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Efficient pricing for European electricity networks – The theory of nodal pricing applied to feeding-in wind in Germany
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Utilities Policy 16 (2008) 284e291 www.elsevier.com/locate/jup
Efficient pricing for European electricity networks e The theory of nodal pricing applied to feeding-in wind in Germany Florian Leuthold*, Hannes Weigt, Christian von Hirschhausen Chair of Energy Economics and Public Sector Management, Dresden University of Technology, 01062 Dresden, Germany Received 27 August 2007; received in revised form 21 December 2007; accepted 23 December 2007
Abstract This paper applies nodal pricing as an economic approach to efficient use of electricity networks utilization for the fairly large German grid. We combine a straightforward welfare maximization with the technical specificities of electricity flows on a realistically large network. The nodal pricing model is applied in order to analyze the impact of extended German wind power production on the power grid. The paper shows that economic modeling, taking into account physical and technical constraints, makes important contributions to the assessment and optimization of system configuration and operation. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Electricity networks; Nodal pricing; Locational Marginal Pricing; Welfare; Wind energy
1. Introduction The efficient use of partly congested network infrastructure is a recurring theme in economics. While the theory of marginal and opportunity cost pricing works well in economic theory, its application to the real world hinges upon the specific technical and institutional characteristics of the sector. This is particularly true for the electricity sector, where e due to physical laws e capacity itself depends on the structure of demand and supply, and is time-variant. This paper presents the application of nodal pricing as an economical approach to congestion pricing in electricity networks to a specific situation in the German electricity grid, the feeding-in of offshore wind energy. Nodal pricing has emerged a powerful and efficient tool of transmission pricing, both in theory and e more recently e in practice. Lessons from North America, Australia, and New Zealand have proven nodal pricing to be workable without * Corresponding author. Tel.: þ49 351 463 39764; fax: þ49 351 463 39763. E-mail addresses: [email protected] (F. Leuthold), hannes. [email protected] (H. Weigt), [email protected] (C. von Hirschhausen). 0957-1787/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jup.2007.12.003
serious technical problems. The European Commission is considering nodal pricing amongst others in the context of the creation of an integrated European electricity market (Brunekreeft et al., 2005). Germany is currently undergoing substantial structural change in the regulation of its electricity industry. It has not only finally implemented a sectoral regulator, as required by the Acceleration Directive 2054/05/EC, but is currently also pondering more efficient network pricing both within and in connection to its neighboring countries. In addition, Germany has pushed strongly for the development of renewable energies and their integration into the existing network. This is particularly the case for wind energy, where Germany had 20.7 GW onshore wind capacities in 2006; in addition about 7 GW are expected to be constructed offshore until 2015 and a long-term goal of 30 GW offshore is considered for 2030 (Brunekreeft et al., 2005). At present, the technicalities of the integration of the offshore wind energy are being studied by industry and government; there is also a debate about the economic costs of integrating large-scale wind power into the German electricity grid (DENA, 2005a). DENA (2005a) concludes that there is a significant impact on electricity transport by large wind capacities. Particularly the integration of large-scale offshore projects can increase
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congestion within the transmission grid as these wind farms are connected only to a few nodes via high voltage DC cables. Thus the problem arises where and to what extent the transmission grid will have to be upgraded to be suited for the expected increase of offshore wind generation over the coming years. We pick up this issue by focusing on the economic possibilities of large-scale wind integration due to a more efficient congestion management mechanism that allows for a better usage of existing transmission grid capacities. While there is an increasing literature on the regulation of the electricity sector, no empirical work has been carried out so far on the implementation of nodal pricing or congestion management in general for Germany. However, there is a comprehensive literature on congestion management on a European level. Boucher and Smeers (2002) analyze the future organization of cross border trade in the European electricity market concluding that the economic principles as proposed by the European Commission in 2001 are not sufficient. Ehrenmann and Smeers (2005) analyze the regulation of cross border trade of electricity (Regulation 1228/2003) in terms of efficient congestion management. They conclude that market coupling e although its implementation is more complex e can path the way to a consistent system integrating the energy and transmission markets. Pe´rez-Arriaga and Olmos (2005) looked at plausible congestion management schemes for the internal electricity market of the European Union. Taking a joint energy and capacity auction as benchmark, they test two alternative approaches: an integrated transmission and energy auction and a coordinated explicit auction of transmission capacity followed by separate energy auctions at the different power exchanges. They propose the latter since it is the most feasible alternative. Our paper takes up the issue of congestion management and focuses on the German market and network. We compare a nodal pricing scheme for the German electricity market to the current pricing scheme assuming a liberalized and competitive electricity market. Our hypothesis is that nodal pricing is more efficient when allocating scarce capacities than uniform pricing which is currently applied in Germany. By using a simplified model of the Central European high voltage grid we can simulate different pricing methods. Furthermore the nodal pricing approach is used to estimate the impacts of large-scale wind power capacities in the North Sea as the obtained nodal prices represent scarcity signals. The objective of this paper is to provide an economic and technical analysis of the issue, whereas technical aspects have dominated the debate in Europe so far. The paper is structured as follows: Section 2 provides a survey of pricing mechanisms used in the electricity sector and suggests that nodal pricing is the most efficient approach. Section 3 develops the model and describes the data that are required for an application to the German electricity grid. Model scenarios and results are reported in Section 4. We are particularly interested in the effects of nodal pricing on the use of wind energy. The section also extends the analysis, beyond the German border, to neighboring countries. We find that nodal pricing leads to a significant increase in total welfare. Section 5 concludes.
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2. Pricing mechanisms in electricity markets The objective of our paper is to estimate possible gains in coping with transmission problems in Germany due to wind energy input by changing the congestion management scheme from uniform pricing to nodal pricing. As electricity transmission follows physical laws, the underlying pricing mechanism has to address these principles in order to lead to efficient capacity usage. Subsequently, we provide a review on different pricing mechanisms and their ability to address the physical characteristics properly. Contrary to ‘‘normal’’ markets such as mineral water, electricity markets are subjected to several technical restrictions that have to be taken into account within a pricing mechanism. Electricity cannot be stored, thus demand and generation have to be equal in each instant; demand is inelastic; and electricity can only be transported using a network in which power flows according to physical and not economic laws. Competitive markets for electricity determine either a uniform marginal price, a few zonal marginal prices, or a set of nodal or Locational Marginal Prices (LMPs). Although theory proves LMPs to be the most efficient, critics find the large number of LMPs e compared to one uniform or several zonal prices e confusing. They claim a uniform- or zonal-based model to be more transparent. The current pricing mechanism in Germany consists of a uniform wholesale price assuming an unrestricted network (‘‘copper plate’’). The location of demand and generation is not considered within the pricing process thus the marginal generator defines the price for the entire system. Transmission expenses are covered by a mixed price calculation levied by the grid company where the demand is located. The fee contains a fixed component for network access and a variable demand charge. This payment covers costs from losses, ancillary services, voltage transformation and access to networks at lower voltage levels. Identical consumers pay the same price for energy transmission, independent from the location within the grid area of the company and the point in time of their consumption, thus no price signal for scare transmission capacity exists. Uniform pricing has also been applied in Finland (since 1998), Sweden (since 1996), Alberta (since 2001), Ontario (since 2002), and most of the continental European countries and has been in operation in the former England/Wales-Pool (1990e2005), PJM (1997e1998) and in the first phase of the New England market from 1999 to 2003 (Ding and Fuller, 2005). It works efficiently only in the absence of congestion. Otherwise, in the case of congestion, an uplift payment is required, which covers overall costs from congestion but does not send adequate market signals (Krause, 2005) Therefore, uniform pricing is not able to ensure an optimal allocation of energy and transmission capacities in the setting of congestion as seen, e.g., in the case of New England (Hogan, 1999). An attempt to solve incentive problems of the uniform pricing approach was to introduce zonal pricing, currently applied in Norway (since 1991), Australia (since 1998), and Denmark (since 2000). The California independent network operator
