This paper proposes a methodology to address the trading strategies of a proactive distribution c... more This paper proposes a methodology to address the trading strategies of a proactive distribution company (PDISCO) engaged in the transmission-level (TL) markets. A one-leader multi-follower bilevel model is presented to formulate the gaming framework between the PDISCO and markets. The lower-level (LL) problems include the TL day-ahead market and scenario-based real-time markets, respectively with the objectives of maximizing social welfare and minimizing operation cost. The upper-level (UL) problem is to maximize the PDISCO's profit across these markets. The PDISCO's strategic offers/bids interactively influence the outcomes of each market. Since the LL problems are linear and convex, while the UL problem is non-linear and non-convex, an equivalent primal-dual approach is used to reformulate this bilevel model to a solvable mathematical program with equilibrium constraints (MPEC). The effectiveness of the proposed model is verified by case studies.
As the power system is developing, the need for new cost-efficient grid solutions is increasing. ... more As the power system is developing, the need for new cost-efficient grid solutions is increasing. A battery energy storage system (BESS) can prove to be a technically and economically feasible alternative to a grid reinforcement. This paper presents a techno-economic analysis of a BESS used to maintain the voltage level in a low-voltage network with periodically large voltage drops. The Backward/ Forward Sweep (BFS) algorithm is used to perform a power flow analysis in a rural distribution grid supplying a cabin field in Norway. The grid-connected BESS has an operating strategy so that voltage deviations in the grid larger than a preset limit are avoided. The BESS also operates efficiently to lower the installed capacity. The costs of installing a BESS to satisfy the operating requirements were found to be 77 % higher than the corresponding line costs. Another important finding is how sensitive the BESS installation is to small changes in the system and that it depends heavily on the time perspective, while these factors barely affect the grid installation.
Målet med denne studien er å finne ut hvordan innføring av elbil i Norge påvirker det norske og e... more Målet med denne studien er å finne ut hvordan innføring av elbil i Norge påvirker det norske og europeiske kraftsystemet og de tilhørende klimagassutslipp. Simuleringsmodellen EMPS blir brukt for å sette opp forskjellige scenarier med og uten elbil. I tillegg undersøkes hvordan resultatene forandrer seg når elbil-innføringen ses i sammenheng med ekstra installert vindkraft i Norge. Dagens kraftverkspark er dominert av fossile kraftverk og dermed går de marginale CO2-utslippene fra kraftsektoren opp ved innføring av elbiler, tilsvarende ca. 73 CO2-g/km. Dersom man ser introduksjon av elbil i sammenheng med utbygging av ekvivalent mengde ny vindkraft i Norge viser simuleringene at de totale utslippene fra kraftsektoren faktisk går noe ned sammenlignet med "null-situasjonen". Man kan i det tilfellet dermed konkludere med at CO2-reduksjonen innenlands som følge av elbiler tilsvarer 1-til-1 utslippene fra de bensin-/diesel-bilene som erstattes
A linear complementarity model is extended with volume-shifting demand response. The model is an ... more A linear complementarity model is extended with volume-shifting demand response. The model is an equilibrium model of the power market. In this paper the model is subjected to a scenario for the northern European power system represented by time series for demand and renewable generation. Investments and dispatch are being computed to study the effect of volume shifting demand response on system adequacy and the potential shift in generation mix. The results show that, within certain limits, the system may benefit from demand response. Further, a sensitivity analysis suggest that demand response may not be enough as the share of renewable energy sources increase. From a system adequacy point of view the results show that demand response can reduce the number of hours with load curtailment, but may increase the amount of energy not served with a cost minimization approach.
This paper presents new methods for ensuring that the energy system of a neighborhood that is des... more This paper presents new methods for ensuring that the energy system of a neighborhood that is designed with the objective of being zero emission is actually operated in a way that allows it to reach net zero emissions in its lifetime. This paper highlights the necessity of taking into account realistic operation strategies when designing the energy system of such neighborhoods. It also suggests methods that can be used in the operation of ZENs to ensure carbon neutrality. An optimization model for designing the energy system of a Zero Emission Neighborhood (ZEN) is first presented and used to produce two designs for a campus in the South of Norway in the case where the amount of PV is limited (PVlim) and when it is not (Base). Several operation approaches are then introduced to compare their operation cost and the CO2 emissions and compensations. These approaches are perfect foresight used as a reference (Ref.), a purely economic model predictive control (E-MPC), an MPC with penalization if deviating from emission targets (EmE-MPC) and a receding horizon MPC where we have a net zero emission constraint over the year (RH-MPC). The resulting energy systems are, in the Base case, PV, heat pumps, a gas boiler and heat storage and, in the PVlim case, a smaller amount of PV, a CHP plant, and heat storage. In the Base case all operation strategies manage to reach net zero emissions, largely due to the passive compensations obtained from the PV. RH-MPC offers the lowest cost. In the PVlim case, the passive effect of the PV is not sufficient to reach net zero emissions and an operation approach specifically taking into account the emissions is necessary. EmE-MPC achieves the lowest emissions but it comes at a much higher cost. We conclude that the best overall strategy is RH-MPC which maintains both the cost and the emission-compensation balance close to the reference case with perfect foresight.
