Master of Science Programme in Sociotechnical Systems
Engineering (STS)
Uppsala U niver sity log otyp eSAMINT-STS 21004
Degree project 15 credits
June 2021
Battery Storage for Grid
Application
A case study of implementing a Lithium-ion storage
system for power peak shaving and energy arbitrage
Eszter Abran
Elin Andersson
Therese Nilsson Rova
Faculty of Science and Technology
Uppsala University, Place of publication Uppsala
Subject reader: Oskar Lindberg Examiner: Joakim Widén
Uppsala U niver sity log otyp e
Battery Storage for Grid Application Eszter Abran
Elin Andersson
Therese Nilsson Rova
Abstract
Large scale Lithium-ion battery energy storage systems (BESS) for stationary power grid application is a developing field among energy storage technologies. Predictions indicate an increased use of the technology which offers a solution to the challenges that the increasing share of intermittent energy sources causes on the power grid. The non-plannability of intermittent power production requires solutions to maintain a stable and reliable power grid. Further commercialization of BESSes is also seen as use increases for electric vehicles and other mobile use.
A distribution grid owner, referred to as the Company, has a power subscription for power that is fed from the regional grid, where additional power peak fees are added when exceeding the subscription limit. This study investigates whether a Lithium-ion BESS can be financially beneficial for the Company by examining two power grid services. The first one is power peak shaving, and the second one is energy arbitrage. Energy arbitrage signifies that the BESS is charged during low electricity prices and discharged during high prices, thus generating profit. This is accomplished by simulating a Lithium-ion BESS in MATLAB (2019) where the studied services are combined. The results show that a Lithium-ion BESS can be used for the purpose of peak shaving and energy arbitrage, although an investment is not profitable for the Company with the current market situation. The sensitivity analysis does however indicate profitability if the current power peak fees and spot prices remain unchanged while the BESS investment cost is reduced by 50%. This decrease in BESS cost is predicted possible within the next decade as BESS demand is expected to increase. The study implies that the main factor effecting the solution to be profitable is the high investment cost.
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Table of contents
Abbreviations ... 2
1. Introduction ... 4
Aims and research questions ... 5
Limitations ... 5
Delimitations... 5
Report structure ... 5
2. Background ... 7
Sweden's electricity grid ... 7
Intermittent energy sources ... 8
The future of the grid... 9
Sweden's electricity market ... 9
Spot market... 10
Intraday market... 11
Grid tariffs ... 11
Energy storage ... 11
Lithium-ion batteries ... 12
Stationary storage market ... 12
Lifetime ... 13
C-number ... 14
Full cycle equivalents ... 14
BESS profit generation ... 14
Power peak shaving ... 14
Energy Arbitrage ... 15
The Company... 15
Power subscription ... 15
3. Methodology ... 17
Working process ... 17
Model of the system... 18
Cost calculations ... 20
Sensitivity analysis ... 22
4. Data ... 23
Collection of datasets ... 23
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Net load ... 24
Power subscription data... 24
BESS parameters ... 25
Choice of parameter values ... 26
5. Results ... 29
Spot market ... 29
Reference price range ... 30
BESS use ... 30
Optimal BESS size ... 32
Subscription limit 70... 33 Subscription limit 66 MW ... 34 Costs ... 35 Profit generation ... 36 Sensitivity analysis ... 37 6. Discussion ... 39 Results... 39 BESS use/size ... 39 Costs ... 40 Profits ... 40 Methodology ... 41 Data ... 42 Sensitivity analysis ... 42 Future prospects ... 43 Further studies ... 44 7. Conclusion... 45 References ... 46 Appendix A ... 50 Appendix B ... 51 Appendix C ... 52 Appendix D ... 53 Appendix E ... 54
Abbreviations
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BESS Battery Energy Storage System
DoD Depth of Charge
EV Electric vehicle kV Kilovolt kW Kilowatt kWh Kilowatt-hour MW Megawatt MWh Megawatt-hour
MSEK Million Swedish Krona
SoC State of Charge
SvK Svenska kraftnät
TWh Terawatt-hour
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1. Introduction
In 2015 the United Nations set several global goals for sustainable development. The goals aim to take urgent action in order to protect the planet from degradation through sustainable consumption and production [1]. One of the goals is to ensure access to affordable, reliable, sustainable, and modern energy for all [2]. After decades of using fossil fuels to produce electricity, the readjustment to clean energy is seen as key to reduce the climate impact. As part of the conversion to less environmentally harmful energy sources, an increase in the usage of fossil free intermittent energy sources is inevitable [3].
The main sources of intermittent powers in Sweden are wind and solar irradiance. Their non-plannability is caused by changes in weather which is part reason for an increase of variability on the electricity grid. As the use of these sources increase in Sweden, the question regarding energy storage becomes a substantial part of the debate concerning a safe and reliable electricity grid [4]. In addition, the Swedish nuclear phase-out,
combined with the expansion of intermittent energy sources makes the electricity production in Sweden increasingly unpredictable. An increased risk of power shortage in southern Sweden due to limited transmission capacity from the northern parts where the majority of the production is located, also contributes to uncertainty [5]. In Sweden, power consumption peaks are high during winter due to electric heating, increasing the strain on the grid as the consumption increases [6]. Time periods of high load peaks and capacity shortage in the grid, combined with intermittent energy production, creates a need for power regulation. A key role in the solution for this increasingly urgent problem could be energy storage systems [7].
For a distribution grid owner and energy producer, here on referred to as the Company, high power peaks lead to costly power fees to the regional grid that provides the local municipality with electricity. Power fees are dependent on a power subscription limit which is based on the highest power peaks in the winter. However, the Company’s average net load during the summer is much lower than the subscription limit, which means that it is not fully utilized. By implementing an energy storage, the peaks can be lowered by storing energy when there is an abundance and using it when there is a greater demand. This allows for a lower power subscription and a more even power consumption from the regional grid. The seasonal dependence of the power peaks also creates opportunity to use an energy storage system for other grid services during times of lower power consumption.
The use and costs of energy storge systems will be analysed for power peak shaving and energy arbitrage. Energy arbitrage signifies that the BESS is charged during low
electricity prices and discharged during high prices, thus generating profits. The services will be analysed in this report by theoretically implementing a Lithium-ion battery energy storage system (BESS) on the Company’s distribution grid.
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Aims and research questions
The study will examine the Lithium-ion BESS technology from a techno-economical perspective and study further if and how an implementation can be favourable for the Company. The report also aims to analyse the economic prospect of using the BESS for energy arbitrage.
The objective of this report is to answer the following research questions: How can a BESS be used for peak shaving and energy arbitrage on the
Company’s distribution grid to generate profit?
Can it profitable for the Company to lower their subscription limit with an installed BESS?
How will BESS use be affected by the predicted development in the field?
Limitations
Data from the Company was used for simulations of power peak reductions. This data consists of the Company’s net load during the years 2016-2019. The energy storage system is not yet applied, and the simulations are therefore a hypothetical investigation using a model for the energy storage system.
