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Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation
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Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation Nanfang Yang a , ∗, Julien Pouget a , Tony Letrouvé a , Cristian Jecu b , Loic Joseph-Auguste b a SNCF Innovation and Research Department, 40 Avenue des Terroirs de France, 75012 Paris, France b EDF R&D, avenue des Renardières, 77250 Moret-sur-Loing, France
Received 6 November 2017; received in revised form 2 May 2018; accepted 25 June 2018 Available online xxxxx
Abstract The railway operator would like to increase train frequency in order to satisfy demand for passenger flow growth in some lines. However the railway power substations (RPSs) are approaching their rating capacities, and have difficulty to supply more. A concept investigated by SNCF is a hybrid railway power substation (HRPS). Renewable energy systems and energy storages are integrated in the RPS to supply power. This paper presents the methodology based on simulation tools to design a HRPS by the electrical infrastructure railway engineering team. The first step uses a temporal power balance and economics linear formulation to optimize the HRPS global design and the energy management (for a time horizon of one year with time step of one hour). In the second step, the energetic macroscopic representation (EMR) is adopted to validate the HRPS global design with the corresponding control and the real time energy management system (time step of one second and simulation time of one week). The case study of Sarry railway power substation reveals that around 9% of the electricity bill can be reduced with the proposed hybridization. c 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights ⃝ reserved.
Keywords: Economic and power flow design; Linear optimization formulation; Energetic macroscopic representation (EMR); Energy management system (EMS); Hybrid railway power substation (HRPS)
1. Introduction and problematic Compared to other passenger transport ways, railway system is the most economic, efficient and environmentfriendly method [6]. However with the growth of traffic flow, the railway power substation (RPS) suffers two problems: one is the voltage drop due to line impedance; the other is that the transformer or power converter connecting to the electric grid (usually transmission line) is approaching its power limit. Besides, the diode bridge based rectifier does not allow sending back power from the catenary to the electric grid in DC railway system. The regenerative braking ∗ Corresponding author.
E-mail address: [email protected] (N. Yang). https://doi.org/10.1016/j.matcom.2018.06.012 c 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights 0378-4754/⃝ reserved.
Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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power has to be dissipated in onboard or/and wayside rheostats, if no other trains are powered simultaneously and catenary voltage is approaching the designed limit [19]. Many studies have been conducted to stabilize DC catenary voltage, to recover the regenerative braking energy, as well as to improve the energy efficiency, with the integration of energy storages. There are two types of energy storage systems: on-board [5,13,10] and wayside [17,7,11,18,8]. The former aims to recover regenerative braking energy so as to improve energy efficiency and to support the catenary-free operation. The wayside energy storages can recover regenerative braking energy through the catenary, improve the power supply quality, as well as participate into peak load shaving. It has been reported that a wayside Nickel–Metal hydride (Ni–MH) battery can recycle as high as 71.4% of the regenerative braking energy in DC railway system [15]. Besides, the price of Lithium-ion battery is falling in recent years, which gives good opportunity to develop new service of battery at wayside, such as the frequency regulation and reactive power compensation. Facing those problems and liberation of electricity market in France, National French Railways Company (SNCF, abbreviation in French) has studied the integration of energy storage system and renewable energies into power substations, to form a hybrid RPS (HRPS), which could be DC or AC type [16]. The energy storage devices and renewable energies are grouped by the DC bus, and then connected to the catenary through a bidirectional power converter. The DC bus can also be connected to the power supply of the railway passenger station in order to supply other services, such as emergency service. The integration of DC microgrid has also been examined in [14], which uses a low-voltage DC bus to interface the energy storage system, renewable energies, the catenary as well as the auxiliary equipment, and similarly in AC type [1]. This new concept, the HRPS, needs to be specified, developed, manufactured, tested and commissioned. Since many years, the development of new system or software is directly done in a prototype. For a HRPS, the prototype is composed of several electric components and an energy management system implemented in electrical railway network. The problematic of the prototype development is the time and resource requirements. Taking HRPS development as an example, it is obvious that each modification or test of a new component, e.g., energy management strategy, has to fulfill restriction on circulating point, send sufficient people to ensure the security as well as track the test in commercial exploitation. To tackle these issues, several virtual steps are available nowadays, such as modeling, representation, simulation, Hardware-In-the-Loop (HIL) test. The focus of this paper is to present an HRPS design methodology by the usage of different tools to achieve optimal sizing, energy management, as well as power flow analysis. The following parts are dedicated to the overview design methodology of hybrid energy systems, which can coordinate different simulation tools together to design and validate the specification, the manufacturing and commissioning of a HPRS. Firstly, the design and optimal sizing will be addressed; then, the simulation and local control with energetic macroscopic representation (EMR) will be presented and discussed. Finally, financial analysis will be conducted to examine the profitability of the proposed HRPS compared to traditional RPSs. 2. HRPS design method: Combination of different simulation tools 2.1. Positioning of simulation tools Modeling is the foundation of system design, and the model gives a mathematical description of system behavior associated with assumptions following an objective. The validity range of this step is defined by assumptions. A system can have different descriptions that can be either structural or functional. The description is an organization of a model that helps to highlight some studied system’s behaviors, thus there is no assumption between a model and its description. The structural description separates the system into different modules according its physical connections. It has advantage on development duration because it is based on pick-and-place blocks following the physical structure, and it may uses derivative to express some block, which requires its future state and cannot be implemented in real-time. Besides, there is not a systemic method to deduce the control structure from system description. The latter approach is based on the functionality separation no matter its physical structure, thus an exclusive rule can be added to avoid the derivative. EMR is a graphical description tool using functionality separations, which has been proposed and specifically designed for the modeling and control of coupled electromechanical system [3]. It decomposes the system into natural interconnected elements according to physical causality principles, where only integration is allowed in system representation [3]. Another positive point is that a systemic control structure could be deduced directly from Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 1. The development path used in this project.
