Cost [M $] Profit related to individual operation [%]
A 8.36 7
B 4.88 27
C 2.35 71
D 4.31 45
The table‘s first column shows how the $ 19.9 M found as costs in full collaboration, should be allocated to the owners, such that every owner has an incentive to join the collaboration. The second column shows the profit the owners gain by collaborating, compared to the situation in which they work individual, wherein it is assumed that plants with the same owner collaborate.
The percentages profit in OPEX and yearly costs are very high for owner C and D since they are the only owners which have to pay a higher price for coal import, caused by importing with Handymax and Panamax instead of Capesize vessels, if they work individually. This penalty is the cause of around 50% of yearly costs for these owners. It is also noteworthy that the costs of demurrage are just a very small portion of the yearly cost, at 0 - 5% and therefore focus on demurrage seems trivial.
The cost allocation structure of the Shapley Value is significantly different from the cost allocation structure calculated by linear division through throughput per owner. The linearly divided cost allocation is found to be infeasible as the combined allocated costs of A, B and C, are larger than the calculated costs of a collaboration between A, B and C, without D. Therefore, owners A, B and C do not have an incentive to incorporate D in a collaboration, although the costs of the entire park decrease if D is incorporated . The Shapley value allocation shows improved results, as any costs related to a sub collaboration is bigger than the combined allocated costs in full collaboration.
This case study brings up a few interesting generic conclusions on sharing facilities in import coal
terminals. The main conclusion is that collaboration brings financial advantages, however a proper
evaluation of the situation is required before rushing into collaboration. At first, handling smaller vessels like Handymax and Panamax brings large additional costs for transport. Therefore collaboration should always strive to create a size in which a situation is created where Capesize vessels can be handled. Second, demurrage costs are just a small fraction of yearly costs and should not be the main focus in collaboration. Finally, the Shapley value cost allocation can propose a cost allocation structure which makes it feasible for players to join a collaboration, in contrast to a linear cost allocation structure.
Samenvatting
Om het chronische energietekort in Pakistan op te lossen, heeft de Pakistaanse overheid in 2013 het Gadani Energy Park aangekondigd. Het project bestaat uit tien kolencenterales in hetzelfde park, elk met een capaciteit van 660 MW. Verwacht wordt dat het park 17 M ton aan kolen per jaar nodig zal hebben. In het huidige concept zullen alle centrales worden bevoorraadt met zeeschepen, die worden uitgeladen aan de gedeelde kade en steiger. Zodra de kolen aan de kust zijn aangekomen, starten de individuele operaties met bandtransporteurs die de kolen naar de opslagfaciliteiten van een centrale transporteren.
Het Gadani Energy Park kan worden gezien als een import droge bulk terminal, door de los-en opslag capaciteiten. Wetenschappelijke literatuur behandelt vooornamelijk de besturing van container terminals en in mindere mate droge bulk terminals. Het delen van faciliteiten is bestudeerd in veel verschillende aandachtsgebieden. In verschillende bestaande situaties is het zo dat bulk-exploitanten samen een op-slagterrein delen, maar een studie naar samenwerking in bulk logistiek ontbreekt. Het doel van dit onderzoek is daarom om inzicht te verschaffen in de effecten van het delen van opslagvelden in droge bulk terminals, door gebruik te maken van het Gadani Energy Park als casus. Dit leidt tot de volgende onderzoeksvraag:
Wat zijn de voor-en nadelen van samenwerking in opslagfaciliteiten in een import kolen terminal?
Om deze vraag te kunnen beantwoorden is eerst een literatuurstudie gedaan naar droge bulk terminals, kolencentrales en het delen van faciliteiten. Er is op dit moment weinig bekend van het Gadani Energy Park en daarom is het conceptueel ontwerp vooral gebasseerd op vuistregels en ervaring. Dit heeft geleid tot een generiek deterministisch model. Hierin is onder andere bepaald dat de operaties voor het blenden van kolen effect hebben op de interne logisitiek van een terminal omdat hiervoor een extra opslagveld nodig is met specifieke stack en reclaim operaties. Omdat er weinig bekend is over de logistieke operaties voor het blenden, worden er in dit rapport een aantal nieuwe evaluaties gemaakt. Zo wordt aangetoond dat blend operaties gedeeld kunnen worden, aangezien een gelijke vorm van samenwerking is gevonden bij de kolencentrales van E.ON op de Maasvlakte II in Rotterdam. Hier worden drie centrales beheerd door hetzelfde bedrijf, maar de centrales hebben verschilende bouwjaren en specificaties. In dit geval worden de centrales toch door dezelfde velden gevoed. Er is ook een vuistregel ontwikkeld om de capaciteit van een veld voor blenden te bepalen aan de hand van de grootte van de kolencentrale die gevoed wordt.
Om stochastische invloeden mee te kunnen nemen in de evaluatie en daardoor conclusies te kunnen maken met betrekking tot redundantie, is een simulate model gemaakt. Als eerste wordt er een model zonder enige vorm van samenwerking en een model met volledige samenwerking gemaakt en vergeleken. Als er een financieel voordeel bestaat in samenwerking, moet een kosten allocatie structuur gemaakt worden, die voor elke speler voordelig is. Dit wordt gedaan met de Shapley waarde, dit is een waarde die gebasseerd is op de gemiddelde kosten die gemaakt worden door een eigenaar toe te voegen aan een samenwerkingsverband.
Om de Shapley waarde te kunnen bepalen, moeten alle mogelijke vormen van samenwerking worden bekeken. Om het aantal mogelijke samenwerkingen te beperken tot een beheersbare set, is een aanname gemaakt over de eigenaren van de kolencentrales. Er is een eigenaar A met vier centrales gedefinieerd, een eigenaar B met drie, een eigenaar C met twee en een eigenaar D met een centrale in het Gadani Energy Park. Omdat samenwerking tussen eigenaren alleen plaats kan vinden als de eigenaren fysiek naast elkaar gepositioneerd zijn, is er een gelimiteerde set aan samenwerkingen mogelijk. Voor deze samenwerkingen wordt het benodigde apparatuur bepaald om de levering van kolen aan de centrales te garanderen en hieraan worden investeringskosten (CAPEX) en operationele kosten (OPEX) gekoppeld.
Het concept zonder samenwerking heeft een starre besturing, met lage bezettingsgraden en veel blokkades. Dit impliceert overcapaciteit, maar weinig redundantie. Wanneer de grootte van het samen-werkingsverband groeit, worden de berekeningen van de benodigde capaciteiten over het algemeen meer gedomineerd door de benodigde capaciteiten dan door de lay-out van de terminal die benodigd is om individuele eigenaren te bereiken. Hierdoor wordt de terminal steeds verder geoptimaliseerd en worden de bezettingsgraden en wachttijden verbeterd. Het aantal stacker en reclaimers wordt zo gereduceerd van 20 stackers en reclaimers in het concept zonder samenwerking, naar 5 stacker reclaimers, 3 stackers en 3 reclaimers in het concept van volledige samenwerking.
