Stochastic Modeling of Natural Gas Infrastructure Development in Europe under Demand Uncertainty.

• Author: Fodstad, Marte
• Position: Report

1. INTRODUCTION

   The Energy Union strategy of the European Union consists of five dimensions: supply security, a fully integrated internal energy market, energy efficiency, emission reduction, and research and innovation. The ambition to create a fully integrated internal energy market shall be achieved by strengthening interconnectors to allow energy to flow freely across the EU. In addition to capacity extensions, technical and regulatory barriers must be overcome. For natural gas, the steps towards a fully integrated market have been set out in three gas directives (98/30/EC, 2003/55/EC, 2009/73/EC). (1)

   The directives establish rules for natural gas transmission, distribution, supply, and storage. This includes rules for market access, authorizations for transmission, distribution, supply and storage of natural gas, and the operation of systems. The motivation for the directives is a full opening up of national gas markets (including LNG) to achieve higher service quality, universal service levels, consumer protection, security of supply, as well as climate change mitigation (see, e.g., EC, 2010, 2016). The objective is to increase competition in national markets and integrate them into regional, and eventually, a single EU-wide market for natural gas. In order to achieve a fully integrated market, it is necessary to strengthen the cross-border gas transportation network in Europe. Cost-effective capacity expansion should consider uncertainty in future developments, specifically considering natural gas production and consumption trends.

   In this paper, we present an analysis of the optimal development of the natural gas infrastructure in Europe based on the natural gas demand and technology scenario studies presented in Knopf et al. (2013). We use a stochastic model to analyze the impact of policy and technology uncertainty on optimal investments in pipelines. The policy dimension varies in terms of the greenhouse gas (GHG) emission reduction targets and the emission trading regimes within the EU. Except for a no policy baseline with a 0% GHG reduction target (BASE), all other scenarios assume either 40% or 80% reduction. The technology dimension varies the availability and technological progress of carbon capture and sequestration (CCS), nuclear power generation, energy efficiency, and renewable energy in five main storylines: default (DEF), default without CCS (noCCS), pessimistic (PESS), efficient (EFF), and green (GREEN). The policy and technology dimensions are combined into eight scenarios: 'BASE', '40%DEF', '40%EFF', '80%DEF', '80%noCCS', '80%PESS', '80%EFF', and '80%GREEN'. The uncertainty considered in this paper is in demand development. In the European Union, reference prices and quantities on which future inverse demand curves are based vary by scenario. In the (aggregated) other regions in the rest of the world, only reference prices are adjusted when calculating the future demand curves for the different scenarios.

   The oil and gas industry is capital intensive and rich in complex operational and strategic planning problems. As such, it has a relatively long history of using computerized decision support for making investment decisions (e.g., Dougherty and Thurnau, 1969). The earliest applications focused on operational planning. Charnes et al. (1954) developed deterministic linear programming models, wherein uncertainty in input parameters is addressed using sensitivity analysis. Dynamic programming was applied to support both transient and steady-state analysis in a natural gas transportation network (Wong and Larson, 1968). Dougherty and Thurnau (1969) presented a computer system for optimal investments in oil wells and pipelines, also based on linear programming.

   Over time, the computational power of computers has increased substantially and off-the-shelf optimization software allows for representation of decision problems in great detail, including the use of integer variables to represent the discontinuous nature of many capacity investment and expansion problems (e.g., Nygreen et al., 1998 and Andre et al., 2009). Gas trade has historically been dominated by long-term contracts. Such a setting more or less warrants a focus on cost minimization, and also reduces the uncertainties faced by the parties involved. Operations and investment models have tended to focus on deterministic cost minimization, finding the cheapest way to fulfill contractual obligations. The opportunities and risks encountered in an increasingly, but not perfectly, competitive and liberalized market are not well-addressed by cost minimization approaches where stochastic profit maximization is more suitable.