the EMR. It has been applied to modeling and control of hybrid electric vehicle [9], hybrid locomotive [13,2], and also wind power system [4]. This would a great help to deduce a systematic control based on the inverse model. In this paper, only functional model (functional description of a system model) is taken into account, which, contrary to a structural model, is based on function association and does not take importance about the physical structure. This step feeds the description to a dynamic simulation and at the power flow simulation platform that is needed in a first approach to define an optimal design. 2.2. HRPS design method The development of such a complicated energy system using the traditional path is really time-consuming and expensive. Therefor a new development path by simulation tools is proposed in order to reduce the development time and costs. The adopted development path is shown in Fig. 1. The requirements of RPS are analyzed by electrocinematic simulation of the electric network railway with the train’s loads to validate the catenary tension standard. This simulation defines the requirement and the localization of the RPS on the railway line. Two different solutions, the traditional and hybrid RPSs, will be studied. For an industrial application, the comparison is based on economic criteria and technical feasibility. The second problematic is provided by the integration of EMS in the design process. Finally the HRPS design is based on three simulation models: an optimization model for the HRPS overall design, a validation model based on EMR and HIL test for the HRPS manufacturing and commissioning. The optimization tool used a temporal power balance and economics linear formulation to optimize the HRPS global design and the anticipative EMS. Thus the size and the temporal used of each components of the HRPS are defined by a linear optimization formulation for each hours of a year [17]. The inputs are the power of the local wind and solar sunrise, the cost of connection and the monthly subscription of the electrical network. After the optimal sizing, the obtained HRPS will be modeled and represented with EMR. This model uses the numerical simulation software MATLAB to verify and validate its well-functioning with the control command and the EMS. The step time is the second and the simulation during a week. To reduce the HRPS manufacturing and commissioning tests costs, a simulation step links the simulation tools with the HRPS manufacturing. This method is developed in collaboration between user and the manufacturer to progressively validate the HRPS. Finally the HRPS is installed and the commissioning tests are simpler. This simulation method can be applied at each hybrid system connected to the electrical grid. 3. Optimal tool for designing the HRPS system and anticipative EMS 3.1. Structure of a HRPS The structure of the studied HRPS is presented in Fig. 2. The hybrid part or DC part groups the renewables energy systems, and storage systems via power converters. The catenary is powered by the transmission grid via power transformers and also the DC bus via the bidirectional power converter. The trains running nearby the power substation absorb power through the pantograph contacting to the catenary. The catenary voltage is determined by the electric grid and the power transformer turns ratio, while DC bus voltage is controlled by the bidirectional power converter linking the DC and AC parts. The wind turbines work in Maximum Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 2. Architecture of the studied HRPS.
Power Point Tracking (MPPT) mode, which is not a controllable energy source. Thus the only controllable source is the battery. The EMS collects power consumption of trains, the power generated by wind turbines as well as the power flowing from the grid to catenary, and then calculates power reference of the battery. 3.2. Optimal design and energy management To study and design a HRPS including electric grid, storage system and renewable production system, a linear optimal formulation is proposed. This optimal sizing takes into account constraints linked to the power substation (type of electrification, the investment and operational cost in electric grid, electricity prices, etc.) and also the reliability and complementarity of the components envisioned for hybridization. With the aid of this optimal sizing tool, it will be possible to evaluate and compare different study case from a technical and economic point of view. For the optimal design phase, system modeling is based on power and financial balances for the global system. Power flow models for railway loads, wind turbine, and storage system in addition to economic models for cost function are here introduced: – For the wind turbine model, the hourly output of wind power system is given as: Pw = ηt · ηg · 0.5 · C p · ρa · Sw · vn3
(1)
with ηt the wind turbine efficiency, ηg the generator efficiency (both obtained from the manufacturer data),C p the power coefficient of wind turbine, ρa the air density, SW the wind turbine rotor swept area and vn the wind velocity. The historical hourly wind speed dataset nearby the substation is adopted to carry out the optimization. – Power flow model of energy storage The storage system considered in the design phase is a battery for railway applications. It is mathematically represented with a power flow model. The unit sizing as well as the optimal reference power of the storage system for one year with an hour time interval are both sought, in order to analyze the optimal operation strategy. 8760 samples for storage power(Psto ) are therefore created. Various losses are considered by introducing energy efficiency parameters (ηbat if Psto > 0 and 1/ηbat if Psto < 0). Positive power values mean that the battery is discharging whereas negative power values correspond to battery charging. Stored energy E sto depends on the initial state of charge and the energy stored every hour during the year is accounted for as follows. E sto (t) = E 0 − 3600s ·
8760 ∑
Psto i
(2)
i=1
– Particular formulation for the power balance is: Pgrid (t) − Psto (t) = Pload (t) − Pw (t)
(3)
Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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with Pgrid being the network power, Psto the energy storage power, Pload the substation load power, and Pw the production of wind turbines. – Objective function, minimization of the global cost (4)
C = C grid + Cinvestment + Copex + C Elecpur chase
with C is the yearly cost of the HRPS, where C grid represents the yearly grid usage cost, Cinvestment represents the yearly depreciated investment cost (investment cost: connection to the transportation grid, transformer, rectifier, purchase of wind turbines, batteries), Copex represents the operation cost: maintenance of infrastructure (transformer, wind turbines and storages) and C Elecpur chase represents the yearly electricity purchase cost from the electricity market. The historical hourly market electricity price dataset in France is adopted to conduct the optimization. – Linear formulation and optimal solver N etwor k The subscription cost Copex is a nonlinear convex formulation as show in (5). To apply a linear optimization process for one year with time step of one hour (2 integer variables, 52 560 binary variables, 788 408 real variables and 1 472 060 constraints), a simplification with a linear formulation is introduced and validated as show in (6). Finally the problem is implemented on the optimal modeling tool YAMILP [12] and solved by CPLEX. ⎞ ⎛ 8760 ( 12 ) ∑ ∑ N etwor k N etwor k N etwor k N etwor k 2 ⎠ ⎝α · √ (5) Copex = a2 · Popex + b · τ c · Popex + ∆Popex Month=1
i=1
N etwor k N etwor k with Copex the subscription cost, a2 , b, τ, c and α contractual coefficient, Popex the contractual power N etwor k subscription, Month the month indices and ∆Popex the positive overrun of the contractual power subscription. ( 8760 ) 12 ∑ ∑( ) N etwor k N etwor k N etwor k N etwor k Copex = a2 · Popex + b · τ2 · Popex + α· ∆Popex (6) Month=1
i=1
with τ2 an equivalent contractual coefficient. 3.3. Case study: Sarry substation on Paris–Lyon High Speed Line 3.3.1. Requirement of RPS The Sarry substation on Paris–Lyon High Speed Line has been used for this study. The load data (automatic meter reading) of 2013 have been used with a modification of the load for the 18:00 to 20:00 interval from Monday to Friday. The traffic load in this time interval has been modified by simulating a frequency of 16 trains per hour per direction. Each train is composed by two high-speed trains. The simulation results showed that the maximum load is 43 MW, which exceeds the substation’s physical limit fixed at 35 MW. To simulate this network constraint and analyze its economic impact, a hybridization of this substation has been done with this specific constraint in the optimal formulation of the problematic. This constraint introduces additional cost of RPS by reinforcement of the electrical connection to the electrical network. 3.3.2. HRPS technologies and electricity prices The Li-ion battery system with 1.1 MW/580 kWh is selected to compose a HRPS, and it has an efficiency of 95% in charge and discharge, and costs 1070 ke. The production of a wind turbine uses the wind speed of a local weather station nearby. The characteristics of wind turbine are: 1670 kW power, 80m rotor diameter, average 32% yield, 3 m/s start-up wind speed, 25 m/s maximum wind speed, costs 1200 ke. The scenario price is the vision 2030 Green Transition prospective marginal costs with 49% of renewable energy, as shown in Fig. 3. 3.3.3. Result of HRPS study case The optimal design model is used to size HRPS components: 1 Li-ion battery and 9 wind turbines. Fig. 4 presents the economic results of HRPS compared to conventional RPS with the same inputs but without an investment in energy storage system and renewable energy systems. The RPS solution is based on electrical reinforcement of the connection to the electric grid. The HRPS does not increase the total cost. The high initial investment is mainly due to the cost of battery. The MWh cost (total cost/total grid energy) is decreased by 15e/MWh from 120e/MWh in RPS to 105e/MWh in HRPS. Thus, the initial investment is compensated by an MWh cost. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 3. Vision 2030 Green Transition.