Power producers use a wide range of decision support systems to manage and plan for sales in the ... more Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market, and they are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either continuously be registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques (gradient boosting and neural networks). A wide range of variables accessible to the models prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.
Generation capacity adequacy is a major issue in most power systems, but there are many approache... more Generation capacity adequacy is a major issue in most power systems, but there are many approaches which can be assessed. Power system planners often define target values for the capacity adequacy, which may be achieved through capacity markets/auctions, capacity reserves, or capacity purchases. Wind power contributes to the generation capacity adequacy of the power system since there is a possibility that wind power will generate in high load situations and thereby decreases the risk of generation capacity deficit compared to the system without this source. The contribution is probabilistic-as it is with any other source, since nothing is 100% reliablebut the capacity value of wind power is significantly smaller compared to the capacity value of conventional fossil-fueled plants. In this article, an overview of the fundamental challenges in the regulation of capacity adequacy as well as how wind power is treated in some selected existing jurisdictions is presented. The jurisdictions that are included are Sweden,
Traditionally, electricity markets have been designed with the intention of disabling producer si... more Traditionally, electricity markets have been designed with the intention of disabling producer side market power or prohibiting exercising it. Nonetheless it can be assumed that players participating in pool markets and aiming to maximize their individual benefits might depart from the optimum in terms of total system welfare. To recognize and analyze such behavior, system operators have a wide range of methods available. In the here presented paper, one of those methods-deriving a supply function equilibrium-is used and nested in a traditional discontinuous Nash game. The result is a case study that shows that marginal cost bidding thermal producers have an incentive to collaborate on scheduling in order to cause similar effects to tacit collusion.
Different possibilities were assessed to supply energy to a large offshore oil and gas area in th... more Different possibilities were assessed to supply energy to a large offshore oil and gas area in the North Sea. The concepts studied involved: (i) onsite power generation by means of simple gas turbine cycles, (ii) full electrification of the plants with power taken from the onshore grid, and (iii) a hybrid solution where power can be either generated onsite or taken from the onshore grid. The analysis included 37 y of the facilities' lifetime and was based on process simulations of the various concepts. The effect of the offshore area electrification was simulated through a model of the power system. The integration of process and power system modelling contributes to the originality and completeness of the analysis. The environmental impact of the concepts was evaluated in terms of cumulative CO2 emissions. The relative economic cost was also assessed to provide a complete picture. The results showed that the advantage of a specific concept over the others was significantly influenced by the future energy policies and the magnitude of the initial investment cost.
Some of the best wind and natural gas resources in Norway are located in rural areas. Hydrogen ca... more Some of the best wind and natural gas resources in Norway are located in rural areas. Hydrogen can be produced from natural gas in combination with carbon capture and storage to utilize the natural gas resources without significant CO2-emissions. The hydrogen can be liquefied and transported to regions with energy deficits. This creates a demand for hydrogen produced from electrolysis of water, which facilitates wind power development without requiring large investments in new transmission capacity. A regional optimization model is developed and used to investigate sizing of the electrolyser capacity and hydrogen storage, as well as regional effects of producing hydrogen from electrolysis. In the model, the transmission grid is represented by dc power flow equations and opportunities for wind power investments in the region are included. The model is used in a case study which shows that hydrogen storage contributes to significantly increase grid utilization, even with small amounts of storage. Increased regional transmission capacity results in more wind power development compared to increased capacity towards the central grid. Hydrogen storage is only profitable to reduce congestion in this deterministic model, thus using hydrogen storage to reduce the costs in the spot market is not profitable.
