Co optimization of natural gas and power infrastructures under uncertainty in vessel docking

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(1)PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE ESCUELA DE INGENIERÍA. CO-OPTIMIZATION OF NATURAL GAS AND POWER INFRASTRUCTURES UNDER UNCERTAINTY IN VESSEL DOCKING. JUAN CARLOS CHUNCHO MOROCHO. Thesis submitted to the Office of Research and Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Advisors: DANIEL OLIVARES QUERO. Santiago de Chile, November 2018 c MMXVII, J UAN C ARLOS C HUNCHO.

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(3) Never consider the study as an obligation but as an opportunity to enter the beautiful and wonderful world of knowledge. Albert Einstein.

(4) ACKNOWLEDGEMENTS. iv. For All.

(5) TABLE OF CONTENTS. ACKNOWLEDGEMENTS. iv. LIST OF FIGURES. vii. LIST OF TABLES. viii. ABSTRACT. ix. RESUMEN. x. 1.. 1. 2.. INTRODUCTION 1.1.. Supply chain of natural gas . . . . . . . . . . . . . . . . . . . . . . . . .. 3. 1.2.. Transportation through LNG vessels . . . . . . . . . . . . . . . . . . . .. 3. 1.3.. Electric Power System . . . . . . . . . . . . . . . . . . . . . . . . . . .. 4. 1.4.. Link between both systems . . . . . . . . . . . . . . . . . . . . . . . . .. 5. 1.5.. State of the art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 7. 1.6.. Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 9. 1.7.. Main Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 9. 1.8.. Document Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 11. MODEL FORMULATION 2.1.. 12. Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12. 2.1.1.. Indices and sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12. 2.1.2.. Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12. 2.1.3.. Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 13. 2.2.. Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14. 2.3.. Mathematical Formulation of the Natural Gas and Power Infrastructure Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 14. 2.3.1. Natural gas system modeling . . . . . . . . . . . . . . . . . . . . . .. 14. 2.3.2. Electric power system modeling . . . . . . . . . . . . . . . . . . . .. 20. v.

(6) 3.. CASE STUDIES Case study in a six nods system . . . . . . . . . . . . . . . . . . . . . . .. 24. Description and data of the case study . . . . . . . . . . . . . . . . .. 24. Chilean System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 24. 3.2.1.. Main considerations of Chilean electricity system . . . . . . . . . . .. 24. 3.2.2.. Natural gas sector in Chile . . . . . . . . . . . . . . . . . . . . . . .. 25. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 26. 3.1.. 3.1.1. 3.2.. 3.3. 4.. RESULTS 4.1.. 27. Expansion results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 27. Modified Chilean System . . . . . . . . . . . . . . . . . . . . . . .. 32. 4.1.1. 5.. 24. CONCLUSIONS. 37. BIBLIOGRAPHY. 38. APPENDICES. 43. A.. 44. APPENDICES A.1.. 6-Zone Test System Data . . . . . . . . . . . . . . . . . . . . . . . . . .. 44. A.2.. Chilean System Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 46. vi.

(7) LIST OF FIGURES. 1.1. NG supply chain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 3. 1.2. Transportation of liquefied natural gas vessel . . . . . . . . . . . . . . . . .. 4. 1.3. Electrical Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 5. 1.4. Link between natural gas infrastructure and electrical power system . . . . .. 6. 1.5. Integration of Gas Network . . . . . . . . . . . . . . . . . . . . . . . . . .. 10. 2.1. Dynamic evolution of LNG in regasification terminal . . . . . . . . . . . . .. 17. 2.2. Mathematics modeling of annual capacity . . . . . . . . . . . . . . . . . . .. 18. 3.1. Case study in a six nods system . . . . . . . . . . . . . . . . . . . . . . . .. 25. 3.2. Simplified diagram of the SIC and SING system of Chile . . . . . . . . . . .. 25. 4.1. Cumulative investment cost in power and NG infrastructure . . . . . . . . .. 28. 4.2. LNG management at terminal LNG2 . . . . . . . . . . . . . . . . . . . . .. 28. 4.3. Number of scheduled LNG vessels . . . . . . . . . . . . . . . . . . . . . .. 29. 4.4. NG consumption by GPG units during the sixth season of year 4 . . . . . . .. 30. 4.5. Evolution of the optimal generation mix . . . . . . . . . . . . . . . . . . .. 31. 4.6. Number of scheduled LNG vessels . . . . . . . . . . . . . . . . . . . . . .. 34. 4.7. Evolution of the optimal generation mix . . . . . . . . . . . . . . . . . . .. 35. 4.8. NG consumption by GPG units during the second of year 5 . . . . . . . . . .. 36. vii.

(8) LIST OF TABLES. 4.1. NG consumption by GPG units in each scenario of RES. . . . . . . . . . . .. 31. 4.2. Summary of Existing and Alternative infrastructure. . . . . . . . . . . . . .. 33. 4.3. Investment in infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . .. 34. A.1. Characteristics of operation and investment of Natural gas Wells . . . . . . .. 44. A.2. Characteristics of operation and investment of Natural Gas Interconnection .. 44. A.3. Characteristics of operation and investment of power plants . . . . . . . . .. 45. A.4. Characteristics of operation and investment of Natural Gas Interconnection .. 45. A.5. Feasible triples for a highly variable Grid . . . . . . . . . . . . . . . . . . .. 46. A.6. Feasible triples for a highly variable Grid . . . . . . . . . . . . . . . . . . .. 50. A.7. Feasible triples for a highly variable Grid . . . . . . . . . . . . . . . . . . .. 53. viii.

(9) ABSTRACT. Natural gas is extensively used in electricity sector and its availability, affordability and infrastructure play a key role in the planning of electric power system. However, this interaction between electricity and gas infrastructure is typically overseen by conventional power system planning tools. In this paper, a stochastic programming-based co-optimazation model is proposed, aiming to minimize the overall capital and operation costs of natural gas and power infrastructures. The proposed model determines the optimal expansion plan considering uncertainty in the arrival of vessels to the regasification terminal whereas the operation of infrastructure is modeled as a second-stage variable. The model is tested thoroughly on a simple 6-zone system and also in an 7-zone 115-generators representation of the chilean power system for a 5-year planning horizon. Results show a relevant connection between the scheduling of vessels, sizing of liquefied natural gas reservoirs and the operation of the electricity system particularly in scenarios of high penetration of renewable energy resource due to operational flexibility provided by natural gas power plants.. Keywords: Natural gas system, Regasification Terminal, Power systems, Pipelines, Generators. ix.

(10) RESUMEN. El gas natural se utiliza extensamente en el sector eléctrico y su disponibilidad, la asequibilidad y la infraestructura desempeñan un papel clave en la planificación del sistema de energı́a eléctrica. Sin embargo, esta interacción entre la electricidad y la infraestructura de gas normalmente es supervisada por las herramientas de planificación de sistemas de energı́a convencionales. En este documento, un modelo de co-optimización basado en programación estocástica es propuesto, con el objetivo de minimizar el costo de inversión y operación del sistema de gas natural y eléctrico. El modelo propuesto determina el plan de expansión óptimo considerando incertidumbre en la llegada de los barcos a los terminales de regasificación mientras que la operación de la infraestructura es modelado como variable de segunda etapa. El modelo se prueba exhaustivamente en un sistema simple de 6 zonas y también en 7 zonas con 115 generadores representando el sistema eléctrico chileno para un horizonte de planificación de 5 años. Los resultados nos revelan una conexión revelante entre la programación de la llegada de los barcos y el dimensionamiento de los reservorios del gas natural licuado y la operación del sistema eléctrico particularmente en escenarios de alta penetración de energias renovables debido a la flexibilidad proporcionada por las unidades eléctricas a gas.. Palabras claves: Planificación de la expansión, planificación de transmisión, pre-despacho, flexibilidad. x.

