Adding solar PV to the Saudi power system: what is the cost of intermittency?

Abstract

With 2015 as a reference year, we quantify the costs of solar photovoltaics (PV) intermittency and integration to power utilities in Saudi Arabia. We define intermittency costs as the costs of ramping and maintaining spinning reserves, while integration costs are defined as the costs of intermittency plus the costs of grid upgrades if they were needed. The operational facets of PV integration to grid operators will be more pronounced with higher PV penetration, so to focus on operation, we exclude capital costs. The cost of intermittency, excluding any benefits attained from PV operation, rises to 1.3 ¢/kWh of energy provided by PV at 20 GW of PV deployment. At low levels of penetration, renewables may impose negligible cost or even confer net benefits. However, above a certain level, costs will always outweigh benefits—what we refer to as the ‘Operational Blend Wall’. It was found that the operational blend wall for Saudi Arabia was 11 GW, based on actual fuel costs prevailing in the period of study. The net financial effect of PV integration on operation, considering both costs of benefits, was an increase of 0.04 ¢/kWh and occurs at 20 GW of PV deployment. The maximum capacity of PV addition considered is 20GW, which represents a 25% penetration level within the power system.

Introduction

Many countries have plans to build substantial amounts of renewable and nuclear power generation. Their motivations vary: for some, it is to lower dependence on natural gas and coal imports, while for others it is to maintain COP21 carbon emissions pledges. In 2015, there were about 2 TW of renewable capacity globally [17], with the majority being hydro plants. Photovoltaics (PV), however, are becoming more attractive to utilities as their costs continue to fall. China, for example, recently announced plans to have 143 GW of PV generation capacity by 2020 [2]. Investment in solar and wind in the United States is outstripping those in conventional power generation [24]. Saudi Arabia also announced a 9.5 GW renewable capacity target by 2023.

PV and wind power technologies are intermittent and fossil fuel-fired generation is typically used to fill in for times of cloud cover or when it’s not windy. Having a large share of intermittent generation means potentially higher ramping magnitudes of dispatchable power plants. Adding to the argument by Joskow [19] that the levelized cost of electricity (LCOE) in itself is not sufficient for comparing intermittent technologies with dispatchable ones, there are additional costs incurred when renewables technologies are added to the mix and not captured by the LCOE—part of these costs is referred to as the cost of intermittency. In other words, there is value associated with dispatchability [9]. This cost of intermittency has been discussed in meetings and forums and not surprisingly has caused controversy (e.g., [10, 11, 22])

With increased penetration of renewable generation worldwide, concerns about the additional costs that have to be borne for accommodating these intermittent technologies are rising [3, 23]. Generation costs of different technologies on a LCOE basis are easy to find, but these figures represent the generation cost of each technology in isolation. When technologies are integrated into a system, the LCOE is unable to capture the ‘‘real’’ costs of the overall system and the interaction among generators to maintain stability and reliability [14]. This concept of system cost is not new; utilities have been dealing with several generation mixes for decades. However, the accumulated experience is mostly related to dispatchable generation technologies only, not intermittent sources [6].

Literature explains the power system’s cost implications that stem from introducing a renewable source into the generation mix (e.g., [11, 13, 22]). The additional costs are mainly due to back-up requirement to cover for reduced output from renewables during cloud cover for example and more frequent or larger ramping flexibility of dispatchable technologies. Although the latter description of intermittency cost is relatively easy to understand qualitatively, the specificity of each country or region hinders generalizing a single figure for this cost [15].

Some countries have liberalized power markets, where the price of electricity is determined by the technology with the highest marginal generation cost. In other countries, the sector is owned by the government and electricity prices are administered and/or subsidized. Irrespective of the prevailing pricing mechanism, renewable penetration results in intermittency costs and generally reduces returns or leads to higher-than-anticipated prices for customers [28]. With that in mind, understanding the quantum of intermittency costs can avoid unexpected burdens on government budget or force abrupt rises in the price of electricity [4].