Delimitations
This report will consider the BESS as a one-unit system, instead of multiple connected batteries. The calculations will exclude annual degradation during a lifetime of the BESS as well as wire and cable losses. Furthermore, the cost calculations for the energy storage system will exclude costs such as subsidies and grants. Because of the rapidly evolving Lithium-ion market prices varying widely over time, the report will restrict the use of data older than 5 years. The reason is to ensure reliable results that reflect the current market and technology state. Power peak shaving and energy arbitrage are the two BESS services investigated in this report. The simulations will hence be restricted to not consider other services.
Lastly, the investigation will not account for the geographical placement of the BESS, and the BESS is assumed to be charged directly from and discharged to the regional grid. The impact of the delimitations and limitations on the result will be discussed later, in Section 6.
Report structure
In Section 2, substantial background information is presented to give an understanding of the subjects discussed in the study. This section includes a presentation of the Swedish electricity grid as well as an introduction to different energy storage options with an emphasis on Lithium-ion BESSes. This is followed by Section 3 that describes
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the methodology of the report as well as an in-depth description of the simulations created for the analysed systems. This section will also include equations for cost calculations. Section 4 presents the data used for the simulations. The outcome is presented in Section 5 where the results of the simulations are presented. The section also includes results of the sensitivity analysis. The results are then discussed in Section 6. The analysis is made based on the results, connecting back to the aims of the report. Further, the impact of limitations and delimitations on the results are analysed as well as the sensitivity analysis. The most important results are then summarized in Section 7.
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2. Background
This section provides background to the study. Sections 2.1 and 2.2 give an overview of the Swedish electricity grid and market which is followed by information regarding grid tariffs and energy storage in Sections 2.3 and 2.4. Further, Lithium-ion BESSes are introduced, which is the investigated technology in this report. Sections 2.5 and 2.6 describe Lithium-ion BESSes and their profit generation. Lastly, the Company is presented as well as information about their current power subscription in Section 2.7. All figure and table descriptions are found in Appendix A and B.
Sweden's electricity grid
The total electricity production in Sweden 2020 was 159 TWh where the majority was generated from nuclear power, hydropower, and wind power [8]. Production of hydro- and wind power is predominant in the north of Sweden while the consumption is concentrated in the southern parts due to a higher population density. The electricity grid is therefore an important system that transmits and distributes electricity between geographical areas within the country and consists of three main levels: the transmission grid, the regional grid, and the distribution grid, as seen in Figure 1 [9].
The transmission grid transfers a large amount of electricity across long distances and is managed and owned by the transmission system operator, Svenska kraftnät (SvK). SvKs goal is to maintain a balance between production and consumption at all times in order to maintain operational reliability [10].
The production input to the transmission grid comes from large electricity generation plants. The transmission grid is then connected to the regional grid. The regional grid is owned by electricity companies and operates as the connection between the
transmission grid and the distribution grid. Lastly, the distribution grids deliver electricity from the regional grids to the customer [9]. The connection between the different grids is shown in Figure 1.
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Figure 1. Sweden's electricity grid: transmission grid (stamnät), regional grid
(regionnät) and distribution grid (lokalnät) [11].
Intermittent energy sources
As the Swedish energy production gravitates towards an increased use of intermittent energy sources, the electricity grid is faced with the limitations that these technologies have. The non-plannability of intermittent power creates an uncertainty aspect to the grid, seen by an increase in variability. This creates a need for further regulation possibilities to ensure stable electricity distribution [12].
The most common sources of intermittent power in Sweden are solar irradiance and wind power [8]. As mentioned earlier, these energy sources are dictated by weather conditions. For Sweden, the weather conditions are signified by drastic changes that vary from cold winters in the north to warm summers in the south, meaning both solar and wind power can be utilized in different parts of Sweden and in different seasons. The intermittent sources tend to complement each other as it is windier during the cooler winter months and the solar irradiance increases during the warmer summer and spring months [13].
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The future of the grid
In all of Europe, the last years have shown structural changes in the electricity market due to a shift to smaller dispersed power plants, from centrally located ones. In Sweden, this trend is evident through the declining profitability of nuclear power, while wind power electricity generation has more than doubled over the last ten years. With falling prices, solar power is also increasing especially among private individuals and property owners, though with limited production during the winter months. The management of the developing share of variable and intermittent power in the system combined with the nuclear phase-out presents a big challenge in the electricity market [14]. The systems need to constantly be balanced along with the uncontrollable production of intermittent sources creates a larger need for energy storage to keep the balance of the system [15].
Sweden's electricity market
Sweden is divided into four bidding areas: SE1, SE2, SE3 and SE4, as presented in Figure 2. The different areas result in varying electricity prices in each area, which is determined by supply, demand, and transmission capacity. The capacity to transfer power between areas is limited by the physical limitations of the grid which may lead to different prices within the different bidding areas. For example, if the transfer capacity between two bidding areas is sufficient, then the electricity price is the same in each area. On the contrary, if the transfer capacity is insufficient, then the price will differ between the areas. Due to more electricity production in the northern parts of Sweden, a large quantity is transported south [16] ,[17].
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Figure 2 Sweden's bidding areas, SE1-SE4 [18].
Nord Pool is the Nordic power market owned by SvK and its counterparts in the various member countries around the Nordic and Baltic. Several actors such as power
producers, suppliers and traders buy and sell electricity on the Nord Pool market, and approximately 90% of the consumed electricity in the Nordic Region is traded on Nord Pool. Nord Pool has a market for trading electricity per hour for delivery the next day, spot market, also called day-ahead market. The intraday market is for short-term market adjustments one hour before delivery to attain grid balance [19], [18].
Spot market
Twelve to thirty-six hours before delivery, the energy is sold and bought for each hour for the following day on the spot market. The actors determine how much electricity to sell or buy in a specific area and at what price. All bids must be settled at 12:00 the day before delivery to be evaluated which later result in a spot market price. The spot market has marginal pricing, which implies actors trade electricity at the settled market price, and not at their original bid. The established spot market price is the same regardless of what kind of production the electricity is produced from [17].
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Intraday market
In supplement to the spot market, the intraday market works as an adjustment for the actors to complement trade volumes. In case of deviations from forecasted production or consumption the actors have the chance to trade until one hour before delivery time. As no forecast can predict exact weather outcome, the intraday market gives room for adjustment. Intraday market trading volumes are small in comparison to the spot market and is therefore majorly used by risk taking companies [17].
Grid tariffs
The limitations of the electricity grid regarding energy supply and distribution capacity puts emphasis on an efficient use of the grid. If load peaks can be levelled out, net losses may decrease, and the hosting capacity of the grid increases which creates opportunities to connect new customers and producers. In addition, levelled out load peaks also increases the flexibility in the grid [20]. To create economic incentives for levelling out loads and to create warning signals for regional capacity shortages different tariffs on the grid are used [21].
One category of tariffs is dependent on the client’s power consumption. The tariff for the power distribution from the distributer is placed on the client. These can for example be between the regional grid as the distributor and the distribution grid as the client. A power tariff can be set in multiple ways and is based on the grid capacity the client either is using, can use or a limit the client has chosen to subscribe to. The latter one is called power subscriptions which are the basis of the current tariffs in the Swedish electricity grid. For each year, an annual power subscription sets a maximum limit of power that a customer can draw from the grid at any time, with additional fees whenever exceeded. The subscription limit is set by the distribution grid owners according to their needs [21].