De eerste resultaten tonen aan dat het verschil tussen geen en volledige samenwerking $ 160 M CAPEX is, dit is een reductie van 47%. The OPEX wordt gereduceerd met 86% tot $ 7.99 M en de jaarlijkse kosten (een combinatie van CAPEX en OPEX door de afschrijving mee te nemen) daalt met 74 % tot $19.1 M voor het gehele park. Omdat de voordelen van samenwerking hiermee zijn aangetoond, is de volgende stap om te bepalen hoe deze voordelen verdeeld moeten worden onder de eigenaren. De kosten allocatie structuur die gemaakt is met de Shapley waarde voor volledige samenwerking wordt getoond in de onderstaande tabel. In deze tabel wordt alleen de jaarlijkse kosten weergegeven omdat dit een samentrekking is van CAPEX en OPEX. Hierbij moet wel de kanttekening worden gemaakt dat de procentuele kostenverdeling er voor CAPEX en OPEX anders uitzien, wat aangeeft dat de eigenaren een ander aandeel hebben in deze kosten.
Eigenaar Jaarlijkse kosten [M $] Voordeel t.o.v. individueel opereren [%]
A 8.36 7
B 4.88 27
C 2.35 71
D 4.31 45
De eerste kolom van de tabel geeft aan hoe de $ 19.9 M aan jaarlijkse kosten in volledige samenwerking verdeeld moet worden onder de eigenaren, zodat elke eigenaar er baat bij heeft om deze samenwerking aan te gaan. De tweede kolom geeft de procentuele voordelen die de eigenaren hiermee halen ten opzichte van individueel werken, waarbij samenwerking binnen de centrales van eenn eigenaar aangenomen is.
De procentuele voordelen in OPEX en jaarlijkse kosten zijn erg hoog voor eigenaar C en D omdat zij de enige eigenaren zijn die een hogere prijs voor hun kolen betalen. Dit wordt veroorzaakt doordat zij kleine spelers zijn en dus kolen moeten importeren via Handymax en Panamax schepen, in plaats van Capesizes. De extra kosten hiervoor zijn in rekening gebracht en nemen ongeveer 50 % in van de jaarlijkse kosten voor deze eigenaren Aan de andere kant is het zo dat wachtgelden maar een kleine portie van de jaarlijkste kosten is, namelijk 0-5%.
De kosten allocatie structuur die bepaald is met de Shapley waarde, is significant anders dan een structuur die bepaald is door de kosten lineair te delen aan de hand van de grootte van de eigenaren. Deze structuur blijkt onuitvoerbaar te zijn omdat een samenwerkingsverband van (ABC), zonder D, meer voordelen geeft aan de eigenaren A, B en C dan een volledige samenwerking. Dit betekent dat als de kosten lineair verdeeld zouden worden onder de spelers in volledige samenwerking, de gecombineerde toegewezen kosten aan A, B en C, hoger zijn de kosten van een samenwerkingsverband (ABC), zonder D. Hierdoor is er voor A,B en C geen stimulans om de volledige samenwerking aan te gaan, terwijl deze wel gereduceerde kosten opleverd voor de terminal in zijn geheel. De Shapley waarde geeft betere resultaten, omdat de toegeweze kosten bij volledige samenwerking hierbij nooit uitstijgen boven de kosten van een andere samenwerking.
De resultaten van deze casus kunnen tot een bepaalde hoogte worden doorgetrokken tot generieke conclusies. De belangrijkste conclusie is dat samenwerking financiele voordelen geeft, maar ook dat er
een goede evaluatie van de situatie nodig is voordat er zomaar een samenwerking wordt aangegaan. Zo kan er worden geconcludeerd dat het primaire doel van samenwerking moet zijn om een grootte te creeren waarbij het haalbaar wordt om louter Capesize schepen binnen te halen. Hiermee kunnen de operationele kosten sterk worden gedrukt. Ook is laten zien dat wachtgelden voor schepen maar een klein deel van de operationele kosten genereren en daardoor niet de focus moet zijn van samenwerking. Ten slotte is laten zien dat de Shapley waarde een kosten allocatie structuur in import kolen terminals bepaald die het haalbaar maakt voor eigenaren op de terminal om een samenwerking aan te gaan. Dit in tegenstelling tot een lineaire kosten verdelings structuur.
Contents
Preface III
Summary V
Samenvatting VII
List of symbols XIV
List of abbreviations XV 1 Introduction 1 1.1 Problem definition . . . 1 1.2 Project definition . . . 3 1.3 Approach . . . 6 1.4 Report structure . . . 7 2 Literature survey 8 2.1 Dry bulk terminals . . . 8
2.2 Coal-fired electricity generation . . . 12
2.3 Game theory . . . 15 3 Terminal functions 17 3.1 Terminal definition . . . 17 3.2 Import . . . 18 3.3 Export . . . 19 3.4 Storage . . . 21 3.5 Terminal assessment . . . 24
4 Deterministic capacity analysis 26 4.1 Import . . . 26 4.2 Export . . . 31 4.3 Storage . . . 33 5 Simulation 37 5.1 Additional assumptions . . . 37 5.2 Import . . . 38 5.3 Export . . . 41 5.4 Storage . . . 43 5.5 Implementation . . . 45
6 Verification and validation 46 6.1 Verification . . . 46 6.2 Validation . . . 48 7 Experimental plan 52 7.1 Measuring KPI‘s . . . 52 7.2 Concepts . . . 53 7.3 Experiments . . . 56
7.4 Required duration and number of replications . . . 57 8 Results 59 8.1 No collaboration . . . 59 8.2 Full collaboration . . . 67 8.3 Intermediate collaboration . . . 73 9 Cost allocation 84 9.1 General definitions . . . 84
9.2 Collaborative game theory at Gadani Energy Park . . . 85
9.3 The core . . . 86
10 Conclusions 89 11 Recommendations 91 Bibliography 92 Appendix A Paper 96 Appendix B Literature survey on coal-fired electricity generation 102 B.1 Coal post-processing steps . . . 102
B.2 Coal-fired power plants . . . 102
B.3 Power plant economics . . . 104
B.4 Power market . . . 104
B.5 Similar projects . . . 105
Appendix C Modelling package 108 Appendix D Conveyor network 110 D.1 Reliability . . . 111
Appendix E Code Simulation model 114 E.1 Route deciscion taking . . . 114
E.2 Coordinate reclaimer . . . 121
Appendix F Registry performed simulation runs 128 Appendix G Raw data 129 G.1 No collaboration . . . 129 G.2 ABCD . . . 133 G.3 (AB)CD . . . 136 G.4 A(BC)D . . . 139 G.5 AB(CD) . . . 141 G.6 (ABC)D . . . 144 G.7 A(BCD) . . . 146
G.8 Full collaboration (ABCD) . . . 148
Appendix I Game theory M.file 152
I.1 Main file: ComputingShapley . . . 152
I.2 Help file: matr . . . 153
List of symbols
C = Cost function [$]
E = Result of Erlang-C formula
N = Number of Players
S = A number of players in a coalition
Wq = Expected waiting time in a queue [hours]
¯
X = Sample mean
a = Availability [%]
ci = Initial costs per player [$]
n = Sample size
s = Number of servers
td = Down period [days]
tw = Working period [days]
t = Student‘s t-distribution [$]
v = Characteristic function [$]
xi = Benefits per player [$]
yi = Allocated costs per player [$]
α = level of significance
= Allowable error [%]
λ = Arrival rate [per day]
µ = Estimated mean
List of abbreviations
CAPEX = Capital Expenditures
DSA = Delft systems approach
DWT = DeadWeigth Tonnage
EMO = Europees Massagoed-Overslagbedrijf
IAT = Inter Arrival Time
KPI = Key Performance Indicator
M = Million
MPP = Maasvlakte Power Plant
MTBF = Mean Time Before Failure
MTTR = Mean Time To Repair
MW = Mega Watt
OPEX = Operational expenditures
PI = Performance Indicator
UMPP = Ultra Mega Power Project
UNCTAD = United Nations Conference on Trade and Development
1
Introduction
In this project, the advantages and disadvantages of collaboration in storage facilities within import dry bulk terminals is evaluated. A case study is found in the form of Gadani Energy Park, which sets a perfect example of a situation wherein a study on the opportunities of facility sharing is viable. Although the research is specified for this case, a large part of the research is generic and can be applied for similar problems.
