How can the electricity markets be better
equipped for the integration of Renewable
Energy Sources?
A simulation of nodal & uniform pricing market
R. Gering (s2346478 & 160676150) DDM Operations Management
11-12-2017
Supervisors
Dr. N.B. Szirbik
Prof. C. Hicks
Abstract
Electricity markets are under renewed interest. With the transition towards “green” electricity the need for flexibility in electricity grids increases. The intermitted nature of Renewable Energy Sources (RES) puts lots of stress on the reliability and stability of the electricity grid. This is asking different operations of the electricity grid. Can this be supported with a nodal market approach instead of a uniform market approach. Thus, what is the effect of a nodal market compared to a uniform market on the flexibility in an electricity grid? A design science approach coupled with a simple optimal power flow simulation model gives directions for answers on this question.
The outcomes suggest that a nodal market design is a better tool to integrate RES into the electricity grid. These outcomes follow the trend the industry now is heading for. This implies, that a nodal market design is better equipped for congestion, prevents losses, is better prepared for the potential use of storage and high integration of RES.
Key terms
Preface
This is the thesis: “how can the electricity markets be better equipped for the integration of Renewable Energy Sources”. A design science approach coupled with the use of a simple simulation model. The writing of this thesis, like many, went with its own ups and downs. Downs like: unfruitful data gathering from Transmission System Operators (TSOs) in the Netherlands; making a dysfunctional fourteen-node model. Ups like, gathering basic
understanding of flow problems and electrical engineering and, finding interesting results in a simplified five-node model.
With all the ups and downs this process never seemed to have bored me, the topic on itself captured my interest from the moment on Dr. Szirbik suggested it to me. I am thankful for the opportunity to work on a topic that fits my interest and future ambitions. However, the project on itself was sometimes frustrating and hard to grasp. Special thanks to my roommates for the long nights trying to solve optimal flow problems. These discussions were both fruitful and amusing at the same time. Special thanks for my parents for supporting me along the way, even during the constant nagging.
This project kept me busy and interested the last 6 to 7 months. I hope it is an interesting read for you.
Rutger Gering
Table of content
1. Introduction p. 5
2. Literature review p. 8
2.1 Grid operations and congestion management p. 8 2.2 Intermitted and uncontrollable nature of RES p. 9 2.3 Network losses p. 11
2.4 Merit order p. 12
2.5 Nodal market design p. 12 2.6 Zonal market design p. 14 2.7 Uniform market design p. 15 2.8 Storage facilities p. 15 2.9 Capacity alleviation methods p. 17 2.10 Market architecture p. 19 3. Power flow examples p. 21
4. Methodology p. 26
4.1 Research questions p. 26 4.2 Design science approach p. 26
4.3 Model p. 28 4.4 Modelling assumptions p. 30 4.5 Math model p. 32 4.6 Scenario settings p. 33 5. Results p. 35 6. Discussion p. 41
7. Limitations and future research p. 48 8. Conclusion and recommendations p. 50
9. Reference list p. 52
10. Appendix 1 p. 56
1. Introduction
In recent years, there has been an increasing interest in congestion management within an electricity grid. The main reason for this renewed interest is the increasing need for flexibility in the electricity grid due to the integration of renewable energy sources (RES) (Neuhoff, 2011). Electricity is seen as a commodity in economic terms, it is bought, sold and traded in different markets at different locations. However, electricity cannot be treated as other commodities, like coffee, copper or oil. Electricity acts different from other commodities when it is bought, sold or traded; it has some peculiarities of its own.
These peculiarities are based on the technical constraints for transporting electricity, which have to be considered in the electricity market (Olmos et al., 2013). There are two important constraints in the electricity market. The first constraint is, how the electricity is transported, in other words, the power flows. The second one, every electricity grid needs to be in balance at all times. The power flows of electricity are hard to influence, due to the fact electricity always takes the path of least resistance. Unlike other commodities electricity cannot be guided on a desired route like a truck, plane or ship. The issue of balancing the electricity grid, so that supply and demand are in balance at all times, is a growing challenge in the power industry across the world.
In a historic perspective the supply side of electricity was controllable and predictable (Baldick, 2012). The demand side however, was viewed as uncontrollable but fairly predictable (Baldick, 2012). In the last decade though, there has been a shift in the way generation and demand in the power industries is viewed upon. The main reason for this shift in view is the drastic growth of renewable energy sources (RES) in the recent years. The amount of wind energy produced increased with 1100% between 2002 and 2012, while the amount of solar energy produced increased with a staggering 2700% over the same period of time (Lannoye et al., 2012). This increase of RES makes the generation side of the balancing mechanism less controllable and predictable, while the demand side does not become more predictable or controllable (Han and Papavasiliou, 2015; Neuhoff, 2011).
shining) are becoming a factor of influence. One way of dealing with this growing problem of balancing the system may be the usage of different market designs. This is the focus of this research.
In most of Europe electricity is traded and sold for an uniform price determined at the end of the day/hour. Most trading is done a day prior the production (Green, 2007; Olmos et al., 2013). This implies that the dispatching decisions are made earlier then optimal regarding the impact of the RES, due to the unpredictable nature of the output of RES. This limits the participation of RES in these markets (Neuhoff, 2011). A uniform market design with different timeframes is commonly used in the electricity wholesale markets. However in the PJM interconnection, ERCOT, New York, New England (USA) and in New Zeeland and Singapore a nodal pricing market is being used. In a nodal market losses and transmission line capacities are accounted for in the marginal value of electricity, at every specific location. These are not accounted for in a uniform market (Green, 2007; Neuhoff, 2011; Olmos et al., 2013). This means that in a nodal market, the price of electricity can differ among locations, based on the losses that occur during the transport of electricity and the transmission line capacities.
When taking a closer look at ERCOT market (Texas). One can see that in the last decade there has been great increase in wind turbines in the state of Texas, to be more specific, in the west of Texas (Baldick, 2012; Bird et al., 2014). In 2004 the installed capacity in the ERCOT market was around 1,200 MW, this grew to more then 8,900 MW in 2009 and continues to grow (Bird et al., 2014; Wan, 2011). This raises questions like: Why is the state Texas, the oil capital of the USA, so effective in integrating RES in their electricity grid? This compared to for example most parts of Europe. What are the reasons that some markets have a higher penetration of RES then others? Could the market design give us some insight in why the penetration of RES is higher in a nodal market?
market design in 2010 (Bird et al., 2014). This resulted in 12% less curtailment of wind energy within 4 years (Bird et al., 2014). The ERCOT market is a leading case for the effect of high wind penetration in an electricity wholesale market (Baldick, 2012). In the ERCOT market nodal prices are used, so energy is valued based on marginal offers per location. Texas had only little storage facilities to provide a flexibility buffer, and they also make heavy use of wind energy (Baldick, 2012). However, renewed interested in storage facilities was created in recent years. This raises questions like: Why is a nodal design so effective in the ERCOT market? What is the effect of nodal design on the flexibility in the network? What are the benefits of a nodal design over a uniform design in terms of curtailment, expenses, flexibility and losses?