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(CAISO) used zonal pricing from 1998 to 2002 (Ding and Fuller, 2005). Zones are network areas within a larger grid. In case of congestion these zones have different prices, whereas the prices within a zone are equal. Zones can either be defined endogenously based on the congestion situation or exogenously as pre-defined and fix zones due to other than economic reasons (e.g. control area of grid operators, federal states). Proponents of zonal pricing claim that it would balance well equity concerns and efficiency goals and is less complex and therefore more transparent to market participants (Alaywan and Wu, 2004). On the other hand, the zonal approach is criticized for its potential of market power abuse during periods of high demand and resulting congestion (Borenstein et al., 2000). Johnsen et al. (1999), however, could not find clear empirical evidence in a study on Norway. On the other hand, Hogan (1999) rejects the model of zonal prices for a number of reasons. He argues that it creates more administrative rules, poorer incentives for investments, demands to pay generators not to generate power, and finally it is much more complicate to define zonal than nodal prices (Hogan, 1999).1 Extending the zonal approach to define each node (representing a physical location on the transmission system including generators and loads) as a single price zone yields a full nodal pricing approach.2 The price at each node reflects the locational value of energy, including the cost of the energy and the cost of delivering it (e.g. losses and congestion). Nodal prices are determined by calculating the incremental cost of serving one additional MW of load at each respective location subjected to system constraints (e.g. transmission limits, maximal generation capacity). Differences of prices between nodes reflect the costs of transmission.3 In order to optimize dispatch in a nodal pricing system, a classic supply and demand equilibrium price has to be developed. The marginal generator is determined by matching offers from generators to bids from loads at each node. This process is carried out for a specific time interval (e.g. every 15 min) at each input and exit node on the transmission grid. The prices take into account the losses and constraints in the system, and generators are dispatched by the system operator, not only in ascending order of offers (or descending order of bids), but in accordance with the required security of the
1
Hogan cites the 1997 PJM attempt to install zonal pricing as an example, where the system collapsed as soon as constraints occurred. Generators rather run than respect transmission constraints e just responding to (distorted) signals from zonal pricing. 2 There are at least three alternative denominations of ‘‘nodal prices’’: ‘‘Locational Marginal Price/LMP’’ (PJM), ‘‘Location Based Marginal Pricing/ LBMP’’ (NYISO) and ‘‘Competitive Locational Prices/CLP’’ (Stoft, 2002). 3 Congestion occurs if both of the following two conditions are fulfilled (Stoft, 2002, p. 392): first, the marginal costs of production differ between nodes. Second, overall demand exceeds supply ability of the ‘‘cheapest’’ generator due to limited production or constrained line capacity. A line constraint can be caused when a particular branch of a network reaches its thermal limit or when a potential overload will occur due to a contingent event on another part of the network (e.g. generator black out). The latter is referred to as a security constraint.
system. This results in a spot market with bid-based, security-constrained, economic dispatch according to nodal prices as proposed by Hogan (2003). Nodal prices reflect the actual situation in the grid more transparently than uniform or zonal prices and represent adequate allocation signals. Nodal prices are one of several important considerations in analyzing where to site additional generation, transmission and load. The implementation of efficient congestion management methods on the basis of nodal pricing is crucial to cope with scarce transmission capacities and to ensure security of supply. Nodal pricing may also save costly investments in transmission lines (Bower, 2004). Nodal pricing was first implemented in New Zealand (1997), followed by some US markets (e.g. PJM 1998, New York 1998, New England 2003) and became part of FERC’s Standard Market Design.4 California is actually redesigning the procedures by which it performs forward scheduling and congestion management; CAISO plans to introduce nodal pricing by 2007 (CAISO, 2005). With the change from zonal to nodal pricing CAISO aims to reduce congestion costs. The initial market design divided the market into three zones allowing for constraints on only two transmission paths. However, most congestion occurs inside the zones resulting in more than 200 million dollars of yearly intra-zonal congestion costs (Price, 2007). With BETTA, a locational approach for generation investment was introduced for the Great Britain grid on the basis of marginal transmission requirements (To¨rnquist, 2005), but no locational wholesale mechanism was implemented. Empirical analyzes using the nodal pricing concept have been provided, e.g. for England/Wales, Austria, Italy and, most recently, for California. Green (2007) developed a 13node model of the transmission system in England and Wales incorporating losses and transmission constraints. The study analyzes the impact of different transmission pricing schemes (LMP, zonal and uniform pricing). Green shows that the introduction of the LMP concept would raise welfare by 1.5% compared to the uniform model on behalf of the larger consumer welfare (þ2.6%) while generator profit would decrease by 1.1%. To strengthen these results, Green applies different values for demand elasticity (0.1, 0.25, 0.4) and shows that the increase of welfare is higher with a larger absolute elasticity value. Stigler and Todem (2005) analyzed the economic impact of a nodal price based congestion management on the Austrian high voltage grid. Against the background of scarce transmission capacities in the East of Austria, the authors developed an optimization model with 165 nodes applicable to the bilateral Austrian electricity market. On the basis of January 2004 data, it was shown where congestion could occur and which prices would have been optimal. Stigler and Todem suggest a division
4
According to Ding and Fuller (2005), nodal pricing was introduced even earlier in some Latin American states (Chile 1982, Argentina 1992, Peru 1993, Bolivia 1994).
F. Leuthold et al. / Utilities Policy 16 (2008) 284e291
of the network into two pricing zones according to their congestion situation.5 Ding and Fuller (2005) provide interesting results regarding the distribution of economic surplus under nodal, uniform and zonal pricings. They show for the Italian 400 kV grid that there is no loss in (total) social surplus using uniform or zonal pricing with a nodal pricing dispatch compared to a full nodal price system (dispatch and pricing nodal-based). The authors therefore calculated optimal dispatch on the basis of an optimal power flow model, respecting transmission constraints and losses while defining uniform (respectively, zonal) prices for financial settlements. The results, however, show that the distribution of economic surplus between supply and demand sides will vary depending on the pricing model. More importantly, the authors reveal perverse incentives for generators that are dispatched at different levels than uniform or zonal prices would suggest. Empirical studies on the integration of wind energy focus either on policy mechanisms (e.g. Buen, 2006; Butler and Neuhoff, 2005) or on technical interactions and investments’ costs due to the highly variable input characteristic of wind turbines (e.g. Barth et al., 2006; Neuhoff et al., 2006). To our knowledge analysis using nodal pricing to estimate the impact of wind capacities has not yet been carried out. 3. Model and data The model is based on a social welfare approach as proposed by Schweppe et al. (1988). The network system operator aims to maximize the welfare. A linear demand function p(qn) representing the consumer behavior and a stepwise generation cost function c( gn) are estimated for each node n. The welfare equals total consumer benefit minus the total costs of generation, which is identical to the sum of consumer and producer surplus, defining the objective function of the model: n Z X
qn
max
1
pðqn Þ dqn
n X
ðcðgn Þgn Þ
ð1Þ
1
0
This objective function is subjected to technical constraints due to the particularities of electricity markets, the nonstorability of the commodity electricity (Eq. (2)), the physical behavior of the grid (Eq. (3)) and capacity restrictions of generators (Eq. (4)): n X
gn ¼
1
n X
qn þ L
ð2Þ
1
Pjk Pjk
ð3Þ
g n gn
ð4Þ
As an electricity market has to be in balance the sum of generation has to equal the total demand plus transmission 5 The most efficient solution to overcome the congestion problem, however, was to build an additional 380-kV line e the so-called ‘Steiermark’-line.
