Techno-economic analysis of MJ class high temperature Superconducting Magnetic Energy Storage (SMES)systems applied to renewable power grids

1 Introduction

China is facing growing international pressures on the issues of climate change, greenhouse gas emission and limited energy resource because of its strong economic growth and the increasing demand for energy. It is important for China to speed up the utilization of renewable energy resource. As one of the most prospective sources of economical renewable energy, wind power can be used to solve the existing and potential environmental and energy shortage problems. In 2015, the additional wind power capacity of China is 30,753 MW which is 48.4% of the global scale and the total installed capacity has reached 33.4% worldwide [1-3]. Based on the accelerating growth of global wind power, Chinese Wind Energy Association (CWEA) thinks that the total installed capacity of China wind power will be 100 GW or more by the end of 2020 [4].

However, we can find the variability of power output is a characteristic of wind energy, resulting in an adverse impact on the power system. Increasing the utilization of wind in power generation presents significant operational challenges in ensuring the grid security and power quality due to this inherent resource variability. In order to reduce the wind power impact over the power quality, high temperature SMES is applied to stabilize substantial power systems with integration of damping out low-frequency power oscillations, avoiding voltage sags and reducing the renewable curtailment. During rich wind period, some wind power has to be curtailed due to that the transmission network does not have sufficient transfer capacity or the receiving system cannot accommodate such amount of fluctuation power securely. SMES can store the abundant wind power when the network is congested and release it back to the system when there is no congestion. However,considering the cost and lifespan of SMES, there is an urgent demand to conduct a cost-beneficial analysis to justify its role in renewable energy generation.

In this paper, an application planning of 5 MJ SMES is added in a practical renewable power system, Zhangbei wind farm. The operation of SMES is evaluated considering the wind turbine failure and the SMES location in Zhangbei wind farm power grid. In addition, a financial feasibility study is conducted by comparing the cost of deploying the SMES and BESS, and the savings from wind power curtailment. The economic analysis provides a useful indication of its practical application feasibility for compensating power system instability with substantial wind power.

2 SMES modelling and operation analysis

2.1 SMES modelling

A three-phase controllable current source model is proposed to simulate the SMES. The energy of a SMES system is stored as the current flowing through a magnet,which is essentially an inductor, therefore a controllable current source for SMES is sufficient for the power system level simulation studies. A Power Converter System (PCS)with two independent PI control regulators for adjusting the output active and reactive power of SMES is proposed using a simplified control method [5].

The SMES simulation models using active power PSMES and reactive power QSMES at simulation time step of i are as the following:

where kp, kI are the control parameters of PI controllers; T is simulation time step; ΔP, ΔQ are the variation of active and reactive power, respectively.

2.2 SMES operation analysis in wind grids

Zhangbei wind power test base was built in 2010 and funded by the National Wind and Solar Energy Storage and Transmission Demonstration Project, the first “Golden Sun Demonstration Project” in China. The initial idea of the wind power test base is to enable the large penetration of renewable power into the main grid in a stable manner by means of energy storage systems. Therefore, the operation of SMES with 5 MJ capacity in Zhangbei wind power test base is investigated considering various fault types and fault locations.

Fig. 1 shows the topology of the wind turbine and the energy storage system in Zhangbei wind power test base.From Fig. 1, we can find that this renewable power grid includes 40 MW solar power generation, 50 MW wind power generation, and 60 MWh battery energy storage systems.

Fig. 1 The topology of Zhangbei wind power test base

The simulation model of Zhangbei wind power test base is established by using PSCAD/EMTDC software, as shown in Fig. 2. The 40 MW PV system is modelled as the power source and the 50 MW wind farm is connected to the 35 kV Bus (Bus 3). The renewable power is delivered to the 110 kV class grid through a boost transformer. Because of the unstable characteristics of the output wind power, the terminal bus of wind turbines is one of the ideal locations for SMES. Since all the renewable generation systems and batteries are connected to the 35 kV Bus, Bus 3 is another potential location for SMES, as shown in Fig. 2. Therefore,15 MW wind turbine failure in the terminal bus is studied in this paper and the performances of the SMES at terminal bus and Bus 3 are investigated.