Comparisons between the uniform, zonal and nodal market models have already been made by multiple researches, however their focal point was the difference in consumer welfare these three markets have (Ding and Fuller, 2005; Green, 2007; Holmberg and Lazarczyk, 2015). As of now, little to no research has been done on what the effects of a nodal and uniform market have on the flexibility in an electricity grid. This study aims to fill this gap.
2. Literature review
2.1 Grid operations and congestion management
In the electricity grid producers and consumers are able to make transaction via a Transmission System Operator (TSO). The electricity grid has a natural monopoly for transporting energy from producers to consumers. In general, one company is in charge of the electricity grid. The reason for this monopoly is that a single company can transmit electricity in more efficient and effective manner, then multiple small ones (Green, 2008). However, this eliminates the possibility of a competitive market. Public authorities regulate TSOs to ensure there is an unbiased market operation for all market participants, while ensuring the reliability of the electricity grid (Han and Papavasiliou, 2015). The TSO is responsible for dispatching electricity at generators nodes to ensure the demand is fulfilled at al nodes in the system (Olmos et al., 2013). A node marks a specific location within the electricity grid. Another responsibility of TSO is to keep the system in balance and to ensure that no transmission line capacity is violated (Olmos et al., 2013).
Transmission lines have physical limits, mostly thermal. This restrains the amount of energy that can travel over a transmission line given the voltage of the transmission line (Olmos et al., 2013). Therefore most common transmission line constraints are related to voltage limits and transmission line capacities (Olmos et al., 2013).
When a physical or operational constraint is reached, the grid is said to be in congestion. In the following example a simple explanation is given. A two-way car lane has a certain capacity of cars it can handle at a certain time. If the capacity of cars is exceeded there will be a traffic jam.
It is the responsibility of the TSO to manage the congestion in the electricity grid. The TSO manages the congestion by choosing which generators to dispatch to fulfil the demand for the least operational costs, without exceeding the transmission lines capacities (Olmos et al., 2013). Location is of importance in congestion management. Just as with traffic control, in electricity grids you have certain routes that are generally known to be a bottleneck. For example, the highway into London is a known bottleneck.
pricing/location marginal pricing (nodal market), zonal pricing (or zonal market), or uniform pricing. Nodal and zonal pricing are flow based capacity allocation methods (Green, 2007; Han and Papavasiliou, 2015; Neuhoff, 2011; Olmos et al., 2013). A nodal and zonal market is defined by giving a location price for electricity at a specific location, while keeping transmission line constraints and losses into consideration (Neuhoff, 2011). An uniform market makes use of an uniform price for electricity at a certain time, the effects for transmission line constraints and losses are not taken into account(Olmos et al., 2013).
A nodal and zonal market considers the maximum capacity of the transmission lines. When the system appears to be in congestion, it deviates from the minimal costs dispatch schedule to ensure no transmission line capacity is exceeded (Olmos et al., 2013). This results in a different value of energy at different location within the nodal and zonal market. While using a nodal or zonal market, congestions have an impact on the price of electricity at a specific location. In a uniform market congestion has no impact on the price. These market designs will be explained in further details in sections 2.5, 2.6 and 2.7. Capacity allocation methods are a non flow-based capacity allocation method, which decouples the electricity market and the transmission capacity market.
Capacity alleviation methods are additional measures that are used if scheduled generation would lead to system constraints violations and in case of unforeseen events. These measures can be i.e. curtailment, re-dispatching and counter trade. These methods will be discussed in section 2.8
2.2 Intermitted and uncountable nature of RES
It is known that the output RES is uncontrollable and is influenced by the weather (Baldick, 2012; Cludius et al., 2016; Mcconnell et al., 2013). Another factor that needs to be mentioned when making a dispatching schedule, also known as generation schedule, is the forecasting error (Baldick, 2012; Cludius et al., 2016). The output of a windmill or a solar panel is slightly different from what is forecasted. This is the intermitted nature of RES, the output could be slightly less or more than forecasted (Baldick, 2012; Cludius et al., 2016; Di Cosmo and Valeri, 2016).
This gives the TSO the task to deal with both the uncontrollable output of RES and its intermitted nature to ensure grid stability and reliability. This puts allot of stress on the balancing mechanism in the electricity grid, which has to compensate for the intermitted nature of RES (Baldick, 2012; Cludius et al., 2016; Di Cosmo and Valeri, 2016). This means that a high integration of RES, because of its intermitted generation, has additional costs coming with it (Di Cosmo and Valeri, 2016). Which implies, that conventional generators, with a controllable output, have to be ready for backup in a sudden drop of RES output (Baldick, 2012; Cludius et al., 2016). This is known as ancillary services or balancing activities. However, these balancing activities are not for free. Balancing costs come from the costs for acquiring generator capacity, ancillary services and the settlement of imbalances that occurs when the real-time market deviates from the scheduled market (Chaves-Avila et al., 2014; Denholm and Hand, 2011). More wind and solar power equals to more balancing activities, both directly and due to forecasting errors.
However, there are some solutions to counter the effect RES have on the electricity grid. Possible solutions are: the increase of the flexibility in the network, add short-term storage that could counter the intermitted nature of RES, and make demand controllable (Baldick, 2012). But what is the reason that the integration of RES in states like Texas and California is higher than the integration of RES in Europa? Are these states better in using the flexibility they have in the network? Another interesting phenomenon is that the demand for storage is higher in markets with a nodal market design compared to markets with a uniform market design (Ruz and Pollitt, 2016).
al., 2014; Han and Papavasiliou, 2015; Neuhoff, 2011). However, the effect of a nodal design on flexibility has not been measured in any of those studies. As of now, little to no research has been conducted on what the effects of different market designs are on the flexibility of electricity grids, this study aims to fill this gap.
2.3 Network losses
Network losses occur while transporting energy form generators towards consumers. The losses are either ohmic losses or losses via the corona effect (Olmos et al., 2013). Ohmic losses are the results of the resistance on a transmission line, where an amount of energy is transferred in heat. Also known as the thermal limits of a transmission line. The corona effect takes place in high voltage line conductors. Electric discharge interacts between the air surrounding the conductors and the conductors themselves which results in losses. This is known as the corona effect (Olmos et al., 2013). Both of these losses result in additional costs for supplying electricity to the consumers. For example the demand for electricity in Newcastle (node B) is 100 MW, while the generator produces 105 MW at Manchester (node A), the extra 5 MW are lost during the transport of the electricity (Green, 2007). As is shown in figure 1.
Due to these network losses the consumer receives less energy than the generators produce (Olmos et al., 2013). The fact that network losses occur while transporting electricity show that the location of the generators compared to the locations of the consumers create a difference in price. According to Olmos et al. (2013, p.51) “losses create geographic
difference in marginal cost for supplying electric energy”. This implies that the exact location
of the generators and consumers influence the cost of meeting a marginal increase in demand. A market design that reflects these losses in the marginal price at a location, will decrease the total losses made in electricity grid (Green, 2007; Olmos et al., 2013; Treinen, 2005).