This first chapter sketches the outline of Gadani Energy Park and defines the problems and project to be evaluated. The project definition includes the research question and scope. Subsequently, the approach of the research will be presented and finally the structure of the report will be introduced.
1.1
Problem definition
The first step in the project is to define an accurate problem definition. In order to accomplish this, the project will be approached from two directions. First the expected problems at Gadani Energy Park are evaluated. Hereafter, a short literature survey will define the knowledge gap in current research which is relevant for this project. By combining these two angles of approach, an accurate project definition can be defined.
1.1.1 Gadani Energy Park
One of the main problems in Pakistan is the nations chronic electricity shortage (Walsh and Masood, 2013). This is mainly caused by increased urbanization over the last decades. As power generation has not been growing along with the population, a gap between demand and supply arose (Amjad et al., 2014). Currently, power breakdowns in the major cities reach ten hours, in rural areas these can reach up to twenty hours (Walsh and Masood, 2013). In 2013, the last Prime Minister, Nawaz Sharif, won the elections with the promise to end the power crisis. However, the shortfall has only increased since then. The shortfall in power is expected to be 3,500 to 6,000 MW, which is a third of Pakistan‘s total demand (Santana, 2013).
In order to face Pakistans chronic power shortage problems, its government announced an ambitious project in August 2013 (Royal HaskoningDHV, 2014). The project aims to establish ten coal-fired power plants at one site, each with a capacity of 660 MW. The project has to take shape in the form of Gadani Energy Park. The power plants will be situated near Gadani, a small coastal village 50 kilometre Northwest of Karachi. Just south of Gadani, seven kilometre coastline is free to be used for the project, as can be seen in figure 1
The provided area of 5,000 acres is in a flat desert (Royal HaskoningDHV, 2014). Gadani and Karachi
are connected via a decent road, which is always accessible. Due to the monsoon around June till
September, heavy rainfall and storms are expected in this time of year. This makes most of the other roads inaccessible.
The Pakistan government is responsible for the development of the power corridor and ensuring fast completion of infrastructural work (Royal HaskoningDHV, 2014). Royal HaskoningDHV is commissioned to plan and design the coal terminal facilities as the berths, protection works, dredging and navigational aids.
Figure 1: Location of Gadani Energy Park, (Google, 2014)
Two power plant units will be installed in public sector, the rest through the private sector (Royal HaskoningDHV, 2014). The private units are to be installed on a built-own-operate and transfer basis. Although no contracts for exploiting the plants have been signed yet, a lot of interest has been shown. The park has to be fully operational in the end of 2017, the first coal must arrive in June 2017 (Royal HaskoningDHV, 2014). It is expected that the Energy Park will require 17 M tonnes of coal per year. The power plants are aligned in front of the water line and share a berth and jetty since all coal will be imported by ship.
In the current concept, all power plants will be supplied via deep sea vessels which are unloaded at the shared unloading facility. The unloaded coal will be brought to the shore via a shared jetty. At the moment the coal arrives at the shore, it is transferred to the dedicated conveyor to the stockyard of one plant, as seen in figure 2.
Expected problems on the project arise with the current design choice of letting every power plant arrange its own coal supply. Since there is no intermediate storage after unloading, the whole cargo of a vessel will be unloaded on one stockyard. The problems of individual coal storage are expected to be:
• Only one unloading line per vessel can be used • Low redundancy throughout the terminal • Rigid control over the terminal
• Overcapacity in equipment and storage
Royal HaskoningDHV expects that sharing facilities for coal storage brings advantages for all the partic-ipants of the collaboration.
Figure 2: Basic concept of Gadani Energy Park
1.1.2 Literature
The Energy Park at Gadani can be seen as an import dry bulk terminal due to its unloading operations and storage capacity. Literature shows extensive research on the design and control of container terminals, however less on bulk terminals. Within literature on bulk terminal design, research has provided answers to basic design decisions and typical characteristics (van Vianen et al., 2012), (Lodewijks et al., 2010), (UNCTAD, 1985). Bulk terminal control mostly focusses on berth occupancy (Robenek et al., 2014), (Jagerman and Altiok, 2003). Current simulations are developed for specific cases, however not for generic solutions (van Vianen et al., 2014). Literature on bulk terminal storage strategies and its effects is limited.
Facility sharing has been researched in many different fields of interest. The gains for each participant in sharing resources can be determined with the use of game theory (Saeed and Larsen, 2010). Game theory applications in cooperation have been extensively used in the fields of, for example, production, insurance and finances (Shoubi et al., 2013). Within logistic applications, examples are found in inter-terminal transport for container inter-terminals (Saeed and Larsen, 2010) and shared transport for production facilities , (Frisk et al., 2010). However, no game theory approach to sharing of bulk storage has been found.
1.2
Project definition
With the problem definition and knowledge gap presented in the previous section, the project can be defined. First the research question will be presented, hereafter the sub-questions and project scope will be determined.
1.2.1 Research question
The objective of this study is to gain insight in the effects of yard sharing in bulk environments, by using the Gadani Energy Park as an example. This objective is set as the previous section proved that little research exists on bulk storage collaboration. At the same time the Gadani Energy Park can provide an example of the effects of collaborating in yard operations. This leads to the following research question:
What are the advantages and disadvantages of collaboration in storage facilities within an import coal terminal?