Fig. 4. Economic results for RPS and HRPS.
Fig. 5. Structural scheme of the wind power system.
4. Simulation tools used for the HRPS validation design and EMS In this section, EMR is adopted to represent the HRPS, and the corresponding control structure is also deduced by using the principle of inversion [4]. Some basic EMR blocks are listed in Appendix. 4.1. Wind power system The wind power system is composed of a three-blade wind turbine, a gearbox, an induction machine, as well as a three-phase rectifier, as shown in Fig. 5. The wind turbine is controlled in power mode to realize maximum power point tracking (MPPT). The detailed modeling procedure of the wind power system can be found in [4], and the resulted description using the EMR and control structure is given by Fig. 6. The control structure can be induced directly from EMR of the wind power system by using the inversion principle, which is usually called as inversion based control (IBC) [4]. The MPPT is realized by a lookup table to inverse the Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 6. EMR and control of the studied wind power system.
Fig. 7. Structural scheme of the battery system.
nonlinear relationship of the aerodynamic characteristic, which takes the turbine speed as input to calculate the optimal torque reference. The following conversion elements without energy accumulation are directly inverted to obtain the current reference, while the converter with energy accumulation is indirectly inverted by a PI controller by using the measurement of the armature current. 4.2. Energy storage system The battery system is connected to the DC bus via a step-up chopper to allow the charging and discharging, as shown in Fig. 7. 4.2.1. Modeling The battery can be viewed as a voltage source, and its output voltage can be calculated by the no-load voltage Vbat and the inner resistance rbat , which are nonlinear functions of the state of charge (SoC). The inductor between the battery and the DC–DC converter is modeled as: di bat L bat = Vbat − Vchop − (Rbat + rbat )i bat (7) dt where Vchop is the low-voltage side voltage of DC–DC converter; i bat is the current flowing out the battery; L bat and Rbat are the inductance and corresponding resistance of the inductor. To respect the principle of causality in EMR, the model can be rearranged in integral form as: Vbat − Vchop i bat = (8) L bat s + Rbat + rbat where s is the Laplace transform calculator. EMR and IBC of the battery system are shown in Fig. 8, where the battery is represented by an energy source, and the inductor is represented by an energy accumulation element. Then the average model of the step-up chopper is given as: { Vchop = m bat Vdc (9) i sto = m bat i bat Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 8. EMR and IBC of the battery system. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. Simplified structure of the DC–AC converter.
where Vdc is the high-voltage side voltage of DC–DC converter; m bat is the modulation ratio of the step-up chopper, which is limited between 0 and 1; and i sto is the output current of the storage system. It is represented in EMR by an element of conversion with additional control port. 4.2.2. Control The control structure can be deduced by using the inversion principle. The conversion elements without energy accumulation are reversed directly; while indirect inversion, e.g., Proportional–Integral controller, is adopted for the conversion elements with energy accumulation. All the control elements are shown in light blue in Fig. 8. 4.3. AC catenary and the grid converter The single-phase AC catenary is powered by high-voltage transmission grid via a power transformer, and also the DC bus via power converters in this HPS, as shown in Fig. 9. 4.3.1. Modeling The voltage of the catenary is determined by the power transformer and the grid voltage, which can be considered as a voltage source, the output voltage decreases with the output current, as expressed as: Vcat = V0 − Rtrans i grid
(10)
where Vcat is the RMS value of the catenary voltage; Vo is the RMS value of secondary side no-load voltage of the transformer; Rtrans is the transformer’s equivalent secondary side resistance; and i grid is the current supplied by the grid at the secondary side of the power transformer. The contact resistance between the pantograph and catenary and also the impedance of the catenary are not considered. The train is modeled as a power load, and the absorbed current depends on the catenary voltage. The train and the electric grid are coupled at the catenary, and this coupling is represented by the element with two squares following the two terminals from left, as shown in Fig. 10. i train = i grid + i cat
(11)
Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 10. EMR and IBC of the DC–AC converter.
where i train represents the current consumed by the train; and i cat represents the current flowing into catenary from DC bus. The wind power system and the storage system are coupled at the DC bus capacitor, which is used to model the dynamic of the DC bus. This can be represented by an energy accumulation element. Their relationships are given as: ilocal = i wind + i sto (12) d Vdc = ilocal − i dc (13) Cdc dt where ilocal is the total current supplied by the local energy sources; and i dc is the DC side current of the DC–AC converter. The power converter connects the single-phase AC catenary and DC bus, and the power can flow between the DC bus and the single-phase catenary. The relationship of currents and voltages can be expressed as: { Vconv = m bus Vdc (14) i dc = m bus i cat di cat L conv = Vconv − Vcat (15) dt where m bus is the average modulation ratio of the converter; L conv represents inductance of the power converter; Vconv is the RMS value of single-phase AC voltage. The average model of this DC–AC converter can be represented in the EMR by a conversion element with energy accumulation and another controlled conversion element. 4.3.2. Control The power converter regulates DC bus voltage and the power exchange between the catenary and DC bus. The controller requires two correctors due to inversions of two elements with energy accumulation. A cascaded control is used to control the current and the voltage, respectively. 4.4. Local energy management system The description and local control of the whole hybrid RPS can be obtained by assembling EMRs of all subsystems, which is shown in Fig. 11. Besides the local control, a central EMS is adopted to achieve optimal operation, which calculates DC bus voltage and battery charging/discharging power references. In France, the electricity bill is composed of three parts: the purchasing of electric energy; the public electric grid usage (TURPE, abbreviation in French) as well as the extra charge to balance the real-time production and consumption taken by RTE. SNCF participates into the day-ahead energy market to buy the required energy according to their power estimation. The real-time production and consumption is balanced by RTE, and it charges users for the gap between estimated and real consumption at each transmission grid connection point, calculated in 30-minute time frame [20]. The grid usage charge can be divided into four parts: one fixed part defined by the subscribed power; another variable part determined by the energy consumption; the third part considering the overtaking power, and the injection charge for sending back power to electric grid. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 11. EMR and control of the studied HRPS.
Fig. 12. Fuzzy logic based EMS of the studied hybrid RPS.