(11) 1. 1. INTRODUCTION Natural gas (NG) is an essential fuel in the operation of electric power systems and it is considered key for the provision of operational flexibility (Zeng et al., 2016). Along with renewable energy sources (RES), gas-fired power generation (GPG) plants have shown an increasing participation in the electricity sector in recent decades (WEC, 2016). According to the Energy Information Administration, NG consumption in the world will increase from 3398 bcm in 2012 to 5748 bcm in 2040 (Conti, 2016), and has shown a sustained growth in the last decade (Bp, 2017). Hence, planning, development and exploitation of this fuel has attracted special attention in recent years (Zhang et al., 2015). Even though GPG plants produce significant greenhouse gas emission, they yield lower environmental impacts with respect to other technologies. For example, CO2 emissions of GPG plants are between 40-50 % lower than coal power plants, and 25-30 % lower than diesel, due to its low carbon content and its high combustion efficiency. (Munoz et al., 2003),(Leung et al., 2014). Due to the growing demand of NG worldwide, requires the development of new infrastructure, including compressors, pipelines, liquefaction terminal (LTs), and regasification terminals (RTs) (Hamedi et al., 2011). This infrastructure will allow the transportation and storage of vast amounts of LNG to supply future demand; however, its development involves a large investment and high operational costs (Toledo et al., 2016). The transportation of NG occurs mainly through pipelines and large vessels. In particular, the maritime transportation of LNG is subject to international regulations and has extremely complex economics and logistics. Furthermore, this activity is subject to unexpected delays and cancellations due to weather conditions and unplanned maintenance of vessels, and also involves complex supply contracts, and variable shipping costs, among others (Stanivuk & Tokić, 2013). For Chile, a net importer of LNG with an increasing demand for this fuel, there is a minimum number and frequency of vessels that need to arrive to the RTs in order to be.

(12) 2. able to supply the local demand; therefore, proper contractual arrangements and logistics are crucial. In particular, the aforementioned tasks need to account for contingencies, such as delayed and cancelled shipments (SIC, 2015). The electricity sector in Chile accounts for nearly 60 % of the total consumption of NG in the country, used in combined-cycle power plants and gas turbines, whereas the remaining 40 % is associated with the residential and industrial consumption (Masenergia, 2011). Hence, a delay in the arrival of LNG to the country may lead to a reduced generation capacity in the national electric system, which could pontentially translate into higher operation costs and, eventually, reliability and security problems in the system. The role of GPGs in power systems is further exacerbated in view of the global trend towards incorporating more RES through energy policies such as Renewable Portfolio Standards, Feed-In Tariffs, and tax incentives (Fell et al., 2012). This higher penetration of RES increases the need for flexible generation resources capable of compensating sudden variation of volatile generation, such as wind and solar power, and introduces new challenges to the real-time operation of power systems (Foley et al., 2013; Leung & Yang, 2012). In this context, the operational flexibility provided by GPG plants (open and combined cycle) and their high ramping capabilities is key to allow a constant demand-supply balance and facilitate the integration of large amounts of RES (Rubio et al., 2008). Given the aforementioned importance of GPGs in the electricity systems, it is highly relevant to analyze the expansion and operation of electric power systems in combination with the dynamics and limitations introduced by the NG infrastructure..

(13) 3. 1.1. Supply chain of natural gas The supply chain of NG begins at the extraction point either from onshore or offshore reservoirs. Then, NG is processed in liquefaction terminals to obtain LNG, which is then transported over long distances by vessels to LNG reservoirs and regasification terminals. Finally, NG is either regasified and distributed through pipelines, or distributed as LNG in trucks to the end consumers. In this thesis, we model the NG infrastructure from the schedule of arrival of LNG vessels to RTs, to the NG en consumer. NG end consumers are categorized as industrial, residential or power generation consumers. Figure 1.1 depicts the aforementioned infrastructure and supply chain.. Natural Gas System System Operator. Exploration / Extraction. Transportation. Refining / Processing. Transportation. Storage. Distribution. Retailing & Commercialization. Natural Gas Modeling. Figure 1.1. NG supply chain. 1.2. Transportation through LNG vessels The process of NG liquefaction consists of lowering the temperature to −160◦ C to convert NG to a liquid state, reducing its volume by 600 times, and making its transportation to RTs around the world viable through tankers or ships. Upon arrival to the RTs and LNG reservoirs in the receiving end, LNG is discharged using unloading arms, and stored at −160◦ C in storage tanks located inland (Zednik et.

(14) 4. Natural Gas Gas Field User. Power Supply. Floating Platfor. Unloading or Re-gasification. Loading. Voyage. Figure 1.2. Transportation of liquefied natural gas vessel al., 2000). After, LNG is typically regasified for transportation through a pipeline network to NG consumers. Figure 1.2 illustrates the aforementioned transportation process. The cost of transportation through vessel depends on many different factors, including the origin and final destination, the supply contract, market rates, seasons, and customs’ procedures and charges. Nevertheless, for simplicity, our analysis considers a constant cost of transportation of 0,173 MMUSD/MMm3 for the LNG arriving to the Chilean RT terminals (Stanivuk et al., 2013).. 1.3. Electric Power System The supply chain of the electric power system consists of fuel supply, power stations, the transmission system, the distribution system, and the consumption, illustrated in Figure 1.3. For simplicity, this work models the supply chain of electricity without including the distribution system, because its impact on the economic and operational decisions of the bulk power system are outside the scope of this work. Thus, in our models hereafter the electricity consumers are assumed to be located at a transmission level. In terms of generation technologies, we consider both conventional (e.g., gas-fired power plants,.

(15) 5. coal-based power plants, hydro-power plants) and energy resources, such as wind and solar plants.. Electrical Power System Generation. Retailing & Commercialization. System and Market Operation (ISO). Transmission. Distribution. Electrical Power Modeling. Figure 1.3. Electrical Power System. 1.4. Link between both systems The link between the electricity and gas system is given by the use of NG in open-cycle and combined-cycle gas power plants, as illustrated in Figure 1.4. The left part of the depicts the NG infrastructure, whereas the electric power infrastructure is shown in the right-hand side. One important aspect of this link is that gas-fired power plants are considered to be a highly flexible resource in power systems, given that they can quickly change their operating point and typically have wide ranges of operation; however, this flexibility is only achievable if the NG supply change is capable of delivering the NG gas to the power station, which is seldom modeled in power systems analysis. In specific, some aspects to be considered in the interaction between NG and power systems are: • The economic/market structures of both systems. • The physical laws and limitations that govern the systems. • The operational protocols of both systems. • Availability of NG at RTs (e.g., scheduling of NG vessels)..

(16) 6. • Management the LNG in reservoirs of RTs.. A. Existing electrical line Electrical line in project. B. Projected structure. PIPE 2. NG pipeline in operation. GN1. S1 Truck Load. PIPE 1 GN2. D. PIPE 3. ~. Bus 1. ~. Bus 2. Bus 3 Electricity Demand. C. Bus 5. LNG1. ~ S2. LGN2. Gas Power 1 Bus 4. Natural Gas System. Bus 7. Electrical Grid. Figure 1.4. Link between natural gas infrastructure and electrical power system.

(17) 7. 1.5. State of the art The interdependence of both NG and power systems infrastructures has been widely studied in the literature. For instance, we have some works. In (Sahin & Shahidehpour, 2009) They show the economic and security issues among to NG and electricity sector in Turkey. The main conclusion of this work is which the electricity sector highly dependent on a secure and trustworthy NG system. In recently works such as (Feijoo et al., 2016) developed a North American Natural Gas Model as they in 2017 Mexico launched its energy reform. Hence, that might seriously affect the natural gas market in North America. Additionally, in (Feijoo et al., 2018) show the future of NG infrastructure in United States. Mainly, their approach is based on pipeline infrastructure planning. Thus, they demonstrated that pipeline in the United states and investments in pipeline capacity will be required due to increasing demand for NG in that country. On the other hand, in (Shahidehpour et al., 2005) the role of the NG infrastructure on the operational schedule of GPGs in a power system is studied. The impact of the reliability of NG supply on the reliability and security of the electricity system is studied in (Rubio et al., 2008) and (Liu et al., 2009), which also discuss how the operation of GPGs in power systems is impacted by the market price of NG. In (Toledo et al., 2016), the impact of NG infrastructure on the operational costs of GPGs in analyzed using an integrated model of NG and power system operations, which includes specific characteristics of both markets. Also, (Diagoupis et al., 2012) proposes an integrated stochastic model to analyze the scheduling of GPG units and other generation resources considering flexible ramp requirements to manage the variability of RES. Different operational metrics are studied in (Devlin et al., 2016) to assess the impact of interruptions in NG supply in a context of high penetration of wind power. Despite the relevant contributions of the aforementioned works, they use simplified models of the NG supply chain and do not consider, for example, the modeling of discrete arrival of LNG vessels to RTs, which is highly relevant in countries that rely strongly on LNG imports. Furthermore, these works do not use chronological time series, which is highly relevant to quantify the flexibility requirements.