287
losses L (Eq. (2)). Furthermore power flows Pjk between two nodes j and k are a direct result of the overall demand and generation situation and have to be lower than the physical capacity limit of a line Pjk . If Eq. (3) is not binding the network is unrestricted. Assuming sufficient generation capacity, the price at each node equals the marginal cost of the last producing unit. If congestion occurs generation and demand have to be shifted, e.g. a more expensive plant at node k has to start-up and an inexpensive one at node j has to lower output to reduce the power flow on line Pjk. Thus the price will increase leading to an uplift on minimal costs of generation as in an unrestricted network: the scarcity price of transmission. While under uniform pricing, the first line restriction can define the price for the whole system, nodal pricing allows for locally differentiated prices. Additionally, installed power plant capacities define the maximal available generation capacities gn (Eq. (4)). To obtain the resulting power flows from an injection at a node, the DC Load Flow Model (DCLF) is used which mean that we do not model the impact of reactive power on the system. The DCLF focuses on the assumption that real power flows Pjk depend on the voltage angle differences Qjk and the line series susceptance bjk between two nodes j and k. While the susceptance is a fixed parameter, the voltage difference varies according to demand and generation in the overall system. Furthermore, the calculation is carried out as per unit calculation and the voltage differences are assumed to be very small. This yields a linear equation for the lossless line flows: Pjk ¼ bjk Qjk
ð5Þ
In addition, losses are important as they increase the amount of generated energy that is needed to supply a certain demand and represent additional cost impacts if energy has to be transported over large distances. Within the DCLF, losses can be calculated with respect to the power flow and the line resistance rjk between two nodes: Ljk ¼ rjk P2jk
ð6Þ
The model is coded in GAMS as a non-linear optimization problem. Regarding data, we choose 1 h as the relevant time frame. Unit commitment decisions and start-up or no-load costs are neglected. Our transmission model is based on the interconnected high voltage network according to UCTE (2004) including 309 nodes and 426 lines of the 380 and 220 kV level. Line characteristics are based on Fischer and Kießling (1989). Node specific generation costs are calculated on a marginal cost basis, including fuel costs but not accounting for operating and service costs. Marginal costs of wind power generation are practically zero, but to respect the cost impact on the whole system by increased balancing and response power costs, an opportunity costs value based on DENA (2005b) is applied within the model. The marginal costs of pump storage plants are based on the assumption that they store energy during the nighttime and have to buy this energy on the market exchange. With an efficiency of 0.75 this results in marginal costs
288
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of 28V/MWh. Table 1 summarizes the assumptions on marginal costs of electricity generation by type of fuel. To obtain a node specific reference demand, the regional GDP (EUROSTAT, 2005) was used. We assume that provinces with high economic output e and, respectively, with a high share in the countries’ GDP e have a high electricity demand. Consequently, the total electricity consumption was divided according to the GDP proportions. Within a province, the demand was distributed equally over all nodes. Based on this reference demand, a reference price taken from the energy exchange spot market and an elasticity of 0.25, the linear demand function is generated and applied for the calculation of the welfare (Eq. (1)). As only 1 h is considered in the model, different load situations are approximated by calculating three reference load cases: an average case, an off-peak case (70% of average load) representing the nighttime and a peak load case (140% of average load) representing noon and evening demand peaks. 4. Scenarios and results Within the first scenario the impact of implementing a nodal pricing mechanism in Germany is analyzed. As theory suggests, we always expect nodal pricing to be superior to uniform pricing in terms of efficiency, thus welfare. First, Germany is considered separately and cross border flows are neglected. In a second step, the grid is extended, now including Denmark, the Benelux, France and the Alpine countries Switzerland and Austria, to estimate the impact on the price situation in Germany. We expect that prices will differ for both cases since the neglecting of cross border flows disregards the opportunity of electricity import and export as well as the impact of congestion in neighboring countries on the German grid. To ensure comparability of these scenarios, the same input data are used. Within the nodal price and uniform price scenario demand and prices could vary, whereas the cost minimization approach works with a fixed given price. None of these scenarios considers the integration of additional offshore wind energy. Thus, the impact on social welfare of introducing a competitive nodal pricing scheme in Germany compared to the current situation is obtained. The results refer to hourly values. All calculations are based on the existing grid and average 2003 demand. The reference price for each country Table 1 Marginal costs of power generation per fuel type Fuel
Marginal costs [V/MWh]
Nuclear power Lignite Coal CCGT Wind Natural gas Fuel oil Running water Pump storage
10.00 15.00 18.00 30.00 4.05 40.00 50.00 0.00 28.00
Source: DENA (2005b), Schro¨ter (2004), own calculation.
Table 2 Results for cost minimization Load
Cost Minimization Welfare [Mio. V]
Demand [GWh]
Losses [MWh]
Off-peak Average Peak
3.19 4.44 5.67
39.4 56.3 76.1
1446 1544 1720
Source: own calculations.
is obtained by calculating the average price in 2003 e the same as for demand calculation e on the power exchange markets. Wind input and availability of water capacities are set to values based on average full load hours. In a first calculation a fixed price of 29.6V/MWh based on the average spot market price is given and a simple cost minimization is modeled. These results are then compared to a calculation based on a full nodal pricing mechanism. As expected the resulting nodal prices are well below the fixed uniform price resulting in a welfare increase of about 0.9% on average (Tables 2 and 3). In a second run the uniform price is not given as fixed parameter but is calculated within the optimization. These results also show that social welfare under nodal pricing is higher than under uniform pricing. In addition the calculations are carried out for the extended grid to estimate the impact of cross border flows. The welfare increase is rather small in the segregate German grid, whereas the surplus in a grid covering eight countries is noteworthy. A social welfare gain of 0.8% under average conditions is possible by implementing a nodal pricing mechanism in the investigated countries. Also a demand increase and a cost decrease can be observed. Additionally, the losses per demand are lower under nodal pricing. Variations in welfare gain between the scenarios result from two facts. First, the introduction of nodal prices allows prices to vary from node to node which means that energy is allocated according to the willingness of pay at each node (presented by the demand curve for each node). Second, the difference between the reference price and the average generation costs varies according to the demand level. In times of low demand (off-peak), mainly cheap base load and wind plants are used; thus the price error is the highest. Because of this the welfare spread between cost minimization and nodal pricing is largest during low load periods. The uniform price in the segregate German grid is about 19.7V/MWh while the average nodal price is 19.1V/MWh.
Table 3 Results for nodal pricing and changes to cost minimization Load
Low High Average
Nodal Pricing Welfare [Mio. V]
Welfare Change (%)
Demand [GWh]
Demand Change (%)
Losses [MWh]
3.23 4.48 5.67
1.3 0.9 0.6
44.4 62.0 79.2
12.9 10.3 8.4
1365 1547 1890
Source: own calculations.
F. Leuthold et al. / Utilities Policy 16 (2008) 284e291
289
Fig. 1. Price comparison with and without nodal prices.
However, prices in Southern Germany are higher under nodal pricing whereas prices in Northern and East Germany are lower (Fig. 1). To estimate the impact of cross border flows these prices were compared to prices obtained in the grid covering all eight countries. The results show that neglecting cross border flows leads to an overestimation of prices in Southern and Western Germany, mainly in BadenWuerttemberg and Bavaria. This is caused by the missing opportunity to import electricity from other countries, i.e. France. On the other hand prices in North-West Germany are slightly underestimated. This is caused by congestion in the Dutch grid limiting the amount of electricity that can be transported within North-West Germany. Even under a uniform pricing regime the integration of neighboring grids leads to a noticeable price reduction to 17.8V/MWh, which is a reduction of 10% compared to the situation in the segregate German grid. Altogether the hypothesis that nodal pricing is superior to uniform pricing can be affirmed. Furthermore, the importance of cross border flows e namely the opportunity to import electricity and the impact of congestion in neighboring countries e is well illustrated. Within the second scenario the impact of additional offshore wind capacities is analyzed using nodal pricing as indicator. At first, the maximum offshore wind capacity for the existing grid is identified. Hereafter, the grid is extended on selected lines. As the increased injection of wind energy is supposed to have an impact on cross border flows, the effects on the Benelux countries are also analyzed. The aim of the first calculation is to find out how much offshore wind energy could be fed into the grid at most without any transmission capacity extension. Offshore wind energy from the North Sea is supposed to be fed in completely at three injection nodes along the coastline (Brunsbu¨ttel, Emden, Wilhelmshaven). Operating the lines at their capacity limit and differing between the three possible load cases, the model
gives three values for input of power with a maximum of 7.9 GW in the high load case. Compared to the nodal pricing model without offshore wind parks, an additional welfare gain of 1% occurs. The average nodal price drops about 10%. In particular, the nodes in Northern Germany benefit from the additional wind energy. Overall, the Southern part of Germany is nearly unaffected by offshore wind generation. In the average load scenario, eight lines are congested, of which six are located in NorthWest Germany. These lines are the first to be checked for a grid upgrade if additional offshore energy is installed. One scenario of German energy policy makers is to install offshore wind energy plants with up to 15 GW.6 To cope with that development an extension of the grid becomes necessary. Within the model four lines are added and two lines are upgraded according to VGE (2000). Although, the grid extension is very ambitious, the maximum offshore capacity could not be raised higher than 13.3 GW. This is caused by additional congestion in North Germany. The results show that a grid extension has to be planed very carefully to maximize the additional benefits. Often new congestions occur in the hinterland grid limiting the effect of the extension. The additional welfare gain compared to the grid without extension is about 0.8%. The average price decreases about 2.5% and again mainly nodes in Northern Germany benefit from the grid extension.7 Surprisingly some nodes have prices
6
The DENA (2005a) grid study even proposes offshore wind capacities of 20 GW until 2020. 7 It must be considered that the network extension investments are not taken into account in the welfare analysis. Furthermore, grid constructions are likely to become political issues. Since only a limited amount of wind energy can be transported to the demand centers in the South, in a nodal pricing regime only Northern Germany would participate in the welfare gain resulting from offshore wind. The analysis shows that the current German electricity grid is not suited for a high amount of offshore capacity.