An important operational aspect of grid-connected large-scale wind turbines is the predictability... more An important operational aspect of grid-connected large-scale wind turbines is the predictability of their network in-feed. Wind power producers acting in day-ahead markets are obliged to predict their production a certain amount of hours in advance. Deviations from the predicted in-feed are alleviated by the grid operator with balancing energy at relatively high costs, depending on the market policy. Thus, the value of the annual production of a wind power producer can be significantly reduced by the penalty costs for balancing energy. This paper investigates to what extent the accuracy of the network in-feed could be improved by combining a wind turbine with a storage device used for balancing the differences between forecasted and actual network in-feed. The relation between the storage capacity and the forecast horizon is investigated to get indications on the limitations and requirements of such a combination. A simulation algorithm is presented together with analysis methods and a case study is used to show their applicability.
This paper presents an analytical method for calculating the operational value of an energy stora... more This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City.
Power producers use a wide range of decision support systems to manage and plan for sales in the ... more Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market. The available tools have advantages and disadvantages and the operators are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. Since only one bid can be submitted each day, this choice can not be avoided. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either be continuously registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques. A wide range of model variables accessible prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.
In recent years, the number of electric vehicles (EVs) has increased rapidly. Due to technologica... more In recent years, the number of electric vehicles (EVs) has increased rapidly. Due to technological advancement, government policies and the focus on reducing greenhouse gas emission, the growth can be expected to continue. Home charging of EVs will often be sufficient for short-distance travel and daily routines. However, EVs still have a limited range. Thus, for longdistance travel, a network of fast charging stations (FCS) is needed. The stochastic nature, high power demand and short duration of EV fast charging, make it in many cases a grid capacity issue rather than an energy issue. Therefore, knowledge about the load profile of FCSs is important. In this paper, a model is developed for the simulation of the aggregated load profile of an FCS. The FCS load model includes a mobility model based on actual traffic flow, EV charging curves and temperaturedependent EV efficiency. Simulations are performed using the Monte Carlo simulation technique, to get a daily load profile for the FCS. Real-world data for the studied FCS in Norway is compared with the results from the simulation to analyze the performance of the FCS load model. The developed load profile for the FCS has a high peak-to-average power ratio, which indicates that the socioeconomic profitability of fast charging stations still is low.
This paper proposes a methodology to address the trading strategies of a proactive distribution c... more This paper proposes a methodology to address the trading strategies of a proactive distribution company (PDISCO) engaged in the transmission-level (TL) markets. A one-leader multi-follower bilevel model is presented to formulate the gaming framework between the PDISCO and markets. The lower-level (LL) problems include the TL day-ahead market and scenario-based real-time markets, respectively with the objectives of maximizing social welfare and minimizing operation cost. The upper-level (UL) problem is to maximize the PDISCO's profit across these markets. The PDISCO's strategic offers/bids interactively influence the outcomes of each market. Since the LL problems are linear and convex, while the UL problem is non-linear and non-convex, an equivalent primal-dual approach is used to reformulate this bilevel model to a solvable mathematical program with equilibrium constraints (MPEC). The effectiveness of the proposed model is verified by case studies.
As the power system is developing, the need for new cost-efficient grid solutions is increasing. ... more As the power system is developing, the need for new cost-efficient grid solutions is increasing. A battery energy storage system (BESS) can prove to be a technically and economically feasible alternative to a grid reinforcement. This paper presents a techno-economic analysis of a BESS used to maintain the voltage level in a low-voltage network with periodically large voltage drops. The Backward/ Forward Sweep (BFS) algorithm is used to perform a power flow analysis in a rural distribution grid supplying a cabin field in Norway. The grid-connected BESS has an operating strategy so that voltage deviations in the grid larger than a preset limit are avoided. The BESS also operates efficiently to lower the installed capacity. The costs of installing a BESS to satisfy the operating requirements were found to be 77 % higher than the corresponding line costs. Another important finding is how sensitive the BESS installation is to small changes in the system and that it depends heavily on the time perspective, while these factors barely affect the grid installation.
