We propose a merchant-regulatory framework to promote investment in the European natural gas network infrastructure based on a price cap over two-part tariffs. As suggested by Vogelsang (J Regul Econ 20:141–165, 2001) and Hogan et al. (J Regul Econ 38:113–143, 2010), a profit maximizing network operator facing this regulatory constraint will intertemporally rebalance the variable and fixed part of its two-part tariff so as to expand the congested pipelines, and converge to the Ramsey-Boiteaux equilibrium. We confirm this with actual data from the European natural gas market by comparing the bi-level price-cap model with different reference cases. We analyze the performance of the regulatory approach under different market scenarios, and identify relevant aspects that need to be addressed if the approach were to be implemented.
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Similar congestion cross-border issues are internationally present in electricity networks in North America and Europe (Rosellón and Weigt 2011; Rosellón et al. 2011, and Jonekren et al. 2014). The need to expand energy networks is also highly motivated by large-scale renewable system projects (Fthenakis et al. 2009).
Regulation (EC) No. 713/2009 of the European Parliament and of the Council of 13 July 2009: “Establishing an Agency for the Cooperation of Energy Regulators”. As we suggest below, an institutional comprehensive European regulatory approach is particularly relevant for our model.
We therefore consider the specific complexities of natural gas networks that are not present in electricity networks, such as seasonal storage facilities and LNG terminals (although we do not model the potential trade-offs between pipeline investment and investment in storage and LNG). While short term storage is also present in electricity markets in the form of pumped hydro the possibility of seasonal storage allows a higher utilization of the gas transportation network during summer months due to increased storage demand. Similar the LNG system can be considered as additional network structure comparable to i.e. a HVDC system in a meshed electricity AC network. However, an LNG terminal provides automatic connection with all other LNG terminals as the network arcs (the ship routes) are unrestricted. In this paper we do not consider spatial or stochastic concerns on networks (as in Ducruet and Beauguitte 2013, and Devine et al. 2014).
Expansion in transmission networks can also be approached from the point of view of consumers who are willing to pay for excess (or buffer) capacity if they dislike facing any period of congestion (Brito and Rosellón 2011).
Nodal or locational marginal pricing is a concept derived from electricity markets which defines prices for each node within a system accounting for production and transport costs, as well as network congestion. Regulation 715/2009 has made the introduction of an entry-exit system that is essentially zonal pricing for natural gas transportation mandatory.
The Ramsey- Boiteaux equilibrium is obtained in a problem where the regulator maximizes social welfare—expressed as the weighted sum of consumer and producer surpluses—subject to the individual rationality constraint of a natural monopoly. A solution to this problem provides the optimal markup between price and marginal cost, which ends up being inversely proportional to the elasticity of demand. In the welfare function, the producer surplus is usually weighted with a factor α less than or equal to one (and greater than zero). The magnitude of α depends on how much the regulator wishes to favor consumer surplus in the welfare criterion. (see Laffont and Tirole 1993).
In a dynamic setting with changing cost and demand functions—and/or non-myopic profit maximization—the chained Laspeyres index (or any other weight index) generates prices that may diverge from the Ramsey structure. Neu (1993), Fraser (1995), and Law (1995a,b) study the effects of the chained Laspeyres restriction under a changing demand function, non-uniform cost changes, and myopic profit maximization, respectively. Ramírez and Rosellón (2002) show that, under high uncertainty, flexible-weight average-revenue regulation might be combined with other regulatory measures so as to achieve a balance between risk management and consumer-surplus maximization. The use of flexible-weights during the initial stages of greenfield gas distribution projects (characterized by volatile cost and demand conditions) is consistent with investment attraction under uncertain dynamic behavior of demand since it is a laxer constraint for firms than fixed-weight regulation. Tariff-basket regulation is used afterwards, once the cost and demand conditions are stabilized.