The overtaking power charge may take a significant part in the electric grid usage cost, if it is not properly considered. In the current case without optimization, the maximum consumption power is chosen to avoid totally the overtaking charge; but this increases a lot the fixed part cost. In this case, the subscribed power is optimized to achieve minimum annual bill. A Fuzzy logic based EMS, as shown in Fig. 12, has been designed in a previous SNCF project CONIFER [17] to limit the overtaking power in order to reduce grid usage cost, to take the advantage of renewable energies with reduction of CO2 emission. 4.5. Numerical simulation To verify the effectiveness of the proposed method, some simulations are carried out in MATLAB/Simulink. The data used for the simulations are measured at the Sarry substation in France on January 12, 2013. The simulation results are shown in Fig. 13. The blue line in the first figure gives the power consumed by trains, which is different form the power from grid in HRPS, but they are equal in traditional RPS. The DC bus voltage is supposed to be constant 1.5 kV. The battery follows the power reference passed by EMS to obtain optimal operation. An economic analysis has been conducted to compare with the traditional railway power substation. The traditional RPS selects the maximum consumption power during one year as the subscribed power; that is 55 MW for this substation. The comparison of electricity bills for a RPS and HRPS is given in Fig. 14. The grid usage takes around 10% of the electricity bill, while energy cost takes the other 90%. Compared to the classic RPS, the HRPS can reduce around 4.09 ke in the electricity bill, that is 9.16%. Focusing on the electric grid Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 13. Power and DC voltage profiles during one day at Sarry substation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 14. The distribution of daily electricity bill.
usage, higher subscribed power makes the constant part of grid usage for RPS twice of that for a HRPS. Although the overtaking charge becomes nonzero in HRPS, the total grid usage can be reduced around 1.08 ke, that is 31.98% of the grid usage for RPS. The total electricity bill reduction can be divided into the contribution of wind power system and battery. The wind power system contributes to around 2.98 ke, that is around 73% of total reduction, and the battery contributes to the rest 1.11 ke, that is around 27%. Considering the initial investment of 7.20 Me for wind power system and 1.07 Me for the battery, their contributions are reasonable. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Table 1 Basic elements of EMR and IBC.
5. Conclusion and perspective This paper discusses the design methodology and numerical simulation of HRPS connected to electric grid with the integration of renewable energies and energy storage system. A hierarchical management structure is proposed with long-term management and local controller. The optimal sizing and long-term operation of energy storage system is solved by using linear programming. With the aid of the EMR and the corresponding control, an AC type HRPS simulation model is constructed to investigate its potential to reduce electricity bill. Sarry substation is taken as the case to demonstrate the advantage of this hybridization. The economic analysis reveals that the proposed hybridization can reduce 9.16% of the electricity bill; especially the public electric grid usage cost can be reduced by nearly 32%. Experimental tests would be scheduled in order to verify the proposed concept and control method in an industrial environment. Appendix The basic elements of EMR and IBC are given in Table 1. References [1] J.A. Aguado, A.J.S. Racero, S. De La Torre, Optimal operation of electric railways with renewable energy and electric storage systems, IEEE Trans. Smart Grid 9 (2) (2018) 993–1001. [2] J. Baert, J. Pouget, D. Hissel, M.C. Pera, Energetic macroscopic representation of a hybrid railway powertrain, in: Proc. of IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, 6–9 Sept. 2011, pp. 1–6. [3] A. Bouscayrol, B. Davat, B.d. Fornel, B. François, J.P. Hautier, F. Meibody-Tabar, M. Pietrzak-David, Multimachine Multiconverter System: application for electromechanical drives, Eur. Phys. J. Appl. Phys. 10 (2) (2000) 131–147. [4] A. Bouscayrol, X. Guillaud, P. Delarue, B. Lemaire-Semail, Energetic macroscopic representation and inversion-based control illustrated on a wind-energy-conversion system using hardware-in-the-loop simulation, IEEE Trans. Ind. Electron. 56 (12) (2009) 4826–4835. [5] S. Hase, T. Konishi, A. Okui, Y. Nakamichi, H. Nara, T. Uemura, Fundamental study on energy storage system for DC electric railway system, in: Proc. of Power Conversion Conference, Osaka, Japan, 2–5 April 2002, pp. 1456–1459. [6] H. Hayashiya, T. Suzuki, K. Kawahara, T. Yamanoi, Comparative study of investment and efficiency to reduce energy consumption in traction power supply: A present situation of regenerative energy utilization by energy storage system, in: Proc. of 16th IEEE International Power Electronics and Motion Control Conference, Antalya, Turkey, 21–24 Sep. 2014, pp. 685–690. [7] H. Kobayashi, T. Saito, K. Kondo, A study on a method to design energy capacity of wayside energy storage devices in DC-electrified railway systems, in: Proc. of 17th European Conference on Power Electronics and Applications, Geneva, Switzerland, 8–10 Sept. 2015, pp. 1–9. [8] T. Konishi, H. Morimoto, T. Aihara, M. Tsutakawa, Fixed energy storage technology applied for DC Electrified railway, IEE J. Trans. Electr. Electron. Eng. 5 (3) (2010) 270–277. [9] T. Letrouve, W. Lhomme, A. Bouscayrol, N. Dollinger, Control validation of Peugeot 3∞8 HYbrid4 vehicle using a reduced-scale power HIL simulation, J. Electr. Eng. Technol. 8 (5) (2013) 1227–1233. [10] T. Letrouve, J. Pouget, F. Chauvet, Hardware-in-the-loop simulation: Hybrid locomotive energy storage system behavior tests, in: IEEEVPPC, Montreal, QC, Canada, 19–22 Oct. 2015, pp. 1–6.
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[11] S. Lin, W. Song, L. Luo, J. Lv, Z. Feng, Research on the capacity of hybrid energy storage system and its control method in rail transit traction grid, in: Proc. of International Conference on Smart Grid and Clean Energy Technologies, Chengdu, China, 19–22 Oct. 2016, pp. 103–106. [12] J. Lofberg, YALMIP : A toolbox for modeling and optimization in MATLAB, in: Proc. of the CACSD Conference, Taipei, Taiwan, 2–4 Sept. 2004. [13] M. Meinert, New mobile energy storage system for rolling stock, in: Proc. of 13th European Conference on Power Electronics and Applications, Barcelona, Spain, 28–10 Sept. 2009, pp. 1–9. [14] S. Nasr, M. Iordache, M. Petit, Smart micro-grid integration in DC railway systems, in: Proc. of IEEE PES Innovative Smart Grid Technologies Conference Europe, Istanbul, Turkey, 12–15 Oct. 2015, pp. 1–6. [15] K. Ogura, K. Nishimura, T. Matsumura, C. Tonda, E. Yoshiyama, M. Andriani, W. Francis, R.A. Schmitt, A. Visgotis, N. Gianfrancesco, Test results of a high capacity wayside energy storage system using Ni-MH batteries for DC electric railway at New York City Transit, in: Proc. of IEEE GreenTech, Baton Rouge, LA, USA, 14–15 April 2011, pp. 1–6. [16] P. Pankovits, M. Ployard, J. Pouget, S. Brisset, D. Abbes, B. Robyns, Design and operation optimization of a hybrid railway power substation, in: Proc. of European Conference on Power Electronics and Applications, Lille, France, 2–6 Sept. 2013, pp. 1–6. [17] P. Pankovits, J. Pouget, B. Robyns, F. Delhaye, S. Brisset, Towards railway-Smartgrid: Energy management optimization for hybrid railway power substations, in: Proc. IEEE PES Innovative Smart Grid Technologies Conference Europe, Istanbul, Turkey, 12–15 Oct. 2015, pp. 1–6. [18] P. Radcliffe, J.S. Wallace, L.H. Shu, Stationary applications of energy storage technologies for transit systems, in: Proc. of IEEE Electrical Power and Energy Conference, Halifax, NS, Canada, 25–27 Aug. 2010, pp. 1–7. [19] T. Ratniyomchai, S. Hillmansen, P. Tricoli, Recent developments and applications of energy storage devices in electrified railways, IET Electr. Syst. Transp. 4 (1) (2014) 9–20. [20] Regulatory Commission of Energy, 2016. [Online]. Available: http://www.cre.fr/reseaux/reseaux-publics-d-electricite/services-systeme-et-m ecanisme-d-ajustement [Accessed 17.10.16].