It is expected that sharing facilities for coal storage brings advantages for all the collaborating par-ticipants. These advantages are expected to be caused by:
• Flexibility in use of unloading systems • Increased redundancy for handling systems
• Decrease of required capacity for handling systems and storage Sub-questions
The research question can be divided in a set of sub-questions. These sub-questions support the main research questions and indicate the required research directions to come to correct conclusions:
• Who are the participants and what are their demands?
• What forms of collaboration are possible and will be evaluated?
• What Key Performance Indicators (KPI’s) are important for participants?
• What preparation activities of coal are necessary at a terminal and to what extend can they be shared?
• How does the arrival and departure processes of coal look like in terms of patterns, quantities and ownership?
• What data is available or retrievable for use in the calculations?
• How can the required terminal characteristics be related to capital expenditures and operational expenditures?
• In what ways can the conveyor network of the terminal be designed for all forms of collaboration and what design will be the best suitable in terms of costs and reliability?
• What are the required equipment and storage capacities of the import bulk terminal of Gadani Energy park for several degrees of collaboration, given the required demand?
• How can a feasible cost allocation structure be defined to fairly distribute the costs generated by a collaboration in storage sharing?
1.2.2 Inside scope
The to-be evaluated concepts of collaboration will lay between two extreme situations, being no collabo-ration at all and full collabocollabo-ration. In between these two extreme situations, several other concepts can be thought of.
In the initial concept, the berth and jetty of the Gadani Energy Park are already designed to be shared by the exploiters of the energy park. The collaboration stops at the moment the coal arrives at the shore. Therefore, this study focusses on potential facility sharing from the point the coal reaches the shore and onwards. The other extreme situation is where all the received coal is stocked in the same yard. From this yard, all plants are supplied directly. In between these two extremes, a few other concepts can be thought of. These concepts are yet to be determined as they depend on the analysis of the current situation.
One important aspect of the influence of the owners of the plants lies in the required coal types. As power plant owners may have different sources and demands for their coal, this may influence the degree of storage facility sharing. Also, decisions have to made on the degree of sharing coal processing facilities as mixing and blending as this influences storage space and machinery.
All concepts will have to be evaluated on required machine and conveyor capacities. These capacities will all have to be determined under acceptable import and export parameters of the terminal, which have yet to be specified. Since the influence of yard sharing is expected to reach all the way through the terminal, the entire terminal will have to be modelled. This includes berth and jetty operations to determine service and waiting times, even though the import configuration will not be changed throughout the concepts. Operations after the transfer of coal to the plant are not regarded, as made visually in figure 3.
Figure 3: The project boundary is depicted by the red line
in order for the participants to choose for collaboration, all the participants should gain in the benefits.
1.2.3 Out of scope
To allow focus in the project on storage sharing, a few project boundaries have to be installed. These boundaries mostly represent borders with other concept studies. To still allow for a representative study, certain assumptions will have to be made based on previous research and experiences. The boundaries installed in this project are:
• Civil works like shipping routes, berth and jetty design and infrastructural issues will not be taken into account.
• Prerequisites required for power plants will not be taken into account. Examples of these are facilities for maintenance, output of electricity and cooling water.
• It should be remembered that the goal of this study is not to design a bulk-terminal. Instead of examining cycle times and moves, the equipment of the terminal in the model will only satisfy peak capacities and efficiency factors. These factors will be retrieved from literature and the experience of Royal HaskoningDHV.
• Although economical reasons may be the most important reasons for owners to decide on collab-oration, other qualitative factors may also influence the owners‘ decisions. Researching social and political preferences of every stakeholder is however considered to be outside the scope of this study. • Environmental and maintenance issues will not be evaluated extensively. These issues will only be
incorporated in the model by terms of statistical probability of unavailability of equipment. • Shipping price determination depends on many factors. In this research, determination of the cost
price of coal will be based on assumptions of similar projects, however no extensive shipping price breakdown and market analysis will be made.
• Preparation activities regarding the coal will only be incorporated when it has logistic consequences. As sampling and weighing of the incoming coal happens without interrupting the flow, this will remain out of scope. Blending will only be considered if it has consequences for the stacking and reclaiming capacities.
1.3
Approach
It is important to realize that the analysis will be made on the Gadani Energy Park, as it sets a perfect scene to compare different degrees of storage sharing for an import dry bulk terminal. However, a large part of the analysis of an import dry bulk terminal is generic. Therefore, the chapters in this report can be divided in generic chapters which can be valid for any kind of import dry bulk terminal, and case specific chapters which use the presented generic approaches and methods to generate conclusions on the case of Gadani Energy Park.
The first step of the research is to thoroughly analyse the generic flows and logistic relations on an import dry bulk terminal qualitatively. The expected flows of goods are mapped and deterministic capacity calculations will be made. The first step towards a quantitative analysis is set by a deterministic model, made in spreadsheets. This model is made to be easily adaptable, such that influence of different input parameters can be evaluated quickly and the model is kept generic.
As discussed, advantages of collaboration are expected to be found in redundancy, flexibility and reliability of the terminal. In order to include important stochastic influences like breakdowns, weather
influences and vessel arrival, a simulation model will be made. The model will be specifically constructed for Gadani Energy Park, which can be adapted such that multiple concepts can be tested and evaluated. After validation and verification, several degrees of collaboration will be simulated. FlexSim will be used as the simulation tool since it comprises strong graphical capabilities and allows for object-oriented simulating.
First, a model without any collaboration and a model with full collaboration is made and compared. If collaboration is found to be beneficial, all the participants should gain in the benefits in order for them to have an incentive to join. Cooperative game theory can be used to determine how this incentive can be created for all the participants. This is done with the Shapley value, which is a method based on the determination of the average costs of adding players to (sub)collaboration such that its worth, and therefore, cost allocation can be determined. Therefore, to find the Shapley Value, all possible collaborations must be evaluated in the simulation model.
The results of these evaluations deliver a very specific conclusion on collaboration in Gadani Energy Park. It is however expected that observed trends and feasibilities of evaluations as cooperative game theory and simulation can be brought to a broader, generic, perspective.
1.4
Report structure
The report will be set-up in the following manner. The analysis is started generic with a literature survey, in which a thorough background knowledge on bulk terminals, coal-fired electricity generation and game theory is developed. Hereafter, the logistic flows of an import dry bulk terminal are illustrated qualitatively by flowcharts, which identify the logistic flows at a terminal and its functions. Chapter 4 will introduce the first capacity calculations. A generic model made with spreadsheets will be introduced by which the required deterministic capacities of the Gadani Energy Park can be calculated. These will later be used as a starting point for the simulation model. The simulation model is introduced in chapter 5 and verified and validated in chapter 6. In chapter 7, the experimental plan will be presented along with the to be evaluated concepts of collaboration. The concepts‘ capacities and performance are presented in chapter 8. Finally, game theory is applied in chapter 9 to define a feasible cost allocation structure at the Energy Park.