2.4 Merit order
The TSO dispatches the generators in the economic order from cheap to expensive, also known as the merit order (Cludius et al., 2016; Di Cosmo and Valeri, 2016; Mcconnell et al., 2013). The integration of RES has an negative effect on the bids made by the generators, also known as the merit order effect (Cludius et al., 2016; Di Cosmo and Valeri, 2016; Mcconnell et al., 2013). The integration of RES has a negative effect on the wholesale electricity price, it also lowers the market power where generators bid strategically (Cludius et al., 2016; Di Cosmo and Valeri, 2016). The effect of wind energy on the generators bids is larger in the early morning, when demand is low (Di Cosmo and Valeri, 2016).
Overall the integration of RES decreases the total system costs via the merit order effect. However, with the integration of RES there is an increase of balancing activities needed to keep the electricity grid stable and reliable, due to the intermitted nature of RES (Di Cosmo and Valeri, 2016). These balancing activities bring costs, nonetheless the extra costs needed due to the integration of RES are less, then the merit order effect RES have on the whole sale electricity price (Cludius et al., 2016; Di Cosmo and Valeri, 2016). When storage capabilities are embedded in the electricity system, the costs of balancing activities is halved (Di Cosmo and Valeri, 2016).
2.5 Nodal market design
As discussed earlier, both network congestion and network losses will result in a difference in the local marginal value of energy among locations in an electricity grid. These locations in a nodal market design are nodes. A schematic picture is given in figure 2, where the dots are nodes and the lines are transmission lines. A node is either a specific location of a generator, a consumer or both. In a nodal market design all network constraints and network losses are considered when a price is made for electricity at a specific location (Green, 2007; Liu et al., 2009; Neuhoff, 2011; Olmos et al., 2013). Congestion and losses in a nodal market design results in different marginal prices for different locations. These different marginal prices fairly reflect the transmission capacity, the lack of generation capacity and the losses that occur during the transportation of the electricity (Olmos et al., 2013).
The nodal price/Location Marginal price (LMP) has three components (Treinen, 2005): • Marginal cost of generation
The concept of nodal pricing/LMP was first developed by Schweppe et al. (1988). The definition of LMP (given by Liu et al., 2009, p.3) is the following: “The Locational Marginal
Price of electricity at a location (node) is defined as the least cost to service the next increment of demand at that location consistent with all power system operating constraints”.
These nodal prices create price signals at specific locations. Through these price signals provided by a nodal market design, consumers will lower their demand when the price of electricity is high. And producers will increase generation, build new generators at certain locations where the price of electricity is regularly high, or even they may build storage facilities when there is a negative price.
In practice, such a market is run by a central TSO. For example in Texas it is run by ERCOT. The TSO, collects all bids form production and consumption. Market clearing is then performed by the TSO considering all network constraints (Olmos et al., 2013). Leading to a generation schedule that theoretically has no need for further capacity alleviation methods. This implies that the flexibility in the network is not needed based on the dispatch schedule, only in case of unforeseen events. In the circumstance of a high penetration of RES in the electricity grid, all of the flexibility resources can be used for the intermitted nature of RES and unforeseen events.
This results in the fact that a nodal market design is the most refined and efficient market design which takes into account the locations of both producers and consumers, without exceeding any constraints (Baldick, 2012; Green, 2007; Olmos et al., 2013).
2.6 Zonal market design
A zonal market design is a simplification of a nodal market design. In a zonal market, there is a marginal price difference between zones/areas. Due to transmission line constraints between these predefined zones the marginal price is different. But the price is not different between the nodes within a zone (Olmos et al., 2013). Normally, a zonal market design has a single market price that applies for all zones, if there is no congestion between zones (Olmos et al., 2013). However, if there is congestion between zones, which restricts the power flow between zones, the price differs among the zones (Olmos et al., 2013).
However, within the zone the prices are uniform and transmission line capacities are not taken into consideration. Within the zones the use of capacity alleviation methods is needed in case of transmission line capacity violations. This results in less flexibility within the zones. After the final dispatch schedule is made, the TSO must use part of its flexibility capacity to alleviate transmission lines that are violated by the schedule. This results to the suggestion that not the full capacity of flexibility can be used for unforeseen events and the intermitted output of RES. Within a zone, one can describe the market as a uniform market. This suggests that capacity alleviations methods are needed within the zone, due to transmission line violations in the dispatching schedule.
Similar like a nodal market design the zonal market design accounts for losses and congestion between the zones. However, these factors are not considered within the zone. Thus within a zone there is a need for a capacity alleviation market to ensure the reliability and stability of the electricity grid (Chaves-Avila et al., 2014; Olmos et al., 2013). In figure 3 a schematic overview of a zonal market design is given.
2.7 Uniform market design
In a uniform market design the price of electricity is constant across the system at a certain time interval (Han and Papavasiliou, 2015; Olmos et al., 2013). In a uniform market generators are dispatched by merit order, form cheapest to expensive generators until the demand is fulfilled (Di Cosmo and Valeri, 2016; Ding and Fuller, 2005; Olmos et al., 2013). Transmission lines and their capacity are not considered while making the dispatch schedule in a uniform market (Ding and Fuller, 2005; Olmos et al., 2013).
Once the dispatch schedule of the energy market is known, the TSO checks whether the generation dispatch schedule violates any transmission lines. If a transmission line is violated, the TSO needs to modify the dispatch schedule or the TSO makes use of network alleviation methods. Thus, tapping into the flexibility capacity to ensure grid stability and reliability, if we talk about the “real-time market”.
During implementation of these alleviation methods, a solution for the transmission line violation with the lowest cost is choosen by the TSO. This is an example of a market-based mechanism to modify the dispatch schedule to ensure the reliability of the electricity grid (Kunz, 2013; Olmos et al., 2013).
An uniform market design is mostly used in a densely meshed electricity grid, with nearly no recurring congestion (Olmos et al., 2013). A nodal or zonal market design is seen as over sophisticated for these kind of electricity grids (Olmos et al., 2013). In an uniform pricing market the revenues are not used to cover the costs of the transmission lines and congestion (Olmos et al., 2013). If re-dispatching is needed, influenced by congestion, the costs for either the producers or consumers will be effected. The uniform market is regularly used in European countries as of now.
2.8 Storage facilities
flexibility for instance if they have quick ramp-up rates or already are running beneath their full capacity. System flexibility is defined as follows: ‘system flexibility is the ability of the
aggregated set of generators to respond to the variation and uncertainty in net load’
(Denholm and Hand, 2011).