Electricity prices in Saudi Arabia are administered and are significantly lower than international averages even after prices were raised at the start of 2016. Recently, Saudi Arabia announced a renewable target of 9.5 GW to be achieved by 2023. As mentioned above, when solar technology penetrates the power system, additional costs will be incurred. For the Saudi case, where the power sector is primarily state-owned, the government can choose to either cover this cost or carry it over to the consumer fully or partially. Between the recently announced target and expected future electricity price increases, modeling this cost will aid policymakers in devising suitable energy policies that minimize costs to society. It will identify one of the key elements of any tariff increase required by the utility to avoid a shortfall in receipts from customers to cover its outlays.

When discussing the introduction of intermittent sources to the power mix, it is only the costs that are often mentioned without recognizing that there are cost saving opportunities [12]. Explicitly, solar in particular can provide energy during the afternoon peak periods and avoid starting expensive and inefficient generators—a process usually referred to as peak-shaving. Depending on the magnitude of the intermittency costs and the costs avoided through peak-shaving, the net effect of introducing renewables into the generation mix could be a reduction in the overall power system cost. At low levels of penetration, renewable technologies may not consistently result in an additional net cost to the operation.

Methodology and assumptions

There are two types of reserves that we consider in our energy economy model:

• The planning reserve margin: Defined as the capacity installed on top of the expected system peak load. Adding PV capacity does not typically contribute to the planning reserve margin.

• Up-spinning reserves: Defined as idling turbines that can react within minutes to a sudden drop in renewable generation. These reserves are more relevant to renewable penetration compared to the planning margin. The costs of maintaining spinning reserves comprise fuel costs and operation and maintenance costs.

With the introduction of PV, the system has to provide back-up for unexpected periods of cloud cover or dust. Also, depending on the magnitude of installed capacity, thermal plants have to be shut down when the sun is shining and restart when it is not; this is known as ramping.

The cost of intermittency has been referred to in the literature by different terms including hidden costs [25] and system costs [1], and has been an active area of research recently. Each country possesses its unique characteristics that would result in a different cost of intermittency [15]. Among the factors that affect the intermittency costs are technology mix, fuel mix, fuel price, and load profile to mention a few. Generation technologies including coal or nuclear are not well suited for continuous ramping to follow the load, while gas turbines are more flexible in that regard. Hence, if a specific mix in a specific country comprises mostly gas turbines, it is expected that more solar penetration can be accommodated. Not only do technical aspects affect the cost of intermittency, but the geographical location of countries, which is directly related to the quality of the solar resource, will result in significant cost implications.

At the initial stages of solar PV deployment, the system can, for the most part, absorb the variability that could be caused given that the system does already operate with some reserves [5]. Hence, accommodating small levels of PV is not considered too problematic. However, as the level of penetrations increases, the variability concern becomes more difficult to handle [16]. The extent to which each system can accommodate solar PV capacity and the subsequent cost associated with this addition is unique to each power system as described above.

We define the cost of intermittency as the cost of operating up-spinning reserves and the cost of ramping existing power plants. The cost of PV integration, on the other hand, is the cost of intermittency plus the cost of building transmission capacity as a result of PV deployment. These costs are shouldered by the power system (not necessarily the PV operator) to ensure system reliability when an intermittent PV plant is operating. This is illustrated in Fig. 1. Additional back-up generation may be built when a certain level of PV penetration is reached. If this is the case, significant costs will be borne. Note that this paper does not consider any social costs or externalities, such as the value of pollution, but rather focuses on operational costs only.

Fig. 1

Source: KAPSARC

Costs of intermittency and integration

We examine the integration of up to 20 GW of national PV capacity, which represents a 25% penetration of generation capacity. The current Saudi power system possesses around 60 GW of thermal capacity, and an additional 20 GW of solar will bring the total capacity to 80 GW with a quarter represented by solar. Indeed, the generation mix of Saudi Arabia will change in the coming few years. Hence, 20 GW of solar PV may no longer be representative of 25% of the available capacity. The objective of this analysis, however, is to study the current status rather than project any future scenarios. As mentioned above, each generation/fuel mix and associated fuel prices would result in different costs of intermittency. Herein, we use the current mix and prices to quantify the cost of intermittency. While acknowledging that the mix will be different in the coming 5–7 years, the results of the paper would still be of value and relevant from a directional and qualitative viewpoint.