Energy storage
Energy storage can offer the opportunity to stabilize the desirable balance between supply and demand in the electricity grid. As a result of a rather new market of energy storage technologies within the electricity grid, there are many technologies in the early stages of development and only a few have come to the stage of large-scale
commercialization. Different energy storage technologies offer various applications depending on specific demand in the electricity grid. Factors such as cost, capacity, efficiency level, energy density, geographical conditions and lifetime are crucial for suitable application, and the current focus areas of application in Europe is energy arbitrage, reduction of power peaks and small-scale use. Energy arbitrage offers a chance to use power storage for economical profit where it takes advantage of electricity prices, this is further explained in Section 2.6.2[22], [15] , [23].
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Some technologies for large-scaled storage are pumped hydropower, hydrogen gas and batteries. Hydropower technique is mature, but currently established predominantly as large-scale facilities and are thus hard to apply on smaller scale storage systems. A big advantage with hydrogen gas storage is the ability to store energy for a long period of time. However, in comparison with other energy storage technologies, hydrogen gas storage has a relatively low efficiency level, around 20-50%. In addition, only a small share of developed hydrogen techniques has reached commercial maturity because of high costs [15], [24].
Of the presented storage technologies BESSes are the fastest developing storage technology today. One type of fast evolving BESS is made of Lithium-ion batteries. With its high energy density and an efficiency of 80-90%, they are more efficient relative to their size than other batteries [15], [24].
Lithium-ion batteries
Due to the fast-evolving development of Lithium-ion battery technology as well as cost reduction projections, it is interesting to investigate the possibility of implementation of such a system for the Company.
Lithium-ion batteries have multiple areas of application, ranging from mobile use such as electrical vehicles (EVs) and power tools to stationary use such as medical devises and large-scale storage systems. A combination of fast reaction time, offering a storage time of minutes to hours, fast installation time and high adaptability has made it
especially suitable for commercial use. In Sweden they are currently found among private customers, public buildings, and real-estate owners [25].
Stationary storage market
Since 2011, when Lithium-ion batteries were first introduced as a large-scale stationary storage technology, the development has been noticeable. The growth in market share is significantly larger compared to other storage options [25]. Sales of the batteries have grown on account for the increasing demand of use in electric vehicles, but also for stationary and industrial use. With the extent of renewable energy generation and consequently the enhancement of storage demand, the use of Lithium-ion batteries is predicted to continue to grow [26].
The constantly developing Lithium-ion battery market for other uses also contributes to the increase in market share for stationary storage. An example is how the cumulative charging of electric vehicles increases the strain on the grid and heighten eventual power peaks even more [27]. The market for EVs is in fast development, with predictions stating an increase of from 70 000 EVs in 2018 to 2,5 million in 2030 in Sweden [28]. In some regions of Sweden, the recent development has resulted in further need for stationary storage in distribution grids. By charging during low demand hours or using stationary storage to charge EVs the power peaks are reduced [27].
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One of the biggest challenges for Lithium-ion batteries used as a large-scale storage system is the cost of investment. Even though the costs for the Lithium-ion batteries have decreased by almost 90% since 2010, there are still challenges to make the investment and use of storage profitable [29]. However, future predictions by
BloombergNEF (2020) declare further cost reductions, with the Lithium-ion battery cost per kilowatt-hour decreasing by 50% until 2030 [22], as exemplified in Figure 3.
Figure 3. Lithium-ion battery price outlook [30].
In a report from the National Renewable Energy Laboratory, W. Cole and A. W. Frazier (2019, [26]) present cost projections of Lithium-ion large-scale battery storage. This declares a cost reduction of 10-52% by 2025 and 21-67% by 2030 [26].
Lifetime
The lifetime of Lithium-ion batteries varies widely between sources depending on publishing year and is partly determined by the circumstances under which they operate. Depth of Discharge (DoD) and number of cycles are two of those qualities, these also vary between reports causing the variety in lifetime. DoD is a percentage value representing how much a battery can be discharged before damaging its performance [31]. A cycle is defined as a complete charge-to-discharge cycle, the number of cycles is what a battery is expected to perform before its capacity is reduced to less than 70-80% of its normal performance. The lifetime of a battery is dependent on calendar life and the number of cycles. The calendar life is the time elapsed before the battery is unusable. There is also a limit of how many full cycles the battery can be used for, referred to in this report as BESS lifetime. Even if the battery does not reach the total number of cycles defined by the BESS lifetime, it is no longer useable after exceeding
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its calendar life. The BESS lifetime can vary between 5250-7300 cycles depending on cycles per year and calendar life [32].
C-number
A battery’s hourly charge or discharge rate is given by its power capacity (measured in kW) which is often seen in relation to its maximum energy capacity, also called storage capacity (measured in kWh). The relation between the power capacity and storage capacity is called the batteries C-number. A battery with a C-number equalling one signifies that the power capacity is the same as the storage capacity, meaning the BESS can fully charge or discharge in one hour. A battery having the C-number four means that 1/4 of the total storage capacity can be discharged or charged in one hour, meaning that when discharging, a fully charged BESS can provide maximum power capacity for four hours [33].
Full cycle equivalents
As mentioned, the aging of a BESS is dependent on calendric lifetime and cyclic lifetime. Because the BESS does not always fully charge and fully discharge when in use, full cycle equivalents, FCE, can be used to estimate the BESS number of full cycles when operating. FCE is described by Equation 1 where Ek represents the energy
difference between each time step k and CBat is the energy storage capacity of the
battery [34].
𝐹𝐶𝐸 = 1
2𝐶𝐵𝑎𝑡∑ |𝐸𝑘|
𝑛
𝑘=1 (1)
BESS profit generation
The flexibility of BESS solutions makes for opportunity to use them for different kinds of services in different levels of the energy system. BESSes are often used for
generating multiple revenue streams, which is seen as a key factor for making BESS projects profitable. Examples of services BESSes can provide in the grid for large-scale applications are frequency regulation, voltage support, arbitrage, peak shaving,
postpone grid investments, capacity reserve, and for integrating renewable intermittent energy production (power stabilization) [35] [36].
Power peak shaving
Like the name suggests, power peak shaving signifies the levelling out or reduction of peaks in load. This can be achieved by using a battery to level out the peaks and thereby reduce costs from power tariffs. If applied proficiently, the power subscription can be lowered and thus lowering the subscription costs for the large company. Another benefit of power peak shaving is that it also can reduce stress on the power grid, which leads to less losses, making power peak shaving a means to increase energy efficiency. This in turn can be used to avoid or postpone grid investments [37].
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Energy Arbitrage
Energy can be bought from the market when prices are low and sold when prices are high. Using these energy market fluctuations in price, often referred to as energy
arbitrage. A battery can be charged during low prices and discharged during high prices, thus creating a profit [37]. The market used for Energy Arbitrage in Sweden is the Nord pool day ahead market.