The flexibility resources are usually fast gas-fired peak plants (Bouchard, 2010; Ruz and Pollitt, 2016). However, storage facilities can be used as flexibility resources as well. Storage can help to balance the electricity grid. Energy is stored when the demand is low and dispatched when peak demands occur (Baldick, 2012; Ruz and Pollitt, 2016). Batteries have the capability to respond on unbalancing events in the grid within 1 second. It can act as an alleviation method to ensure the balance in the system (Ruz and Pollitt, 2016). Storage can counter the effect of the intermitted nature of RES, for both the forecasting error effect (unforeseen events) and the mismatch between scheduled supply and demand (the early morning example) (Ruz and Pollitt, 2016).
As mentioned in section 2.2, the demand in the USA for storage facilities/batteries is larger than in Europe (Ruz and Pollitt, 2016). Market design could play a role in the difference of demand. Negative prices occur both in European markets and in USA markets (Bird et al., 2014; Bolton, 2016; Zhou et al., 2016). In both markets flexibility is needed, especially when RES are integrated in the electricity grid. Currently storage facilities are more expensive then the gas-fired peak plants (Bouchard, 2010; Ruz and Pollitt, 2016). However, there is a decline in the price of storage facilities due to the economic of scale and learning curve of these technologies, think about the efforts of Tesla in Australia (Bouchard, 2010; Ruz and Pollitt, 2016; Zhou et al., 2016).
prices occurred for 10 per cent of the time (Zhou et al., 2016). With the increase of wind energy in the electricity grid negative prices occur more frequent. This creates opportunities for storage facilities as it is a potential solution for the intermitted nature of RES (Baldick, 2012; Denholm and Hand, 2011; Ruz and Pollitt, 2016; Zhou et al., 2016).
2.9 Capacity alleviation methods
Capacity alleviation methods are generally used when there is a violation on a transmission line, either due to the final dispatch schedule or due to unforeseen events (Kunz, 2013; Vries, 2001). When there are transmission line violations in the final dispatch schedule, either a zonal or uniform market design is used (Baldick, 2012; Green, 2007; Olmos et al., 2013). Unforeseen events, they are deviations from the final dispatch schedule, will happen more regularly due to the intermitted nature of RES (Baldick, 2012). The “real-time” market has to respond to these unforeseen events, it does this by making use of capacity alleviation methods. These capacity alleviation methods make use of the flexibility capacity of an electricity grid.
2.9.1 Re-dispatching
Re-dispatching is one of the first capacity alleviation methods that is generally used by the TSO (Vries, 2001). Thus, when a line is above its maximum capacity, in order to ensure stable and reliable electricity grid, alleviation is needed in that line. The power flow that is on that specific line needs to decrease. Power flows can travel in both direction on a transmission line, left and right so to speak (Arroyo and Galiana, 2005; Green, 2007; Olmos et al., 2013; Overbye et al., 2004). The direction cancel each other out, so when 10 MW is moving to the right and 10 MW is moving to the left, the net power flow and that line will be 0 MW.
The TSO is responsible for grid stability and thus responsible for the re-dispatching activities if needed (Vries, 2001). The TSO needs to reimburse the generators that will support to alleviate the system with generating the needed electricity, however the amount of electricity in the grid is now exceeding the demand (Vries, 2001). The overproduced electricity is thrown away to ensure that the electricity grid is in balance This is also known as curtailment (Baldick, 2012; Olmos et al., 2013; Vries, 2001). The TSO uses re-dispatching without informing all market players, and therefore it does not give direct price signals to the market to adjust their trading pattern (Vries, 2001).
2.9.2 Countertrading
Countertrading is a specific implementation of the method of re-dispatching, however it is a market oriented method (Vries, 2001). Because the TSO does not make use of a market when the re-dispatching method is used. The TSO acquires the generation capacity needed to without any interaction of market player. When countertrading is used, market players can put a price on the available flexibility capacity in a certain hour. The TSO then decides which generators to use based on the cheapest bid and the location of the generator (Green, 2008; Vries, 2001). Countertrading is thus a market form of re-dispatching. Countertrading is based on the same principles as re-dispatching in terms of alleviating the electricity grid, but the TSO has to enter a market to do so (Vries, 2001). Through an open bidding process, the TSO buys and sells electricity in such a manner that the power flow on the capacity exceeding transmission line falls within the boundaries (Vries, 2001). The TSO will receive prices in the bidding process, which are higher than the market price. But this creates more transparency in the market (Vries, 2001). In a nodal market transmission line capacities are honoured, and generators place bids. It could be that while making the dispatching schedule, a nodal market design already makes use of countertrading, by ensuring that the transmission lines of cheap generators are not exceeding its capacities, via countertrading with other cheap generators. This will enable the TSO to dispatch more of the cheap electricity. However, no evidence has been found so far of this hypothesis.
2.9.3 Curtailment
fluctuating output (Baldick, 2012). According to Dr. J. Veldman, curtailing an 8 MW wind turbine costs around € 10.000 per stoppages in cumulative costs. It is known that both “grey” and “green” energy sources have different start and stoppage costs. However in this thesis the assumption is made that “grey” energy sources can be stopped at a moment notice, without any major cost.
2.10 Market architecture
The world is adopting RES rapidly into the grids, which puts more stress on the balancing activities. There is an increasing need for more flexibility in the electricity grids around the world. This flexibility must ensure the reliability and stability of the electricity grid. The responsibilities of a TSO are the following (Abiff et al., 2011; Green, 2008):
• Ensuring grid reliability in real time
• Making of dispatching schedules based on weather predictions and historical demand data
• Overseeing the ancillary service market, and ensuring there is enough flexibility capacity to meet the grids reliability goals
• Transmission congestion management
• Interconnection. Where to place new generators and where to expand transmission lines.
The activities described above are the core activities to ensure grid reliability and stability in the “real-time” market.
Nonetheless, with the expected high penetration of RES into the electricity grid and its intermitted nature, TSO has a harder time balancing the system. The TSO has to deal with a reasonable predictable but uncontrollable demand just like before (Baldick, 2012). However, now the supply is intermitted, reasonable predictable and less controllable (Baldick, 2012; Green, 2008). Matching unknown supply and demand with each other is hard. This requires solutions to ensure the grid is stable and reliable. This will change the architecture of the grid that is needed to adopt these RES into the grid. Solutions are needed in the grid. Hereby you may think about controllable demand, storage, demand response and vehicle to grid for example (Alizadeh et al., 2016; Denholm and Hand, 2011; Lund et al., 2015; Zhou et al., 2016). Al these possible solutions have one thing in common: the creation of extra flexibility in the electricity grid. This extra flexibility can either make the demand or supply more controllable, which implies that balancing the electricity grid becomes easier.
This raises the questions which market model is better fit to articulate this change in the electricity grid. Green (2008) made a six-point list, on how to judge an electricity wholesale market:
• Ensure the efficient day-to-day operations of the generation section
• Signal the need for investment in generations and demand side management • Promote efficient location choices for these investment
• Compensate the owners of existing generation assets • Be simple, transparent and stable as possible
• Be political implementable
3. Power flow examples
Power flows behave in a specific manner, especially when there are more than 1 routes available between certain nodes. Electricity always travels in the direction of the least resistance (Green, 2007; Olmos et al., 2013; Treinen, 2005). The model that will be used in this simulation is an approximation of this behavior.