F. Leuthold et al. / Utilities Policy 16 (2008) 284e291
290 Table 4 Overview of scenario results
Demand-case Fixed price Nodal Offshore pricing þ8 GW þ13 GW Welfare [Mio. V/h] Low Average High Average price Low [V/MWh] Average High
3.19 4.44 5.64 29.60 29.60 29.60
3.23 4.48 5.67 13.98 17.07 19.07
3.27 4.54 5.75 12.82 15.42 18.39
3.29 4.57 5.80 11.79 15.06 17.30
Source: own calculations.
below their costs of generation. This can be explained by the welfare maximization approach. If additional demand at one node enables supplying a different node with a higher willingness to pay, the welfare gain from this node can be higher than the loss from the first node. This phenomenon only occurs in few cases in Northern Germany to enable a higher flow of cheap wind energy to the South. In addition, some nodes have to pay a higher price caused by congestion (Table 4). Power flows according to relative line impedances. Hence, additional wind energy can cause unintended but inevitable cross border flows, thus causing congestion in neighboring grids. Therefore, the situation in the Benelux is estimated within a further step of the analysis. We want to find out if the German wind capacities lead to additional congestion and therefore to a price increase in the Benelux. First a base case with average reference values for 2003 is calculated. In the second step, the grid is extended and additional wind capacities are installed to estimate the impact of the ambitious German offshore objectives. The wind capacities are increased according to EWEA (2003) and DENA (2005a) as projected for 2015. As all further assumptions are just as in the first scenario, the analysis can be considered as an estimation of increased wind input in the actual grid situation. The analysis shows a welfare gain in cases of high wind input for both the existing grid and the extended grid. This goes along with increased demand, reduced losses and reduced costs. The comparison of prices in the Benelux shows no price spikes caused by high wind input during average load cases in the existing grid. In peak situations, however, prices in Southern Belgium increase in case of high wind input due to congestion. The extension of both grid and wind capacities yields different results. The already planned grid extensions8 relax the situation at the FrencheBelgian border and lead to price reductions in Belgium. In case of high wind input e including offshore capacities in Germany e prices in Belgium will drop compared to the actual situation, while in the Northern parts of the Netherlands a remarkable price increase occurs. This is caused by congestion at the DutcheGerman border due to high wind energy supply in Northern Germany. Although the results differ for the actual and extended situation, the impact
8 Namely the upgrade of the 300-kV line between Avelin and Avelgem to a double circuit system and the planned line between Aubange and Moulaine.
of North-Western European wind capacities is obvious. In both the existing and the extended grid price increases in times of high wind input are observable. The analysis shows that the integration of additional offshore capacities is practicable and feasible to a certain extent. It causes problems if the grid is not properly extended. Nodal pricing can help to decide which extension measurements are necessary to cope with the changed situation. 5. Conclusion In this paper, we argue for an efficient allocation of scare electricity capacity using a nodal pricing approach. We have shown for Germany that nodal pricing is economically superior to a uniform price mechanism currently applied in most European electricity markets. Moreover, it has been illustrated that there is an additional welfare increase of about 1% on average in case of additional offshore wind input into the German power grid. However, the results show that there is a limit of offshore wind energy construction at about 8 GW without network extension. Beyond that we were forced to extent the modeled grid in order to cope with further wind energy input. However, the hinterland grid cannot carry the burden and congestion occurs on lines leaving the adjacent nodes. Consequently, the prices at the inland nodes do not differ significantly as their demand is accommodated by the same generating pool as before. Furthermore it could be shown that the increased implementation of offshore capacities in Northern Germany affects the neighboring grids, namely the Benelux. Therefore, not only the grid situation within Germany can be taken into account when deciding where and how to extend the grid. Hence, the efforts of the European Union to strive for greater coordination among European countries are economically justified as it helps to reduce the mentioned external effects. Future research should develop the concept of nodal pricing further, and to put it onto a dynamic context: in particular, the static version of nodal pricing does not provide incentives to extend the (congested) grid efficiently. One of the main tasks, therefore, is to develop a theoretical model to combine incentive regulation of a transmission owner with the appropriate signals for network extension. References Alaywan, Ziad, Wu, Tong, 2004. Transitioning the California market from a zonal to a nodal framework: an operational perspective. In: Proceedings of the Power Systems Conference and Exposition, vol. 2, pp. 862e867. Barth, Ru¨diger, Brand, Heike, Meibom, Peter, Weber, Christoph, 2006. A stochastic unit-commitment model for the evaluation of the impacts of integration of large amounts of intermittent wind power. In: Proceedings of the 9th International Conference on Probabilistic Methods Applied to Power Systems Stockholm. Borenstein, Severin, Bushnell, James, Wolak, Frank, 2000. Diagnosing Market Power in California’s Deregulated Wholesale Electricity Market. PWP064. University of California Energy Institute. Boucher, Jacqueline, Smeers, Yves, 2002. Towards a common European electricity market e paths in the right direction. Still far from an effective design. Journal of Network Industries 3, 375e424.
F. Leuthold et al. / Utilities Policy 16 (2008) 284e291 Bower, John, 2004. Electricity Infrastructure & Security of Supply: Should EU Governments Invest in Transmission Capacity? Oxford Institute for Energy Studies. Brunekreeft, Gert, Neuhoff, Karsten, Newbery, David, 2005. Electricity transmission: an overview of the current debate. Utilities Policy 13, 73e93. Buen, Jorund, 2006. Danish and Norwegian wind industry: the relationship between policy instruments, innovation and diffusion. Energy Policy 34, 3887e3897. Butler, Lucy, Neuhoff, Karsten, 2005. Comparison of Feed in Tariff, Quota and Auction Mechanisms to Support Wind Power Development. Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge. CAISO, 2005. Market Redesign and Technology Upgrade. Locational Marginal Pricing (LMP) Study 3B. California Independent System Operator. DENA, 2005a. dena-Netzstudie, Energiewirtschaftliche Planung fu¨r die Netzintegration von Windenergie in Deutschland an Land und Offshore bis zum Jahr 2020. Tech. rep., Deutsche Energie-Agentur. DENA, 2005b. Summary of the Essential Results, Planning of the Grid Integration of Wind Energy in Germany Onshore and Offshore up to the Year 2020. Tech. rep., Deutsche Energie-Agentur. Ding, Feng, Fuller, J. David, 2005. Nodal, uniform, or zonal pricing: distribution of economic surplus. IEEE Transactions on Power Systems 20, 875e882. Ehrenmann, Andreas, Smeers, Yves, 2005. Inefficiencies in European congestion management proposals. Utilities Policy 13, 135e152. EUROSTAT, 2005. Regional Gross Domestic Product Retrieved January 9, 2006. Available from: http://epp.eurostat.cec.eu. EWEA, 2003. Wind Power Targets for Europe: 75,000 MW by 2010. The European Wind Energy Association, Brussels, Belgium. Fischer, Reinhard, Kießling, Friedrich, 1989. Freileitungen e Planung, Berechnung, Ausfu¨hrung. Springer Verlag, Berlin, Heidelberg. Green, Richard, 2007. Nodal pricing of electricity. How much does it cost to get it wrong? Journal of Regulatory Economics 31, 125e149. Hogan, William W., 1999. Transmission Congestion: the NodaleZonal Debate Revisited. Harvard University, John F. Kennedy School of Government, Center for Business and Government.