2
Literature survey
In order to allow focus on the relevant aspects of the research, it is important to define previous work on which this study can be based. A literature survey is made on three focus areas which are relevant for the research on storage sharing and the case of Gadani Energy Park. First, dry bulk terminals will be analysed. Hereafter, the characteristics of coal-fired power plants and finally the basics of game theory will be evaluated.
2.1
Dry bulk terminals
The primary function of a dry bulk terminal is the receipt of goods, consisting of loose, dry, unpacked materials (Willekes, 1999). Since the Gadani Energy Park comprises import and storage facilities for coal, it can be regarded as an owner-oriented coal import bulk terminal. Such terminals are characterized by (Willekes, 1999):
• Goods flow directed from the sea vessel towards the land destination. • Specialisation in handling of a fixed number of products.
• The terminal also functions a strategic buffer.
• The terminal often includes transformation functions as blending, sizing, screening, grinding and mixing.
A terminal consists of an entrance system, a stockyard system and an exit system (Schott and Lodewi-jks, 2007). In import terminals, the entrance system often is the sea-side where bulk carriers deliver the coal. The bulk carriers are unloaded by grab cranes or continuous ship unloaders after which the material is dropped in a bunker that feeds the conveyors. A system of belt conveyors transports the material to a stockyard system with a stacker that stacks the material on a stockyard. In this terminal, the exit system comprises the coal-fired power plants. More on coal-fired power plants will be explained in section 2.2. Due to the behaviour of power plants, the stockyard management philosophy tends to a pull system, as the terminals clients (power plants) request coal from the yard.
As stated earlier, most research in port development focusses on container terminals(Umang, 2014), (Robenek et al., 2014). However, research in bulk terminals is completely different due to the necessity to account for the cargo type and the locations of the fixed equipment facilities as conveyors. Existing literature in bulk terminals focusses mostly on rules-of-thumb in design, berth occupancy and terminal-specific simulations.
2.1.1 Rules-of-thumb in terminal design
Current dry bulk terminal design is often based on rules-of-thumb, which are mostly determined by calcu-lations from the operations research and previous experience (Robenek et al., 2014). The most important rules-of-thumb found in literature will be presented in this section.
Equipment performance specifications
In literature, three types of capacities have been defined, which are often related to quay cranes but may account for every type of terminal equipment (UNCTAD, 1985):
• Peak capacity: Maximum hourly handling rate. This rate is the capacity to which the subsequent equipment should be designed.
• Rated Capacity: The rate during unloading from a specific point in a vessel. This is determined by analysing one crane cycle.
• Effective Capacity: The average hourly rate of tonnage discharged during unloading of the entire cargo of a ship, excluding scheduled non-working periods.
Experience has learned that the peak capacity is often around 2.5 times higher than the effective capacity and 2 times higher than the rated capacity (Ligteringen and Velsink, 2000). Although this factor may be different throughout applications, the effective capacity is seldom more than 50% of the peak capacity (Schott and Lodewijks, 2007). Royal HaskoningDHV generally uses a factor 0.5 between peak and effective capacity and a factor 0.6 between effective and rated (also called nominal) capacity, this factor is called the through ship efficiency factor.
The chosen unloading capacity should be big enough to unload a vessel within the acceptable service time. The unloading factor, which is defined as the installed unloading capacity divided by the minimum required capacity, proves to be 3 to 4.5 at similar sized terminals (van Vianen et al., 2011).
Ship-handling capacity of the terminal is determined by an analysis of both the number of berths and the handling rates of the ship-(un)loaders (UNCTAD, 1985). Doubling the number of (un)loaders handling one ship generally doesn’t double the throughput. Typical throughput factors are around 1.75, 2.25, 2.60 and 2.85 for two, three, four or five (un)loaders per berth respectively.
Stacking and reclaiming factors, defined as the installed capacity divided by necessary capacity, are generally found to lie between 5.5 - 9 for stacking and 4 to 8 for reclaiming in dry-bulk import terminals (van Vianen et al., 2011). An alternative to the use of fixed conveyors, stackers and reclaimers is the use of mobile stackers and wheel loaders. This is often used for very small installations since the wheel loaders only have capacities up to 100-200 tonnes per hour, depending on the capabilities of the driver and transportation distance (UNCTAD, 1985).
When designing a bulk terminal, one of the most important design considerations is trying to increase the utilization of terminal equipment (Lodewijks et al., 2010). Today, utilizations of 40% to 50% are generally found (Lodewijks et al., 2010).
Conveyor routing
Within conveyor design, a distinction can be made between one-way and bi-way conveyors (Lodewijks et al., 2010). Bi-way conveyors often show a lower availability due to its higher complexity. Also, the transfer points are more complex. However, the total length of conveyors is lower with using bi-way conveyors.
A by-pass in a bulk terminal means that it is possible to transport the coal straight from the ship unloaders to the exit system without intermediate storage (Lodewijks et al., 2010). By-passing is often considered to reduce storage area, it does however introduce complexity in terminal control and synchro-nization. Balancing the incoming and outgoing stream in terms of capacity has therefore proven to be crucial in order to optimize a by-pass option. Typical by-pass percentages are less than 5% compared to annual throughput.
Storage capacity
The storage system is primarily used to decouple ingoing flow from outgoing flow such that the flows
are balanced. In import terminals, the output usually occurs at a much lower rate than the input
(UNCTAD, 1985). The stockyards enable different transportation modes with different rates to function independently, thereby avoiding delays (van Vianen et al., 2012). This causes the terminal to act as a buffer from which typical variation in stockyard level can be seen in figure 4.
Figure 4: A typical variation in stock level (UNCTAD, 1985)
A stock of around 10% of annual throughput is generally accepted, however examples exist where it can be as low as 3% (Lodewijks et al., 2010), or as high as 2 months (16%) (van Vianen et al., 2014). These percentages may vary due to the number of types of products stored. In terminals that are ded-icated for one application, for example coal for a power plant, the size is primarily related to a safety margin in number of tonnes of material required for a certain number of days of operation of the power plant (Schott and Lodewijks, 2007).
Redundancy and Reliability
If a specific task on a specific area can be performed by more than one machine, then this task has machine redundancy (Lodewijks et al., 2010). In general, the more products a terminal handles, the more important machine redundancy is. If by-passing is not possible, machine redundancy also becomes more of an issue.
Generally, a terminal should be designed in such a way that there is a sufficient number of ways to get to any desired destination (Schott and Lodewijks, 2007). In some cases it means that bi-way conveyors are used, in other cases multiple stacker reclaimers are used in-line.