The power flow distribution on a transmission system is a function of (Treinen, 2005): • Location and amount of generation
• Location and amount of supply
• The relative resistance of the various paths between generation and supply.
The examples used below are from Treinen (2005), these examples will help to understand the nodal market and its pricing activities.
3.1 Example 1
In figure 4 an example is given of a three-node network, where all three lines have the same resistance and losses are not considered. 1 MW is generated at node A and the demand is 1 MW at node C. Two-third of the power generated at node A will flow directly using line 1 (A-B) towards node C, and one-third will flow through lines 2 (B-C) and 3 (A-C) towards node C. We assume there are no losses in this example, just to keep the example simple (Green, 2007; Overbye et al., 2004; Treinen, 2005).
3.2 Example 2
Unconstrained nodal example without losses
The case of an unconstrained nodal example is given in figure 5. As we can see the demand at node A is 0 MW, at node B it is 50 MW, and at node C it is 200 MW. The generators are located in nodes A and C. The block offer/bid of node A is $10/MWh with a max capacity of 450 MW. At node C the block offer/bid is $20/MWh with a max capacity of 250 MW. The line capacities are:
• Line 1: 300 MW • Line 2: 400 MW • Line 3: 200 MW
The total demand is 250 MW, and the lowest block offer can fulfill the complete demand of 250 MW for $10/MWh. This will results in the following line flow:
• Line 1: (1/3*200 MW) + (2/3*50 MW) = 100 MW • Line 3: (2/3*200 MW) + (1/3*50 MW) = 150 MW • Line 2: (1/3*200 MW) – (1/3*50 MW) = 50 MW
In line 2, there are two opposites direction of power flow present, which results in a power flow of 50 MW. This is a way to relieve congested lines, which will be further explained in example 3. The nodal price without losses will be $10/MWh, due to the fact that the price of 1 MW extra at each node can still be fulfilled by the generator in node A.
3.3 Example 3
Constrained nodal example without losses
!
Figure'6')'Power'flow'example'3'part'1
The case of the constrained nodal example is given in figure 6. As we can see the demand at node A is equal to 0 MW. At node B equal to 75 MW. And at node C equal to 325 MW. The generators are located at node A and C, with corresponding block offers/bids of $10/MWh with a maximum capacity of 450 MW and $20/MWh with a maximum capacity of 250 MW. The line capacities are the following:
• Line 1: 300 MW • Line 2: 400 MW • Line 3: 200 MW
If the transmission line capacity were overlooked the power flows would be the following: • Line 1: (1/3*325 MW) + (2/3*75 MW) = 158 MW
• Line 3: (2/3*325 MW) + (1/3*75 MW) = 242 MW • Line 2: (1/3*325 MW) – (1/3*75 MW) = 83 MW This would be the power flow in a uniform market design.
of the demand, but this causes that transmission line 3 is violated. Thus, an amount of energy needs to be dispatched at node C in order to alleviate transmission line 3.
The demand at node C is equal to 325 MW; the demand at node C is 75 MW. For this specific case, we need to dispatch the more expansive generator B and curtail some energy of generator A. The output of Generator A will be the following:
• Output A = (325 – output B) + 75.
75 MW of the output of generator A is used to serve the demand at node B. While 325-ouput B is used to serve demand at node C.
This leads to the following power flow equation for line 3: • (2/3) * (325 – output B) + (1/3) * 75 = 200 MW
Solving this equation says that output B equals 62.5 MW and this ensures that the output of generator A equals to 262.5 + 75 = 337.5 MW.
Now the power flows on the lines will be the following: • Line 1: (1/3*262.5 MW) + (2/3*75 MW) = 128 MW • Line 3: (2/3*262.5 MW) + (1/3*75 MW) = 200 MW • Line 2: (1/3*262.5 MW) – (1/3*75 MW) = 63 MW
In order to determine the nodal price/LMP at each node we need to add 1 MW of demand at each node to determine the change in total generation costs considering the transmission constraints.
• Node A: can be served by the generator at node A (hereafter called G1) for an extra cost of $10/MWh, nodal price is $10.
• Node C: cannot be served by G1 since any dispatch by G1 has to cover an additional 1 Mw of load at node C which would increase the flow on line 3 and this violates the transmission constraint of 200 MW. This results that an additional 1 MW at node C has to be served by the generator at node C (hereafter called G2), which results in a nodal price of $20MW/h
next equation will be used: ∆G1+ ∆G2= 1MW. This results in an additional flow on line 3 as (1/3*∆G1 - (1/3*(1-∆G1) = 0. This results in 2/3*∆G1 = 1/3 which leads to ∆G1=1/2 MW. Since ∆G1+ ∆G2= 1MW !∆G2=1/2 MW. This results in a nodal price of $15MW/h, a combination of the cost of the generators at nodes A and B. This is shown in figure 7.
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4. Methodology
First the formulation of the research questions will be discussed. Then the design science approach will be discussed. This will be followed by an explanation of the simulation model and the data used. The next part will give the modelling assumptions. At the end a brief discussion of the math and at last the scenario settings will be given.
4.1 research questions
The following questions are the foundation of the research:
• Why is the state Texas, the oil capital of the USA, so effective in integrating RES in their electricity grid? Compared to European countries. !
• What are the reasons that some markets have a higher penetration of RES then others? • Could the market design give us some insight in why the penetration of RES is higher
in a nodal market?
• Why is a nodal design so effective in the ERCOT market?
• What is the effect of nodal design on the flexibility in the network?
• What are the benefits of a nodal design over a uniform design in terms of curtailment, expenses, flexibility and losses?
These research questions resulted in the final research question:
• What is the effect of a nodal market compared to a uniform market on the flexibility in an electricity grid?
4.2 Design science approach
A design science approach is based on the connection between knowledge and practical problems. These methods show us that designing useful models and things might produce scientific knowledge (van Aken et al., 2016; Wieringa, 2009). Practical problems are resolved by changing the world, in such a manner that it will be more in line with the stakeholders goals. This needs an investigation of stakeholders goals and the evaluation of possible solutions (Wieringa, 2009). This is considered using the regulative cycle approach of design science with the following four steps (Wieringa, 2009)
1. Problem investigation 2. Solution design 3. Design validation
4.2.1 Problem investigation
The first step involves the problem investigation. One of the main questions of this thesis is the question why certain areas, like Texas, are able to integrate more RES into their power grid then for example, Europe or the Netherlands. This question evolved for me in the research focus of markets models, namely the nodal market design and the uniform market design. With the following research question: what is the effect of a nodal market compared to a uniform market on the flexibility in an electricity grid? One can describe this question as a solution-driven investigation; both markets models are dealing with the need for flexibility in an electricity grid differently (Wieringa, 2009). This problem investigation leads to these market design and their functionally in terms of flexibility, as of now, and what is needed in the future.