(18) 8. of power systems with high penetration of RES (Dincer, 2000). In relation to infrastructure planning, some works have tackled the issue of combined power system and gas infrastructure expansion planning. In (Qiu et al., 2016), a co-optimization of NG and power systems infrastructure is carried, which involves an iterative process to guarantee that security standards are met in both systems. Authors in (Liu et al., 2010) assume the existence of an independent gas-and-electricity system operator that is able to make decisions outside the traditional jurisdictions of electric and gas operators in order to maximize the general welfare of the coordinated systems. Similarly, (Qiu et al., 2015) proposes a co-planning formulation under uncertainty in electricity demand based on mixed integer nonlinear programming, which is subject to the well known scalability and convergence issues of such techniques. Authors in (Barati et al., 2015) also propose a co-planning tool based on nonlinear programming and genetic algorithms introducing a new approach to solve NG flow equations; however this work does not account for uncertainty. Uncertainty in electricity demand in the co-planning framework is also considered in (Zhao et al., 2017), (Qiu et al., 2016), (Liu et al., 2010), and (Qiu et al., 2015) using two-stage stochastic programming. Several other works, including (Unsihuay et al., 2007)(Bakken et al., 2007)(Salimi et al., 2015)-(Sanchez et al., 2016) propose models for the integrated planning of 2 or more energy infrastructures (including gas and electricity) considering detailed network flow equations of each energy carrier; however, these works do not consider the effect of the whole LNG supply chain: LTs, transportation vessels, RTs, and storage. In this regard, to the best of the authors knowledge, the only work that models the impact of the whole LNG supply chain in the integrated power system and gas infrastructure planning is (Unsihuay-Vila et al., 2010); nevertheless, this work does not take into consideration uncertainty in the LNG supply (e.g., arrival of vessels). Furthermore, (Unsihuay-Vila et al., 2010) uses a basic representation of the operation based on energy blocks as opposed to chronological time-series, which tends to neglect the flexibility requirements of the electricity system..

(19) 9. 1.6. Proposed Work In this work, we aim to analyze the interaction between the NG and power infrastructure and its impact on the economic expansion and operation of the electric power system. Thus, as a first step we propose a new stochastic programming-based co-optimization model aiming to minimize the overall capital and operation costs of NG and power infrastructures. The proposed model determines the optimal expansion plan considering uncertainty in the arrival of vessels to the regasification terminal, where the operation of the infrastructure is modeled as a second-stage variable. The proposed model is used to run several case studies to analyze the interaction between the physical and economic features of both systems. Figure 1.5 depicts the input parameters and decision variables used in the proposed model. The proposed model is based on the following key assumptions: (i) Upon arrival to a RT terminal, the loading of LNG from the vessel to the LNG reservoir is assumed to be instantaneous. (ii) All the LNG transported by the vessels must be unloaded to the RTs. (iii) There is no congestion in vessels’ arrival to RTs (i.e., RTs are always available to unload LNG). (iv) Seasons are represented by chronological time-series consisting of four representative days (v) LNG reservoirs at RTs must be able to store the remaining LNG at the end of the season plus 1 additional vessel, which is equivalent to assuming that LNG vessels arrive evenly distributed in time, and NG consumption is homogeneous throughout the season.. 1.7. Main Contributions The main contributions of this thesis are:.

(20) 10. - Existent production a transport - Expected Demand. - Existen Generation and Transport - Expected Demand. - Candidate production and transport. Gen. Liquefied natural gas (LNG) re-gasification terminals are operated in a similar way as natural gas producers. Natural Gas Model. Outputs. P. g,t,h. Gen. Gen. g. g,t,h. =η ·D. - Optimal gas expansion plan. - Installation time of any new facilities - The optimal dispatch of the gas system. Planning and operation natural gas system. - Candidate generation and Transmision. Electrical Model. Outputs. - Hourly Generation profile of RES - Cyclic Operation - Capabilities of Chilean Generators. - Optimal Generation and Transmission Expansion Plan - Installation date of new Facilities with regional location - Generators Dispatch. Planning and operation electrical system. Figure 1.5. Integration of Gas Network • The consideration of investment decisions in RTs and optimal scheduling of LNG vessels under uncertainty in a combined NG and power infrastructure tool. • The use of chronological time-series to account for the flexibility requirements imposed by RES in the analysis of the interdependence of NG and power infrastructures. • A thorough discussion is presented regarding the interaction of NG and power infrastructures for different levels of penetration of RES in a Chilean case study..

(21) 11. 1.8. Document Structure This work is structured as follows. In Chapter 2 we describe in detail the proposed mathematical model developed to solve the co-optimized expansion problem. Chapter 3 describe a simple 6-zone system, and also a reduced version of Chile’s integrated gas and electricity system. Chapter 4 discusses the application of the proposed model to a simple case study and presents an analysis of Chile’s integrated gas and power system. Finally, conclusions are presented in Chapter 5..

(22) 12. 2. MODEL FORMULATION 2.1. Nomenclature 2.1.1. Indices and sets t, k. Stages of planning and season. d, h. Days and hours. i, z. Subsystem and zone. j, g. Infrastructure and generator. p, l. Pipelines and lines.. T ,K. Sets of planning horizons and seasons .. D, H, S. Sets of days, hours and scenarios. J,P. Set of LNG RTs and pipelines. AGas , AEle. Set of NG and electrical zones. G Gas , G Hy. Set of NG and hydro generator units.. G, Gc , Lc. Set of candidate generator units to build.. 2.1.2. Variables G iG j,t , ip,t. State of the NG supply terminal j and gas pipeline project l at stage t.. E iE g,t , il,t. State of the NG supply terminal j at stage t.. S iW z,t , iz,t. Linear variable of solar and wind power plant in load zone z, at stage t. cE g,s,t,k,d,h. Power generation cost at stage t, hour h, scenario s on day d, and season k. pE g,s,t,k,d,h. Generating of power plant g at stage t, during the hour h on day d and season k in [MW/h]. pSs,z,t,k,d,h. Generating of solar power plant in load zone z at stage t during the hour h on day d and season k in [MW/h]. pW s,z,t,k,d,h. Generating of wind power plant in load zone z at stage t during the hour h on day d and season k in [MW/h].

(23) 13. pcur,W s,z,t,k,d,h. Wind energy curtailed in load zone z, at stage t during the hour h on day d and season k. pcur,S s,z,t,k,d,h. Solar energy curtailed in load zone z, at stage t during the hour h on day d and season k. E fs,z,t,k,d,h. Power supplied by transmission from zone z to z 0 at stage t and during the hour h, day d, season k, in [MW/h].. G fl,s,t,k,d,h. Interchange NG l at stage t during the hour h on day d and season k in [MMm3 /h]. dE s,z,t,k,d,h. Electricity deficit at zone z at stage t during the hour h on day d and season k, in [MW/h].. pG j,s,t,k,d,h. Supply of natural gas from the regasification terminal j at stage t and during the hour h on day d and season k in [MMm3 /h]. cExp j,t , uj,t,k. Expansion of LNG reservoirs and Integer number of how many ships do you buy terminal j in season k, year t. sj,t,k,s. Volume of LNG in terminal j at season k, year t, scenario s in [M M m3 /h]. 2.1.3. Parameters θs , πs. Probability that scenario s will happen. ηd , r. Scale factor of NG infraestructure and electric system and discount rate [%]. CjG , CgE. Annualized investment costs of RT j and conventional power plant g in [MMUSD], respectively.. CpG , ClT. Annualized investment costs of gas pipeline project p and transmission line l in [MMUSD], respectively.. CW , CS. Annualized investment costs of wind and solar plants per MW built in [MMUSD], respectively.. σlG , σlE E. Loss factor of interchange NG and transfer capacity at stage t in [%].. ,Pg pE g. Minimal and maximal bounds of power output of generator g in [MW/h].. S GW t , Gt. Hourly generation profile of an unitary wind, solar generation in time t..