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Hogan, William W., 2003. Transmission Market Design. KSG Working Paper No. RWP03e040. Johnsen, Tor Arnt, Verma, Shashi Kant, Wolfram, Catherine, 1999. Zonal Pricing and Demand-side Bidding in the Norwegian Electricity Market. PWP-063. University of California Energy Institute. Krause, Thilo, 2005. Congestion Management in Liberalized Electricity Markets e Theoretical Concepts and International Application. EEH e power systems laboratory, Swiss Federal Institute of Technology, Zurich. Neuhoff, Karsten, Ehrenmann, Andreas, Butler, Lucy, Cust, Jim, Hoexter, Harriet, Keats, Kim, Kreczko, Adam, Sinden, Graham, 2006. Space and Time: Wind in an Investment Planning Model. Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge. Pe´rez-Arriaga, Ignacio J., Olmos, Luis, 2005. A plausible congestion management scheme for the internal electricity market of the European Union. Utilities Policy 13, 117e134. Price, James E., 2007. Market-based price differentials in zonal and LMP market designs. IEEE Transactions on Power Systems 22 (4), 1486e1494. Schro¨ter, Jochen, 2004. Auswirkungen des europa¨ischen Emissionshandelssystems auf den Kraftwerkseinsatz in Deutschland. Master’s thesis, Berlin University of Technology, Institute of Power Engineering. Schweppe, Fred C., Caramanis, Michael C., Tabors, Richard D., Bohn, Roger E., 1988. Spot Pricing of Electricity. Kluwer, Boston. Stigler, Heinz, Todem, Christian, 2005. Optimization of the Austrian electricity sector (control zone of VERBUND APG) under the constraints of network capacities by nodal pricing. Central European Journal of Operations Research 13, 105e125. Stoft, Steven, 2002. Power System Economics. IEEE Press, Piscataway, NJ. To¨rnquist, Jonas, 2005. UK Transmission System Challenges. The Office of Gas and Electricity Markets. UCTE, 2004. Interconnected Network of UCTE. Abel Druck, Dortmund. VGE, 2000. Elektrizita¨ts-Verbundsysteme in Deutschland. Verlag Glu¨ckauf, Essen.
Utilities Policy 16 (2008) 284e291 www.elsevier.com/locate/jup
Efficient pricing for European electricity networks e The theory of nodal pricing applied to feeding-in wind in Germany Florian Leuthold*, Hannes Weigt, Christian von Hirschhausen Chair of Energy Economics and Public Sector Management, Dresden University of Technology, 01062 Dresden, Germany Received 27 August 2007; received in revised form 21 December 2007; accepted 23 December 2007
Abstract This paper applies nodal pricing as an economic approach to efficient use of electricity networks utilization for the fairly large German grid. We combine a straightforward welfare maximization with the technical specificities of electricity flows on a realistically large network. The nodal pricing model is applied in order to analyze the impact of extended German wind power production on the power grid. The paper shows that economic modeling, taking into account physical and technical constraints, makes important contributions to the assessment and optimization of system configuration and operation. Ó 2008 Elsevier Ltd. All rights reserved. Keywords: Electricity networks; Nodal pricing; Locational Marginal Pricing; Welfare; Wind energy
1. Introduction The efficient use of partly congested network infrastructure is a recurring theme in economics. While the theory of marginal and opportunity cost pricing works well in economic theory, its application to the real world hinges upon the specific technical and institutional characteristics of the sector. This is particularly true for the electricity sector, where e due to physical laws e capacity itself depends on the structure of demand and supply, and is time-variant. This paper presents the application of nodal pricing as an economical approach to congestion pricing in electricity networks to a specific situation in the German electricity grid, the feeding-in of offshore wind energy. Nodal pricing has emerged a powerful and efficient tool of transmission pricing, both in theory and e more recently e in practice. Lessons from North America, Australia, and New Zealand have proven nodal pricing to be workable without * Corresponding author. Tel.: þ49 351 463 39764; fax: þ49 351 463 39763. E-mail addresses: [email protected] (F. Leuthold), hannes. [email protected] (H. Weigt), [email protected] (C. von Hirschhausen). 0957-1787/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jup.2007.12.003
serious technical problems. The European Commission is considering nodal pricing amongst others in the context of the creation of an integrated European electricity market (Brunekreeft et al., 2005). Germany is currently undergoing substantial structural change in the regulation of its electricity industry. It has not only finally implemented a sectoral regulator, as required by the Acceleration Directive 2054/05/EC, but is currently also pondering more efficient network pricing both within and in connection to its neighboring countries. In addition, Germany has pushed strongly for the development of renewable energies and their integration into the existing network. This is particularly the case for wind energy, where Germany had 20.7 GW onshore wind capacities in 2006; in addition about 7 GW are expected to be constructed offshore until 2015 and a long-term goal of 30 GW offshore is considered for 2030 (Brunekreeft et al., 2005). At present, the technicalities of the integration of the offshore wind energy are being studied by industry and government; there is also a debate about the economic costs of integrating large-scale wind power into the German electricity grid (DENA, 2005a). DENA (2005a) concludes that there is a significant impact on electricity transport by large wind capacities. Particularly the integration of large-scale offshore projects can increase
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congestion within the transmission grid as these wind farms are connected only to a few nodes via high voltage DC cables. Thus the problem arises where and to what extent the transmission grid will have to be upgraded to be suited for the expected increase of offshore wind generation over the coming years. We pick up this issue by focusing on the economic possibilities of large-scale wind integration due to a more efficient congestion management mechanism that allows for a better usage of existing transmission grid capacities. While there is an increasing literature on the regulation of the electricity sector, no empirical work has been carried out so far on the implementation of nodal pricing or congestion management in general for Germany. However, there is a comprehensive literature on congestion management on a European level. Boucher and Smeers (2002) analyze the future organization of cross border trade in the European electricity market concluding that the economic principles as proposed by the European Commission in 2001 are not sufficient. Ehrenmann and Smeers (2005) analyze the regulation of cross border trade of electricity (Regulation 1228/2003) in terms of efficient congestion management. They conclude that market coupling e although its implementation is more complex e can path the way to a consistent system integrating the energy and transmission markets. Pe´rez-Arriaga and Olmos (2005) looked at plausible congestion management schemes for the internal electricity market of the European Union. Taking a joint energy and capacity auction as benchmark, they test two alternative approaches: an integrated transmission and energy auction and a coordinated explicit auction of transmission capacity followed by separate energy auctions at the different power exchanges. They propose the latter since it is the most feasible alternative. Our paper takes up the issue of congestion management and focuses on the German market and network. We compare a nodal pricing scheme for the German electricity market to the current pricing scheme assuming a liberalized and competitive electricity market. Our hypothesis is that nodal pricing is more efficient when allocating scarce capacities than uniform pricing which is currently applied in Germany. By using a simplified model of the Central European high voltage grid we can simulate different pricing methods. Furthermore the nodal pricing approach is used to estimate the impacts of large-scale wind power capacities in the North Sea as the obtained nodal prices represent scarcity signals. The objective of this paper is to provide an economic and technical analysis of the issue, whereas technical aspects have dominated the debate in Europe so far. The paper is structured as follows: Section 2 provides a survey of pricing mechanisms used in the electricity sector and suggests that nodal pricing is the most efficient approach. Section 3 develops the model and describes the data that are required for an application to the German electricity grid. Model scenarios and results are reported in Section 4. We are particularly interested in the effects of nodal pricing on the use of wind energy. The section also extends the analysis, beyond the German border, to neighboring countries. We find that nodal pricing leads to a significant increase in total welfare. Section 5 concludes.