The literature research was conducted in a funnel form approach, starting with a broad research of flexibility in electricity grids. After this broad research the main focus on market models was made. The stakeholders of these market models and their goals are the following, (Baldick, 2012; Green, 2007, 2008; Olmos et al., 2013):
• TSO - ensuring grid reliability, minimal transmission congestion costs, minimal generation costs, market needs to be simple, transparent and stable, increasing RES. • Market participants consumer side - a reliable and stable electricity grid, minimal
costs, increasing RES.
• Market participants generation side - efficient day-to-day operation of generators and its market, signal need for investment in generation, signal the location of generation investments needed, fairly compensated for service provided, increase RES.
• Government - increase RES, transparent and stable market, implementable, governable.
These goals need to transform into design criteria, so that the market design can be validated. Green (2008) developed six criteria that a wholesale electricity market should have. It is based on the criteria used to evaluate he different market designs:
1. Ensure the efficient real-time operation of the generation sector
6. Be politically implementable
As our focus lays on the real-time market and the effect these market models have on flexibility, it is not possible to make an in-depth evaluation of all criteria.
4.2.2 Solution design
The second step involves the design of potential solutions. The literature research and the trends in the industry show promising insights in the market design topic in terms of flexibility. This specified the boundaries of the research namely the market design and the real-time market. The real-time market demonstrates the flexibility that is necessary, while making a dispatching schedule of an electricity grid.
These points of interest indicate a solution-driven investigation (Wieringa, 2009). Specifically, the uniform and nodal market design in a real-time setting. So, for the design of the possible solutions, two areas of interest are explored.
4.2.3 Design validation
The internal validity (that is: if the design is implemented will it fit the criteria) will be based on both qualitative and quantitative results (Wieringa, 2009). The qualitative part is focusing on literature research and the trends in the industry. While the quantitative part, a simulation model of a five-node network, focuses on numerical results. In the discussion, links between the qualitative parts and quantitative parts have been made. This creates recommendations based on the criteria previously mentioned. The trade-offs between these models will be discussed in the discussion (Wieringa, 2009). As the solution is specifically designed for the electricity market, it is unlikely the solution can be used in other markets based on the peculiarities of electricity as a commodity.
4.2.4 Implementation and evaluation
Based on the fact that no market has changed from a uniform market design to a nodal market design and vice versa, it is not possible to implement and evaluate the different market models in the real world. This step of the regulative cycle was not feasible in this research.
As mentioned before, the quantitative research shall be conducted via a comparison of simulations of a nodal electricity pricing model and an uniform electricity pricing model. A simulation model is a simplification or abstraction of the assumed real world, where the assumed real world is a simplification of the real world (Karlsson, 2016; Law, 2003). The simulation model should have enough detail to answer the research question. However, the solutions of the simulation model should be validated whether or not it has an accurate picture of the real world systems which, is the objective of the study (Law, 2003).
This research is a solution-driven investigation; Karlsson (2016) refers to this type of research as axiomatic, which stands for obtaining a solution within the defined model.
The solution of this thesis provides insight in the behaviour of flexibility capacity in different market models.
Law (2003) provided a seven-step approach of conducting a successful simulation as can be seen in figure 8. These seven steps have been followed during the model development.
4.3.1 Model
The model that will be used in in the simple simulation study is based upon a five-point node model with five transmission lines connecting them. In figure 9 a schematic picture is given of the five-point nodal model. On a transmission line electricity can flow both in “left” and “right” direction. The arrows in figure 9 (nodal model correction) indicates the direction of the positive power flow; the opposite direction will be denoted in negative numbers.
The uniform price is calculated based on the merit order, so the highest bid that is dispatched equals to the uniform price, plus an equal distribution of the costs for the losses. The LMPs are calculated by adding one demand at a certain node, while keeping the demand of all other nodes the same as the original parameters. The total generation costs increase equals to the LMP at that specific node.
The input data used in the simulation models comes from two sources. The input of the demand and the maximum capacity is based on the work of Hutcheon and Bialek (2013). The data that Hutcheon and Bialek (2013) gathered is based on data of the real electricity grid in the winter of Europa in 2009. The demand and maximum capacity data that is used in this model is a distilled version of the data available (Hutcheon and Bialek, 2013). The demand and capacity data used in our model come from the following countries: the Netherlands, Belgium, France and Germany. The data used for the bids of the generators is based on PJM interconnection, a TSO that is based in the east of the United States of America. In this data set, the generator type is not shown for competitive reasons, only the bids. Based on the work of Cludius et al. (2016), an estimation was made for what bids are coupled to what generator type. These data sources were used to validate the simulation model by making use of real-world numbers.
A black-box diagram can be found in appendix 1 of the simulation model, its inputs, parameters and outputs. The model is built in Excel 2010, using the CPLEXX solver add-in function. The model is a linear optimization problem, with an approximation of the optimal power flow of electricity.
4.4 Modelling assumptions
To build the simulation model the following assumptions were made:
I. Demand is always fulfilled; this can be done with the available generation capacity. II. Demand is inelastic, which means that the demand is not influenced with a price
the “real-time” market, it will influence the “day-ahead” market. The “real time” market illustrates the main issues of congestion management and thus the need for flexibility. Based on the idea that in the “day-ahead” market there is more time and information to adequately change the dispatch schedule, this extra time and information is not available in the “real-time” market.
III. The bids of the generator are block offers. This implies that the same price is used for all the available capacity of that certain generator. An example of a block offer is illustrated in figure 10
IV. Losses are expressed in a percentage of the amount of energy that flows on a transmission line. Due to the complicated nature of the losses this approximation is made.
V. All transmission lines have the same resistance and length. This assumption is made for two reasons. The first reason is that the power flow on transmission line 2,3 and 4 can be calculated as done in example 1, 2 and 3. The second reason is that every line has the same percentage of losses.
VI. Curtailment is only measured for RES generators; the assumption is made that “grey energy sources” can be turned down at a moment notice. Based on the idea that one cannot turn of the wind or sun, but can stop fuelling a “grey” generator.
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4.5 Math model
Objective function
The objective of both the uniform market model and nodal market model is to minimize the generation costs. In a nodal market the transmission line capacities are considered as opposed to the uniform market.
The nodal market has multiple objectives according to literature (Green, 2007; Olmos et al., 2013; Treinen, 2005):
1. Minimize the total costs of generation 2. Minimize the total energy losses 3. Minimize the re-dispatching costs 4. Minimize the total adjustment costs Objectives 2-4 are subject to objective 1.
This indicates that the optimal dispatching schedule in both markets is based on the minimization of the total costs of generation, which is given in equation 1.
1: !"#!!!(!") = !"#!!(!"!)
! !
Constraints
Generator maximum output, equation 2:!!!"!!"# ≥ ! !"