When there is market power in generation, prices will not reflect the marginal cost of production (Joskow and Tirole 2005). Generators in constrained regions will tend to withdraw capacity to bring their generation price up, and this will overestimate the cost-saving gains from investments in transmission. Further, dominance in the market for financial transmission rights (FTRs) provides incentives to curtail output (demand) to make FTRs more valuable. Thus, the effectiveness of the HRV model to incentivize efficient expansion of the transmission network is severely affected when assumptions on perfect competition are lifted.
The Hogan et al. mechanism is further tested using simplified grids in Northwestern Europe (Benelux) and in Pennsylvania, New Jersey, Maryland (PJM) with realistic generation structures in Rosellón and Weigt (2011) and Rosellón et al. (2011), respectively. This testing results in the NO expanding the network such that prices develop in the direction of marginal costs. The nodal prices that were subject to a high level of congestion converge to a common marginal price level, and consumer and producer surpluses converge to the Ramsey-Boiteaux welfare optimal values.
This formulation allows storage in the summer season and withdrawal in the winter season but does not account for storage between the years. Consequently, if it would be optimal to store quantities of natural gas longer than one calendar year the model will not capture this possibility.
The number of network users is normalized to 1 thus the variable F indicates the total amount collected via the fixed fee. How the charge is imposed on the different natural gas consumers (e.g. industry vs. households) and how changes in the number of consumers are accounted is not addressed.
The extension plan could typically be carried out by an ISO.
The reference prices have been chosen to obtain a feasible match between the used cost information for production and transport and the resulting locational marginal prices given the observed demand level. Due to the perfect competitive market setting, highly aggregated nature of the model and lack of uncertainty and stochastic elements the obtained price results are on average below real observed market prices.
Lumpiness can lead to suboptimal investment results as the full investment block may be too big or too small compared to the economic optimum. Consequently, the obtainable results of a discontinuous investment setup are likely to result in a lower profit/welfare outcome while the general investment incentive provided by the regulatory approach should still hold.
Note that the producer surplus includes non-European countries while the extension is limited to pipelines within Europe. We will analyze the influence of regional limitation in Section 5.
This decline of the fixed fee is due to the increased demand and reduced production patterns that lead to a gradual price increase over time. As the regulatory cap is fixed without RPI or X modifications in the simulations the only adjustment possibility for the Transco is the fixed fee. If an appropriate RPI or X factor would be introduced the fixed fee would likely remain relatively stable over time once the basic investments are finalized.
Transport costs are treated similar i.e. cost on inner-region pipelines are fully accounted for while cross-border transport costs are split equally.
In reality one can assume that for cross-border projects a form of cooperation between the participating Transcos will emerge. However, including this decision process into the model frame is beyond the scope of this paper.
A test case in which cross-border lines are treated like inner-region lines shows an investment pattern solely focused on the connection Slovakia-Austria and no alternative routes. This can be interpreted as indication that the presented investment pattern is a result of the benefit split.
A test case with only Austria as participating country also showed significant investment volumes.
This is obtained by letting the model solve two deterministic cases in sequence. First the base case model is solved from 2010 until 2025 with the base case dataset. The results up to 2016 are then fixed and the model is solved again with the new dataset for 2017 up to 2025. The model formulation is not altered.
Test cases with the altered dataset and a single run of the model shows similar investment volumes in both the regulatory setting and under welfare maximization.
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Hannes Weigt acknowledges support from the Robert Schuman Centre for Advanced Studies at the European University Institute as the research reported in this paper was partly carried out while he was a Jean Monnet Fellow at the Florence School of Regulation. Juan Rosellón acknowledges support from a Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme, as well as from Conacyt (p. 131175).