Fig. 2 Simulation schematic diagram of total system configuration in Zhangbei wind power test base

The whole system operates in normal condition before t = 1.0 s, but some wind turbines fail to work after t = 1.0 s creating a power deficit of 15 MW with a fault duration of 0.1 s. The system suffers large power fluctuations with the failure of 15 MW wind turbine and it takes 0.25 s for the entire system to return to the stable condition (at t =1.25 s),seen in Fig. 3. In Fig. 3, Pw is the output power of the wind turbines and PBus3 is the power of Bus 3.

Fig. 3 Electric power simulation result of wind turbines and Bus 3 with wind turbine failure without SMES

A SMES with 5 MJ capacity is applied to both terminal bus and Bus 3 to validate its ability for compensating the power fluctuation. Fig. 4 (a) and (b) show the simulation results of this condition. We find that the power fluctuations on both terminal bus and Bus 3 are compensated very well by SMES. The maximum and minimum values of power Pwt and the power fluctuation factor kp, with and without SMES are calculated and compared in Table 1.

The power fluctuation factor kp is defined using the rated power PN (PN =1 pu) which can give the maximum rate of change in electric power adequately for wind grids.

where Pmax and Pmin are the maximum and minimum power values respectively after the fault takes place.

We find that when SMES is located at Bus 3, with the wind turbine failure, the peak-peak value of Pwt is 0.4 pu. However, it is 0.15 pu when SMES is located at the terminal bus which is the smaller value in the two conditions. This 0.15 pu value is observed to be 20% of the peak-peak value of power without SMES. The simulation results show that the SMES can smooth the wind power system within a few milliseconds effectively. Moreover,the location of SMES is important for the effective power fluctuation compensation. The SMES has a better performance when it is close to the fault location.

Fig. 4 Simulation results with wind turbine failure with SMES: (a) SMES installed on Bus 3; (b) SMES installed at terminal bus

Table 1 Comparison of electric power fluctuation before and after compensation with SMES

Power value Pw /pu SMES operation mode Minimum/pu Maximum/pu Peak-topeak value/pu kp/100%Without SMES 0.30 1.05 0.75 75%With SMES on Bus 3 0.65 1.05 0.40 40%With SMES on terminal Bus 0.90 1. 05 0.15 15%

3 Cost and benefit modelling of SMES

Future investments in electrical grid can come from network thermal limitation violations, the aging of components, and other factors [6]. Only the second one is considered here.

3.1 Investment deferral

In normal conditions, the investment horizons n of the circuit under a given load growth rate can be identified as:

where RC is the network’s rated capacity; r is the chosen load growth rate; D is current loading level; n is investment horizon which is the number of years required for investment.

Rearranging and taking the logarithm of it gives:

If the SMES/BESS is integrated into the network, it can absorb the excessive wind power and store it when the network is congested. The stored energy can be then released to the network when the network is not congested.In this case, the network’s new investment horizon is calculated with:

where, SC is the maximum energy storage capacity, and nnew is the new investment horizon.

The benefit for network investment is assessed in terms of the deferral in present value of future reinforcements of components [7]. It is quantified by comparing the annual present values of future reinforcements in networks, given in (7), with and without the SMES. The final benefit ΔPV is the sum of the change in present values of all network components.The mathematical formula of the evaluation is described as:

where, PVi and n,i are the present values of future investment and reinforcement horizon of component i without SMES; PVnew,i and nnew,i are its new present values of future investment with SMES; N is the component number in network; Asseti is the assest cost of component i; d is the discount rate; AnnuityFactor is the annuity factor which is between 0 and 1.

3.2 Payback period

Another key parameter to justify or measure the cost/benefit of an investment is payback period, which is normally expressed in years. Generally, to calculate a more exact payback period, Payback Period (PR) equals the amount to be invested divided by the estimated annual net cash flow. It can also be derived by using:

Where, ny is the number of years after the initial investment when the last negative value of cumulative cash flow occurs; pn is the value of cumulative cash flow when the last negative value of cumulative cash flow occurs; p is the value of cash flow at which the first positive value of cumulative cash flow occurs.

4 Financial analysis of SMES in Zhangbei

This section compares the costs of building a new network for energy output and a SMES for energy storage to avoid the curtailment of the abundant wind energy in Zhangbei wind farm based on the theory discussed in last section.

4.1 Scenario 1: wind curtailment

Wind power will be curtailed by assuming that the energy would congest the grid. The cost will be the lost value of curtailed wind power. Thus, this value can be quantified by timing wind curtailment amount with the unit price of electricity tariff of Zhangbei wind farm, as shown in Table 2 [8].