(24) 14. FzW , FzS. Capacity factor of solar and wind power plant in load zone z. EzW , EzS. Existing capacity factor of solar and wind power plant in load zone z. ship CjB , Vlng. Regasification terminal capacity and the volume that a ship transports in [MMm3 ] j. G. C lng , P j. Gas and transportation cost in [USD] and maximal bounds of natural gas in [MMm3 /h].. 2.2. Overview This chapter we present a combined NG and power system infrastructure planning model that uses a 2-stage stochastic programming approach to account for uncertainty in the arrival of LNG vessels to RTs. Decision variables in the model include the number of vessels scheduled to arrive each season, size of LNG reservoirs, investment in new RTs and pipelines, and power generation and transmission investment. Uncertainty is represented by 2 scenarios: 1) All scheduled vessels arrive to the RTs, and 2) 10% of the scheduled vessels do not arrive. The proposed model considers chronological time-series of representative days to better represent flexibility requirements introduced by RES, and it is used to analyze the interactions between power and gas infrastructures in the context of high-penetration of RES. 2.3. Mathematical Formulation of the Natural Gas and Power Infrastructure Planning 2.3.1. Natural gas system modeling This section discusses in detail the mathematical modeling of the natural gas and power systems in the context of its combined expansion planning. Moreover, the model considers the total present value the sum of equivalent annualized investment cost plus the annual operating costs. The equivalent annualized investment costs of an.

(25) 15. infrastructure two systems is defined as: γy Inf raestructuras =. ICj,g E + (O&M )j,g .(pG j )o(pg ).(T ) γt. Where ICj,g is the total capital investment of infrastructure g or j and (O&M )j,g is operation and maintenance cost of the structure. Therefore, the annualized capital recovery factor remains like this. γt =. 1 (1 + r)t−1. (2.1). The expansion planning problem of the Natural gas network, used in this thesis can be. IvCost. formulated from the following way. X X X G G Exp Exp G G = γt (Cj ij,t + C cj,t ) + Cp ip,t (2.2) t∈T. j∈J. p∈P. The three terms in equation (2.2) represent the annualized investment cost in new RTs, the annualized investment cost of expansion of reservoirs, and the annualized investment cost in new gas pipelines, respectively. Operation costs depend upon the total number of ships scheduled to arrive to the RTs (uj,t,k ) and corresponding gas and transportation cost (C lng ), and the cost of unserved demand (cG ) in the NG system. In order to account for possible cancellation of ships, different scenarios are considered where parameter θs represents the percentage of scheduled ships that effectively arrive to the RTs in scenario s of probability πs ; thus, variables uj,t,k are first-stage decisions in the model. The operational variables are defined in a chronological time series of representative days indexed by d, with corresponding weights ηd . Mathematically, the operation costs of the NG infrastructure can be expressed as: OpCost =. XXX s∈S t∈T k∈K. πs γt. X j∈J. (θs C. lng. X. ship Vlng uj,t,k +C o soj,t )+. d∈D. ηd. X X. Cguns dG s,i,t,k,d,h. . h∈H i∈Agas. (2.3).

(26) 16. The objective of this problem is to minimize the annualized investment and expected operating costs, and the following constraints must be considered:. 2.3.1.1. Modeling the status of the capacity of reservorios State on/off of the project expansion capacity of regasification terminals t ∈ T Exp cExp j,t ≤ cj,t+1 , ∀j ∈ J. (2.4). 2.3.1.2. Modeling of natural gas capacity Maximal bounds of natural gas dispatch, given the state iG j,t and for all t ∈ T , h ∈ H, j ∈ J in (2.5) G. G pG j,s,t,k,d,h − ij,t P j ≤ 0. (2.5). 2.3.1.3. Modeling of gas flow in pipelines Maximal bound of NG interchange between subsystems i and i0 , given the state iG p,t of the transmission. And for all p ∈ {i, i0 }, p ∈ P, t ∈ T , h ∈ H, s ∈ S, d ∈ D, k ∈ K. View (2.6) G. G |fp,s,t,k,d,h | − iG p,t F p ≤ 0. (2.6). 2.3.1.4. Modeling the status of the project State on/off of the project along the planning horizon: Those binary variables reflect the unique activation or construction of each unit, RT and pipeline and apply to t ∈ T G iG p,t ≤ ip,t+1 , ∀p ∈ P. (2.7). G iG j,t ≤ ij,t+1 , ∀j ∈ J. (2.8).

(27) 17. S2 +1.M <= CT. S0 K0. K1. K2. SEASON. K3. K4. K6. Figure 2.1. Dynamic evolution of LNG in regasification terminal 2.3.1.5. Modeling capacity and expansion of regasification terminal To determine the terminal capacity, it is proposed according to the following modeling criteria: the optimization model will decide how many ships arrive in each season, will assess the volume of season and compute how much volume will be left over for next season. In addition, it is assumed that the RT does not have the storage capacity to receive the total energy of the year at start from first season, as in practice the RT needs to receive one ship at a time with a slack. Consequently, model will decide maximum storage capacity of LNG that it needs at any moment of time. The design criteria for RT capacity are as follows: According to assumption raised, the season is represented by four representative days, hence it is necessary that at the beginning or end of those days, plus a 1 LNG ship, are able to be stored in the RT. To illustrate the aforementioned, consider Figure 2.1, which show the volume evolution of six seasons in a year, where the ship volume peak is in season k2 , thus, at this point the RT must be able to store s2 + Vlng of. LNG. Then, we have the following equation ship soj + Vlng ≤ CjB + cExp j,t , J ∈ J , t = 1. (2.9). In order to constrain the maximum volume of LNG stored in each RT, we assume that the most critical condition in terms of storage capacity corresponds to the arrival of a new.

(28) 18. 1 year. K5 SEASON. 2 year. K6. K1. Figure 2.2. Mathematics modeling of annual capacity vessel right at the beginning of a each season. This is equivalent to assuming an evenly distributed demand of LNG and evenly distributed arrival of vessels within each season. Thus, the RT capacity constraints are as follows: ship sj,t,k,s + Vlng ≤ CjB + cExp j,t , k ∈ K, t ∈ T. (2.10). sj,t,k,s ≥ 0, k ∈ K, t ∈ T , s ∈ S, j ∈ J. (2.11). sj,k̄=6,t,s = soj,t+1 , t ∈ T \ {tf }, s ∈ S, j ∈ J. (2.12). The Figure 2.2 shown consideration to determine annual capacity of the TR, the volume ship of the year T . This new volume will be the of final season is considered Sj,6,t,s plus Vlng. initial volume for next upcoming year. In other words, equation (2.9) ensures that the volume of LNG available at the beginning of each year of the planning horizon equals the volume stored at the end of the previous year, for each RT..

(29) 19. 2.3.1.6. The NG balance constraint in the Terminal Regasification A gas balance equation is established for each season in the plannning horizon as follows: ship Γ sj,t,(k−1),s + (Vlng uj,t,k ) θs − sj,t,k,s. =. X. η d pG j,s,t,k,d,h ,. k > 1 (2.13). d∈D h∈H. where the left-hand side represents the volume of LNG evacuated from the terminal in each season, and the right-hand side represents the energy supplied to the different end consumers throughout the system, weighted by representative days.. 2.3.1.7. The Supply/demand balance of NG energy The following equation represents the gas-balance equations in each node of the gas network. NG pipelines indexed by p have attributes r(p) and s(p) which represent the receiving and sending zones of each pipeline in the network. The volume of gas at the receiving end of the pipelines is penalized by the efficiency factor σpG < 1 to account for NG losses throughout the network. Sets Ji and GiGas represent the sets of RTs and GPG units in zone i ∈ AGas .. X j∈Ji. pG j,s,t,k,d,h +. X p:r(p)=i. G σpG fp,s,t,k,d,h −. X. G fp,s,t,k,d,h. p:s(p)=i. G + dG s,i,t,k,d,h = Di,t,k,d,h +. X g∈GiGas. Gas cE (2.14) g,s,t,k,d,h i ∈ A. G Moreover, dG s,i,t,k,d,h is the NG deficit, and Di,t,k,d,h stands for the local NG. This equation. takes into account any demand from the electricity grid and is represented by cE g,t,h,s,d,k ..