The analysis is carried out using the KAPSARC Energy Model (KEM) for Saudi Arabia for a long-run 2015 year [20, 21]; by long-run, it means we take annualized capital costs for investment, and consider that the power sector is able to make investment decisions by taking a long-term view. KEM is a bottom-up model covering six supply-side sectors in the Saudi energy economy and, within these, the electricity sector works toward minimizing its total system cost. The model considers the low fuel prices paid by Saudi utilities (in 2015) and the characteristics of the power capacity then, while simultaneously accounting for the activities of the other sectors modeled.

As there are four regions in KEM for Saudi Arabia, we also test their regional breakdowns. In addition to the base case in which solar is not present, we explore various regional placement cases for PV for every 1 GW of capacity increment; A1 to A15, as the cases are called, are detailed in Table 3 in Appendix A, and may establish a range for the integration costs. For example, A1 describes a scenario where the solar PV capacity is equally distributed in the eastern, central and southern regions, whereas A6 describes a scenario where all the capacity is added in the western region. Note that we dictate the PV capacity additions and placements, and then we let the model arrive at the optimal operation to meet demand.

This analysis could be carried out by specifying the national capacity and then having the model optimize for the regional placements. However, there are near-optimal solutions that may become optimal when considering a more disaggregate regional analysis or other costs that we may be missing. So we wanted to test different cases. Providing a range of solutions also allows policymakers to consider other costs and benefits that could be attained from solar deployment.

The costs of ramping

We have also added the costs of ramping dispatchable power plants to KEM for Saudi Arabia. Ramping involves higher maintenance costs due to the fatigue of mechanical equipment. It is important to note that power plants ramp up or down regardless of renewables, so the incremental cost of ramping is relevant when assessing the costs of PV integration [5]. The costs of ramping are derived from Van den Bergh and Delarue [29], as shown in Table 1.

Table 1 Ramping costs for existing power plant technologies in Saudi Arabia

KEM for Saudi Arabia represents electricity demand in the form of hourly load segments. The 24-h day is broken into eight load time segments to ensure model tractability, so the change of operation would be taken every 2–4 h. We also make the assumption in this analysis that 20% of existing open-cycle gas turbines can be converted to combined-cycle plants in the long-run framework.

The costs of maintaining spinning reserves

Gas turbines are used as potential back-up since they make up the majority of existing capacity in Saudi Arabia. The contribution of spinning reserves is a share of the power being generated by PV in each load segment, which is approximated to be 20%. Although these gas turbines would not be generating electricity as reserves, there are fixed operation and maintenance (O&M) costs associated with them. Further, they consume fuel at a fraction of a fully generating turbine. The fuel costs are summarized in Table 2. We know PV investments would not be made at these regulated prices (e.g., [20]), so we explore the operability of the system by considering the PV capacity as already existing—i.e., without considering its investment cost.

Table 2 Administered fuel prices for utilities in Saudi Arabia

Results and discussion

Effects of increasing PV capacity on generation

We show in Fig. 2 the electricity generation share by technology in steps of 10 GW of PV. At 20 GW of capacity, PV’s average share in generation is 17%. As seen, generation of electricity is only slightly sensitive to regional placement and varies because of the different solar radiation levels regionally. Since demand varies by region, additional transmission lines may be required to transport electricity from one region to another, based on where PV is deployed. In the base case 38.3 TWh of electricity is transmitted between regions. If for example, 20 GW of PV is installed in the southern region then 74.5 TWh would be transmitted inter-regionally, which would require 12.9 GW of additional transmission capacity to the western and central regions. The costs associated with the additional transmission lines and extra generation due to losses contributes to the total power system cost.

Fig. 2

Annual generation (TWh) shares by technology in the power sector as PV capacity is increased (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

Introducing solar capacity can increase the total ramping required from the system. As more PV capacity is built, additional thermal capacity is likely to be shut down in the morning, or at least operate at a lower output, and started up again in the evening. Figure 3 shows the incremental ramping costs in relation to a case where no additional PV capacity is deployed. The cost of ramping in the base case is around $5.5 million, which is small compared to the total system costs. The increase is largest with PV capacity installed in the south and least when installed in the central region.

