Synergetic optimization operation method for distribution network based on SOP and PV

0 Introduction

With the development of renewable energy generation technologies,more distributed generations (DGs) have been integrated into distribution network [1,2].Owing to the uncertainty,intermittence,and fluctuation of DGs,several problems emerge,including out-of-limit voltage and complicated power flow,threatening power quality and grid security [3,4].Thus,it is important to explore new optimization operation methods for distribution networks.

Owing to the growth of power electronics technology,several flexible intelligent devices have been integrated into distribution networks.This provides an effective means for eliminating the aforementioned problems.Soft open points (SOPs) are typical power electronic devices.They generally consist of two back-to-back-mounted voltagesource inverters.They are typically installed at the positions of traditional tie switches.SOPs are widely used in supply recovery [5,6],reliability assessment [7],and optimization of distribution networks [8,9].They have advantages such as load balancing and power flow control,which show great potential for optimizing system operation.In addition,SOPs can provide reactive power compensation.This improves the reactive power and voltage levels of the system.Furthermore,the consumption rate of distributed generation is promoted.

Aiming at different scenarios,researchers have developed and optimized different models for SOP.To improve the performance of an active distribution network against the uncertainty of renewable energy sources,Rezaeian-Marjani et al.[10] introduced a novel SOP model in the forward-backward load flow method.The optimization results comprehensively consider the active power losses,feeder load-balancing index,and investment cost of the SOP.Zhao et al.[11] proposed an optimization strategy and control technology for distribution networks with SOP,photovoltaics (PV),and battery energy storage systems.Coordinated operations and economic benefits were obtained.Zhang et al.[12] developed a two-stage robust optimization model to maximize the hosting capacity of PV generation for a distribution network with SOP and electric vehicles.An optimal scheduling scheme was obtained,and the dispatching of the SOP contributed to the improvement of the maximum hosting capacity of the PV generators.The present study indicates that SOP plays an important role in distribution network optimization operation.Although the SOP has a good optimization effect,coordination optimization with other devices shows great potential for the optimization of complicated distribution networks.

DG technology has developed rapidly in response to the requirements of sustainable development and environmental friendliness [13].Power electronics-based DGs can not only output active power but also control reactive power as long as they satisfy certain constraints [14].This is an excellent characterization that can be utilized in distribution network optimization operations.Rezaei et al.[15] proposed a decentralized method based on fuzzy logic to control the reactive power of DGs.Simulation results indicate that the proposed method can effectively control the voltage of the DG connection bus.Singh et al.[16] performed reactive power optimization for an integrated DG distribution system.Low power loss,low voltage variation,and low investment were achieved.Varma et al.[17] proposed the nighttime application of a PV solar farm to regulate voltage.The PV system could be used as a static synchronous compensator at night.Owing to the large ratio of resistance to reactance (R/X) of the distribution network,curtailment of the active power is beneficial for avoiding a voltage rise [18].However,the curtailment of active power decreases the consumption rate of DG and causes economic losses for DG owners [19].Calderaro et al.[20] proposed a coordinated local control approach based on a mixed distribution network sensitivity analysis.This control can regulate the reactive/active power to achieve voltage regulation and maximize the active power produced by the DGs.Thus,it is important to build an optimization model with proper constraints and objective functions.By comprehensively considering active power curtailment and reactive power compensation,DG optimization can be achieved.

The optimization operation of a distribution network is generally a multi-objective optimization problem.Two methods were used to prepare the solutions.One is to convert the multi-objective optimization problem into a single-objective optimization problem by adding a weight.This method is easy.However,the weight of each objective should be set,which leads to strong subjectivity.The other is to obtain the Pareto front using multi-objective intelligent optimization algorithms.Several multi-objective intelligent algorithms have been utilized,including enhanced artificial bee colony (EABC) algorithm [21],multi-objective particle swarm optimization (MOPSO) algorithm [22],teaching learning-based optimization (TLBO) algorithm [23],and non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) [24].