Table 2 Wind curtailment in Zhangbei wind farm

Annual generation(MWh)Items Energy price(dollars /kWh)Curtailment percent (%)Farm Capacity(MW)Value 0.08 46296.07 30 50

4.2 Scenario 2: network investment

In order to avoid the curtailment of wind energy, a network can be introduced to enhance the capability of the 110 kV transmission line, and the parameters are seen in Table 3.

Table 3 Network investment in Zhangbei wind farm

Items Unit cost(dollars/km)Current loading(MW)Value 74680 58 100 70 Length(km)Capacity(MW)

4.3 Scenario 3: SMES and BESS investment

Another method to avoid the wind curtailment is to install the SMES system which can absorb the abundant energy and release energy when it is needed. The SMES investment in wind curtailment can be gotten according to Table 4 with a lifespan of 30 years and the cost of SMES includes the cost of refrigerators and converters. The annual investment of BESS with a lifespan of 30 years is given with the unit cost of 300 $/kWh [9].

Table 4 SMES Investment

Cost(dollars)ItemsEnergy(MJ)Charging efficiency(100%)Capacity(MW)Available capacity(MW)Life span(year)Value 5 1720000 80 15 12 30

4.4 Comparison and discussion

The two systems are developed to accommodate the abundant wind energy in Zhangbei wind farm. The investment cost and benefit are summarized in Table 5.From Table 5, we can find that if no action is taken to handle the curtailment of the wind energy, the annual loss of income is 1012 k$ for Zhangbei wind farm. If a new network is constructed to enhance the energy transmission, the expected lifetime of the network is assumed to be 20 years, then the annual investment cost of the network is 46.05 k$. With the newly-built network,the curtailment of the wind energy can be avoided, thus the benefit of the network is the saved wind curtailment of 1012 k$. In addition, if a 5 MJ SMES and a 60 MWh BESS are connected to the system, the energy storage systems can avoid the energy congestion and make full use of the wind energy. The SMES and BESS systems are expected to work well in 30 years. In this way, the annual costs for the SMES and BESS systems are 57.33 k$ and 600 k$, respectively. Thus, their annual benefit is the saved wind curtailment of 1012 k$.

Therefore, we find that by introducing the network and energy storage systems (SMES, BESS), the overall cost is reduced as the curtailment of wind energy can be saved. However, with the given cost in Table 5, the investment of network is only 7% of the investment of SMES and BESS. This is mainly due to the fact that the cost of superconductor and battery is relatively high at the moment, but the cost is expected to decline. SMES would be more attractive in future because it can stabilize the output of wind farms. From this aspect, it can be inferred that SMES can save the cost of other devices which are required to stabilize the voltage and frequency for wind power penetration. Therefore, SMES can still be promising to be applied in wind farms with batteries.

Table 5 Annual cost and benefit

Items Cost (k$/year) Benefit (k$/year)Wind curtailment 1012 —Network investment 46.05 1012 Investment(SMES and BESS) 657.33 1012

5 Conclusions

This paper presents the recent development in MJ-scale SMES systems with the application to enhance the efficiency of a real wind farm. The technique is compared with the conventional network in terms of economic aspect to avoid the wind energy curtailment. From the study, the following conclusions are reached:

1) SMES is promising in stabilizing the output of wind energy output. Two connection points are analyzed in PSCAD/EMTDC and SMES can smooth the output of the wind energy better when it is close to the fault location.

2) A method to analyze the feasibility of applying SMES in the power system is proposed based on the annual aging model of the SMES system. The benefit for network investment is assessed in terms of the deferral of future reinforcement of components.

3) Compared with the traditional network, SMES currently is not competitive in construction costs but the overall benefit of SMES and BESS could be promising since they can stabilize renewable energy output together and the cost of the superconductor could continue declining with the technology advance.

Acknowledgements

This work is a cooperation research work both by China Electric Power Research Institute and the University of Bath, UK. It is funded by the National Key Research and Development Plan, Energy Storage Technology of 10MW Level Redox Battery (2017YFB0903504), China State Grid Corporation science and technology project (DG71-16-002,DG83-17-002) and the international cooperation project between China and United Kingdom, RAEng Newton Research Collaboration Programme of UK/1415134.