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Original articles
Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation Nanfang Yang a , ∗, Julien Pouget a , Tony Letrouvé a , Cristian Jecu b , Loic Joseph-Auguste b a SNCF Innovation and Research Department, 40 Avenue des Terroirs de France, 75012 Paris, France b EDF R&D, avenue des Renardières, 77250 Moret-sur-Loing, France
Received 6 November 2017; received in revised form 2 May 2018; accepted 25 June 2018 Available online xxxxx
Abstract The railway operator would like to increase train frequency in order to satisfy demand for passenger flow growth in some lines. However the railway power substations (RPSs) are approaching their rating capacities, and have difficulty to supply more. A concept investigated by SNCF is a hybrid railway power substation (HRPS). Renewable energy systems and energy storages are integrated in the RPS to supply power. This paper presents the methodology based on simulation tools to design a HRPS by the electrical infrastructure railway engineering team. The first step uses a temporal power balance and economics linear formulation to optimize the HRPS global design and the energy management (for a time horizon of one year with time step of one hour). In the second step, the energetic macroscopic representation (EMR) is adopted to validate the HRPS global design with the corresponding control and the real time energy management system (time step of one second and simulation time of one week). The case study of Sarry railway power substation reveals that around 9% of the electricity bill can be reduced with the proposed hybridization. c 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights ⃝ reserved.
Keywords: Economic and power flow design; Linear optimization formulation; Energetic macroscopic representation (EMR); Energy management system (EMS); Hybrid railway power substation (HRPS)
1. Introduction and problematic Compared to other passenger transport ways, railway system is the most economic, efficient and environmentfriendly method [6]. However with the growth of traffic flow, the railway power substation (RPS) suffers two problems: one is the voltage drop due to line impedance; the other is that the transformer or power converter connecting to the electric grid (usually transmission line) is approaching its power limit. Besides, the diode bridge based rectifier does not allow sending back power from the catenary to the electric grid in DC railway system. The regenerative braking ∗ Corresponding author.
E-mail address: [email protected] (N. Yang). https://doi.org/10.1016/j.matcom.2018.06.012 c 2018 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights 0378-4754/⃝ reserved.
Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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power has to be dissipated in onboard or/and wayside rheostats, if no other trains are powered simultaneously and catenary voltage is approaching the designed limit [19]. Many studies have been conducted to stabilize DC catenary voltage, to recover the regenerative braking energy, as well as to improve the energy efficiency, with the integration of energy storages. There are two types of energy storage systems: on-board [5,13,10] and wayside [17,7,11,18,8]. The former aims to recover regenerative braking energy so as to improve energy efficiency and to support the catenary-free operation. The wayside energy storages can recover regenerative braking energy through the catenary, improve the power supply quality, as well as participate into peak load shaving. It has been reported that a wayside Nickel–Metal hydride (Ni–MH) battery can recycle as high as 71.4% of the regenerative braking energy in DC railway system [15]. Besides, the price of Lithium-ion battery is falling in recent years, which gives good opportunity to develop new service of battery at wayside, such as the frequency regulation and reactive power compensation. Facing those problems and liberation of electricity market in France, National French Railways Company (SNCF, abbreviation in French) has studied the integration of energy storage system and renewable energies into power substations, to form a hybrid RPS (HRPS), which could be DC or AC type [16]. The energy storage devices and renewable energies are grouped by the DC bus, and then connected to the catenary through a bidirectional power converter. The DC bus can also be connected to the power supply of the railway passenger station in order to supply other services, such as emergency service. The integration of DC microgrid has also been examined in [14], which uses a low-voltage DC bus to interface the energy storage system, renewable energies, the catenary as well as the auxiliary equipment, and similarly in AC type [1]. This new concept, the HRPS, needs to be specified, developed, manufactured, tested and commissioned. Since many years, the development of new system or software is directly done in a prototype. For a HRPS, the prototype is composed of several electric components and an energy management system implemented in electrical railway network. The problematic of the prototype development is the time and resource requirements. Taking HRPS development as an example, it is obvious that each modification or test of a new component, e.g., energy management strategy, has to fulfill restriction on circulating point, send sufficient people to ensure the security as well as track the test in commercial exploitation. To tackle these issues, several virtual steps are available nowadays, such as modeling, representation, simulation, Hardware-In-the-Loop (HIL) test. The focus of this paper is to present an HRPS design methodology by the usage of different tools to achieve optimal sizing, energy management, as well as power flow analysis. The following parts are dedicated to the overview design methodology of hybrid energy systems, which can coordinate different simulation tools together to design and validate the specification, the manufacturing and commissioning of a HPRS. Firstly, the design and optimal sizing will be addressed; then, the simulation and local control with energetic macroscopic representation (EMR) will be presented and discussed. Finally, financial analysis will be conducted to examine the profitability of the proposed HRPS compared to traditional RPSs. 2. HRPS design method: Combination of different simulation tools 2.1. Positioning of simulation tools Modeling is the foundation of system design, and the model gives a mathematical description of system behavior associated with assumptions following an objective. The validity range of this step is defined by assumptions. A system can have different descriptions that can be either structural or functional. The description is an organization of a model that helps to highlight some studied system’s behaviors, thus there is no assumption between a model and its description. The structural description separates the system into different modules according its physical connections. It has advantage on development duration because it is based on pick-and-place blocks following the physical structure, and it may uses derivative to express some block, which requires its future state and cannot be implemented in real-time. Besides, there is not a systemic method to deduce the control structure from system description. The latter approach is based on the functionality separation no matter its physical structure, thus an exclusive rule can be added to avoid the derivative. EMR is a graphical description tool using functionality separations, which has been proposed and specifically designed for the modeling and control of coupled electromechanical system [3]. It decomposes the system into natural interconnected elements according to physical causality principles, where only integration is allowed in system representation [3]. Another positive point is that a systemic control structure could be deduced directly from Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 1. The development path used in this project.