(30) 20. 2.3.2. Electric power system modeling Similar to the NG system, the model considers both operational and planning costs associated with the electric power system, in order to minimize the combined operational and investment costs of both systems. The investment costs include the costs associated with new conventional generation units, transmission lines, and wind and solar power generation units. Wind and power generation is separated from other conventional units given that their investment is modular and, therefore, it can be represented using continuous variables (as opposed to binary variables for other units). Thus, the investment cost is formulated as follows:. IvCost. X X X X E E W W S S E E Cl il,t + (C iz,t + C iz,t ) = γt Cg ig,t + t∈T. g∈Gc. l∈Lc. (2.15). z∈AEle. where CgE corresponds to the investment cost of power generation unit g, ClE is its investment cost of transmission lines and C W , C S conform the investment cost of renewable generation plants. Similarly, the operational costs of the electric power system. OpCost =. XXXX d∈D t∈T s∈S h∈H k∈K. πs ηd γt. X g∈G. cE g,s,t,k,d,h +. can be represented as: X uns E Ce ds,z,t,k,d,h (2.16). z∈AEle. where the first term corresponds to the power generation costs of units, and the second term represents the cost of energy not served. Also, the term πs corresponds to the scenario probability, ηd is the weight of the representative days, and the γt is the discount factor..

(31) 21. 2.3.2.1. Generation in Natural Gas Thermal Power Plants The gas-fired power plants g are limited by the availability and transportation constraints of the resource, and depends on heat rate the fuel, ηgG and it is applied for each t ∈ T , h ∈ H, s ∈ S, d ∈ D , k ∈ K and g ∈ G gas as indicated in (2.17) G G Gas pE g,s,t,k,d,h = ηg cg,s,t,k,d,h , g ∈ G. (2.17). 2.3.2.2. Renewable portfolio standard The model considers the enforcement of a renewable portfolio standard (RPS). As a planning constraint, a percentage GRP S of the generation in each year of the planning horizon must be delivered by wind or solar generation units. Mathematically: XXX X k∈K d∈D h∈H. z∈Aele. S (pW s,z,t,k,d,h + ps,z,t,k,d,h ) ≥ RP S. G. X. (pW s,z,t,k,d,h. +. pSs,z,t,k,d,h ). +. X. pE g,s,t,k,d,h. (2.18). g∈G. z∈Aele. 2.3.2.3. State on/off of the project The total capacity construction of conventional and renewable units, transmission lines should be done in merely one period into planning horizon and forcing such infrastructure appear built in the next year. That apply for t ∈ T View 2.19-2.22 E E Ig,t ≤ Ig,t+1. ∀g ∈ Gc. (2.19). E E Il,t ≤ Il,t+1. ∀g ∈ Lc. (2.20). W W Iz,t ≤ Iz,t+1. ∀z ∈ Aele. (2.21). S S Iz,t ≤ Iz,t+1. ∀z ∈ Aele. (2.22).

(32) 22. 2.3.2.4. Generation of units The generation cost of each unit is formulated as a convex piece-wise linear function of their power generation. Each linear segment m ∈ M is characterized by parameters αg,m and βg,m , as follows: E cE g,s,t,k,d,h ≥ αg,m pg,s,t,k,d,h + βg,m wg,s,t,k,d,h g ∈ G. (2.23). In the case of hydro-power plants, the generation cost is given by a single linear segment (i.e., a linear cost function) with a different αg,m,k in each season k ∈ K. For the different case studies, parameters αg,m,k were obtained from Unsihuay-Vila et al. (2010), and from data of the economic operation of the Chilean power. 2.3.2.5. Maximum and minimum generation capacities The following constraints apply for each t ∈ T , h ∈ H, s ∈ S, d ∈ D and k ∈ K and restrict the units capacity. In addition, relies into state of the operation variable wg,t,h,s,d,k . E ≤ pE P g wg,s,t,k,d,h g,s,t,k,d,h. (2.24). E P g wg,s,t,k,d,h ≥ pE g,s,t,k,d,h. (2.25). 2.3.2.6. Renewable limit The equations (2.26), (2.27) proposed are limited by the renewable profile, capacity existing at the zone z and the capacity factor of allocated resource and applies for each t ∈ T , h ∈ H, s ∈ S, d ∈ D and k ∈ K. Moreover, the renewable energy may be curtailed from system cur,W W W W W pW s,z,t,k,d,h + ps,z,t,k,d,h = Gt Fz (Ez + iz,t ). (2.26). S S S S pSs,z,t,k,d,h + pcur,S s,z,t,k,d,h = Gt Fz (Ez + iz,t ). (2.27) (2.28).

(33) 23. 2.3.2.7. The power balance constraint This constraint allows to balance hourly demand in the different load zones of electrical system, and considers the transmission amoung the zones z, z 0 throughout a network transport model, and apply forg ∈ G, t ∈ T , h ∈ H, s ∈ S, d ∈ D and k ∈ K X. pE g,s,t,k,d,h. +. pW s,z,t,k,d,h. +. pSs,z,t,k,d,h. X E E E + σl fs,z,t,k,d,h − fs,z,t,k,d,h + z 0 ∈Vz. g∈Gz. E dE s,z,t,k,d,h = Di,t,k,d,h (2.29). 2.3.2.8. The transfer capacity between load zones These constraints are within the limits of projected lines to build plus existing lines. X X E E E F l (z, z 0 )iE F l (z, z 0 ) ft,h,s,d,k + ≤ l,t (2.30) l∈Lc. l∈Ltc. 2.3.2.9. Electric dispatch operation A generation plant can only operate whether this has been built. E iE g,s,t,k,d,h ≤ ig,t , g ∈ G. (2.31). 2.3.2.10. Ramp Constraints The bounds of ramp rate we propose is to quantify flexibility that can electrical units deliver to the system, and are formulated as follow. E up pE g,s,t,k,d,h − pg,s,t,k,d,h−1 ≤ Rg wg,s,t,k,d,h , g ∈ G. (2.32). E dw pE g,s,t,k,d,h−1 − pg,s,t,k,d,h ≤ Rg wg,s,t,k,d,h , g ∈ G. (2.33) (2.34). Equations (2.31) , (2.32) y (2.33) applies g ∈ G, t ∈ T , h ∈ H, s ∈ S, d ∈ D and k ∈ K.

(34) 24. 3. CASE STUDIES The model is tested thoroughly on a simple 6-zone system and also in an 7-zone 115-generators representation of the chilean power system . In the case the Chilean power system we assume that the Central Interconnected System (SIC) and Northern Interconnected System (SING) are already interconnected and conform the National Electric Power System (CEN).. 3.1. Case study in a six nods system 3.1.1. Description and data of the case study The 6-zone test system consists of a 3-node power system interconnected with a 3-node NG system through GPG units, as illustrated in Figure 3.1. NG is supplied by vessels to the RT located at NG1, NG demand is located in zones NG2 and NG3, either as direct NG consumption or GPG units. Existing power system infrastructure is depicted in solid black lines, existing NG infrastructure in solid blue lines, and expansion alternatives are depicted in violet. We analyze the results of our model in the expansion of the 6-zone system for 3 different cases of RES penetration (RPS): 10%, 25% and 50% of the generation from RES.. 3.2. Chilean System 3.2.1. Main considerations of Chilean electricity system In the larger Chilean system case study, only two season are considered in each year, where each season is represented by two representative days. Each representative day consists of 24 hours and has a weight associated with the number of days it represents in a season..