Fig. 3

Incremental ramping costs of thermal power plants with increasing PV capacity (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

Costs of PV intermittency

Additional ramping costs are a small fraction of the intermittency costs, where the majority comes from maintaining spinning reserves. Figure 4 shows the costs attributed to spinning reserves and ramping as installed PV capacity is raised. The cost of intermittency is near zero in the base case and rises to around $600 million when 20 GW of PV is added. This cost of intermittency to the power utilities would range from near zero cents per kilowatt-hour (¢/kWh) without new PV capacity, to just over 0.20 ¢/kWh with 20 GW of PV if shared by all consumers or 1.3 ¢/kWh for each unit generated by PV; this range is illustrated in Fig. 5. The cost specific to electricity generated from PV plants would be neglected in a typical LCOE calculation.

Fig. 4

Overall cost of intermittency, i.e., costs of ramping dispatchable plants and maintaining spinning reserves as a result of increasing PV capacity, but excluding added transmission (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

Fig. 5

The cost of intermittency in Saudi Arabia shared by all electricity consumers as the share of PV is raised (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

Because no two countries have identical installed power systems, comparing costs of intermittency between countries should be undertaken with caution. This is illustrated in a study conducted by the United Kingdom Energy Research Center (UKERC). It is found that, if the share of wind turbine capacity in the UK is capped at 20 percent and the costs of intermittency were shared by all consumers, the impact on UK electricity prices would be in the range of 0.10–0.15 pence per kWh, or 0.18–0.27 ¢/kWh in 2006 dollars [11]. Comparing the Saudi and British power systems, it is worth noting that:

• Fuel prices in Saudi Arabia are low.

• A significant portion of the generation capacity in the UK was, at that time, comprised of coal-fired steam turbines, which are more expensive to ramp compared to gas turbines.

• The UK study analyzed intermittency caused by wind penetration. Wind energy is considered less predictable than solar conditions in Saudi Arabia, which results in more ramping and necessitates more flexible generation capacity.

The cost of intermittency is only one part of the story, as the addition of PV brings with it benefits to the power system. PV generation displaces the use of thermal plants during the middle of the day. The operational costs of thermal plants, as shown in Fig. 6, are declining because of lower variable operating costs and reduced costs of fuels. The fuels are purchased by utilities at below market rates, as shown by the middle column in Table 2. So the cost reductions would increase if fuel was priced higher. Matar et al. [20] showed that deregulating fuel prices in Saudi Arabia would induce investment in solar technologies. Therefore, crude oil and refined products become more costly to use in power plants instead of PV.

Fig. 6

Operating costs of thermal power plants as PV capacity is increased (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

The change in operation of existing thermal generators would also alter the thermal efficiency of generating plants, as highlighted in Fig. 7. Due to investments in converting gas turbines to combined-cycle plants, and building new combined-cycle plants, the long-run efficiency of generating plants increases to around 43.7%; this is mainly due to meeting reserve margin requirement, which is the same through the cases as PV does not contribute toward the requirement. Typically, the least efficient turbines would operate in the middle of the day in the summer, and by displacing them, the overall efficiency increases. As more capacity is introduced, more of the lower efficiency gas turbines would no longer operate.

Fig. 7

Efficiency of generating thermal plants with increasing integration of PV (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

The one outlier is building the capacity in the central region. Initially efficiency is increased as gas turbines are forgone. At about 8 GW of added PV capacity, the more efficient combined-cycle turbines in the day time begin to be removed from operation; this is made apparent in Fig. 7. The same is true for other regional placements, but occurs at higher PV capacity additions.

Overall, when looking at the operation of the electricity system and neglecting the capital spent on PV, the additional cost of PV integration is limited at 0.04 ¢/kWh, if the costs were shared between all consumers. As shown in Fig. 8, with the PV capacity shared between the southern and central regions (regional allocation A14), however, up to 11 GW can be added with a slightly lower system cost. The figure also shows that the difference between regional distributions that result in the highest and lowest power system costs at 20 GW of PV is $108 million; that is less than 2% of the system cost with no PV added. Many regional placement decisions achieve a near least-cost scenario and should not be neglected.

Fig. 8

Entire system costs as integrated PV capacity is increased (A1 to A15 are the regional placement cases we examine, detailed in Appendix A)

The economic limit of adding PV capacity: the ‘operational blend wall’

We have established that this cost of intermittency is dependent on region-specific attributes, including the flexibility of existing power generation mix. It is found that up to a certain level of PV capacity addition, the total costs of operating the generators and the grid begin to outstrip the benefits for the power sector. That point in our analysis for Saudi Arabia thus far is 11 GW based on the 2015 power system.