the EMR. It has been applied to modeling and control of hybrid electric vehicle [9], hybrid locomotive [13,2], and also wind power system [4]. This would a great help to deduce a systematic control based on the inverse model. In this paper, only functional model (functional description of a system model) is taken into account, which, contrary to a structural model, is based on function association and does not take importance about the physical structure. This step feeds the description to a dynamic simulation and at the power flow simulation platform that is needed in a first approach to define an optimal design. 2.2. HRPS design method The development of such a complicated energy system using the traditional path is really time-consuming and expensive. Therefor a new development path by simulation tools is proposed in order to reduce the development time and costs. The adopted development path is shown in Fig. 1. The requirements of RPS are analyzed by electrocinematic simulation of the electric network railway with the train’s loads to validate the catenary tension standard. This simulation defines the requirement and the localization of the RPS on the railway line. Two different solutions, the traditional and hybrid RPSs, will be studied. For an industrial application, the comparison is based on economic criteria and technical feasibility. The second problematic is provided by the integration of EMS in the design process. Finally the HRPS design is based on three simulation models: an optimization model for the HRPS overall design, a validation model based on EMR and HIL test for the HRPS manufacturing and commissioning. The optimization tool used a temporal power balance and economics linear formulation to optimize the HRPS global design and the anticipative EMS. Thus the size and the temporal used of each components of the HRPS are defined by a linear optimization formulation for each hours of a year [17]. The inputs are the power of the local wind and solar sunrise, the cost of connection and the monthly subscription of the electrical network. After the optimal sizing, the obtained HRPS will be modeled and represented with EMR. This model uses the numerical simulation software MATLAB to verify and validate its well-functioning with the control command and the EMS. The step time is the second and the simulation during a week. To reduce the HRPS manufacturing and commissioning tests costs, a simulation step links the simulation tools with the HRPS manufacturing. This method is developed in collaboration between user and the manufacturer to progressively validate the HRPS. Finally the HRPS is installed and the commissioning tests are simpler. This simulation method can be applied at each hybrid system connected to the electrical grid. 3. Optimal tool for designing the HRPS system and anticipative EMS 3.1. Structure of a HRPS The structure of the studied HRPS is presented in Fig. 2. The hybrid part or DC part groups the renewables energy systems, and storage systems via power converters. The catenary is powered by the transmission grid via power transformers and also the DC bus via the bidirectional power converter. The trains running nearby the power substation absorb power through the pantograph contacting to the catenary. The catenary voltage is determined by the electric grid and the power transformer turns ratio, while DC bus voltage is controlled by the bidirectional power converter linking the DC and AC parts. The wind turbines work in Maximum Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 2. Architecture of the studied HRPS.
Power Point Tracking (MPPT) mode, which is not a controllable energy source. Thus the only controllable source is the battery. The EMS collects power consumption of trains, the power generated by wind turbines as well as the power flowing from the grid to catenary, and then calculates power reference of the battery. 3.2. Optimal design and energy management To study and design a HRPS including electric grid, storage system and renewable production system, a linear optimal formulation is proposed. This optimal sizing takes into account constraints linked to the power substation (type of electrification, the investment and operational cost in electric grid, electricity prices, etc.) and also the reliability and complementarity of the components envisioned for hybridization. With the aid of this optimal sizing tool, it will be possible to evaluate and compare different study case from a technical and economic point of view. For the optimal design phase, system modeling is based on power and financial balances for the global system. Power flow models for railway loads, wind turbine, and storage system in addition to economic models for cost function are here introduced: – For the wind turbine model, the hourly output of wind power system is given as: Pw = ηt · ηg · 0.5 · C p · ρa · Sw · vn3
(1)
with ηt the wind turbine efficiency, ηg the generator efficiency (both obtained from the manufacturer data),C p the power coefficient of wind turbine, ρa the air density, SW the wind turbine rotor swept area and vn the wind velocity. The historical hourly wind speed dataset nearby the substation is adopted to carry out the optimization. – Power flow model of energy storage The storage system considered in the design phase is a battery for railway applications. It is mathematically represented with a power flow model. The unit sizing as well as the optimal reference power of the storage system for one year with an hour time interval are both sought, in order to analyze the optimal operation strategy. 8760 samples for storage power(Psto ) are therefore created. Various losses are considered by introducing energy efficiency parameters (ηbat if Psto > 0 and 1/ηbat if Psto < 0). Positive power values mean that the battery is discharging whereas negative power values correspond to battery charging. Stored energy E sto depends on the initial state of charge and the energy stored every hour during the year is accounted for as follows. E sto (t) = E 0 − 3600s ·
8760 ∑
Psto i
(2)
i=1
– Particular formulation for the power balance is: Pgrid (t) − Psto (t) = Pload (t) − Pw (t)
(3)
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with Pgrid being the network power, Psto the energy storage power, Pload the substation load power, and Pw the production of wind turbines. – Objective function, minimization of the global cost (4)
C = C grid + Cinvestment + Copex + C Elecpur chase
with C is the yearly cost of the HRPS, where C grid represents the yearly grid usage cost, Cinvestment represents the yearly depreciated investment cost (investment cost: connection to the transportation grid, transformer, rectifier, purchase of wind turbines, batteries), Copex represents the operation cost: maintenance of infrastructure (transformer, wind turbines and storages) and C Elecpur chase represents the yearly electricity purchase cost from the electricity market. The historical hourly market electricity price dataset in France is adopted to conduct the optimization. – Linear formulation and optimal solver N etwor k The subscription cost Copex is a nonlinear convex formulation as show in (5). To apply a linear optimization process for one year with time step of one hour (2 integer variables, 52 560 binary variables, 788 408 real variables and 1 472 060 constraints), a simplification with a linear formulation is introduced and validated as show in (6). Finally the problem is implemented on the optimal modeling tool YAMILP [12] and solved by CPLEX. ⎞ ⎛ 8760 ( 12 ) ∑ ∑ N etwor k N etwor k N etwor k N etwor k 2 ⎠ ⎝α · √ (5) Copex = a2 · Popex + b · τ c · Popex + ∆Popex Month=1
i=1
N etwor k N etwor k with Copex the subscription cost, a2 , b, τ, c and α contractual coefficient, Popex the contractual power N etwor k subscription, Month the month indices and ∆Popex the positive overrun of the contractual power subscription. ( 8760 ) 12 ∑ ∑( ) N etwor k N etwor k N etwor k N etwor k Copex = a2 · Popex + b · τ2 · Popex + α· ∆Popex (6) Month=1
i=1
with τ2 an equivalent contractual coefficient. 3.3. Case study: Sarry substation on Paris–Lyon High Speed Line 3.3.1. Requirement of RPS The Sarry substation on Paris–Lyon High Speed Line has been used for this study. The load data (automatic meter reading) of 2013 have been used with a modification of the load for the 18:00 to 20:00 interval from Monday to Friday. The traffic load in this time interval has been modified by simulating a frequency of 16 trains per hour per direction. Each train is composed by two high-speed trains. The simulation results showed that the maximum load is 43 MW, which exceeds the substation’s physical limit fixed at 35 MW. To simulate this network constraint and analyze its economic impact, a hybridization of this substation has been done with this specific constraint in the optimal formulation of the problematic. This constraint introduces additional cost of RPS by reinforcement of the electrical connection to the electrical network. 3.3.2. HRPS technologies and electricity prices The Li-ion battery system with 1.1 MW/580 kWh is selected to compose a HRPS, and it has an efficiency of 95% in charge and discharge, and costs 1070 ke. The production of a wind turbine uses the wind speed of a local weather station nearby. The characteristics of wind turbine are: 1670 kW power, 80m rotor diameter, average 32% yield, 3 m/s start-up wind speed, 25 m/s maximum wind speed, costs 1200 ke. The scenario price is the vision 2030 Green Transition prospective marginal costs with 49% of renewable energy, as shown in Fig. 3. 3.3.3. Result of HRPS study case The optimal design model is used to size HRPS components: 1 Li-ion battery and 9 wind turbines. Fig. 4 presents the economic results of HRPS compared to conventional RPS with the same inputs but without an investment in energy storage system and renewable energy systems. The RPS solution is based on electrical reinforcement of the connection to the electric grid. The HRPS does not increase the total cost. The high initial investment is mainly due to the cost of battery. The MWh cost (total cost/total grid energy) is decreased by 15e/MWh from 120e/MWh in RPS to 105e/MWh in HRPS. Thus, the initial investment is compensated by an MWh cost. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 3. Vision 2030 Green Transition.