The Company
The Company operates in the bidding area SE3. It owns and maintains the local
electricity grid and hence the distribution of electricity from the regional grid to the city area. Furthermore, the Company also produces energy through wind and solar power.
Power subscription
As a distribution grid owner, the Company has a power subscription for the power drawn from the regional grid. The yearly power subscription is based on the maximum load for each year to avoid additional fees when exceeded. There is thus an economic incentive to level out the load to cut the load peaks and thereby lower the power subscription and associated cost [20]. The Company has a consumption level that exceeds the production on the distribution grid which results in a dependence on import of power from the regional grid. To secure sufficient power the production in the distribution grid is complemented by imported power from the regional grid. The net load is therefore a description of the total power imported from the regional grid. In Figure 4 the Company’s net power consumption from the regional grid for the years 2016-2019 is shown. The hours when the net load exceeds the subscription limit are referred to as periods of high load, remaining hours are referred to as periods of low load. The referred hours change when changing the power subscription level. The Company’s subscription limit is currently set at 70 MW.
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Figure 4 The Company’s net load and power subscription level over the years 2016-2019
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3. Methodology
This section presents the method that is used for the investigation. Initially, the working process for the investigation is presented in Section 3.1. Section 3.2 introduces the models and explains the simulations. Flow charts are also presented to visualize how the models operate. Further, Section 3.3 defines the equations used for cost calculations such as investment and power subscription costs. The section is concluded with an explanation of the sensitivity analysis in Section 3.4. All equations are found in Appendix C and parameter abbreviations are found in Appendix D and E.
Working process
To fulfil the aim and answer the research questions the following processes summarize the workflow of the study. Figure 5 presents a schematic image with three main
processes: Information Gathering, Simulation, Evaluation & Conclusion.
Figure 5. Working process timeline.
The first step in the working process is Information Gathering which includes the gathering of relevant information needed for the study. To get a perception of the studied system, publications regarding energy storage systems, and more specifically about Lithium-ion storage systems, is analysed. This leads into data collection of datasets and parameters being used in the simulations, this is further described in Section 4. Parameters used for the BESS simulations were collected from various sources, to give a broader perception of the current market situation. Lastly, two flowcharts are created to represent the algorithms for charging and discharging the BESS for the two services.
The second step of the process is Simulation. The algorithms are made in MATLAB (2019), along with the main script that simulates the complete process. These are later extended with cost calculations. The annual costs are subtracted from the annual
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revenues from the services to calculate the profits generated. Lastly, the remaining costs from the BESS investments are calculated. If all costs are zero, the break-even is
reached during its lifetime. This calculation is made for various BESS sizes to investigate the optimal BESS size for different subscription limits.
The third and last step is Evaluation & Conclusions where results from the
accomplished simulations are obtained and evaluated. Further, a sensitivity analysis is formed by investigating the sensitivity of the study.
Model of the system
To analyse the system, a model over the BESS is created. Peak shaving is used during periods of high load to reduce power peaks. During the longer periods of low load, the need of reducing power peaks is not as relevant as the low load does not entail risks of exceeding the subscription limit. To get the most use out of the BESS throughout the whole year, it is therefore examined for a combination of power peak shaving and energy arbitrage use.
The analysis is achieved by creating two primary models in MATLAB, one for using the BESS for peak shaving, and one for energy arbitrage. The data for the Company’s net load consumed from the regional grid is processed by splitting the net load data into time blocks and simulating the results one block at a time. The length of a time block is an input to the model. For each time block, an algorithm decides which of the two models will be applied for the BESS use during the current block. If there are any peaks above the subscription limit in the time block, the BESS will be used for peak shaving, and if not, it will be used for arbitrage. After processing the entire four years of data accordingly and summarizing the results for each time block, a cost calculation is made. The cost calculation calculates the cost of the BESS as well as the cost of grid fees using the processed net load data. The arbitrage profits are however calculated for each block the BESS is used for arbitrage and then summarized. The two models also output the full cycle equivalents (FCE), which are summarized to show the total number of cycles. Lastly, a comparison is made between the old net load and the processed net load to calculate the change in power peaks above the subscription limit.
Figure 6 presents the process for the BESS when used for peak shaving. The process displayed by the model restarts every hour. The first step is monitoring whether the net load, Pnet, is less than the subscription limit, Plim. If so, the algorithm checks if the BESS
is fully charged or not by controlling the BESS State of Charge, SoCBESS which
corresponds to the battery’s current charge level. If the BESS needs to be charged, it is charged as long as Pnet is lower than Plim, until the end of the hour or until the BESS
reached its maximum capacity, maxcap. If Pnet exceeded the limit, the BESS is
discharged instead, with the condition that the BESS has a SoC that is higher than the minimum capacity mincap, meaning the battery can be used. The BESS is discharged as
long as Pnet is higher than Plim, until the end of the hour or until the BESS reached its
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charged in one hour and is set by the lowest of the following values: the maximum charging capacity in one hour, the maximum capacity that can be charged without Pnet
exceeding the subscription limit, or the capacity until the BESS reaches maxcap. The
same method is used for determining maxdischarging, but with mincap and maxdischarging until
the peak is reduced to Plim.
Figure 6. Flow chart representing the hourly BESS process for peak shaving.
Figure 7 represents the BESS process when used for energy arbitrage. For this model, a 3rd degree polynomial curve is created using MATLAB basic fitting functions, polyfit and polyval for each processed time block. The curves are created for each block of time this model is activated, based on the spot prices corresponding to the time block. The 3rd degree polynomial is chosen over the use of a mean value of the spot prices due to the seasonal changes in price. The polynomial allows a more optimized utilization of the BESS with more revenue than if mean values would have been used. The curve is then used as a reference, pref, for a range consisting of an upper and lower limit. The
range decides when to charge or discharge the BESS. A spot price lower than the lower limit, pdown, results in the BESS being charged. It is charged as long as pspot is lower than
pdown or until the end of the hour or until the BESS reached its maximum capacity,
maxcap. A spot price higher than the upper limit, pup, resulted in the BESS being
discharged. It is discharged as long as pspot is higher than the upper limit, pup, until the
end of the hour or until the BESS reached its minimum capacity, mincap. For each time
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Figure 7. Flow chart representing the hourly BESS process for energy arbitrage.
Cost calculations
The total cost for the BESS includes the investment cost as well as total grid costs including the extra fees for power peaks that were not entirely reduced by the BESS. The total annual extra fee for peaks over the subscription limit is described by:
𝑆𝑡𝑜𝑡 𝑓𝑒𝑒𝑠 = (∑(𝑃𝑛𝑒𝑡− 𝑃𝑙𝑖𝑚)) × 𝑃𝑓𝑒𝑒 (1)
Where Pnet [kW] signifies the net load, Plim [kW] is the subscription limit and Pfee
[SEK/kW] is the fee for exceeding the limit.