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2. Pricing mechanisms in electricity markets The objective of our paper is to estimate possible gains in coping with transmission problems in Germany due to wind energy input by changing the congestion management scheme from uniform pricing to nodal pricing. As electricity transmission follows physical laws, the underlying pricing mechanism has to address these principles in order to lead to efficient capacity usage. Subsequently, we provide a review on different pricing mechanisms and their ability to address the physical characteristics properly. Contrary to ‘‘normal’’ markets such as mineral water, electricity markets are subjected to several technical restrictions that have to be taken into account within a pricing mechanism. Electricity cannot be stored, thus demand and generation have to be equal in each instant; demand is inelastic; and electricity can only be transported using a network in which power flows according to physical and not economic laws. Competitive markets for electricity determine either a uniform marginal price, a few zonal marginal prices, or a set of nodal or Locational Marginal Prices (LMPs). Although theory proves LMPs to be the most efficient, critics find the large number of LMPs e compared to one uniform or several zonal prices e confusing. They claim a uniform- or zonal-based model to be more transparent. The current pricing mechanism in Germany consists of a uniform wholesale price assuming an unrestricted network (‘‘copper plate’’). The location of demand and generation is not considered within the pricing process thus the marginal generator defines the price for the entire system. Transmission expenses are covered by a mixed price calculation levied by the grid company where the demand is located. The fee contains a fixed component for network access and a variable demand charge. This payment covers costs from losses, ancillary services, voltage transformation and access to networks at lower voltage levels. Identical consumers pay the same price for energy transmission, independent from the location within the grid area of the company and the point in time of their consumption, thus no price signal for scare transmission capacity exists. Uniform pricing has also been applied in Finland (since 1998), Sweden (since 1996), Alberta (since 2001), Ontario (since 2002), and most of the continental European countries and has been in operation in the former England/Wales-Pool (1990e2005), PJM (1997e1998) and in the first phase of the New England market from 1999 to 2003 (Ding and Fuller, 2005). It works efficiently only in the absence of congestion. Otherwise, in the case of congestion, an uplift payment is required, which covers overall costs from congestion but does not send adequate market signals (Krause, 2005) Therefore, uniform pricing is not able to ensure an optimal allocation of energy and transmission capacities in the setting of congestion as seen, e.g., in the case of New England (Hogan, 1999). An attempt to solve incentive problems of the uniform pricing approach was to introduce zonal pricing, currently applied in Norway (since 1991), Australia (since 1998), and Denmark (since 2000). The California independent network operator
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(CAISO) used zonal pricing from 1998 to 2002 (Ding and Fuller, 2005). Zones are network areas within a larger grid. In case of congestion these zones have different prices, whereas the prices within a zone are equal. Zones can either be defined endogenously based on the congestion situation or exogenously as pre-defined and fix zones due to other than economic reasons (e.g. control area of grid operators, federal states). Proponents of zonal pricing claim that it would balance well equity concerns and efficiency goals and is less complex and therefore more transparent to market participants (Alaywan and Wu, 2004). On the other hand, the zonal approach is criticized for its potential of market power abuse during periods of high demand and resulting congestion (Borenstein et al., 2000). Johnsen et al. (1999), however, could not find clear empirical evidence in a study on Norway. On the other hand, Hogan (1999) rejects the model of zonal prices for a number of reasons. He argues that it creates more administrative rules, poorer incentives for investments, demands to pay generators not to generate power, and finally it is much more complicate to define zonal than nodal prices (Hogan, 1999).1 Extending the zonal approach to define each node (representing a physical location on the transmission system including generators and loads) as a single price zone yields a full nodal pricing approach.2 The price at each node reflects the locational value of energy, including the cost of the energy and the cost of delivering it (e.g. losses and congestion). Nodal prices are determined by calculating the incremental cost of serving one additional MW of load at each respective location subjected to system constraints (e.g. transmission limits, maximal generation capacity). Differences of prices between nodes reflect the costs of transmission.3 In order to optimize dispatch in a nodal pricing system, a classic supply and demand equilibrium price has to be developed. The marginal generator is determined by matching offers from generators to bids from loads at each node. This process is carried out for a specific time interval (e.g. every 15 min) at each input and exit node on the transmission grid. The prices take into account the losses and constraints in the system, and generators are dispatched by the system operator, not only in ascending order of offers (or descending order of bids), but in accordance with the required security of the
1
Hogan cites the 1997 PJM attempt to install zonal pricing as an example, where the system collapsed as soon as constraints occurred. Generators rather run than respect transmission constraints e just responding to (distorted) signals from zonal pricing. 2 There are at least three alternative denominations of ‘‘nodal prices’’: ‘‘Locational Marginal Price/LMP’’ (PJM), ‘‘Location Based Marginal Pricing/ LBMP’’ (NYISO) and ‘‘Competitive Locational Prices/CLP’’ (Stoft, 2002). 3 Congestion occurs if both of the following two conditions are fulfilled (Stoft, 2002, p. 392): first, the marginal costs of production differ between nodes. Second, overall demand exceeds supply ability of the ‘‘cheapest’’ generator due to limited production or constrained line capacity. A line constraint can be caused when a particular branch of a network reaches its thermal limit or when a potential overload will occur due to a contingent event on another part of the network (e.g. generator black out). The latter is referred to as a security constraint.
system. This results in a spot market with bid-based, security-constrained, economic dispatch according to nodal prices as proposed by Hogan (2003). Nodal prices reflect the actual situation in the grid more transparently than uniform or zonal prices and represent adequate allocation signals. Nodal prices are one of several important considerations in analyzing where to site additional generation, transmission and load. The implementation of efficient congestion management methods on the basis of nodal pricing is crucial to cope with scarce transmission capacities and to ensure security of supply. Nodal pricing may also save costly investments in transmission lines (Bower, 2004). Nodal pricing was first implemented in New Zealand (1997), followed by some US markets (e.g. PJM 1998, New York 1998, New England 2003) and became part of FERC’s Standard Market Design.4 California is actually redesigning the procedures by which it performs forward scheduling and congestion management; CAISO plans to introduce nodal pricing by 2007 (CAISO, 2005). With the change from zonal to nodal pricing CAISO aims to reduce congestion costs. The initial market design divided the market into three zones allowing for constraints on only two transmission paths. However, most congestion occurs inside the zones resulting in more than 200 million dollars of yearly intra-zonal congestion costs (Price, 2007). With BETTA, a locational approach for generation investment was introduced for the Great Britain grid on the basis of marginal transmission requirements (To¨rnquist, 2005), but no locational wholesale mechanism was implemented. Empirical analyzes using the nodal pricing concept have been provided, e.g. for England/Wales, Austria, Italy and, most recently, for California. Green (2007) developed a 13node model of the transmission system in England and Wales incorporating losses and transmission constraints. The study analyzes the impact of different transmission pricing schemes (LMP, zonal and uniform pricing). Green shows that the introduction of the LMP concept would raise welfare by 1.5% compared to the uniform model on behalf of the larger consumer welfare (þ2.6%) while generator profit would decrease by 1.1%. To strengthen these results, Green applies different values for demand elasticity (0.1, 0.25, 0.4) and shows that the increase of welfare is higher with a larger absolute elasticity value. Stigler and Todem (2005) analyzed the economic impact of a nodal price based congestion management on the Austrian high voltage grid. Against the background of scarce transmission capacities in the East of Austria, the authors developed an optimization model with 165 nodes applicable to the bilateral Austrian electricity market. On the basis of January 2004 data, it was shown where congestion could occur and which prices would have been optimal. Stigler and Todem suggest a division
4
According to Ding and Fuller (2005), nodal pricing was introduced even earlier in some Latin American states (Chile 1982, Argentina 1992, Peru 1993, Bolivia 1994).