! ≥ !"!!"#
Total system load, equation 3: !!"! = !"
System balance, equation 4: !!! +! !!! = !"
Line losses, equation 5: !!,!= ! !!!!"#!− (!!!!"#∗!!!) = !!!!!!"
Electricity in and outcome in a node, equation 6: !!!!" = !!!!"#!− !!
Transmission line capacity (only in the uniform market), equation 7: !! ≤ ! !!!!"#
Parameters
PL = system load
Di = demand at node i (i = 1-5)
On,x = loss on line n direction x (n =1 -5)(x = 1,2)
Fp = factor of losses
Ni out = flow out of node i (i = 1-5)
Ni in = flow into node i (i = 1-5)
Ln = flow on line n (n = 1-5)
Decision variable
PDi = power dispatched at node i (i = 1-5)
4.6 Scenario settings
In order to analyse the differences between a uniform and nodal market model, different scenarios were used to show how the models behave under the same circumstances. Twelve scenarios were conducted with different parameter settings, to ensure the differences between the two market models are shown. The scenarios are numbered from 1.1 to 6.2 Table 1 shows the distinguished features of these scenarios.
Scenario number Features
1.1 Low loss factor, “grey sources”, high transmission line capacity
1.2 High loss factor, “grey sources”, high transmission line capacity
2.1 Low loss factor, “grey sources”, transmission line violation
transmission line violation
3.1 Low loss factor, RES, transmission line violation
3.2 High loss factor, RES, transmission line violation
4.1 Low loss factor, RES, transmission line violation, curtailment
4.2 High loss factor, RES, transmission line violation, curtailment
5.1 Low loss factor, RES, transmission line violation, negative generator bid
5.2 High loss factor, RES, transmission line violation, negative generator bid
6.1 Low loss factor, RES, transmission line violation, negative generator bids, curtailment
6.2 Low loss factor, RES, transmission line violation, negative generator bids, curtailment
Table 1 - Scenario settings
The parameters in all experiments were set in such a manner that it shows all differences the model is able to show, while using the data that is explained in section 4.2.
5. Results
In this thesis, two different market models (uniform and nodal market) are presented and observed. In the scenarios that are described, the parameters were altered in such a manner that different aspects of these two market models show up. For all the results and parameters, see appendix 2.
In this section, scenario 4.1 will be further explained including its results, followed by a short summary of the other scenarios and their interesting outcomes.
Scenario 4.1
The parameters of scenario 4.1 shown in table 2
Scenario 4.1 Parameters Parameters Node Demand in MW Generator fuel type Generator minimum capacity in MW Generator maximum capacity in MW Generators bid in euro per MW/h Transmissi on line Capacity in MW Losses on line in percentage I 1461,59 Gas 0 811,54 76,93 1 800 3 II 508,06 Wind 0 814,84 9,50 2 250 III 842,40 - - - - 3 450 IV 476,09 Wind 0 1461 12,13 4 250 V 1134,44 Coal 0 1746 46,87 5 600
Table 2 - Parameter setting scenario 4.1
As shown in table 2 the major demand is at node I and V. There are two RES of the four generators in this network. These RES (node II and IV) are the cheapest generators followed by the coal and finally the gas generator. The transmission line capacities are rather small if we quickly compare them with the demand. This might lead to possible transmission line violations in the uniform market.
Table 3 - Generation dispatch schedule scenario 4.1
The dispatch schedule of both models is not the same. This indicates that there is congestion in the grid. This is shown in table 4. In the uniform market the dispatch schedule violates transmission line 1, 3, 4 and 5 with a total amount of 462 MW.
In the nodal model no transmission line is violated as is to be expected for such a market. However due to transmission line constraints, and thus congestion, the full capacity of the wind generator at node IV is not used. This shows that curtailment of 166,14 MW of green electricity is needed in this particular situation, as shown in table 5.
Scenario 4.1
Variables
Node Dispatch schedule LMP model in MW Dispatch schedule UMP model in MW
I 811,54 489,32
II 814,84 814,84
III - -
IV 1294,90 1461
V 1569,10 1746
Table 4 - Results UMP model scenario 4.1
Scenario 4.1
Results UMP model
Scenario 4.1 Results LMP model Node LMP in euros Curtailment in MW Transmission line Transmission line violation in MW Total generation cost in euros I 81,58 - 1 - 159446,36 II 79,14 - 2 - Total losses III 48,32 - 3 - 68,17 IV 12,13 166,14 4 - V 46,87 - 5 -
Table 5 - Results LMP model scenario 4.1
The total cost of generation is higher in the nodal market (LMP model) then in an uniform market (UMP model). This indicates congestion, which is shown by the transmission line violation. Furthermore, fewer losses are made in a nodal market compared to a uniform market. This is one of the sub-objectives of a nodal market. If we take a closer look to the different LMP prices at each node, it shows us that overall the costs for the consumer will be lower than these costs in an uniform market, except for nodes I and II (only small increases). However, the LMP price at node IV does not give the system a price incentive to act upon the curtailment of green energy at node IV.
Scenario 1.1
In scenario 1.1 the system is not in congestion, which is shown by the total cost of generation. For both models the costs are equal: € 271.571,75. In both markets, the same dispatching schedules are used, which causes the costs and losses to be exactly the same. However, the LMP per node is different, due to the specific loss component for each node, which is used in a LMP price. This fact explains why the LMPs differ from each other.
Scenario 1.2
Scenario 2.1
Scenario 2.1 makes use of “grey” generators only; there is a slight difference in total cost of generation. Which, indicates that the system is in congestion. Transmission line 2 is violated by 210,81 MW in the uniform market. Furthermore, the total losses in the uniform market are slightly lower than those in the nodal market.
Scenario 2.2
The parameters that changed compared to scenario 2.1 are the transmission line capacity of line 1 and 2 and the loss factor that is increased to 10% instead of 3%. Transmission line 2 and 3 are violated in the uniform market by a total of 186,73 MW. This means that the system is in congestion. The losses are slightly more in the nodal market then in the uniform market. The LMP at nodes I and II are respectively high compared to the other LMP prices and the uniform price, more or less doubled.
Scenario 3.1
Scenario 3.1 has three generators, where one is RES. The total generation costs in the nodal market are higher than those in the uniform market. In the uniform market transmission line 2 and 3 are violated with a total amount of 405,93 MW. The total losses in the nodal market are lower than those in the uniform market.
Scenario 3.2
The parameter that is changed in regard to scenario 3.1 is just the loss factor of 3%.It is now 10%. The same phenomenon happens in this scenario with regards to scenario 3.1. in the uniform market transmission lines 2, 3 and 5 are violated in this scenario for a total amount of 579,7 MW.
Scenario 4.2
Scenario 5.1
Scenario 5.1 has four generators, specifically two RES and two “grey” generators. One of those RES generators made a bid of -€ 3 to sell its electricity. The total generation costs are higher in the nodal market then in the uniform market. The losses are more in the uniform market then in the nodal market. Finally, transmission lines 2, 3 and 5 are violated in the uniform market for an amount of 932,65 MW.