The total annual fees, Totfees [SEK], for a specified subscription limit is given by:
𝑇𝑜𝑡𝑓𝑒𝑒𝑠 = (𝐺𝑓𝑒𝑒 × ∑ 𝑃𝑛𝑒𝑡) + 𝑆𝑡𝑜𝑡 𝑓𝑒𝑒𝑠 (2) The cost of the region grid fee is signified by Gfee [SEK/kWh], as earlier, Pnet is the net
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The total grid cost, Gcost [SEK] is calculated as:
𝐺𝑐𝑜𝑠𝑡 = 𝑃𝑙𝑖𝑚 × 𝑆𝑐𝑜𝑠𝑡 + 𝑇𝑜𝑡𝑓𝑒𝑒𝑠 (3) Where Plim is the subscription limit, Scost is the subscription cost [SEK] and Totfees as
mentioned the total fees [SEK].
The total investment cost for the BESS, BESSinv [SEK/kWh], is given by:
𝐵𝐸𝑆𝑆𝑖𝑛𝑣 = 𝐶𝐵𝐸𝑆𝑆× 𝑐 (4)
Here, the investment cost in dollars is signified by CBESS [USD/kWh] and c is the
currency converter from USD to SEK.
The total cost for the BESS, BESScost [SEK] is:
𝐵𝐸𝑆𝑆𝑐𝑜𝑠𝑡 = 𝐵𝐸𝑆𝑆𝐸 × 𝐵𝐸𝑆𝑆𝑖𝑛𝑣 (5)
Where BESSE [kWh] is the installed energy capacity.
Finally, the total cost, Totcost [SEK] is given by:
𝑇𝑜𝑡𝑐𝑜𝑠𝑡 = 𝐵𝐸𝑆𝑆𝑐𝑜𝑠𝑡 + 𝐺𝑐𝑜𝑠𝑡 (6)
Where, as mentioned, BESScost is the total cost for the BESS and Gcost is the total grid
cost.
When presenting the results, the profit is defined as the net savings made by
implementing a BESS on the system. Here the profits or costs of lowering the limit are not considered. For a reduced subscription limit the total system cost is defined as costs of having a reduced limit without an installed BESS compared to costs using the same limit but with an installed BESS. The net savings are then the difference in system costs. In order to take into consideration, the one-time investment cost of the BESS, the investment cost is estimated by dividing it by the BESS lifetime, thus making it
comparable to the yearly savings the BESS is generating. The annual BESS investment cost is then subtracted to the sum of annual revenues generating by peak shaving and arbitrage.
An algorithm is also created to generate profit results for different sizes of BESSes for a specific subscription limit. This is used to evaluate which size BESS is the most optimal
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for each subscription limit, from a cost perspective. The script implements BESSes of different sizes on the system and uses the profit definition (as described above) to evaluate the total accumulated profits over the BESS lifetime, which is then used to determine the optimal BESS size. The accumulated lifetime profits for each size BESS are also presented in a plot.
Sensitivity analysis
In order to examine the robustness of the results produced, a sensitivity analysis is performed that analyses which effect a change in certain parameters have on the system. The sensitivity analysis investigates the effect on the break-even time of an installed BESS caused by a percentage change in a chosen parameter, while all other parameters remain unchanged. A change of three chosen parameters is investigated, which are chosen as the most interesting parameters to investigate based on the background information provided in this report.
The first examined parameter is the BESS cost. This is chosen due to the rapid price development occurring the last decade, along with cost projections showing a continued price reduction over the next decade. Due to these rapid changes in the parameter, this is seen as an interesting parameter for which to investigate its effect on the break-even time. The second parameter is the power peak fee. This is due to the fee being the incentive to level out the load, meaning it can be changed depending on the needs of the transmission and regional grid. The last parameter is spot prices. These affect the
revenues from arbitrage, generated by using the BESS on the spot market. This
parameter is chosen in order to be able to analyse the arbitrage revenues impact on the economic prospects of a BESS investment.
The changes of the parameters were produced by multiplying the parameter with a percentage. For the BESS cost and power peak fees, the cost defined in the model are multiplied by the investigated percentage. For the spot market, the change in this market is estimated by assuming there would be a percentual augmentation or reduction of the variation in prices, meaning a bigger/smaller difference between the peaks and the valleys of the spot price. This is simulated by multiplying the income with a percentage, with the income being calculated as the accumulated costs from charging subtracted from accumulated profits from discharging for a time block.
The break-even year is then estimated by applying a 5MW/20MWh BESS on a 70 MW subscription limit system. The yearly revenues made by arbitrage and peak shaving is subtracted from the BESS investment cost until the remaining BESS investment cost is below zero, meaning break-even was reached. The amount of yearly revenues needed is the break-even time. The time is estimated in whole years and to compensate for this, the resulting plot is interpolated using MATLAB's basic fitting tool for plots.
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4. Data
This Section presents the data used to execute the simulations to obtain the result. Section 4.1 describes the collection of the data. This is then divided in to three separate descriptions of every data set used, Sections 4.1.1 - 4.1.3. Section 4.2 presents detailed information and significant parameters for Lithium-ion energy storage systems. This is followed by a presentation of choice of parameters used for the BESS simulations in Section 4.3.
Collection of datasets
The performed simulations are based on a dataset of the netload provided by the Company. The netload is given in kW and is presented as hourly values between the period 2016-01-01 00:00 and 2019-12-31 23:00, meaning the simulation results are based on a total of four years of hourly data. Used market data of spot prices is collected from Nord Pool with a time range corresponding to the same period of time as the given netload from the Company. Spot prices are also given on an hourly basis. All collected datasets are downloaded as Excel files and imported to MATLAB. Datasets are stored as vectors to be used in the simulations. As the datasets include a leap year, the results that are presented as annual mean values have more data values than the exact length of four normal years.
Spot prices
Spot prices for the investigated period are collected from Nord Pool for the SE3 area. Prices are given in SEK/MWh for each hour between 2016-2019 and are utilized to analyse eventual profit generation when using the BESS for arbitrage. Figure 8 illustrates the hourly spot prices for SE3 from 2016 to 2019
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Figure 8. Spot prices for SE3 between years 2016-2019.
Net load
To perform the simulations, data of the Company’s net power consumption from the regional grid was provided. The data is previously presented in Figure 4. The data shows that for the current subscription level of 70 MW, a peak lasts an average of three hours with three hours as the median value as well. The minimum peak time is one hour, and the maximum is 13 hours. The mean peak size is 218 MWh, with the biggest peak having a size of 979 MWh and the smallest peak has a size of 70 MWh. If
lowering the subscription limit, the peaks will last for a longer time period and increase in size, which can be seen in Figure 4. For a subscription level of for example 64 MW, the peaks duration has increased to an average of 4.6 hours and median of 4 hours, and an average size of 310 MWh.
Power subscription data
The cost data for the Company’s power subscription was provided by the Company. The cost associated with the power subscription is 246 SEK/kW, a fixed cost dependent of the set level for the subscription. The current power subscription is set at a level of 70 MW. This fee will be referenced to as the subscription limit cost. Additional fees of 41 SEK/kW are imposed for each hour the net power consumption from the regional grid exceeds the power subscription limit, here on referred to as power peak fees. Finally, there is a transfer fee of 0.011 SEK/kWh, which is always charged for each kWh imported from the regional grid, independently of the power subscription limit.