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For the MPEC formulation the optimization problem defined in Section 3.1.1 is reformulated as Mixed Complementarity Problem (MCP) by deriving the KKT conditions. In addition to the variables presented in Section 3 the shadow prices on the different capacity constraints are indicated by λ and the shadow price on the storage balance by μ. The demand function P(D) is represented by a linear function in the form of P = a – bD, with a and b as intercept and slope of the function, respectively. Only the shadow variable on the storage balance, μ, and the nodal price, P, are free variables; all other variables are positive with a lower bound of zero. The MCP formulation of the lower level problem is as follows:
The regulatory model (Regulated) consists of the objective function (Eq. 10), the regulatory constraint (Eq. 11), the new investment definition (Eq. 13) and the above described MCP formulation of the market clearing with new capacity (invested by the Transco) added as indicated by Eq. 12. For reference simulations with a purely profit maximizing Transco (Profit Based) the regulatory constraints (Eq. 11) is omitted and the fixed fee F is set to zero, otherwise the model is identical to the regulatory model. For the Welfare Benchmark model the upper level is changed by replacing the profit objective with the welfare objective function (Eq. 14) and omitting the regulatory constraint. The remainder of the model is again similar to the regulatory model. The No Extension base case counterfactual consists only of the above described MCP model.
In the welfare-optimal benchmark problem of 3.1.3 (the basic market model, No Extension), the objective function is concave (thus, with convex level curves), and the constraints define convex sets. Therefore, the theorem of separating hyper planes applies. For the bi-level programming model in the regulatory model (Subsection 3.1.2, Regulated), under convexity of the choice set in the lower-level problem, the KKT conditions are necessary and sufficient so that inserting them into the upper-level provides a solution (see Gabriel et al. 2013).
The MPEC models are incorporated into GAMS and solved using the NLPEC solver while the MCP base case model is solved using PATH. The solve options for NLEPC are set to utilize inner-product reformulation of the MPEC formulation which transfers the lower level MCP into a system of equalities and inequalities while retaining the upper level equations in their stated form (NLPEC solver manual, section 3.1). Other solver options (e.g., penalty approaches) led to a reduced performance and lower objective values.
The obtained solutions are necessarily only local optima. All models are solved using the same starting point (the solution of the base case MCP model, which provides all market outcomes when no extensions take place). We performed tests with multiple other starting points (e.g., no preliminary market results, arbitrary extension patterns). However, they all provide a slightly lower objective value while the basic result pattern remained unchanged. The Welfare Benchmark model can also be solved without reformulation as MPEC by keeping the welfare objective (Eq. 14) and the basic market representation (Eqs. 2–9) adjusted for the new capacity extension (Eqs. 12 and 13). This version of the Welfare Benchmark model is a quadratically constrained programming (QCP) problem due to the quadratic part in the objective function (the gross consumer surplus) while all other aspects remain linear. The QCP version can be solved using the CPLEX solver providing the global optima to the problem.
Comparing the results of the derived local optima obtained from the MPEC solution with the global optima of the QCP solution shows a difference in the resulting welfare value of ca. 0.01 %. The main differences between the two solutions are the exact timing and quantity of the added capacity, although the general extension pattern with respect to corridors, overall quantity and time schedule is similar. The development of consumer and producer surplus, demand, production, and prices are identical in both model formulations. Thus, the MPEC version of the Welfare Benchmark provides a respectable fit to the global solution. As the Regulated model in turn converges towards the Welfare Benchmark we consider the obtained locational end results as sufficient for our analysis.
If the spatial and temporal resolution is to be extended and more complex market structures (e.g. strategic company behavior on the wholesale market) or investment behavior (e.g. lumpiness and decommissioning) are to be considered the NLPEC reformulation may not be sufficient requiring direct model reformulations/decompositions of the underlying MPEC model (e.g., see Gabriel and Leuthold 2010; Siddiqui and Gabriel 2013).
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Neumann, A., Rosellón, J. & Weigt, H. Removing Cross-Border Capacity Bottlenecks in the European Natural Gas Market—A Proposed Merchant-Regulatory Mechanism. Netw Spat Econ 15, 149–181 (2015). https://doi.org/10.1007/s11067-014-9273-3
- Natural gas network