The overall reliability of a terminal is composed of the individual reliabilities of the equipment (Lodewi-jks et al., 2010). Reliability is then defined as the average percentage of working time the material flow is guaranteed. Little scientific data is known on breakdowns. Although a lot of practical data may exist among terminal operators, it is not often presented in literature. Royal HaskoningDHV uses a 5% down-time for corrective maintenance for unloaders and 2% for conveyors. It is thereby often assumed that preventive maintenance is planned during non-operational hours. Percentages for operational availabil-ity of terminal equipment differ among scientific sources. General percentages for all equipment of 70% (Willekes, 1999) and 80% (Lodewijks et al., 2010). The most extensive analysis is shown in Table 1.
Table 1: Typical model input for capacity and availability (Lodewijks et al., 2009)
Equipment name Availability [%] Work Time Distribution [days] Overland conveyor 97 Exponential(14)
Stockyard conveyor 97 Exponential(21) Jetty conveyor 97 Exponential(21) Ship loader 97 Uniform(1,5) Stacker 90 Normal(20,5) Reclaimer 85 Table(2,0,6,70,45,100)
In this table, the work time distribution stands for a working period tw. The reclaimer downtime
shows a table function, which is Delphi Tomas specific command. It gives a distribution wherein there is 0% chance that the downtime is in the region 0-2 days, 70% chance that the downtime is 2-6 days and
30% that the downtime is 6-45 days. After a working period, a down period follows which length tdcan
be determined as follows:
td= tw(
100
a − 1) (1)
Where the ’a’ represents the availability. The working period and down period together make the equipment work-down cycle time. parallel to literature (Lodewijks et al., 2009), the downtime is taken
to be uniformly distributed between 0.5 tdand 1.5 td.
2.1.2 Berth allocation
Within berth allocation, several objectives can be thought of, such as minimization of service times, port stay time (or vessel port time), berth utilization, number of rejected vessels or deviation between actual and planned berthing schedules(Robenek et al., 2014). Two of the most important performance measures are the port time per vessel and the berth occupancy (Jagerman and Altiok, 2003).
Vessel port time is the duration of time of the vessel from the point of arrival at the port to the departure from the berth (Jagerman and Altiok, 2003). Normally, the terminal operator and ship-owners agree on a maximum time a ship is allowed to lay in the port. Berth utilization is the proportion of time the berth is occupied by a vessel. Berth time is the total time a vessel spends at the berth, including things like paperwork and repair works . Typical vessel port times vary from 1 to 6 days, berth utilization varies between 50 and 75%, depending on the number of berths. Higher berth occupancies may be possible but are not desirable since the stochastic behaviour of vessel arrival times may lead to unacceptable demurrage costs. Demurrage costs are charges the terminal operator pays to a shipowners if the handling of its ship takes longer than allowed. On the other hand, Dispatch is paid by the shipowners to the terminal operators if a vessel is handled faster than agreed
The stochastic behaviour of inter arrival time of vessels is due to the fact that the arrival pattern depends location, routes, weather conditions, tidal activity, swells and unexpected failures or stoppages. Arrivals of vessels are assumed to be scheduled, however vessels are known to often not reach their estimated time of arrival. It is therefore common to use an Erlang-2 distribution for variations in vessel inter arrival times for bulk terminals (van Vianen et al., 2014). It is however unrealistic to assume the inter arrival time to be fully stochastic, since the vessels are planned. To adapt for these stochastic influence, a range of time within which vessels will arrive is determined. In literature, a lay period is defined as the period within which a vessel is expected to arrive at the port(Jagerman and Altiok, 2003). In other literature, this term is known as the time window (Lodewijks et al., 2009). This period is defined in literature to be between 12 hours (Asperen et al., 2003) and 15 days (Jagerman and Altiok, 2003). Naturally, an economical optimum is found when immediate berthing is possible for a majority of vessels (UNCTAD, 1985). It is important to search for a robust design which can cope with a variety of future traffic.
The relation between berth occupancy and ship time at port is based on queueing theory (UNCTAD, 1985). Vessel arrivals are often planned in bulk operations, however in reality the vessels often do not arrive at the estimated time of arrival (Asperen et al., 2003). Often, an Erlang 2 distribution is used for the service time distribution and the inter-arrival time distribution. However, examples exist where an Erlang-4 distribution for service times is proposed (van Vianen et al., 2014). The berth can be allocated discrete (fixed positions), or continuous (dynamic positions) which increase the flexibility and occupancy of the berth (Umang, 2014).
Berth allocation algorithms have been provided as the results of an optimization problem for ship queues (Jagerman and Altiok, 2003). Since storage location influence the internal travel distance and
storage efficiency, the berth allocation has also been researched in combination with yard assignment problems (Robenek et al., 2014). However, due to large scale of terminal logistics and case-specific influences, no generic methods exist yet on the optimization of the logistics within an entire bulk terminal (Umang, 2014). The current optimization techniques of the complete system are therefore often based on heuristics.
2.1.3 Bulk terminal simulation
Simulation is often used in environments with stochastic behaviour and non-linear characteristics (van Vianen et al., 2012), (Monrad and King, 2008). In a bulk-terminal these are due to a difference between expected and real arrival times of vessels, natural phenomena and machines-and conveyors breakdowns. Also, by varying parameters of a simulation model, several concepts can be evaluated quickly without costly capital investments(UNCTAD, 1985). In order to obtain the full benefits from simulations, a good and accurate set of traffic forecasts and future handling rates is crucial. Although some terminal specific simulations exist (van Vianen et al., 2012), (Monrad and King, 2008), (Harris et al., 2007), (Bugaric and Petrovic, 2007), no generally applicable simulation has been found.
Besides its design, a terminals‘ logistic control and maintenance of bulk equipment mainly determine a terminal‘s success (Lodewijks et al., 2010). Optimizing the logistics control of a terminal may increase the utilization of machines. Normally, the routing of conveyors and usage of equipment is planned by a terminal planner (van Vianen et al., 2012). Because of the earlier described uncertainties, planning in advance is unusual.
2.2
Coal-fired electricity generation
In the case of Gadani Energy Park, the exit system of the import terminal is realized by ten coal-fired power plants. In order to correctly define the output and understand the required preparative processes, a literature survey is made on coal and coal-fired power plants. This section will present the findings which are necessary to determine the logistic flows within the scope. The working principles of the power plants are considered outside the direct scope of the project. However, since the author believes a basic understanding of the behaviour of power plants is required to understand its demands, all this information can be found in appendix B
2.2.1 Coal
Coal is considered to be one of the major bulk cargoes (UNCTAD, 1985). In 2013, steam coal trade volume reached 1,250 Mtonnes globally (Royal HaskoningDHV, 2014). Despite the concerns about global warming and hazardous emissions, coal is currently experiencing an increase in use in electricity generation due to its relative low price (see figure 5), tensions in other energy markets and the high energy demand growth in especially China and India (Haftendorn and Holz, 2008). In 2013, coal accounted for 41% of the world‘s electricity generation (International Energy Agency, 2014), as can be seen in figure 6.