To explore this issue further, we look at this ‘operational blend wall’ for crude oil prices that range from the 2015 administered price of $4.24/bbl to $7/bbl (2016 price was $6.35/bbl). We want to avoid a potential price response by the power utilities in the form of investment in new technologies, so we cap the increase of the oil price to a modest value. We ensure that any built capacity without an oil price change is maintained until $7/bbl is reached. Moreover, that is the only price we adjust, as we would like to maintain the relative order of fuel use; crude oil is the last fuel of choice due to its price and lower thermal efficiency.

Fig. 9 shows the maximum capacity addition that results in lower overall power system costs than a case with no addition for a given administered crude oil price. Under the current crude oil price, the grid operator would have a lower total cost than a case without PV with 11 GW of installed PV capacity. As the price of the fuel rises, so does the maximum capacity that achieves a lower cost to the system. At $7/bbl crude oil, this capacity addition reaches 52 GW, which is sufficiently high for our analysis. These optimal capacities are not distributed the same way regionally as you change the oil price.

Fig. 9

Maximum PV capacity addition that yields lower operational power system costs than a case without PV for a range of crude oil prices

This specific blend wall applies to the Saudi power sector as a whole. It is valid for the prevailing capacity and electricity market situation in Saudi Arabia, and is strictly operational, i.e., no PV capital costs are included. As part of the power sector, the utility may bear the investment cost, or a power purchase agreement (PPA) may be established between itself and a solar PV developer. In such a scenario, the excess of the price paid to the new PV generators over the average consumer tariff would also have to be covered by the utility and will depend on the details of the PPA.

A rise in the cost to power system would likely bring about an increase in the electricity price. Furthermore, customers in aggregate would react by reducing their consumption. Without making assumptions as to the price elasticity of demand in Saudi Arabia, we reduce electricity use by 10% to observe how this PV integration wall changes following a change in demand. Shown in Fig. 9, the economic limit would be reached at lower PV capacity additions. This relationship is somewhat intuitive, as the operation costs of thermal plants are lower without the introduction of PV capacity.

Conclusion

The additional power system cost of introducing PV technology into the generation mix, part of which is referred to as the cost of intermittency, has been incorporated and quantified in the KAPSARC Energy Model for Saudi Arabia. It is the cost of maintaining a reliable grid with an intermittent renewable technology in the mix. In this analysis, a total PV capacity of 20 GW was deployed in 1 GW increments into the current Saudi generation mix, in various regional placements.

Essentially, the cost of intermittency entails two types of costs to the power utilities: additional ramping costs and spinning reserve costs. Without renewables, generators ramp only to deal with load fluctuations. However, as renewables penetrate the system, these dispatchable generators have to now address supply and demand variability simultaneously. Although ramping incidents may increase in number or magnitude when renewables are introduced, this addition does not have significant financial implications. The cost of intermittency is mainly attributed to maintaining spinning reserves.

Conventionally, the literature accepts that introducing solar in the power mix would reduce overall generation costs at the initial stages of penetration. As seen in this paper, this widely accepted notion is not always true. Power generation costs may actually immediately rise even at the first stages of solar PV deployment. Thus, careful analysis is warranted to ensure that the (1) capacity to be introduced and (2) the location(s) at which this capacity is to be built be optimal to achieve cost reductions.

On the cost saving side, introducing PV capacity means that inefficient generators would not be started, and the effective efficiency of the generating plants would thus improve. However, beyond a certain PV capacity, the avoided costs of operating inefficient plants and the higher generating efficiency begin to pale in comparison to the costs incurred.

When considering total electricity system cost, which includes the cost of intermittency plus any required grid upgrades or additional generation capacity to maintain the planning reserve margin, but not PV investments, the generation costs would increase by a net of 0.04 ¢/kWh at most; this value occurs at an installation of 20 GW in the eastern region. The least-cost scenario would yield lower generation costs than the base case, and would occur at an addition of 5 GW shared evenly between the central and southern regions.

Up to 11 GW of capacity, which includes the 2023 target of 9.5 GW announced, can be introduced evenly between the southern and central regions with lower power system cost. This PV integration wall rises as the price of crude oil offered to the utilities increases, reaching 52 GW of PV capacity at $7/bbl. The integration wall drops as the demand for electricity declines due to lower costs of thermal plants in initial operation. The relationship between the cost of generation, its potential effect on price and, therefore, demand, and the integration wall can be further studied.