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BESS parameters
Parameters for the BESS are in-front-of-the-meter values. In-front-of-the-meter refers to larger-scale energy production that feed into the grid. These are managed by the
electricity producers [38].
Jülch (2016) presents costs for battery storages of the sizes 100 MW / 70 GWh and 100MW / 400 MWh through a compilation of market data. The battery calendric lifetime was set to 20 years and the BESS lifetime to 7000 cycles at a DoD of 80%. Meaning 350 cycles/year at an efficiency of 95%. Future predictions for Lithium-ion batteries in 2030 were presented in improved parameter values such as DoD at 100% and 10000 cycles during a lifetime of 20 years resulting in 500 cycles/year [31].
Apricum (2016) presents two examples of energy storage where the lifetime is set to 15 years with 350 cycles/year with a DoD of 80% and an efficiency of 92% [32].
A report by Fu et al. (2018, [39]) is based on analysis of 8 publications which are used for determining benchmark pricing for battery systems. This BESS pricing includes the battery cost, which is the cost of the battery itself, the developer cost, sale tax, EPC overhead, installation labour and equipment, electrical BOS, structural BOS and the battery central invertor. For a 60 MW/240 MWh sized battery with a duration of 4 hours the battery price is given by 209 $/kWh. The collective cost for the total system is 380 $/kWh. The storage system cost itself is thus given by extracting the battery cost from the total cost which results in a storage system cost of 171 $/kWh [39].
The projections presented in Cole and Frazier (2019, [26]) of utility scaled Lithium-ion storage systems provides future benchmark prices based on 25 publications analysed for the report. The BESS cost is presented in $/kWh because it is the most common way to present storage costs for battery systems, for comparison purposes this can be converted to $/kW by multiplying by the duration (in this case 4). The projections for future costs were based on interpolation between points in 2018, 2020, 2025, 2030 and 2050. Prices are given as storage system costs and the 2018 starting point is given by Fu et al. (2018, [39]). As stated in Section 2.5, predictions suggests that a 10-52% decrease will be achieved by 2025. The parameters used from Cole and Frazier (2019, [26]) is a 15-year calendric lifetime and a 365 cycles/year cyclic lifetime. With an efficiency at 85% and a duration of 4 hours. [26].
The Lazard (2020) report presents pricing for multiple batteries and areas of application. For use of a battery in-front-of-the-meter with a duration of 4 hours the calendric
lifetime was set to 20 years, a BESS lifetime of 350 cycles/year and with a DoD of 90%. The efficiency was given by a span of 85-93% and the initial cost is 164-309 $/kWh. These values were all calculated for a storage size of 100 MW / 400 MWh [40]. An article written by BloombergNEF (2020) states how battery prices have varied over the years and predict further decrease in future prices in combination with forecasts
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regarding increase in the total installed storage capacity world-wide. The article is based on current weighted-average prices for Lithium-ion batteries. For 2020 the market average for the battery cost is given by 137 $/kWh, a 90% decrease from the price found in 2010. No other battery-parameters are presented in the article [29]. An overview of the presented data is compiled in Table 1.
Table 1. Summary of parameter values.
Source BESS calendar life [years] BESS lifetime [cycles/year] BESS cost [$/kWh] Battery cost [$/kWh] DoD [%] Efficiency [%] Duration (1/C) [h] Jülch [31] (2016) 20 350 - - 80 95 - Apricum [32] (2016) 15 350 - - 80 92 - Fu et al. [39] (2018) - - 380 209 - - 4 Cole and Frazier, NREL [26] (2019) 15 365 380 (342) - - 85 4 Lazard [40] (2020) 20 365 - 164-309 90 85-93 4 BloombergNEF [29] (2020) - - - 137 - - -
Choice of parameter values
The parameters for the calculations are chosen from the reports presented in Section 4.2. The steep decline in BESS-price places an emphasis on using as recent values as
possible. Some values are chosen by taking the mean of the different values presented in Table 1 and some are chosen by weighing the different values and their descriptions against each other. The reasoning behind each chosen value is presented below, and then summarized in Table 2.
BESS calendar life: For the calculations, the calendar lifetime is chosen as a mean value of the different values presented by the sources. This leads to a mean BESS calendar life of 18 years.
BESS lifetime: The BESS lifetime is an indication of how many cycles/years the BESS is constructed for. This is used as a reference point when evaluating the simulation results and not an input into the simulation. For the reference point, a
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mean of the values presented in Table 1 is used, which resulted in a BESS lifetime of 358 cycles/year.
BESS cost and Battery cost: The chosen battery cost is 137 $/kWh from BloombergNEF [29], a market average for 2020. Due to the rapidly decreasing Lithium-ion battery costs the most representative value of the current market situation is the most recent one. The BESS cost is given by the battery cost and the storage system cost. The latter is taken from Fu et al. ((2018, [39]) and is 171 kWh/$ [39]. The new BESS cost with an updated battery cost is thus given by:
137 + 171 = 308 $/𝑘𝑊ℎ (7)
DoD: The Depth of Discharge is chosen using the mean value of the different values presented in Table 1, which leads to a mean DoD of 85%.
Efficiency: The efficiency is chosen by taking a mean value of the values listed in Table 1. This results in an efficiency of 90%.
Duration: The parameter values from each source are often dependent on a C-number for the batteries. The C-C-number chosen for the BESS is based on peak duration which, as mentioned in section 4.1.2, ranges from 3 to about 4.5 hours for a subscription limit between 70-64 MW. Therefore, this report only
considers batteries with a duration of 4 hours, meaning a C-number of 0.25.
Table 2. Chosen BESS parameter values.
Parameters Chosen values
BESS calendar life [years] 18
BESS lifetime [cycles/year] 358
BESS cost [$/kWh] 308
Battery cost [$/kWh] 137
DoD [%] 85
Efficiency [%] 90
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The cost parameters are given by the sources in $USD. The results of this report are given in SEK, and for converting between the currencies the mean value for exchange rate over the years 2016-2019 is calculated. The exchange rate is thereby set to 1 $USD = 9.39 SEK [41].
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5. Results
This section presents the result of the study. Results are introduced by Section 5.1 presenting the BESS utilization for energy arbitrage followed by the associated reference price range in Section 5.2. This leads to Section 5.3 presenting how a BESS operates when shaving power peaks. Further, Section 5.4 presents the optimal BESS size for different subscription limits. This is followed by a closer analyse two cases by looking at their use, cost and profits (Sections 5.5-5.8). Costs for subscription limits analysed in Section 5.4 are also presented here. Finally, results from the sensitivity analysis are presented in Section 5.9. All figure and table descriptions are found in Appendix A and B.
Spot market
Figure 9 presents the result of how the spot market price is used to determine when to charge and discharge the BESS. It is presented as an hourly spot market price in SEK as a function of the time during 2016-2019. The green line in Figure 9 represents actual spot market prices while the red line follows the price trend and is the reference price. The purple and yellow lines show the lower and upper limit, which represents the price range that the algorithm is adapting to.