(35) 25. Hydro2. LNG1. Hydro1. Wind1. ~ ~. Expansion Infrastructure Existing Electrical System Existing NG Infrastructure. ~ E1. NG1. T2. T1. T3. Solar 1. T4. ~. PIPE 1. T5. gas power2. ~. gas power1. E2. ~. ~. E3. ~. ~. Nuclear2. NG2. T6. Nuclear1. gas power4. NG3. ~. gas power3. ~. ~. Coal2. Coal1. Load 1. LNG2 PIPE 2. Figure 3.1. Case study in a six nods system Knot Substation. Hydroelectric power plants Thermoelectric plants. Diego de Almagro. Quillota Polpaico. Temuco Santiago. Carrera Pinto. Paine. Cardones. Alto Jahuel. Rapel. SIC 3. Rancagua. Esmeralda. Cerro Colorado. Gas Field Mejillones. Pozo Almonte. Los Cóndores. Lagunas. Tarapacá. San Fernando. SING1. Collahuasi. Itahue. Huaco. Gas Field Quinteros. La Serena. Minsal Oeste. Linares. Mejillones. Alto Norte Coloso. Central Salta Mantos Blancos OHiggins. Andes. Lomas Bayas. Nueva Zaldívar Domeyko. Zaldívar. SING2. Parral Concepción San Vicente Hualpen. La Unión Osorno. SIC 4. Puerto Montt. El Indio Pan Azucar. Ancoa. Chuquicamata Laberinto Capricornio. Los Lagos. Curicó. Radomiro Tomic. Esmeralda Antofagasta. Gas Field Quinteros Maitencillo. El Alba. Crucero Tocopilla Atacama Chacaya. Pullinque Valdivia. Guacalda. Arica Parinacota. SIC 5. SIC 1. Copiapó. Cerro Navia San Antonio. Iquique. Gas Field Quinteros. Taltal San Luis Valaparaiso. Ovalle. Punta Barranco. SIC 2. Pugueñun Ancud. Malles Castro. Chillán Charrua Illapel. Escondida. Los Angeles. Los Vilos. Figure 3.2. Simplified diagram of the SIC and SING system of Chile 3.2.2. Natural gas sector in Chile In Chile, nowadays has two main regasification terminals in operating, Quinteros and Mejillones which supply NG to the SIC and SING, respectively. The GNL Quintero terminal has a regasification capacity of 15 MMm3 /day and owned by Enel (ex-Endesa), ENAP and Metrogas. Enel uses the available gas for its combined cycle plants in San Isidro, ENAP has industrial consumers and it supplies its refineries and Metrogas sells to.

(36) 26. residential users. Besides, Metrogas and ENAP whether they have surplus of available gas are sold to Colbun and AES Gener to generate electric power in certain periods of year. The general idea of this thesis is assess of synergy among natural gas infrastructure and power electrical system and will shows the variations in the operation and planning of electrical system particularly in scenarios of high penetration of renewable energy resources. We assume that the connection points of electric network with the gas infrastructure are the Enel, Colbun and AES Gener plants. This links of both systems in our study are the systems SIC1, SIC2, SIC3, SIC4 and SIC5 in the case of terminal Quinteros. Regarding the terminal Mejillones there are small gas pipelines that supply some combined cycle plants and mining industry of SING. In this work the SING is divided into two zones as SING1 and SING2, such as shown in Figure 3.2.. 3.3. Implementation The planning horizon is 5 years, and begins the first day of January. A discount rate of 8%/year is assumed. The electric power demand of the first year was obtained from official data of the Chilean ISO (Nacional, 2018). Representative days were sampled from 365 demand scenarios, corresponding to the electric power demand of year 2015, using hierarchical clustering and the dynamic time warping distance metric (Berndt & Clifford, 1994),(Liao, 2005). Each representative day is characterized by profiles of demand, and wind and solar power generation. For the following years in the planning horizon, an annual load growth rate of 4% is assumed. As previously mentioned, the scenarios where a 100% and 90% of the scheduled LNG vessels arrive to the RTs are assigned probabilities of 0.8 and 0.2, respectively. The computational experiments were performed using a Dell PowerEdge R630 server, with 32 GB of RAM, an Intel Xeon CPU E5-2620 v3 @ 2.40GHz, running Ubuntu 14.04 (Linux)..

(37) 27. 4. RESULTS We present our results for the different test cases using the formulations previously described. All cases are analyzed separately, providing table comparison and final generation mix figures to effectively illustrate the results.. 4.1. Expansion results Figure 4.1 shows the cumulative investment of electrical and NG new infrastructures throughout the planning horizon for the 3 different levels of penetration of RES. It can be observed that in the scenarios of 10%, 25% and 50% RPS, the terminal LNG1 is required to be built in the first year. This is justified by the need to provide NG to the electric power system and satisfy the industrial customers, for which the existing terminal LNG2 does not have enough capacity. Additionally, as it is to be expected, the pipeline PIPE1 is built at the beginning of the planning horizon in order to evacuate the NG from LNG1. As the level of penetration increases, additional investment in transmission lines is required in order to handle the high power injections from RES. In particular, line TL2 is built at the beginning of the first year, in the scenario of 50% RPS. Besides, in the same scenario, a new GPG unit is built in the fifth year in order to provide flexible power to cope with RES variability. Finally, the hydro unit Hydro2 is built in all 3 scenarios of RPS, which used in part to provide base load generation and also flexible power. Figure 4.2 shows the management of LNG throughout the planning horizon (seasonal resolution) for the scenario of 50 % RES penetration in terminal LNG2. In particular, the figure depicts the volume of LNG in terminal LNG2 at the end of each season. The figure also illustrates the existing LNG capacity of reservoirs located at LNG2. It is important to note that the actual level of LNG stored in the reservoir never comes too close to its maximum capacity in order to comply with the LNG capacity reserve equation (4). For instance, in the fourth season of year 3, the difference between the peak volume of LNG.

(38) 28. TERMINALS TRANSMISSION LINES GPGs. WIND PIPELINES HYDRO. 2500. 1500 1000. Year 4. 10 % RES. 50 % RES. 25 % RES. 10 % RES. Year 3. 50 % RES. 25 % RES. 10 % RES. Year 2. 50 % RES. 25 % RES. 10 % RES. Year 1. 50 % RES. 25 % RES. 10 % RES. 0. Year 5. 50 % RES. 500 25 % RES. MMUSD$. 2000. Figure 4.1. Cumulative investment cost in power and NG infrastructure. 0,4. Volumen MMm3. Renewable Integration 50 % TERMINAL LNG2. Volumen LNG1 Initial Capacity. 1 : January-February 2 : March-April 3 : May-June 4 : July-August 5 : September-October 6 : November-December. 0,35. 0,08. 0,3 0,25 0,2 0,15 0,1 0,05 0. 1. 2. 3 4 5 Year 1. 6 1. 2. 3 4 5 Year 2. 6. 1. 2. 3 4 5 Year 3. 6 1. 2. 3 4 5 Year 4. 6 1. 2. 3 4 5 Year 5. 6. Figure 4.2. LNG management at terminal LNG2 and the capacity of the reservoir is ∆ = 0,08 MMm3 , which corresponds to the volume of an LNG vessel. The regasification capacities of terminals LNG1 and LNG2 are 10 and 15 MMm3 /daily, respectively, both having the same initial storage capacity. Additionally, NG provided from LNG1 to NG consumers needs to be transported over longer distances through the NG network of pipelines, with the associated losses. This difference results in a higher value of LNG managed by LNG2 in comparison with LNG1. This higher value of LNG managed by LNG2 is more relevant in the cases where GPG units operate as base-load power plants, which in this study case occurs in scenarios of low RES penetration. As the.

(39) 29. RES penetration increases (e.g., scenarios of 25% and 50%), the operation of GPG units shift from base-load to cycling units, in which case the systemic value of such unit migrates to the flexibility they can provide. As a result, we observe from Figure 4.3 that in the scenario of 10% of RES penetration the total number of vessels arriving to LNG terminals is 33 and 102 for LNG1 and LNG2, respectively. In the case of a 25% RES penetration, the number of vessels in terminals LNG1 and LNG2 change to 36 and 94, respectively, which corresponds to a reduction of 5 vessels in total in the whole planning horizon (i.e., higher LNG consumption) that can be attributed to GPG plants being mildly shifted from base-load generation by RES-based generation. Noteworthy, the number of vessels in the LNG terminals tend to draw near, which results in a higher operational flexibility as compared with terminal LNG2 concentrating most of the LNG management. This operational flexibility is required by the system due to the higher net-load variability introduced by RES. Finally, in the scenario of 50% RES penetration the number of vessels arriving to terminals LNG1 and LNG2 is 35 and 87, respectively, which represents a reduction of 13 vessels with respect to the 10% scenario that can be attributed to the GPG units no longer playing the role of base-load units.. The number of vessels in terminals in LNG1 and LNG2 LNG2. Year 2. Year 3. 6. 6. Year 4. 50 % RES. 12 9. 6. 25 % RES. 6. 10 % RES. 6. 50 % RES. 6. 10 % RES. 6. 23 16 18. 50 % RES. 8. 22 22 20. 25 % RES. Year 1. 6. 10 % RES. 9. 50 % RES. 6. 25 % RES. 6. 10 % RES. 6. 50 % RES. 21 20 18. 25 % RES. 17 19 15. 10 % RES. 19 17 16. 25 % RES. LNG1. Year 5. Figure 4.3. Number of scheduled LNG vessels The phenomenon of GPG units operating as cycling units for high levels of RES penetration can also be observed more directly from the NG consumption by GPG units in the 3 scenarios of RPS, shown in Figure 4.4. The figure shows the profiles of NG.