Appendices

Appendix A: Regional placement cases

The 15 placement cases examine a wide range of regional deployment scenarios, as highlighted in Table 3. We steadily add 1 GW to the national PV capacity from 0 to 20 GW. The first 4 look at deployment of all the capacity in different combinations of 3 regions. The fifth scenario distributes the national capacity evenly across all the regions. Cases A6 to A9 stipulate capacities are installed in one of the four regions. Lastly, capacities in the remaining 5 cases are evenly deployed in different combinations of 2 regions. This produces 300 cases.

Table 3 Regional PV placement cases in our analysis

See Table 3.

Appendix B: Updating the KAPSARC energy model for Saudi Arabia

We updated KEM to account for recent activity as much as possible. We describe here the steps taken to update the model calibration to 2015. Power load demands were calculated for all combinations of region, season and type of day using 2015 hourly load profiles obtained from the Saudi Electricity Company (SEC). 2015 power generation capacities are sourced from ECRA and correspondence with SEC.

In the upstream sector, updated production values for crude oil, methane, ethane and other natural gas liquids were taken from Saudi Aramco [27]. International market prices for Arabian crude grades were taken from the Saudi Arabian Monetary Agency (SAMA) [26] and the Middle East Economic Survey. Those 2014 values were then adjusted to 2015 by growth rates in Oxford Economics’ Global Economic Model projections (using the January 2016 database). The administered naphtha and propane prices offered to petrochemicals firms in Saudi Arabia are calculated relative to the 2015 naphtha cost and freight price in Japan.

We updated the refining capacities in Saudi Arabia using the IHS Midstream database. In 2015, the YASREF refinery was added to the western region’s aggregate capacity. The 2013 demand for cement, petrochemicals and refined products were scaled up to 2015 by the gross domestic product growth and elasticities used by Matar et al. [20]. The exports of those products are the sectors’ gross output minus domestic demand; the 2015 gross outputs for those exporting sectors are taken from Oxford Economics’ Global Industry Model (using the November 2015 database). Municipal water demand is scaled by population growth.

Photovoltaics experience degradation over time. Similar to annualizing capital costs over their lifetime, we have to also annualize their output. This is done by taking the effective degradation throughout their life and applying it to the capacity in which the power sector makes the initial investment. We consider up to a 1% per year reduction in output following Jordan and Kurtz [18] for crystalline silicon PV in desert climates.

Appendix C: Cost of ramping up and down in KEM

The costs of ramping are estimated in the model as,

$$- {\text{ELrampcst}}_{\text{ELpd}} \mathop \sum \limits_{\text{v,ELf}} \left( {\frac{{{\text{ELop}}_{\text{ELpd,v,ELl,ELs,ELday,ELf,r}} }}{{{\text{ELlchours}}_{\text{ELl}} }} - \frac{{{\text{ELop}}_{\text{ELpd,v,ELl - 1,ELs,ELday,ELf,r}} }}{{{\text{ELlchours}}_{{{\text{ELl}} - 1}} }}} \right) + {\text{ELrampupcst}}_{\text{ELpd,ELl,ELs,ELday,r}} - {\text{ELrampdncst}}_{\text{ELpd,ELl,ELs,ELday,r}} = 0$$

(1)

The equation enforces a cost for the utilities to change power output between time periods. In Equation 1:

• ${\text{ELrampcst}}_{\text{ELpd}}$ are the costs of ramping in USD per ∆MW for each dispatchable power plant, ${\text{ELpd}}$, divided by the number of hours in each load segment, season and day type. The value for the ramping costs used in the analysis is shown in Table 1.

• ${\text{ELop}}_{\text{ELpd,v,ELl,ELs,ELday,ELf,t,r}}$. are decision variables for generation in MWh by plant type, vintage, load segment, seasonal period, day type, fuel and region.

• ${\text{ELrampupcst}}_{\text{ELpd,ELl,ELs,ELday,r}}$ and ${\text{ELrampdncst}}_{\text{ELpd,ELl,ELs,ELday,r}}$ are the total ramping costs in USD.