Fig. 4. Economic results for RPS and HRPS.
Fig. 5. Structural scheme of the wind power system.
4. Simulation tools used for the HRPS validation design and EMS In this section, EMR is adopted to represent the HRPS, and the corresponding control structure is also deduced by using the principle of inversion [4]. Some basic EMR blocks are listed in Appendix. 4.1. Wind power system The wind power system is composed of a three-blade wind turbine, a gearbox, an induction machine, as well as a three-phase rectifier, as shown in Fig. 5. The wind turbine is controlled in power mode to realize maximum power point tracking (MPPT). The detailed modeling procedure of the wind power system can be found in [4], and the resulted description using the EMR and control structure is given by Fig. 6. The control structure can be induced directly from EMR of the wind power system by using the inversion principle, which is usually called as inversion based control (IBC) [4]. The MPPT is realized by a lookup table to inverse the Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 6. EMR and control of the studied wind power system.
Fig. 7. Structural scheme of the battery system.
nonlinear relationship of the aerodynamic characteristic, which takes the turbine speed as input to calculate the optimal torque reference. The following conversion elements without energy accumulation are directly inverted to obtain the current reference, while the converter with energy accumulation is indirectly inverted by a PI controller by using the measurement of the armature current. 4.2. Energy storage system The battery system is connected to the DC bus via a step-up chopper to allow the charging and discharging, as shown in Fig. 7. 4.2.1. Modeling The battery can be viewed as a voltage source, and its output voltage can be calculated by the no-load voltage Vbat and the inner resistance rbat , which are nonlinear functions of the state of charge (SoC). The inductor between the battery and the DC–DC converter is modeled as: di bat L bat = Vbat − Vchop − (Rbat + rbat )i bat (7) dt where Vchop is the low-voltage side voltage of DC–DC converter; i bat is the current flowing out the battery; L bat and Rbat are the inductance and corresponding resistance of the inductor. To respect the principle of causality in EMR, the model can be rearranged in integral form as: Vbat − Vchop i bat = (8) L bat s + Rbat + rbat where s is the Laplace transform calculator. EMR and IBC of the battery system are shown in Fig. 8, where the battery is represented by an energy source, and the inductor is represented by an energy accumulation element. Then the average model of the step-up chopper is given as: { Vchop = m bat Vdc (9) i sto = m bat i bat Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 8. EMR and IBC of the battery system. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. Simplified structure of the DC–AC converter.
where Vdc is the high-voltage side voltage of DC–DC converter; m bat is the modulation ratio of the step-up chopper, which is limited between 0 and 1; and i sto is the output current of the storage system. It is represented in EMR by an element of conversion with additional control port. 4.2.2. Control The control structure can be deduced by using the inversion principle. The conversion elements without energy accumulation are reversed directly; while indirect inversion, e.g., Proportional–Integral controller, is adopted for the conversion elements with energy accumulation. All the control elements are shown in light blue in Fig. 8. 4.3. AC catenary and the grid converter The single-phase AC catenary is powered by high-voltage transmission grid via a power transformer, and also the DC bus via power converters in this HPS, as shown in Fig. 9. 4.3.1. Modeling The voltage of the catenary is determined by the power transformer and the grid voltage, which can be considered as a voltage source, the output voltage decreases with the output current, as expressed as: Vcat = V0 − Rtrans i grid
(10)
where Vcat is the RMS value of the catenary voltage; Vo is the RMS value of secondary side no-load voltage of the transformer; Rtrans is the transformer’s equivalent secondary side resistance; and i grid is the current supplied by the grid at the secondary side of the power transformer. The contact resistance between the pantograph and catenary and also the impedance of the catenary are not considered. The train is modeled as a power load, and the absorbed current depends on the catenary voltage. The train and the electric grid are coupled at the catenary, and this coupling is represented by the element with two squares following the two terminals from left, as shown in Fig. 10. i train = i grid + i cat
(11)
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Fig. 10. EMR and IBC of the DC–AC converter.
where i train represents the current consumed by the train; and i cat represents the current flowing into catenary from DC bus. The wind power system and the storage system are coupled at the DC bus capacitor, which is used to model the dynamic of the DC bus. This can be represented by an energy accumulation element. Their relationships are given as: ilocal = i wind + i sto (12) d Vdc = ilocal − i dc (13) Cdc dt where ilocal is the total current supplied by the local energy sources; and i dc is the DC side current of the DC–AC converter. The power converter connects the single-phase AC catenary and DC bus, and the power can flow between the DC bus and the single-phase catenary. The relationship of currents and voltages can be expressed as: { Vconv = m bus Vdc (14) i dc = m bus i cat di cat L conv = Vconv − Vcat (15) dt where m bus is the average modulation ratio of the converter; L conv represents inductance of the power converter; Vconv is the RMS value of single-phase AC voltage. The average model of this DC–AC converter can be represented in the EMR by a conversion element with energy accumulation and another controlled conversion element. 4.3.2. Control The power converter regulates DC bus voltage and the power exchange between the catenary and DC bus. The controller requires two correctors due to inversions of two elements with energy accumulation. A cascaded control is used to control the current and the voltage, respectively. 4.4. Local energy management system The description and local control of the whole hybrid RPS can be obtained by assembling EMRs of all subsystems, which is shown in Fig. 11. Besides the local control, a central EMS is adopted to achieve optimal operation, which calculates DC bus voltage and battery charging/discharging power references. In France, the electricity bill is composed of three parts: the purchasing of electric energy; the public electric grid usage (TURPE, abbreviation in French) as well as the extra charge to balance the real-time production and consumption taken by RTE. SNCF participates into the day-ahead energy market to buy the required energy according to their power estimation. The real-time production and consumption is balanced by RTE, and it charges users for the gap between estimated and real consumption at each transmission grid connection point, calculated in 30-minute time frame [20]. The grid usage charge can be divided into four parts: one fixed part defined by the subscribed power; another variable part determined by the energy consumption; the third part considering the overtaking power, and the injection charge for sending back power to electric grid. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 11. EMR and control of the studied HRPS.