Figure 5: Coal price development, (International Energy Agency, 2014)
Power production is realized with steam coal, which has a higher calorific coefficient than coking coal (Haftendorn and Holz, 2008). The recent advance of ”clean-coal” technologies increases the effort to reduce greenhouse gas emissions and therefore extend the viability of coal-based electricity generation.
Figure 6: World energy consumption by fuel (U.S. Energy Information Administration (EIA), 2013)
The global seaborne trade of coal can be found in figure 7. All major coal exporting countries can be considered as safe countries in political terms and no sudden supply disruption on political grounds can be expected (Haftendorn and Holz, 2008). Short supply disruption due to natural disasters or social tensions may however occur. Even though coal is the cheapest fossil fuel, its transport costs take up a considerable portion. In order to ensure viability of the plant throughout its lifetime, a secure fuel supply agreement is vital (Breeze, 2005). In most situations, the coal-fired power plant is therefore placed close to the coal supplying mine. It is important to remember that the actual demand of coal from plants are considered stochastic in nature and may vary up to 21% of the planned demand (Conradie et al., 2008).
Figure 7: Global coal trade, (International Energy Agency, 2014)
2.2.2 Coal Blending
Coal qualities vary over sources and even coalfields (Liu and Sherali, 2000). Often, a large portion of coal is bought in long-term contracts to guarantee a safe supply. However, raw material prices become more and more subject to fluctuation, which leads to an increase in additional short-term supply contracts and opportunity driven sourcing. The newest boilers and furnaces however require a feed of material with consistent quality to achieve a high efficiency (Lieberwirth, 2012). To achieve this, coal blending is required, which makes the planning of efficient processes more difficult. Other coal processing steps are considered to be part of the power plant technology and therefore not considered to be in the direct scope of this research. Appendix B does however give an overview of these processes for the interested reader.
Blending is known as the process of mixing multiple different materials with the aim to create a blend with an average level of certain parameters. Typical quality parameters are caloric value, carbon content, ash content, sulphur content, organic content, fine coal fraction, grain size distribution, density and stone fraction (Conradie et al., 2008), (Lieberwirth, 2012). It would suffice to only choose high-quality coal for the plant, however this has proven to be infeasible in practice due to its price and limited supply quantity. Mine stockyards often already blend raw materials originating from various mining faces within a mine or from different mines in the area. Since most plants buy coal from different mines however, an additional blending step is required.
Mixed-integer programming models have been presented to determine the optimal shipping and blend-ing decisions (Liu and Sherali, 2000), (Conradie et al., 2008), (Shih, 1997). In literature, each power plant has its own blending formula, depending on the design specifications of its burning furnaces (Shih, 1997). In real life however, examples exist where the blend is not altered for every power plant but merely used to create an average coal quality. For example, at the Maasvlakte in Rotterdam, three E.on power plants from different years of construction are supplied by the same blending bed. Unfortunately, literature on the influence of blending on terminal logistics is limited.
2.2.3 Future outlook
A decision to build a coal-fired power plant will depend on many factors as fuel availability, environmental hazards and the costs of alternatives (Breeze, 2005). A typical lifetime of a power plant is 30 years. Therefore, some guesses have to be made about its future performance and costs over its lifetime. Also,
economic factors as the changing of energy markets and value of money have to be taken into account as well as insurances and debt repayment terms. It is expected that the price of coal will rise, however over the lifetime of a new plant no dramatic move is likely (Breeze, 2005).
One of the major attractions of coal is its abundance (Breeze, 2005). According to the World Energy Council‘s survey of Energy Resources, the recoverable world resources in steam coal is 1,038 billion tonnes. At current consumption rates, the reserves can provide energy for 132 years (World Coal Association, 2014b).
As can be seen in figure 8, investment in coal-fired power supply is still very actual. The energy demand created by coal is expected to rise 0.6% globally until 2035 (International Energy Agency, 2014).
Figure 8: Investment in global energy supply, (International Energy Agency, 2014)
Most environmental studies indicate that environmental benefits can be gained in shifting from fossil fuels to renewable sources of energy (Breeze, 2005). However, since power generation industry shifted from public to the private sector in the past 30 years, cost has become more decisive than ever. Governments may impose legislation and surcharges to influence industry, but it can not direct the private sector. Short-term return on investments are most important in the private sector (Breeze, 2005). Most renewable technologies however are capital expensive but cost very little to run. This is amplified by the fact that changing to renewable energy sources will affect the whole power generation industry, including transmission, distribution and structures.
2.3
Game theory
Game theory is known as the study of strategic decision making. It’s name has originated from the decisions that are made in board games, however the theory is not only used these games. It is mainly applied in economics, sociology and computer sciences to generate a rational quantitative incentive to make a certain decision. Game theory offers a framework within which the strategic interaction between players is studied.
Game theory studies comprise non-cooperative games (which are often board-games like problems) and cooperative games. The problem studied in this research is found to be a cooperative game, wherein (in contrast to non-cooperative games) binding agreements can be made between players. Often, the players in a joint activity can achieve more financial benefits in a collaboration than individually (Shoubi et al., 2013). If all players collaborate to reach the most benefits, the ‘grand coalition‘ is reached.
In game theory, players are assumed to decide rationally, such that a quantitative game can be defined (Frisk et al., 2010). Within cooperation, questions often arise on how the joint costs or profits should
be allocated among the members. If the involved parties do not see their portion in total cost or profit fairly, they will not be willing to cooperate. Cooperative game theory emerged as an effective approach to find an adequate incentive to motivate the parties for participation in a joint venture.
In order to find a solution in a cooperative game, the games must specify three elements (Saeed and Larsen, 2010), (Zhang et al., 2010):
• The players of the game
• Strategies available to each player at each decision point • Pay-offs for each outcome
In cooperative games, coalitions are defined. A coalition is a subset of the set of players which coordinate strategies and agree on how the total pay-off is to be divided among the members. The game is created by specifying a value for every possible coalition.
The extra costs an additional player brings to a coalition is called marginal contribution (Shoubi et al., 2013). The marginal contribution of a player can be used to measure its bargaining power. The players will choose a coalition according to their estimate of the way the payment will be divided over the members. The challenge therefore is to allocate the pay-off of a coalition in a fair way. An efficient pay-off is called an imputation and must fulfil the following requirements (Frisk et al., 2010):
• Efficiency (the sum of the individual gains equals the total gain) • Uniqueness
• Symmetry (equal payouts to symmetric players)
• Additivity (allocation to a player in a sum of two games is the sum of allocations in the individual games)
A set of imputations must be deduced such that no player can profit by deviating strategy. This can be done with the use of several methods. When no coalition or player in a set-up can be found which can benefit by altering its decision to collaborate, the core solution has been found (Shoubi et al., 2013).