Målet med denne studien er å finne ut hvordan innføring av elbil i Norge påvirker det norske og e... more Målet med denne studien er å finne ut hvordan innføring av elbil i Norge påvirker det norske og europeiske kraftsystemet og de tilhørende klimagassutslipp. Simuleringsmodellen EMPS blir brukt for å sette opp forskjellige scenarier med og uten elbil. I tillegg undersøkes hvordan resultatene forandrer seg når elbil-innføringen ses i sammenheng med ekstra installert vindkraft i Norge. Dagens kraftverkspark er dominert av fossile kraftverk og dermed går de marginale CO2-utslippene fra kraftsektoren opp ved innføring av elbiler, tilsvarende ca. 73 CO2-g/km. Dersom man ser introduksjon av elbil i sammenheng med utbygging av ekvivalent mengde ny vindkraft i Norge viser simuleringene at de totale utslippene fra kraftsektoren faktisk går noe ned sammenlignet med "null-situasjonen". Man kan i det tilfellet dermed konkludere med at CO2-reduksjonen innenlands som følge av elbiler tilsvarer 1-til-1 utslippene fra de bensin-/diesel-bilene som erstattes
A linear complementarity model is extended with volume-shifting demand response. The model is an ... more A linear complementarity model is extended with volume-shifting demand response. The model is an equilibrium model of the power market. In this paper the model is subjected to a scenario for the northern European power system represented by time series for demand and renewable generation. Investments and dispatch are being computed to study the effect of volume shifting demand response on system adequacy and the potential shift in generation mix. The results show that, within certain limits, the system may benefit from demand response. Further, a sensitivity analysis suggest that demand response may not be enough as the share of renewable energy sources increase. From a system adequacy point of view the results show that demand response can reduce the number of hours with load curtailment, but may increase the amount of energy not served with a cost minimization approach.
This paper presents new methods for ensuring that the energy system of a neighborhood that is des... more This paper presents new methods for ensuring that the energy system of a neighborhood that is designed with the objective of being zero emission is actually operated in a way that allows it to reach net zero emissions in its lifetime. This paper highlights the necessity of taking into account realistic operation strategies when designing the energy system of such neighborhoods. It also suggests methods that can be used in the operation of ZENs to ensure carbon neutrality. An optimization model for designing the energy system of a Zero Emission Neighborhood (ZEN) is first presented and used to produce two designs for a campus in the South of Norway in the case where the amount of PV is limited (PVlim) and when it is not (Base). Several operation approaches are then introduced to compare their operation cost and the CO2 emissions and compensations. These approaches are perfect foresight used as a reference (Ref.), a purely economic model predictive control (E-MPC), an MPC with penalization if deviating from emission targets (EmE-MPC) and a receding horizon MPC where we have a net zero emission constraint over the year (RH-MPC). The resulting energy systems are, in the Base case, PV, heat pumps, a gas boiler and heat storage and, in the PVlim case, a smaller amount of PV, a CHP plant, and heat storage. In the Base case all operation strategies manage to reach net zero emissions, largely due to the passive compensations obtained from the PV. RH-MPC offers the lowest cost. In the PVlim case, the passive effect of the PV is not sufficient to reach net zero emissions and an operation approach specifically taking into account the emissions is necessary. EmE-MPC achieves the lowest emissions but it comes at a much higher cost. We conclude that the best overall strategy is RH-MPC which maintains both the cost and the emission-compensation balance close to the reference case with perfect foresight.
Power producers use a wide range of decision support systems to manage and plan for sales in the ... more Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market, and they are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either continuously be registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques (gradient boosting and neural networks). A wide range of variables accessible to the models prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.
Generation capacity adequacy is a major issue in most power systems, but there are many approache... more Generation capacity adequacy is a major issue in most power systems, but there are many approaches which can be assessed. Power system planners often define target values for the capacity adequacy, which may be achieved through capacity markets/auctions, capacity reserves, or capacity purchases. Wind power contributes to the generation capacity adequacy of the power system since there is a possibility that wind power will generate in high load situations and thereby decreases the risk of generation capacity deficit compared to the system without this source. The contribution is probabilistic-as it is with any other source, since nothing is 100% reliablebut the capacity value of wind power is significantly smaller compared to the capacity value of conventional fossil-fueled plants. In this article, an overview of the fundamental challenges in the regulation of capacity adequacy as well as how wind power is treated in some selected existing jurisdictions is presented. The jurisdictions that are included are Sweden,
Traditionally, electricity markets have been designed with the intention of disabling producer si... more Traditionally, electricity markets have been designed with the intention of disabling producer side market power or prohibiting exercising it. Nonetheless it can be assumed that players participating in pool markets and aiming to maximize their individual benefits might depart from the optimum in terms of total system welfare. To recognize and analyze such behavior, system operators have a wide range of methods available. In the here presented paper, one of those methods-deriving a supply function equilibrium-is used and nested in a traditional discontinuous Nash game. The result is a case study that shows that marginal cost bidding thermal producers have an incentive to collaborate on scheduling in order to cause similar effects to tacit collusion.