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Reference price range
The reference price range for the arbitrage model shows an effect on the revenue. If there is a large range, there will be less eligible times for when to charge, and less eligible times for when to discharge. Simulations using a large range showed a lower number of full cycle equivalents performed by the BESS, meaning the BESS was used less. Even though prices for charging and discharging differed more, the lower degree of use leads to less total arbitrage revenue of the BESS. On the contrary, a smaller range leads to an increased BESS use due to more hours fulfilling the criteria of being above or below the range limits. Despite the more frequent BESS use, a narrow range leads to less profits, as the price difference between charging and discharging also becomes smaller. This led to a range of the reference price +34.5 SEK for the charging reference and reference price -32.5 SEK for the discharging reference.
As mentioned in the methodology, the spot price range is set by a polynomial fitting for each time block the arbitrage model is used. This results in a reference price range that follows the spot prices well. How well the reference price follows the changes in spot prices also showed an effect on the revenues. Using the model for a shorter time frame led to a greater adaptation, and a longer time led to a less precise adaptation. Testing different length of time blocks showed that a time block of 140 hours led to the most profits for a battery of 5 MW.
BESS use
As seen in Figure 10, the BESS can be used to reduce power peaks. The figure is based on a scenario where the subscription limit is 70 MW, which represents the current limit, combined with a BESS of the size 10 MW/40 MWh and C-number 0.25. The orange in the figure represents the load without BESS use whilst the blue represents the netload with BESS use. The orange parts of the plot represent the old net load, when it is visible the BESS use leads to a change in the net load in that moment. However, when it is not visible the old and new net loads are the same. This is apparent when the load exceeds the subscription limit but can also be seen during periods of low load. This is when the BESS is used for energy arbitrage, both when charged and discharged which in turn is regulated by the price range. In this scenario peaks decreased by 86% when compared to peaks without BESS use, which means that not all power peaks above the limit are reduced. If the BESS power capacity is not big enough the power peak is only cut to a certain extent. This is seen in periods with multiple peaks in a short period of time and can also be caused by the BESS not being charged entirely between peaks. Simulations also show that the peaks can be decreased by 100% if the BESS has a big enough capacity, independent of the subscription limit. The BESS used for simulating also stores enough energy to sell it back to the grid at times of low net load, which is seen when the curve goes below zero, meaning the net load is negative.
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Figure 10. Net load with subscription limit Plim =70 MW and BESS =10 MW/40 MWh.
In Figure 11 the peak reduction is shown for a lower limit (66 MW) and a smaller battery (5 MW/20 MWh). This is to show the manner in which the BESS shaves the peaks. As seen in the figure, when the BESS does not have the capacity to shave all peaks, nor does it have opportunity to fully recharge before the next peak. The first peaks will be cut resulting in the following peaks being cut to a lower degree.
Figure 11. Zoom in of net load with subscription limit Plim = 66 MW and BESS =5 MW/
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Optimal BESS size
Figure 12 presents cost after the BESS calendric lifetime as a function of BESS power capacities from 0 to 12 MW. This is investigated for four different subscription limits: 64, 66, 68 and 70 MW. If the cost after 18 years is zero, the system has reached its break-even. Costs are calculated by comparing the costs using a BESS for a specified subscription limit compared to having a limit of 70 MW without a BESS. Results show the optimal BESS size for each subscription limit, as the minimum value of each plot. As seen in Figure 12, none of the systems reaches their break-even after 18 years with an installed BESS. For a subscription limit 70 MW, zero is reached for a BESS of size 0 MWh, meaning the optimal size is no BESS at all. This is further presented in Table 3.
Figure 12. Cost after BESS lifetime for different power subscription limits and BESS sizes.
Table 3 presents the results from the simulations with optimal BESS size and peak reduction for each investigated subscription limit. From the presented values it is noticeable that a lower subscription limit requires more capacity from an installed BESS. Further, a lower subscription limit and more capacity result in a larger share of power peak reduction. As seen, the optimal BESS power capacity for 70 MW is 0 MW, which indicates that installing a BESS of any investigated size while maintaining the limit at 70 MW is not optimal.
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Table 3. Simulation results of optimal BESS power capacity and peak reduction.
Subscription limit [MW] Optimal BESS power capacity [MW] Peak shaving [%] 64 8 59 66 5 47 68 2 25 70 0 0
The performance of the BESS is presented in Section 5.5 and 5.6 by closer examination of two of the subscription limits, 70 MW and 66 MW with an installed BESS of 5 MW/20 MWh. This is to examine the implementation of a BESS with the Company’s current subscription limit of 70 MW as well as a lowered one of 66 MW. The size of the BESS is chosen based on the optimal BESS size for the 66 MW limit.
Subscription limit 70
The result on the net load for a power subscription of 70 MW when applying a BESS of size 5 MW/20 MWh is presented in Figure 13. The figure shows BESS use for both power peak reduction and energy arbitrage use during 2016-2019.
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The result of power subscription at 70 MW and an installed BESS of size 5 MW/20 MWh is presented in Table 4. As seen the BESS use results in a 60% peak decrease compared to the net load without BESS use. Further, the number of hours used for the different services are presented where there is a noticeable difference between hours used for peak shaving and for energy arbitrage. As seen in the table energy arbitrage appears to be the dominating service. The total use for the BESS is seen by the number of cycles which is given by 368 cycles per year which results in 6624 cycles during the BESS lifetime of 18 years.
Table 4. Results for Plim = 70 MW and BESS = 5 MW/20 MWh.
Peak decrease [%] 60
Peak shaving [hours/year] 490
Energy arbitrage [hours/year] 8276
Cycles [cycles/year] 368
Subscription limit 66 MW
The result of a reduced power subscription to 66 MW and an installed BESS of size 5 MW/20 MWh is presented in Figure 14. The figure shows BESS use for both power peak reduction and energy arbitrage during the years 2016-2019.
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Results present a peak reduction of 47 % compared to non-BESS use with the limit at 66 MW, meaning the limit was reduced before the installation of the BESS. Similarly, to the 70 MW subscription limit results in Section 5.5, the BESS is predominantly used for energy arbitrage and far less used for peak shaving. Number of cycles per year is 358 cycles, which represents a total of 6444 cycles for 18 years (the BESS lifetime).
Table 5. Results for Plim = 66 MW and installed BESS = 5 MW/20 MWh.
Peak decrease [%] 47
Peak shaving [hours/year] 840
Energy arbitrage [hours/year] 7926
Cycles [cycles/year] 358
Costs
Costs for the system are presented in Table 6 according to the subscription limits and their optimal BESS sizes that were presented in Section 5.4. Costs considered here are subscription cost, power peak fee and transfer fee, which make up the total grid cost. The BESS investment cost is also given for each case. All costs of the grid are yearly costs except the BESS investment cost which is a one-time cost. As seen in Table 6 the subscription cost is reduced as the limit is reduced, however the fees for power peaks increase as more peaks go over the limit. The transfer fee stays approximately the same, since the netload transferred to and from the grid stays approximately the same. The minor changes in these costs are a result of the BESS efficiency. The biggest cost difference is seen in BESS investment costs, where the highest cost is given for the biggest battery presented, 8 MW / 32 MWh with a cost of 92,55 million SEK.