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of the network into two pricing zones according to their congestion situation.5 Ding and Fuller (2005) provide interesting results regarding the distribution of economic surplus under nodal, uniform and zonal pricings. They show for the Italian 400 kV grid that there is no loss in (total) social surplus using uniform or zonal pricing with a nodal pricing dispatch compared to a full nodal price system (dispatch and pricing nodal-based). The authors therefore calculated optimal dispatch on the basis of an optimal power flow model, respecting transmission constraints and losses while defining uniform (respectively, zonal) prices for financial settlements. The results, however, show that the distribution of economic surplus between supply and demand sides will vary depending on the pricing model. More importantly, the authors reveal perverse incentives for generators that are dispatched at different levels than uniform or zonal prices would suggest. Empirical studies on the integration of wind energy focus either on policy mechanisms (e.g. Buen, 2006; Butler and Neuhoff, 2005) or on technical interactions and investments’ costs due to the highly variable input characteristic of wind turbines (e.g. Barth et al., 2006; Neuhoff et al., 2006). To our knowledge analysis using nodal pricing to estimate the impact of wind capacities has not yet been carried out. 3. Model and data The model is based on a social welfare approach as proposed by Schweppe et al. (1988). The network system operator aims to maximize the welfare. A linear demand function p(qn) representing the consumer behavior and a stepwise generation cost function c( gn) are estimated for each node n. The welfare equals total consumer benefit minus the total costs of generation, which is identical to the sum of consumer and producer surplus, defining the objective function of the model: n Z X
qn
max
1
pðqn Þ dqn
n X
ðcðgn Þgn Þ
ð1Þ
1
0
This objective function is subjected to technical constraints due to the particularities of electricity markets, the nonstorability of the commodity electricity (Eq. (2)), the physical behavior of the grid (Eq. (3)) and capacity restrictions of generators (Eq. (4)): n X
gn ¼
1
n X
qn þ L
ð2Þ
1
Pjk Pjk
ð3Þ
g n gn
ð4Þ
As an electricity market has to be in balance the sum of generation has to equal the total demand plus transmission 5 The most efficient solution to overcome the congestion problem, however, was to build an additional 380-kV line e the so-called ‘Steiermark’-line.
287
losses L (Eq. (2)). Furthermore power flows Pjk between two nodes j and k are a direct result of the overall demand and generation situation and have to be lower than the physical capacity limit of a line Pjk . If Eq. (3) is not binding the network is unrestricted. Assuming sufficient generation capacity, the price at each node equals the marginal cost of the last producing unit. If congestion occurs generation and demand have to be shifted, e.g. a more expensive plant at node k has to start-up and an inexpensive one at node j has to lower output to reduce the power flow on line Pjk. Thus the price will increase leading to an uplift on minimal costs of generation as in an unrestricted network: the scarcity price of transmission. While under uniform pricing, the first line restriction can define the price for the whole system, nodal pricing allows for locally differentiated prices. Additionally, installed power plant capacities define the maximal available generation capacities gn (Eq. (4)). To obtain the resulting power flows from an injection at a node, the DC Load Flow Model (DCLF) is used which mean that we do not model the impact of reactive power on the system. The DCLF focuses on the assumption that real power flows Pjk depend on the voltage angle differences Qjk and the line series susceptance bjk between two nodes j and k. While the susceptance is a fixed parameter, the voltage difference varies according to demand and generation in the overall system. Furthermore, the calculation is carried out as per unit calculation and the voltage differences are assumed to be very small. This yields a linear equation for the lossless line flows: Pjk ¼ bjk Qjk
ð5Þ
In addition, losses are important as they increase the amount of generated energy that is needed to supply a certain demand and represent additional cost impacts if energy has to be transported over large distances. Within the DCLF, losses can be calculated with respect to the power flow and the line resistance rjk between two nodes: Ljk ¼ rjk P2jk
ð6Þ
The model is coded in GAMS as a non-linear optimization problem. Regarding data, we choose 1 h as the relevant time frame. Unit commitment decisions and start-up or no-load costs are neglected. Our transmission model is based on the interconnected high voltage network according to UCTE (2004) including 309 nodes and 426 lines of the 380 and 220 kV level. Line characteristics are based on Fischer and Kießling (1989). Node specific generation costs are calculated on a marginal cost basis, including fuel costs but not accounting for operating and service costs. Marginal costs of wind power generation are practically zero, but to respect the cost impact on the whole system by increased balancing and response power costs, an opportunity costs value based on DENA (2005b) is applied within the model. The marginal costs of pump storage plants are based on the assumption that they store energy during the nighttime and have to buy this energy on the market exchange. With an efficiency of 0.75 this results in marginal costs
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of 28V/MWh. Table 1 summarizes the assumptions on marginal costs of electricity generation by type of fuel. To obtain a node specific reference demand, the regional GDP (EUROSTAT, 2005) was used. We assume that provinces with high economic output e and, respectively, with a high share in the countries’ GDP e have a high electricity demand. Consequently, the total electricity consumption was divided according to the GDP proportions. Within a province, the demand was distributed equally over all nodes. Based on this reference demand, a reference price taken from the energy exchange spot market and an elasticity of 0.25, the linear demand function is generated and applied for the calculation of the welfare (Eq. (1)). As only 1 h is considered in the model, different load situations are approximated by calculating three reference load cases: an average case, an off-peak case (70% of average load) representing the nighttime and a peak load case (140% of average load) representing noon and evening demand peaks. 4. Scenarios and results Within the first scenario the impact of implementing a nodal pricing mechanism in Germany is analyzed. As theory suggests, we always expect nodal pricing to be superior to uniform pricing in terms of efficiency, thus welfare. First, Germany is considered separately and cross border flows are neglected. In a second step, the grid is extended, now including Denmark, the Benelux, France and the Alpine countries Switzerland and Austria, to estimate the impact on the price situation in Germany. We expect that prices will differ for both cases since the neglecting of cross border flows disregards the opportunity of electricity import and export as well as the impact of congestion in neighboring countries on the German grid. To ensure comparability of these scenarios, the same input data are used. Within the nodal price and uniform price scenario demand and prices could vary, whereas the cost minimization approach works with a fixed given price. None of these scenarios considers the integration of additional offshore wind energy. Thus, the impact on social welfare of introducing a competitive nodal pricing scheme in Germany compared to the current situation is obtained. The results refer to hourly values. All calculations are based on the existing grid and average 2003 demand. The reference price for each country Table 1 Marginal costs of power generation per fuel type Fuel
Marginal costs [V/MWh]
Nuclear power Lignite Coal CCGT Wind Natural gas Fuel oil Running water Pump storage
10.00 15.00 18.00 30.00 4.05 40.00 50.00 0.00 28.00
Source: DENA (2005b), Schro¨ter (2004), own calculation.
Table 2 Results for cost minimization Load
Cost Minimization Welfare [Mio. V]
Demand [GWh]
Losses [MWh]
Off-peak Average Peak
3.19 4.44 5.67
39.4 56.3 76.1
1446 1544 1720
Source: own calculations.
is obtained by calculating the average price in 2003 e the same as for demand calculation e on the power exchange markets. Wind input and availability of water capacities are set to values based on average full load hours. In a first calculation a fixed price of 29.6V/MWh based on the average spot market price is given and a simple cost minimization is modeled. These results are then compared to a calculation based on a full nodal pricing mechanism. As expected the resulting nodal prices are well below the fixed uniform price resulting in a welfare increase of about 0.9% on average (Tables 2 and 3). In a second run the uniform price is not given as fixed parameter but is calculated within the optimization. These results also show that social welfare under nodal pricing is higher than under uniform pricing. In addition the calculations are carried out for the extended grid to estimate the impact of cross border flows. The welfare increase is rather small in the segregate German grid, whereas the surplus in a grid covering eight countries is noteworthy. A social welfare gain of 0.8% under average conditions is possible by implementing a nodal pricing mechanism in the investigated countries. Also a demand increase and a cost decrease can be observed. Additionally, the losses per demand are lower under nodal pricing. Variations in welfare gain between the scenarios result from two facts. First, the introduction of nodal prices allows prices to vary from node to node which means that energy is allocated according to the willingness of pay at each node (presented by the demand curve for each node). Second, the difference between the reference price and the average generation costs varies according to the demand level. In times of low demand (off-peak), mainly cheap base load and wind plants are used; thus the price error is the highest. Because of this the welfare spread between cost minimization and nodal pricing is largest during low load periods. The uniform price in the segregate German grid is about 19.7V/MWh while the average nodal price is 19.1V/MWh.
Table 3 Results for nodal pricing and changes to cost minimization Load
Low High Average
Nodal Pricing Welfare [Mio. V]
Welfare Change (%)
Demand [GWh]
Demand Change (%)
Losses [MWh]
3.23 4.48 5.67
1.3 0.9 0.6
44.4 62.0 79.2
12.9 10.3 8.4
1365 1547 1890
Source: own calculations.