(40) 30. 0,05. Consumption (MMm^3). January-February. 10 % RES 25 % RES 50 % RES. NG Consumption by GPGs. 0,04. 0,03. 0,02. 0,01. 0. Representative day 1. Representative day 2. Representative day 3. Representative day 4. Figure 4.4. NG consumption by GPG units during the sixth season of year 4 consumption by GPG units over the 4 representative days of the sixth season (Nov-Dec) of year 4, for different scenarios of RES penetration. It is important to note that the modeling does not assume that these representative days are consecutive and/or equally weighted; however, the figure is capable of illustrating the generation pattern of GPG units for different RPS scenarios. It is clearly observed from the figure how GPG units shift their role from base-load units with smooth generation profiles to cycling units with progressively deeper cycles. Table 4.1 presents the total NG consumption by GPG units in each of the 5 years of the planning horizon, for scenarios of 10%, 25% and 50% of RES penetration, and the percent reduction of NG consumption with respect to the scenario of 10% RPS. It is observed from the results that NG consumption by GPGs is mildly reduced from the scenario of 10% to 25% RES penetration, roughly between 8 and 9% reduction. However, in the case of 50% penetration, the reduction in NG consumption by GPGs spikes between nearly 42% and 28% with respect to the 10% case. These results are in line with the previous observations, which indicate that GPG units are mildly shifted from their role of base-load units in the case of 25% of RES penetration, but with an increasingly relevant role as cycling units. Furthermore, the role as cycling units continues to be relevant in the case of 50% RES penetration, but the NG consumption drops drastically as a result of GPG units no longer providing base-load power..

(41) 31. Table 4.1. NG consumption by GPG units in each scenario of RES. Year [MMm3], 10 RPS [MMm3], 25 RPS [MMm3], 50 RPS 1 2 3 4 5. 10.03 10.77 11.61 12.58 13.59. 9.19 (-8.3 %) 9.81 (-8.9 %) 10.61 (-8.6 %) 11.42 (-9.2 %) 12.39 (-8.8%). 5.82 (-41.9 %) 6.39 (-40.7 %) 7.01 (-39.6 %) 8.77 (-30.3 %) 9.76 (-28.2 %). Figure 4.5 show the optimal share of electricity generation by technology for the 3 different scenarios of RPS, and for each of the 5 years in the planning horizon. It is observed that generation is dominated by hydro, gas, coal-fired, and wind power plants, with marginal participation of solar and nuclear power plants, which is consistent with the discussion of higher flexibility requirements in the system, which cannot be provided by nuclear plants. The least expensive source of flexible power in terms of operational costs are hydro power plants; however, they suffer from variable availability throughout the seasons, which is accounted for as different generation costs (opportunity costs) in the model; therefore, GPG units become competitive as providers of flexibility. As a result, hydro power plants tend to operate as base-load generation and GPG units tend to cycle more. WIND SOLAR GAS HYDRO NUCLEAR. 10000. Electricity Generation. 8000 6000 4000. Year 1. Year 2. Year 3. Year 4. Figure 4.5. Evolution of the optimal generation mix. Year 5. 50 % RES. 25 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 10 % RES. 0. 10 % RES. 2000. 25 % RES. Generation (GWh). 12000. COAL.

(42) 32. 4.1.1. Modified Chilean System 4.1.1.1. Description of the Modified Chilean NG and Power Systems The proposed model is now applied to a modified version of the Interconnected Chilean NG and electricity systems. As in case study 1, both systems are physically linked through GPG units. The total installed capacity in the Chilean system is 22,36 GW with an annual peak load of approximately 13,25 GW. In order to test the proposed model in a tighter demand-supply balance condition we have reduced the available installed capacity to 15 GW, which is achieved by mainly reducing the available hydro-power capacity. Thus, the modified system presents a strong concentration of thermal power plants, including GPG units (4140 MW), coal-fired power plants (4720 MW), and diesel units (2839 MW), followed by a smaller but significant presence of hydro power plants (1884 MW). It should be noted that the purpose of this case study is to validate the proposed model in a realistic test system that resembles the characteristics of the Chilean power system; however, the results cannot be use to extrapolate future requirements or patterns in the actual system. Chile has two main RTs in operation, GNL Quinteros and GNL Mejillones, which supply NG to industrial, residential and electric power sectors. The GNL Quinteros terminal has a regasification capacity of 15 MMm3 /day, whereas GNL Mejillones has a regasification capacity of 5 MMm3 /day with storage capacities of 334 Mm3 and 187 Mm3 , respectively. A summary of existing infrastructure and alternative investments in the Chilean NG and power system is presented in Table 4.2, whereas the topology of the system and location of the main existing infrastructure is depicted in Figure 3.2. Note that even though no new RTs and pipelines are considered as alternatives of investment in the planning horizon, the reservoirs in each existing RT can be expanded for additional storage capacity..

(43) 33. Table 4.2. Summary of Existing and Alternative infrastructure. Item. Existing. Invesment Alternative. OC GPGs CC GPGs Coal oil Hydro Wind Solar Transmission Line RTs. 18 (4140 MW) – 28 (4720 MW ) 45 (2839 MW ) 24 (1884 MW ) (909 MW) (599 MW ) 14 (5270 MW) 2. Pipelines. 5. – 24 (9500 MW) 18 (6180 MW) 6 (1800 MW ) – Unlimited Unlimited 36 (56041 MW) No new RTs, but storage capacity can be expanded –. 4.1.1.2. Results and Discussion of the Chilean case study The investment results for the Chilean case study are shown in Table 4.3, for different scenarios of RPS. It is observed that new conventional generation units correspond to combined-cycle GPGs power plants in all the scenarios. Furthermore, the investment in new conventional generation is the same in all 3 scenarios; however, the role of such units in the operation of the system changes. In the scenario of 10% RPS, the GPG units in the system produce nearly 30,468 GWh in the 5th year of the planning horizon, in the scenario of 25% RPS produce 26,537 GWh, and in the scenario of 50% RPS produce nearly 17,551 GWh. These productions correspond to 62,7%, 54,6% and 36,1% aggregate capacity factors for the GPG units in the system, respectively, which is justified by the need to provide flexible power to cope with RES variability. The scenarios of 10%, 25% and 50% RPS result in investments of 2,941 MW, 7,959 MW and 17,964 MW of RES (wind and solar), respectively. It is observed that the investment is solar power plants is lower than wind power plants in all the scenarios, given the lower annualized investment cost for wind power technology ($660/kW vs. $1000/kW) and high capacity factors assumed for wind energy in the test system. Additionally, solar power plants are unable to contribute firm power during night hours; thus, it must be.