Fig. 12. Fuzzy logic based EMS of the studied hybrid RPS.
The overtaking power charge may take a significant part in the electric grid usage cost, if it is not properly considered. In the current case without optimization, the maximum consumption power is chosen to avoid totally the overtaking charge; but this increases a lot the fixed part cost. In this case, the subscribed power is optimized to achieve minimum annual bill. A Fuzzy logic based EMS, as shown in Fig. 12, has been designed in a previous SNCF project CONIFER [17] to limit the overtaking power in order to reduce grid usage cost, to take the advantage of renewable energies with reduction of CO2 emission. 4.5. Numerical simulation To verify the effectiveness of the proposed method, some simulations are carried out in MATLAB/Simulink. The data used for the simulations are measured at the Sarry substation in France on January 12, 2013. The simulation results are shown in Fig. 13. The blue line in the first figure gives the power consumed by trains, which is different form the power from grid in HRPS, but they are equal in traditional RPS. The DC bus voltage is supposed to be constant 1.5 kV. The battery follows the power reference passed by EMS to obtain optimal operation. An economic analysis has been conducted to compare with the traditional railway power substation. The traditional RPS selects the maximum consumption power during one year as the subscribed power; that is 55 MW for this substation. The comparison of electricity bills for a RPS and HRPS is given in Fig. 14. The grid usage takes around 10% of the electricity bill, while energy cost takes the other 90%. Compared to the classic RPS, the HRPS can reduce around 4.09 ke in the electricity bill, that is 9.16%. Focusing on the electric grid Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Fig. 13. Power and DC voltage profiles during one day at Sarry substation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 14. The distribution of daily electricity bill.
usage, higher subscribed power makes the constant part of grid usage for RPS twice of that for a HRPS. Although the overtaking charge becomes nonzero in HRPS, the total grid usage can be reduced around 1.08 ke, that is 31.98% of the grid usage for RPS. The total electricity bill reduction can be divided into the contribution of wind power system and battery. The wind power system contributes to around 2.98 ke, that is around 73% of total reduction, and the battery contributes to the rest 1.11 ke, that is around 27%. Considering the initial investment of 7.20 Me for wind power system and 1.07 Me for the battery, their contributions are reasonable. Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.
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Table 1 Basic elements of EMR and IBC.
5. Conclusion and perspective This paper discusses the design methodology and numerical simulation of HRPS connected to electric grid with the integration of renewable energies and energy storage system. A hierarchical management structure is proposed with long-term management and local controller. The optimal sizing and long-term operation of energy storage system is solved by using linear programming. With the aid of the EMR and the corresponding control, an AC type HRPS simulation model is constructed to investigate its potential to reduce electricity bill. Sarry substation is taken as the case to demonstrate the advantage of this hybridization. The economic analysis reveals that the proposed hybridization can reduce 9.16% of the electricity bill; especially the public electric grid usage cost can be reduced by nearly 32%. Experimental tests would be scheduled in order to verify the proposed concept and control method in an industrial environment. Appendix The basic elements of EMR and IBC are given in Table 1. References [1] J.A. Aguado, A.J.S. Racero, S. De La Torre, Optimal operation of electric railways with renewable energy and electric storage systems, IEEE Trans. Smart Grid 9 (2) (2018) 993–1001. [2] J. Baert, J. Pouget, D. Hissel, M.C. Pera, Energetic macroscopic representation of a hybrid railway powertrain, in: Proc. of IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, 6–9 Sept. 2011, pp. 1–6. [3] A. Bouscayrol, B. Davat, B.d. Fornel, B. François, J.P. Hautier, F. Meibody-Tabar, M. Pietrzak-David, Multimachine Multiconverter System: application for electromechanical drives, Eur. Phys. J. Appl. Phys. 10 (2) (2000) 131–147. [4] A. Bouscayrol, X. Guillaud, P. Delarue, B. Lemaire-Semail, Energetic macroscopic representation and inversion-based control illustrated on a wind-energy-conversion system using hardware-in-the-loop simulation, IEEE Trans. Ind. Electron. 56 (12) (2009) 4826–4835. [5] S. Hase, T. Konishi, A. Okui, Y. Nakamichi, H. Nara, T. Uemura, Fundamental study on energy storage system for DC electric railway system, in: Proc. of Power Conversion Conference, Osaka, Japan, 2–5 April 2002, pp. 1456–1459. [6] H. Hayashiya, T. Suzuki, K. Kawahara, T. Yamanoi, Comparative study of investment and efficiency to reduce energy consumption in traction power supply: A present situation of regenerative energy utilization by energy storage system, in: Proc. of 16th IEEE International Power Electronics and Motion Control Conference, Antalya, Turkey, 21–24 Sep. 2014, pp. 685–690. [7] H. Kobayashi, T. Saito, K. Kondo, A study on a method to design energy capacity of wayside energy storage devices in DC-electrified railway systems, in: Proc. of 17th European Conference on Power Electronics and Applications, Geneva, Switzerland, 8–10 Sept. 2015, pp. 1–9. [8] T. Konishi, H. Morimoto, T. Aihara, M. Tsutakawa, Fixed energy storage technology applied for DC Electrified railway, IEE J. Trans. Electr. Electron. Eng. 5 (3) (2010) 270–277. [9] T. Letrouve, W. Lhomme, A. Bouscayrol, N. Dollinger, Control validation of Peugeot 3∞8 HYbrid4 vehicle using a reduced-scale power HIL simulation, J. Electr. Eng. Technol. 8 (5) (2013) 1227–1233. [10] T. Letrouve, J. Pouget, F. Chauvet, Hardware-in-the-loop simulation: Hybrid locomotive energy storage system behavior tests, in: IEEEVPPC, Montreal, QC, Canada, 19–22 Oct. 2015, pp. 1–6.
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Please cite this article in press as: N. Yang, et al., Techno-economic design methodology of hybrid energy systems connected to electrical grid: An application of hybrid railway power substation, Mathematics and Computers in Simulation (2018), https://doi.org/10.1016/j.matcom.2018.06.012.