Table 6. Optimal BESS power capacity for each subscription limit and their costs.
Subscription limit [MW] 64 66 68 70
Optimal BESS power capacity [MW] 8 5 2 0
Subscription cost [MSEK/year] 15,74 16,24 16,73 17,22
Power peak fee [MSEK/year] 7,01 4,71 3,52 2,47
Transfer fee [MSEK/year] 3,211 3,205 3,199 3,194
Total grid cost [MSEK/year] 25,97 24,15 23,45 22,89
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Looking closer at the two investigated cases of subscription limits 66 MW and 70 MW with an installed BESS of 5 MW/20 MWh, Table 7 presents the associated costs. The results shows that the annual power peak fees are reduced and as a result, the total grid cost decreases. The subscription cost is reduced for Plim = 66 MW, as expected for lower
subscription limits. The BESS investment cost is the same for both cases and thus the primary cost difference is given by the power peak fee, the transfer fee stays once again approximately the same.
Table 7. Costs for Plim= 66 MW and Plim= 70 MW with BESS = 5 MW/20 MWh.
Subscription limit [MW] 66 70
BESS power capacity [MW] 5 5
Subscription cost [MSEK/year] 16,24 17,22
Power peak fee [MSEK/year] 4,71 0,96
Transfer fee [MSEK/year] 3,2048 3,2051
Total grid cost [MSEK/year] 24,15 21,39
BESS investment cost [MSEK] 57,84 57,84
Profit generation
Table 8 presents the profit outcomes for the two cases investigated. As seen, an implementation of a BESS when the subscription limit is set to 66 MW generates a profit of 1,36 MSEK/year. Revenue generated by peak shaving is 4,34 MSEK/year and revenue generated by arbitrage is 0,23 MSEK/year, while the BESS investment costs are 3,21 MSEK/year. The implementation of the same BESS size when the limit is set to 70 MW has a negative profit, meaning the system costs 1,47 MSEK/year. Here peak shaving generates 1,50 MSEK/year, and the arbitrage generates 0,24 MSEK/year, while the BESS cost remains at 3,21 MSEK/year. The negative profit is due to expensive investment prices for the BESS combined with less generated revenues, which do not cover the BESS investment costs.
The last column in Table 8 presents the total system cost. This represents the annual profit subtracted by annual cost for the subscription limit, the transfer fees and the fees for the remaining power peaks.
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Table 8. Profit generation for BESS = 5 MW/20 MWh for Plim = 66 MW and Plim =70
MW.
*Profit: Including initial BESS cost **Revenue: not including initial BESS cost
Sensitivity analysis
The result of the sensitivity analysis is presented in Table 9 and visualized in Figure 15. The sensitivity analysis is based on the power subscription limit at 70 MW with an installed BESS of 5 MW/20 MWh. By changing the BESS cost, power peak fee and spot market prices one at a time, different break-even times are obtained. Without changing the parameters, the break-even time is 34 years.
A decrease of the power peak fee resulted in an increase of break-even time. This result is due to the power peak fee being directly related to the savings of power peak shaving, and thus the break-even time. Higher fee costs imply a better use of the BESS as more money is saved by its use. If the fee price decreases then so does the profitability of the BESS, resulting in longer break-even.
An increase of the BESS cost parameter increases the break-even time. Higher BESS cost entails a larger sum that has to be paid off, leading to a longer break-even time. As presented in Table 9 and also seen in Figure 15, a 50% decrease of BESS cost results in a break-even time of 17 years. This indicates that the investment of a BESS will be profitable within the BESS lifetime of 18 years.
Changing of the spot market prices resulted in a much less obvious effect on the even time. An increase in the difference of low and high prices of 50% entails a break-even time of 31 years while a decrease of 50% entails 36 years. There is therefore only a slight alteration when increasing and decreasing, meaning a variation in spot market prices has less of an impact on the break-even time than the BESS cost and power peak fee.
Subscription limit (Compared to itself) 66 70
Profit* [MSEK/year] 1.36 -1.47
Revenue** from peak shaving [MSEK/year] 4.34 1.50
Revenue** from arbitrage [MSEK/year] 0.23 0.24
BESS investment cost [MSEK/year] 3.21 3.21
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Table 9. Sensitivity analysis of the break-even time.
Change of parameter [%] Power peak fee BESS cost Spot market prices 50 23 51 31 40 25 47 32 30 27 44 32 20 29 41 33 10 31 37 33 0 34 34 34 -10 37 31 34 -20 41 28 34 -30 46 24 35 -40 52 21 35 -50 61 17 36
Figure 15 presents the change of the break-even time with respect to change in
parameters. The figure visualizes the trends of the three analysed parameters, based on Table 9.
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6. Discussion
This section analyses the results by discussing different perspectives of the executed simulations. Results are discussed according to BESS use, costs, and profits in Sections 6.1.1-6.1.3. This is followed by discussions of the methodology and data in Sections 6.2 and 6.3. Results from the sensitivity analysis are discussed in Section 6.4, this section also includes future prospects for BESS use. Lastly, further studies are discussed in Section 6.5.
Results
The study has shown that it is possible to utilize a Lithium-ion battery energy storage system to reduce power peaks. By charging the BESS during low power demand and discharging during high power demand, the Company can reduce costly power peaks that exceed the power subscription limit by up to 100%. Even though its technically possible to reduce all peaks the BESS size needed for that amount of peak shaving would not be a profitable investment. This is apparent when looking at investment costs for different batteries in Table 6 in relation to its peak shaving ability presented in Table 3. Even though the BESS is technically applicable for the desired purpose, profit generation within the BESS lifetime is crucial for the profitability of the investment for the Company.
Simulation results presented in Table 3 for the subscription limit 70 MW, implies that an investment of a BESS is not profitable for any of the investigated BESS sizes. It shows that the optimal BESS size for 70 MW subscription limit is 0 MW, which entails that the best option for the Company is to not invest in a Lithium-ion BESS.
BESS use/size
When investigating the net load, it was noted that power peaks often came in clusters, meaning there were multiple peaks in a shorter time span. When examining closely how the BESS shaves the peaks in a cluster, the results showed that the BESS will not have time to fully recharge between peaks. This led to the BESS only being able to cut the first peaks, often leaving the following peak reduced to a lower degree or not at all. As shown in Figure 11, the first lower peaks are completely cut, leaving the bigger peaks at the same size as without BESS use or only a bit lower. In some cases, only the
beginning of a peak is cut. This corresponds with the simulation algorithm, as it cuts a peak with full capacity one at a time without considering which peak is the highest or how many peaks there are in the cluster. This is due to the objective of the BESS use for peak shaving being to reduce the net total of all peaks, as the height of the highest peak does not affect the fees, only the net total.
Investigations could be made into how this behaviour could affect the balance of the grid, and how the power subscription works as an incentive. This would be interesting from the perspective of the transmission system operator, Svenska kraftnät. The way in