   Quantitative energy market models addressing game-theoretic behavioral aspects started to arise in the 1980s (e.g., Haurie et al., 1987 and Mathiesen et al., 1987). Since then, gradually more natural gas market models have been developed with finer time granularity, a broadening geographical coverage, and a more detailed representation of the actors in the market. Examples of such models are GASTALE (Boots et al., 2004; Egging and Gabriel, 2006, Lise et al., 2008), GASMOD (Holz et al., 2008), EGM/WGM (Egging et al., 2008, Gabriel et al., 2012), GGM (Holz et al., this issue), Columbus (Hecking and Panke, 2012), and MultiMOD (Egging and Huppmann, 2012; Huppmann and Egging, 2014). These models share the equilibrium modeling approach that allows for representation of imperfect competition and explicit inclusion of a transportation network so that the interplay of market power and infrastructure bottlenecks and their impact on optimal capacity expansion can be analyzed.

   Uncertainty is not commonly addressed in these models. The first representation of uncertainty (oil price) in an oligopolistic European natural gas market setting was proposed and solved by Haurie et al. (1987). Zhuang and Gabriel (2008) present a stochastic mixed complementarity problem (MCP) with a stylized application inspired by the North American market. Egging (2010, 2013) and Egging and Holz (2016) present and apply a multi-period stochastic version of the GGM with endogenous capacity expansions. Zheng and Pardalos (2010) address demand and local production uncertainty in a stochastic mixed integer program for minimizing expected costs of future gas transport and investments in the pipeline network and regasification terminals. Their model contains integer variables in the second stage and the authors implement an advanced Benders decomposition scheme to solve data instances that consider modest networks but large scenario trees. Goel and Grossmann (2004) look at decision dependent scenario trees for investments in natural gas production under uncertainty of the size of and maximum production rate from gas reserves. An interesting aspect, highly complicating the numerical tractability, is that they implement a decision dependent scenario tree wherein uncertainty about reserves is only resolved if the decision to explore them is actually made.

   Two aspects ignored by most natural gas market literature are the nonlinear relationships between capacities and pressures and gas quality. In Midthun et al. (2009), the system effects of natural gas transportation networks and their impact on economic analyses are discussed. However, for onshore pipelines, additional compressors can be installed to increase gas pressure, and thereby, capacity at any point in a network. Li et al. (2011) address various aspects of gas quality in their two-stage stochastic mixed integer program, by designing an optimal gas transportation network for maximizing expected profits under uncertainty of needed throughput capacities at various points in the network. (2) The resulting model bears elements of non-convex pooling problems. The authors implement an advanced decomposition algorithm to solve two cases. A commonality among Zheng and Pardalos (2010), Goel and Grossmann (2004), and Li et al. (2011) is that the presence of integer variables in a multi-stage stochastic setting requires an advanced Benders-type decomposition approach to solve the model for data instances containing relatively modest networks. This makes these models not suitable for data instances representing the European gas market in a global context over a long time horizon. Bi-level models explicitly address the sequential and closed-loop nature of agent decisions in markets. The resulting models are very hard to solve and need advanced, customized solution approaches (e.g., Siddiqui and Gabriel, 2013). In a more general perspective, stochastic transportation network planning approaches can apply to natural gas networks. The characteristics of traffic demand uncertainty and network degradability in Siu and Lo (2008) can translate to demand uncertainty and pipeline disruptions in a natural gas network. Patil and Ukkusuri (2007, 2008) and Ukkusuri and Patil (2009) propose bi-level optimization approaches for transportation network design under demand uncertainty. Patil and Ukkusuri (2007) develop a two-stage mathematical program with equilibrium constraints, and they implement the model on a small test network. Ukkusuri and Patil (2009) extend this work to a multi-period network design formulation. Patil and Ukkusuri (2008) develop two-stage stochastic programs balancing network expansion costs with congestion reduction. Although interesting and insightful, the models presented in these papers do not scale up to a detailed enough representation of the European natural gas market.

   The remainder of this paper is organized as follows. In Section 2, we present the Ramona model, which we used for our analysis, and the corresponding input data. In Section 3, we discuss the impact of uncertainty in demand on investment decisions. We also present detailed results from analysis of the different scenarios. In Section 4, we conclude and present thoughts and ideas related to future work and shortcomings of...