Scenario 5.2
The only parameter that is changed compared to scenario 5.1 is the loss factor. It is increased from 3% to 10%. The outcomes have the same patterns as described in scenario 5.1. In the uniform market transmission lines 2, 3 and 5 are violated for a total amount of 1305,23 MW.
Scenario 6.1
Scenario 6.1 has four generators, of which two are RES. Both of those RES generators made negative bids to sell their electricity. The total generation costs are more in the nodal market then in the uniform market. The losses are more in the uniform market then in the nodal market. Transmission lines 1, 3 and 4 are violated in the uniform market for an amount of 155.36 MW. Finally, in the nodal market at node I 106,39 MW of “green” energy is curtailed.
Scenario 6.2
Parameters Scenario 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 5.1 5.2 6.1 6.2 Number of generators 4 4 4 4 3 3 4 4 4 4 4 4 Number of RES generators 0 0 0 0 1 1 2 2 2 2 2 2
High loss factor x x x x x x
Negative bids x x x x
Nodal market model
Scenario 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 5.1 5.2 6.1 6.2
Total costs of generation = = + + + + + + + + + +
Total losses = = + + - - - -
Large LMP difference x x x x x x
Curtailment x x x x
Uniform market model
Scenario 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 5.1 5.2 6.1 6.2
Total costs of generation = = - - - -
Total losses = = - - + + + + + + + +
Transmission line violations
x x x x x x x x x x
Where x:yes, +:higher then other model, -:lower then other model and =:equal to other model
6. Discussion
The particular five-node model with different market models shows certain behaviour that is reoccurring in different scenarios. In this section, the results will be further analysed, links to industry will be pointed and links to the literature review will be made. The criteria from the methodology section will be discussed in the last section.
In all scenarios except scenarios 1.1 and 1.2 the total costs of generation is higher in a nodal market then in the uniform market, before capacity alleviation methods are used. According to the extant knowledge this is expected, due to the congestion in scenario 2.1 troughs 6.2. A nodal market fairly reflects the transmission line capacity, lack of generation capacity and losses occurring during the transportation from generator to consumer (Olmos et al., 2013). Thus, the total cost of generation is higher in a nodal market then a uniform market, the dispatch schedule deviates form the cheapest schedule due to the transmission line constraints (Green, 2007; Olmos et al., 2013). An uniform market lacks the ability, to send a price incentive to a certain location based on losses, congestion or the lack of generator capacity (Green, 2008; Olmos et al., 2013). A uniform market dispatching schedule is based on merit order of generators bids, without accounting for congestion. This implies that, the total costs of generation before capacity alleviation methods, is less in a uniform market then in a nodal market. Thus, the wholesale electricity price in a uniform market with a high penetration of RES is less than in a nodal market. Which results, that the merit order effect that RES have on the wholesale electricity price, is bigger in a uniform market then in a nodal market.
However, this raises the question whether the lower generation costs in a uniform market is less or equal to the costs that occur due to capacity alleviation methods. These total generations cost findings, give us some indication in terms of flexibility. In an uncongested electricity grid the total generation costs are equal in a nodal and uniform market. However, in scenario 2.1 through 6.2 this is not the case. This translates in transmission line violations in the uniform market models, as shown in table 6 and thus congestion.
generators that could alleviate the system, also known as the flexibility of the system, is limited. This flexibility in the electricity grid is used to ensure the reliability and stability of the grid in case of unforeseen events (Kunz, 2013; Vries, 2001). The intermitted natures of RES or demand drops/peaks, are seen as unforeseen events. So using the flexibility in the electricity grid to correct the transmission line violations, which are the effect of a uniform market dispatching schedule, seems sub optimal. Making extra use of re-dispatching, curtailing and countertrading, when it is not necessary makes the market less efficient. Especially, when the need for flexibility in the electricity grid increases due to penetration of RES, which has an intermitted nature (Baldick, 2012; Green, 2008; Neuhoff, 2011; Ruz and Pollitt, 2016).
If we see the amount of flexibility that is available in an electricity grid as a solid block of 2 by 4 as is shown on the left side of figure 11. The amount of flexibility after alleviating the system due to scheduled violations in a uniform market is, for example 2 by 2. While, the amount of flexibility in a nodal market stays, 2 by 4 as is shown on the right in figure 11.
Based on the idea a nodal market does not need to alleviate the system due to scheduled transmission line violations. So, by using a uniform market model, the amount of flexibility in the network for unforeseen events is smaller compared to a nodal market model. Therefore it makes sense to adopt a nodal market in an electricity grid where congestion is a recurring phenomenon. Especially when integrating RES sources in the electricity grid. In that case, the need for flexibility increases due to the intermitted nature of RES. It would be unwise for using flexibility resources to alleviate the system due to transmission line violations based on a uniform dispatch schedule. Especially when the demand of RES grows, the flexibility in an electricity grid needs to grow accordingly (Denholm and Hand, 2011; Green, 2008). However, market design has an influence on the way flexibility is used and needed; a nodal market enables the usage of the flexibility more efficient then a uniform market does.
In most of the scenarios the total losses are less in a nodal market then in a uniform market. Which is not a surprising finding, because of the sub objective of a nodal market: minimize
the total system losses (Green, 2007; Olmos et al., 2013; Treinen, 2005). Therefore, the nodal market shows it is more efficient in most cases in terms of losses and thus transport.
Another reoccurring incident is shown in four scenarios (4.1, 4.2, 6.1 and 6.2): curtailment of “green” energy in the nodal market model. In these scenarios, the transmission line capacity prevents these “green” generators to export its full capacity to the other nodes of the network for fulfilling the demand. This will not happen in the uniform market, because transmission line capacities are not taken into account. The merit order of dispatching generators upholds in a uniform market and wind turbines are known for their low bids (Blanco, 2009; Di Cosmo and Valeri, 2016; Mcconnell et al., 2013). While a nodal market accounts for transmission line constraints, and thus differentiates from the merit order of dispatching generators (Baldick, 2012; Green, 2007, 2008; Olmos et al., 2013).
In Texas (before 2010) a zonal market design was used. In Texas most of its wind energy is generated in the West, while most of the demand for electricity is in its capital Houston (the East) and the North. In the past the limited capacity of the transmission lines between the zones, prevented the West to export its electricity to the East and North, thus leading to curtailment (Baldick, 2012; Fink et al., 2009). After 2010 Texas adopted a nodal market design. In 2009 research by the National Renewable Energy Laboratory (NREL) found that the curtailment percentage in Texas of wind and solar power was approximately 17%. This dropped drastically to 2-4% in 2013 (Bird et al., 2014). Might it be that the introduction of nodal market design played a role in this drastic drop of green energy curtailment? Or, are other factors influencing this drop of curtailment as well?