(44) 34. combined with additional investment in other conventional generation technologies. Finally, the transmission investments in the system increase for more aggressive RPS policies, due to the need to reinforce the system in order to transport the generation from RES sources to the load centers. Regarding the NG infrastructure, it is observed that Table 4.3. Investment in infrastructure. Item. 10 RPS. 25 RPS. 50 RPS. CC GPGs Wind Solar Transmission Line Storage Capacity. 4 (1400 MW) 2888 MW 53 MW 5 (5400 MW) –. 4 (1400 MW) 7649 MW 310 MW 6 (6800 MW) –. 4 (1400 MW) 11892 MW 6072 MW 7 (8200 MW) –. The number of vessels in Quinteros and Mejilllones terminals. 70. 48. 71. Year 3. 50 44. 71. 49. 69. Year 4. 69. 48. 50 % RES. 69. 63. 10 % RES. 10 % RES. 49. Year 2. 51 44. 10 % RES. 50 % RES. Year 1. 70. 10 % RES. 70. 50 % RES. 46. 64. 51 44. 25 % RES. 66. 25 % RES. 10 % RES. 66. 63 43. 50 % RES. 51. 25 % RES. 63 42. 50 % RES. 50. 25 % RES. 62. Mejillones. 25 % RES. Quintero. Year 5. Figure 4.6. Number of scheduled LNG vessels additional storage capacity in Quinteros and Mejillones terminals is not required, given the lower capacity factors observed for GPG units. In terms of LNG management, Figure 4.6 shows the number of vessels arriving to each RT over the 5-year planning horizon, for different scenarios of RES penetration. It is observed that the total number of vessels required in each year is lower in the scenario of 50% RPS as compared with the 10% and 25% RPS, which is consistent with the GPG units having to cycle more to compensate RES variability..

(45) 35. Figure 4.7 shows the energy generated by each technology in each year of the planning horizon. In particular, it is observed that during year one the energy generated by GPG units the scenarios of 10%, 25% and 50% RPS is the 28,153 GWh, 24,911 GWh and 16,261 GWh, which requires 2,421 MMm3 , 2,142 MMm3 and 1,398 MMm3 of NG, respectively. This means that approximately 30, 26 and 17 vessels arrive to the RT terminals, respectively for each scenario of RPS, for power generation purposes. Additionally, it is observed from the figure that power generation is dominated by coal, gas, hydro, solar and wind power plants, with marginal participation of diesel units. While GPG units feature high variable generation costs as compared with coal-fired and hydro power plants, their participation in the electricity production is still significant due to their contribution to the power system’s flexibility required to manage RES variability. On the other hand, coal-fired power plants feature lower variable operation costs; however, their production is significantly reduced due to their lack of flexibility. Hence, it is observed that the required flexibility is being provided mainly by GPG units and hydro power plants. The later highlights the need to jointly consider power and gas networks in operational and planning models for power systems, given the key role of GPG units in. Generation (GWh). the provision of energy and flexibility in a future system dominated by variable RES. GAS SOLAR WIND HYDRO PETROLEUM. 70000. 60000. Electricity Generation. COAL. 50000 40000 30000 20000. Year 1. Year 2. Year 3. Year 4. Figure 4.7. Evolution of the optimal generation mix. Year 5. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 50 % RES. 25 % RES. 10 % RES. 10000.

(46) 36. Consumption (MMm^3). 0,6 0,5. NG Consumption by GPGs. 10 % RES 25 % RES 50 % RES. 0,4 0,3 0,2 0,1 0. Representative day 1. Representative day 2. Figure 4.8. NG consumption by GPG units during the second of year 5 Figure 4.8 illustrates the aggregate NG consumption profiles for all the GPG units during the 2 representative days of the second season (Jul-Dec) of year 4, for different scenarios of RES penetration. This results further support the argument of GPG units playing a role of cycling units for high levels of RPS penetrations, due to the high variability observed in the daily NG consumption for generation purposes; on the other hand, GPG units operate as base load plants, with a much more stable generation output, in the scenarios of 10% and 25% RPS..

(47) 37. 5. CONCLUSIONS This paper proposed a stochastic programming formulation to solve the co-optimized expansion of integrated electricity and NG infrastructures. The important role of GPG units and their inherent flexibility in an RES-dominated power system highlights the need to properly model the LNG supply chain in operation and planning models the electricity system. In particular, the inadequate scheduling of LNG vessels and delayed investment decisions will not only have an impact on the availability of GPG units for electricity production, but may also limit the production of other variable RES units leading to episodes of high operation costs, and potentially high electricity prices. In this regard, the study cases studies clearly showed the important role of GPG units in the provision of flexibility in power systems with high penetration of RES, and how the operation of the power system influences the operation of the gas network and the scheduling of LNG vessels arriving to RTs. Future research will focus on the incorporation of additional details in the modeling of LNG suppply chain, including specific characteristics of LNG supply contracts (e.g., take-or-pay), and the modeling of compressors and pressure constraints in the network..

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(53) APPENDICES.

(54) 44. A. APPENDICES A.1. 6-Zone Test System Data. Table A.1. Characteristics of operation and investment of Natural gas Wells Sub. Annualized Name State Min Max Investment MMm3/h MMm3/h MMUS$. NG1 LNG1. 0. 0. 15. 105. NG3 LNG2. 1. 0. 10. 5. Table A.2. Characteristics of operation and investment of Natural Gas Interconnection Annualized From. To. Name. State. Min. Max. Investment. MMm3/h. MMm3/h. MMUS$. NG1. NG2. PIPE1. 1. 0. 15. 5. NG2. NG3. PIPE2. 0. 0. 15. 105.

(55) 45. Table A.3. Characteristics of operation and investment of power plants Annualized State Min Max Investment MW/h MW/h MMUS$. Sub. Name. E3. Coal1. 1. 0. 200. -. E2. Nuclear1. 1. 0. 200. -. E1. Hydro1. 1. 0. 500. -. E2. Gas1. 1. 0. 400. -. E3. Gas3. 1. 0. 400. -. E2. Nuclear2. 0. 0. 200. 212. E3. Coal2. 0. 0. 200. 67. E1. Hydro2. 0. 0. 500. 208. E2. Gas2. 0. 0. 400. 35,49. E3. Gas4. 0. 0. 400. 35,49. Table A.4. Characteristics of operation and investment of Natural Gas Interconnection Annualized From. To. Name. State. Min. Max. Investment. MMm3/h. MMm3/h. MMUS$. E1. E2. TL1. 1. 0. 200. 5. E1. E3. TL3. 1. 0. 200. 5. E2. E3. TL5. 1. 0. 200. 5. E1. E2. TL2. 0. 0. 200. 55. E1. E3. TL4. 0. 0. 200. 55. E2. E3. TL6. 0. 0. 200. 55.

(56) 46. A.2. Chilean System Data. Table A.5. Feasible triples for highly variable Grid, MLMMH. Units Fix. Variable PMIN PMAX. 1. 758.42. 75.32. 2. 1235.42 77.43. 121.67 248.57. SING2 carbon 2. 3. 1257.29 77.43. 121.67 252.97. SING2 carbon 3. 4. 738.14. 80.04. 90.52. 148.52. SING1 carbon 4. 5. 395.51. 86.49. 44.28. 79.58. SING2 carbon 5. 6. 396.46. 84.3. 44.27. 79.77. SING2 carbon 6. 7. 634.52. 80.65. 66.27. 127.67. SING2 carbon 7. 8. 616.58. 79.03. 66.66. 124.06. SING2 carbon 8. 9. 769.85. 79.1. 79. 154.9. SING2 carbon 9. 10. 815.08. 77.21. 79. 164. SING2 carbon 10. 11. 764.88. 74.28. 83.8. 153.9. SING2 carbon 11. 12. 655.39. 75.42. 55.83. 131.87. SING2 carbon 12. 13. 633.38. 75.79. 56.14. 127.44. SING2 carbon 13. 14. 710.11. 75.33. 60. 142.88. SIC1. carbon - petcoke 1. 15. 710.11. 75.42. 60. 142.88. SIC1. carbon - petcoke 2. 16. 681.39. 73.97. 60. 137.1. SIC1. carbon - petcoke 3. 17. 691.23. 74.17. 60. 139.08. SIC1. carbon - petcoke 4. 18. 654.55. 74.17. 60. 131.7. SIC1. carbon - petcoke 5. 19. 1172.92 77.43. 94.4. 236. SING2 carbon 14. 20. 1172.92 77.43. 94.4. 236. SING2 carbon 15. 21. 607.33. 70. 122.2. SIC4. carbon 16. 22. 1602.73 73.46. 128.99 322.48. SIC4. carbon 17. 23. 1237.48 73.67. 110. SIC3. carbon 18. 73.67. 83.9. 152.6. 248.99. Zone. Technology. SING2 carbon 1. Continued on next page.