Different possibilities were assessed to supply energy to a large offshore oil and gas area in th... more Different possibilities were assessed to supply energy to a large offshore oil and gas area in the North Sea. The concepts studied involved: (i) onsite power generation by means of simple gas turbine cycles, (ii) full electrification of the plants with power taken from the onshore grid, and (iii) a hybrid solution where power can be either generated onsite or taken from the onshore grid. The analysis included 37 y of the facilities' lifetime and was based on process simulations of the various concepts. The effect of the offshore area electrification was simulated through a model of the power system. The integration of process and power system modelling contributes to the originality and completeness of the analysis. The environmental impact of the concepts was evaluated in terms of cumulative CO2 emissions. The relative economic cost was also assessed to provide a complete picture. The results showed that the advantage of a specific concept over the others was significantly influenced by the future energy policies and the magnitude of the initial investment cost.
Some of the best wind and natural gas resources in Norway are located in rural areas. Hydrogen ca... more Some of the best wind and natural gas resources in Norway are located in rural areas. Hydrogen can be produced from natural gas in combination with carbon capture and storage to utilize the natural gas resources without significant CO2-emissions. The hydrogen can be liquefied and transported to regions with energy deficits. This creates a demand for hydrogen produced from electrolysis of water, which facilitates wind power development without requiring large investments in new transmission capacity. A regional optimization model is developed and used to investigate sizing of the electrolyser capacity and hydrogen storage, as well as regional effects of producing hydrogen from electrolysis. In the model, the transmission grid is represented by dc power flow equations and opportunities for wind power investments in the region are included. The model is used in a case study which shows that hydrogen storage contributes to significantly increase grid utilization, even with small amounts of storage. Increased regional transmission capacity results in more wind power development compared to increased capacity towards the central grid. Hydrogen storage is only profitable to reduce congestion in this deterministic model, thus using hydrogen storage to reduce the costs in the spot market is not profitable.
An important operational aspect of grid-connected large-scale wind turbines is the predictability... more An important operational aspect of grid-connected large-scale wind turbines is the predictability of their network in-feed. Wind power producers acting in day-ahead markets are obliged to predict their production a certain amount of hours in advance. Deviations from the predicted in-feed are alleviated by the grid operator with balancing energy at relatively high costs, depending on the market policy. Thus, the value of the annual production of a wind power producer can be significantly reduced by the penalty costs for balancing energy. This paper investigates to what extent the accuracy of the network in-feed could be improved by combining a wind turbine with a storage device used for balancing the differences between forecasted and actual network in-feed. The relation between the storage capacity and the forecast horizon is investigated to get indications on the limitations and requirements of such a combination. A simulation algorithm is presented together with analysis methods and a case study is used to show their applicability.
This paper presents an analytical method for calculating the operational value of an energy stora... more This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City.
Power producers use a wide range of decision support systems to manage and plan for sales in the ... more Power producers use a wide range of decision support systems to manage and plan for sales in the day-ahead electricity market. The available tools have advantages and disadvantages and the operators are often faced with the challenge of choosing the most advantageous bidding strategy for any given day. Since only one bid can be submitted each day, this choice can not be avoided. The optimal solution is not known until after spot clearing. Results from the models and strategy used, and their impact on profitability, can either be continuously registered, or simulated with use of historic data. Access to an increasing amount of data opens for the application of machine learning models to predict the best combination of models and strategy for any given day. In this article, historical performance of two given bidding strategies over several years have been analyzed with a combination of domain knowledge and machine learning techniques. A wide range of model variables accessible prior to bidding have been evaluated to predict the optimal strategy for a given day. Results indicate that a machine learning model can learn to slightly outperform a static strategy where one bidding method is chosen based on overall historic performance.
In recent years, the number of electric vehicles (EVs) has increased rapidly. Due to technologica... more In recent years, the number of electric vehicles (EVs) has increased rapidly. Due to technological advancement, government policies and the focus on reducing greenhouse gas emission, the growth can be expected to continue. Home charging of EVs will often be sufficient for short-distance travel and daily routines. However, EVs still have a limited range. Thus, for longdistance travel, a network of fast charging stations (FCS) is needed. The stochastic nature, high power demand and short duration of EV fast charging, make it in many cases a grid capacity issue rather than an energy issue. Therefore, knowledge about the load profile of FCSs is important. In this paper, a model is developed for the simulation of the aggregated load profile of an FCS. The FCS load model includes a mobility model based on actual traffic flow, EV charging curves and temperaturedependent EV efficiency. Simulations are performed using the Monte Carlo simulation technique, to get a daily load profile for the FCS. Real-world data for the studied FCS in Norway is compared with the results from the simulation to analyze the performance of the FCS load model. The developed load profile for the FCS has a high peak-to-average power ratio, which indicates that the socioeconomic profitability of fast charging stations still is low.
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Papers by Magnus Korpås