2.3.1 Shapley Value
The Shapley Value is one of the many methods to find a feasible cost allocation structure in a cooperative game. It is often used in literature since it gives a relatively simple, comprehensive and well performing method (Shoubi et al., 2013), (Burg, 2012), (Frisk et al., 2010).
The Shapley value is equal to the average costs of adding a player in the coalition (Shoubi et al., 2013). When a participant enters a coalition, he is allocated marginal costs, by which the total costs of the coalition rises when he joins. The Shapley value is the average marginal cost of the participants, if the participants enter in a completely random order. For a game with n players, the cost allocation with the Shapley Value can be calculated by the following formula (University of British Columbia, Department of Computer Science, ): yi= 1 N ! X i∈S,S⊆N |S|!(|N | − |S| − 1)![C(S) − C(S − i)], (i ∈ N ) (2) With:
yi = Allocated costs to player i
|S| = The number of players in the coalition S C(S) = The cost function of the coalition S
C(S − i) = The cost function of the coalition S when player i has been deleted from the coalition
If player i was added to the coalition, his contribution of costs to the coalition is c(S) - c(S-1). This contribution is multiplied by |S|!, which represents the amount of different ways the set S could have been formed before i joins. (|N | - |S| - 1)! represents the number of ways the remaining players could be added to the existing coalition. The summation in this formula is the summation over all coalitions that contain the participant i. Since there is no guarantee that the Shapley value solution is stable (it is not necessarily in the core solution), this should be checked by also determining the core of the game and evaluating whether the solution is within the core.
2.3.2 Similar projects in game theory
Similar problems as the one described here have been found in other fields of interest. One relevant example is found in the port of Karachi, where the multiple forms of collaboration between four con-tainer terminals is analysed (Saeed and Larsen, 2010). Here, expected advantages of collaboration are found in a higher market prices, better utilization of combined capacity and an increase in efficiency of storage facilities. These advantages are expressed in total gains per coalition. By defining the marginal contribution of each player, an efficient pay-off is found to determine an economic incentive for every player. Remarkably, a profit allocation proportional to size or throughput is not found to be feasible, thereby proving the benefits of game theory.
Another project is found in collaborative timber transportation in Sweden (Frisk et al., 2010). In this case, the potential savings of integrated transportation planning of eight lumber yards is studied. Here, different methods to find a core solution are evaluated and cost savings of up to 14% have been realized. Finally, a relevant example is found in the water distribution and storage throughout different regions (Shoubi et al., 2013). The advantages of collaboration between the multiple regions is evaluated with game theory.
3
Terminal functions
In order to define the functions and logistic flows of the terminal, a generic flowchart analysis of the terminal is made. In this way, the terminal is defined qualitatively and the required handling activities of a import dry bulk terminal will be presented. Also, a categorization is introduced which is used throughout the report to help define and structure the deterministic capacity calculations and simulation model in later chapters.
The flowchart will be presented in several levels of detail. This approach is adopted from the ”Delft systems approach” (DSA) (Veeke et al., 2008). One of the main advantages of using the DSA‘s way of zooming in on functions is that it allows structuring in defining the terminal step by step. This contributes to the structuring of the report, as the characteristics of the terminal can be described per function. The chapter closes with an evaluation of how an import dry bulk terminal should be assessed. The Key Performance Indicators as well as Performance indicators used to determine how well a terminal performs are presented here.
3.1
Terminal definition
At the lowest level of detail, the terminal operates to handle product, which is in this case defined as the steam coal. As described earlier, the terminal consists of an entrance system, a storage system and an exit system. These are translated to the functions ’import’, ’store’ and ’export’, as can be seen in figure 9.
Figure 9: Flowchart at lowest level of detail
When zooming in on the ”import”, ”store” and ”export” function, the path of the coal and required handling operations become clear, as is seen in figure 10. Here, the bold arrows represent material flow and the thin dashed arrows represent information flow. The arrows all point towards the exit function: A return flow is not expected and therefore a return conveying system is therefore not required.
Figure 10: Flowchart in higher detail level
In this chapter, all the functions within import, store and export will be evaluated. Some functions will prove to be dependent on the level of collaboration within the terminal. This model is however kept generic, when a case-specific configuration is presented, this will be noted.
3.2
Import
The entrance system is defined as seen in figure 11. The import system starts with the unload function, where the vessels are unloaded by the quay cranes. The quay cranes require information on the vessel arrivals for them to be planned and know which type of vessel arrives, as this influences its efficiency. The quay cranes unload the cargo of the vessels into hoppers, which will transfer the coal onto conveyors in order to transport the coal over the jetty to the shore.
Figure 11: Flowchart of import function
Unload
The unloaders can either be chosen as grab unloaders or continuous unloaders. It should be remembered that grab unloaders can be designed for a wide range of vessel sizes (not with the same efficiency for each vessel category), while continuous loaders are less flexible with regard to vessel sizes and especially have difficulties to handle smaller ships (Royal HaskoningDHV, 2014). Although continuous unloaders have a higher overall efficiency and more constant discharge rate, they require more maintenance as they house more mechanically moving parts (Haskoning, 1989). In addition, Grab cranes handle wet sticky materials (like coal in some cases) better than continuous unloaders. Due to its flexibility, grab unloaders seem beneficial for bulk terminals expecting a range in vessel sizes, however choosing a particular type of unloader is considered outside the scope of the research.
Hopper
The function of the hopper is to translate the discontinuous flow from the unloader to a more continuous flow on the conveyor. It is not considered in the scope of this project to determine the capacity of the hopper.
Transport
The conveyors connecting the berths to the transfer tower on the shore should be able to handle the peak capacity of the ship unloaders.
3.3
Export
The final function group in figure 9 represents the export system. It is however considered as the second group since its results will help define the storage function. The export function comprises the transport from the stockpile to the power plants. Figure 12 shows the functions within export. After reclaiming within the store function, the coal is transported towards the blending beds where the coal is blended in order to feed coal to the power plants with constant characteristics. At the blending beds, the coal is reclaimed and conveyed to the daybins before it enters the system of the power plants. At this point the coal is considered to be out of the scope of this research.
Figure 12: Flowchart of export function
Transport
All transport should suffice to transport coal in the desired capacity generated by the reclaimers. It should be remembered that both single way as double way conveyors can be utilized. Stackers and reclaimers can be bypassed by making use of so-called hammerheads. Although these can be expensive in use, they allow coal to by-pass stackers and reclaimers and move on to the daybins directly.
Stack
From the main stockpiles, coal is transported to the blending beds, where the blending occurs. The stacking of longitudinal blending piles is often performed in a so-called chevron arrangement (Poultney et al., 1997). With this method, the stacker moves back and forth in the direction of the pile (Petersen, 2004), this can be seen in figure 13.