F. Leuthold et al. / Utilities Policy 16 (2008) 284e291
289
Fig. 1. Price comparison with and without nodal prices.
However, prices in Southern Germany are higher under nodal pricing whereas prices in Northern and East Germany are lower (Fig. 1). To estimate the impact of cross border flows these prices were compared to prices obtained in the grid covering all eight countries. The results show that neglecting cross border flows leads to an overestimation of prices in Southern and Western Germany, mainly in BadenWuerttemberg and Bavaria. This is caused by the missing opportunity to import electricity from other countries, i.e. France. On the other hand prices in North-West Germany are slightly underestimated. This is caused by congestion in the Dutch grid limiting the amount of electricity that can be transported within North-West Germany. Even under a uniform pricing regime the integration of neighboring grids leads to a noticeable price reduction to 17.8V/MWh, which is a reduction of 10% compared to the situation in the segregate German grid. Altogether the hypothesis that nodal pricing is superior to uniform pricing can be affirmed. Furthermore, the importance of cross border flows e namely the opportunity to import electricity and the impact of congestion in neighboring countries e is well illustrated. Within the second scenario the impact of additional offshore wind capacities is analyzed using nodal pricing as indicator. At first, the maximum offshore wind capacity for the existing grid is identified. Hereafter, the grid is extended on selected lines. As the increased injection of wind energy is supposed to have an impact on cross border flows, the effects on the Benelux countries are also analyzed. The aim of the first calculation is to find out how much offshore wind energy could be fed into the grid at most without any transmission capacity extension. Offshore wind energy from the North Sea is supposed to be fed in completely at three injection nodes along the coastline (Brunsbu¨ttel, Emden, Wilhelmshaven). Operating the lines at their capacity limit and differing between the three possible load cases, the model
gives three values for input of power with a maximum of 7.9 GW in the high load case. Compared to the nodal pricing model without offshore wind parks, an additional welfare gain of 1% occurs. The average nodal price drops about 10%. In particular, the nodes in Northern Germany benefit from the additional wind energy. Overall, the Southern part of Germany is nearly unaffected by offshore wind generation. In the average load scenario, eight lines are congested, of which six are located in NorthWest Germany. These lines are the first to be checked for a grid upgrade if additional offshore energy is installed. One scenario of German energy policy makers is to install offshore wind energy plants with up to 15 GW.6 To cope with that development an extension of the grid becomes necessary. Within the model four lines are added and two lines are upgraded according to VGE (2000). Although, the grid extension is very ambitious, the maximum offshore capacity could not be raised higher than 13.3 GW. This is caused by additional congestion in North Germany. The results show that a grid extension has to be planed very carefully to maximize the additional benefits. Often new congestions occur in the hinterland grid limiting the effect of the extension. The additional welfare gain compared to the grid without extension is about 0.8%. The average price decreases about 2.5% and again mainly nodes in Northern Germany benefit from the grid extension.7 Surprisingly some nodes have prices
6
The DENA (2005a) grid study even proposes offshore wind capacities of 20 GW until 2020. 7 It must be considered that the network extension investments are not taken into account in the welfare analysis. Furthermore, grid constructions are likely to become political issues. Since only a limited amount of wind energy can be transported to the demand centers in the South, in a nodal pricing regime only Northern Germany would participate in the welfare gain resulting from offshore wind. The analysis shows that the current German electricity grid is not suited for a high amount of offshore capacity.
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290 Table 4 Overview of scenario results
Demand-case Fixed price Nodal Offshore pricing þ8 GW þ13 GW Welfare [Mio. V/h] Low Average High Average price Low [V/MWh] Average High
3.19 4.44 5.64 29.60 29.60 29.60
3.23 4.48 5.67 13.98 17.07 19.07
3.27 4.54 5.75 12.82 15.42 18.39
3.29 4.57 5.80 11.79 15.06 17.30
Source: own calculations.
below their costs of generation. This can be explained by the welfare maximization approach. If additional demand at one node enables supplying a different node with a higher willingness to pay, the welfare gain from this node can be higher than the loss from the first node. This phenomenon only occurs in few cases in Northern Germany to enable a higher flow of cheap wind energy to the South. In addition, some nodes have to pay a higher price caused by congestion (Table 4). Power flows according to relative line impedances. Hence, additional wind energy can cause unintended but inevitable cross border flows, thus causing congestion in neighboring grids. Therefore, the situation in the Benelux is estimated within a further step of the analysis. We want to find out if the German wind capacities lead to additional congestion and therefore to a price increase in the Benelux. First a base case with average reference values for 2003 is calculated. In the second step, the grid is extended and additional wind capacities are installed to estimate the impact of the ambitious German offshore objectives. The wind capacities are increased according to EWEA (2003) and DENA (2005a) as projected for 2015. As all further assumptions are just as in the first scenario, the analysis can be considered as an estimation of increased wind input in the actual grid situation. The analysis shows a welfare gain in cases of high wind input for both the existing grid and the extended grid. This goes along with increased demand, reduced losses and reduced costs. The comparison of prices in the Benelux shows no price spikes caused by high wind input during average load cases in the existing grid. In peak situations, however, prices in Southern Belgium increase in case of high wind input due to congestion. The extension of both grid and wind capacities yields different results. The already planned grid extensions8 relax the situation at the FrencheBelgian border and lead to price reductions in Belgium. In case of high wind input e including offshore capacities in Germany e prices in Belgium will drop compared to the actual situation, while in the Northern parts of the Netherlands a remarkable price increase occurs. This is caused by congestion at the DutcheGerman border due to high wind energy supply in Northern Germany. Although the results differ for the actual and extended situation, the impact
8 Namely the upgrade of the 300-kV line between Avelin and Avelgem to a double circuit system and the planned line between Aubange and Moulaine.
of North-Western European wind capacities is obvious. In both the existing and the extended grid price increases in times of high wind input are observable. The analysis shows that the integration of additional offshore capacities is practicable and feasible to a certain extent. It causes problems if the grid is not properly extended. Nodal pricing can help to decide which extension measurements are necessary to cope with the changed situation. 5. Conclusion In this paper, we argue for an efficient allocation of scare electricity capacity using a nodal pricing approach. We have shown for Germany that nodal pricing is economically superior to a uniform price mechanism currently applied in most European electricity markets. Moreover, it has been illustrated that there is an additional welfare increase of about 1% on average in case of additional offshore wind input into the German power grid. However, the results show that there is a limit of offshore wind energy construction at about 8 GW without network extension. Beyond that we were forced to extent the modeled grid in order to cope with further wind energy input. However, the hinterland grid cannot carry the burden and congestion occurs on lines leaving the adjacent nodes. Consequently, the prices at the inland nodes do not differ significantly as their demand is accommodated by the same generating pool as before. Furthermore it could be shown that the increased implementation of offshore capacities in Northern Germany affects the neighboring grids, namely the Benelux. Therefore, not only the grid situation within Germany can be taken into account when deciding where and how to extend the grid. Hence, the efforts of the European Union to strive for greater coordination among European countries are economically justified as it helps to reduce the mentioned external effects. Future research should develop the concept of nodal pricing further, and to put it onto a dynamic context: in particular, the static version of nodal pricing does not provide incentives to extend the (congested) grid efficiently. One of the main tasks, therefore, is to develop a theoretical model to combine incentive regulation of a transmission owner with the appropriate signals for network extension. References Alaywan, Ziad, Wu, Tong, 2004. Transitioning the California market from a zonal to a nodal framework: an operational perspective. In: Proceedings of the Power Systems Conference and Exposition, vol. 2, pp. 862e867. Barth, Ru¨diger, Brand, Heike, Meibom, Peter, Weber, Christoph, 2006. A stochastic unit-commitment model for the evaluation of the impacts of integration of large amounts of intermittent wind power. In: Proceedings of the 9th International Conference on Probabilistic Methods Applied to Power Systems Stockholm. Borenstein, Severin, Bushnell, James, Wolak, Frank, 2000. Diagnosing Market Power in California’s Deregulated Wholesale Electricity Market. PWP064. University of California Energy Institute. Boucher, Jacqueline, Smeers, Yves, 2002. Towards a common European electricity market e paths in the right direction. Still far from an effective design. Journal of Network Industries 3, 375e424.
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