As mentioned previously,the SOP holds flexible control over the power flow.PV provides green electricity and reactive power compensation.The synergetic control of SOP and PV offers an effective means of improving power quality,particularly in the case of a large number of PVs in the distribution network.Thus,a synergetic optimization method for the SOP and PV was proposed.Considering the power quality,economy,and consumption rate of the PVs,the voltage deviation,network loss,and ratio of PV abandonment were selected as the objective functions in the optimization operation model.The SOP,PV,and load models were constructed.The PV model was improved by considering the three reactive power output modes of the PV inverter.Loss of the SOP and fluctuations of the PV and load were considered.Power flow and voltage amplitude constraints were introduced.NSGA-Ⅱ,NSGA-Ⅲ,and MOPSO were used and compared to solve the model.

The main contribution of this study is the proposal of a synergetic optimization model for the SOP and PV.To leverage the PV capacity and decrease the influence of the PV active power injection,the active power and reactive power output of the PV are both optimized.The PV model was improved by considering the three reactive power output modes of the PV inverter.To avoid excessive occupation of capacity by the reactive power output and ensure the consumption rate of PV,the ratio of PV abandonment was selected as an objective function.Thus,both voltage quality and environmental friendliness were considered.In addition,the synergetic optimization of the SOP and PV further improved the power control ability.The active power transmission of the SOP suppresses the power fluctuations of the PV system and load.The reactive power generation of the SOP complements that of the PV system for the reactive power compensation.The power quality was further improved.The proposed model provides a novel optimization operation method for flexible interconnected active distribution network.

The remainder of this paper is organized as follows.The modeling of the SOP,PV,load and optimization operations is described in Section 1.The solution algorithms and strategies are described in Section 2.Case simulations are presented and discussed in Section 3.Finally,the conclusions are presented in Section 4.

1 Establishment of optimization model

1.1 Function and model of SOP

The SOP is an important device for power flow control.Three pieces of equipment can accomplish the SOP function,including a static synchronous series compensator (SSSC),back-to-back voltage source converter (B2B VSC),and unified power flow controller (UPFC).Compared with the others,the B2B VSC can control the active and reactive power in four quadrants,leading to a wider power-flow control range.The B2B VSC was used for the subsequent analysis.

During normal operation,the SOP operates in the PQVdcQ control mode.The active power is exchanged among the ports.The law of conservation of active power should be satisfied as follows:

where is the active power of SOP injection in Port i and m is the quantity of SOP ports is the active power loss of the SOP at Port is the reactive power of the SOP injection in Port is the SOP loss coefficient.

For each Port i of SOP,the capacity constraints should be satisfied as

where is the rated capacity of SOP’s Port i.

1.2 Operation model of PV

PV systems transform solar energy into electricity.The active power output mainly depends on the light intensity,environmental temperature,and area of photovoltaic panel.The power-output curve of a PV system on a typical day is shown in Fig.1.As shown in Fig.1,the PV displays strong randomness and fluctuation.This fluctuation makes it difficult to optimize power flow,which lowers power quality.In addition to active power,the reactive power capability of PV inverters has attracted significant attention for voltage control [25].Thus,both the active and reactive powers of the PV were optimized in this study.

Fig.1 The power output curve of PV in a typical day

The active power of PV is limited aswhere is the active power output of is the active power of the maximum power point tracking for PVi.

The reactive power output of PV should satisfy the capacity constraint as

where is the reactive power output of PVi. is the capacity of PVi.

To analyze the influence of different reactive power output modes on optimization performance,three reactive power output schemes were compared and discussed.The reactive power output ranges of the three schemes are shown in Fig.2.The first scheme is the power-factor control scheme.The power factor of photovoltaic inverter was maintained above 0.95.The power factor constraint is defined as follows:

Fig.2 The reactive power output range of PV inverter with three schemes.The black line represents Scheme 1.The blue line represents Scheme 2.The red line represents Scheme 3

The second scheme is a direct reactive power output scheme.The reactive power output should not be more than±0.31 of the rated capacity.It can be described as

The third scheme is the night static var generator (SVG)scheme.The reactive power output can be described as follows:

Three reactive power output modes are designed using these three schemes in different combinations.Concrete information for the three modes is presented in Table 1.

Table 1 Three reactive power output modes

1.3 Model of load

The load of the power system is the electricity consumed by the electrical equipment.They are typically continuous,periodic,and random.Load characteristics are important for the regulation,operation,and management of power systems.To consider load fluctuations,resident,commercial,and industrial loads were analyzed and connected to the system.Typical load curves are shown in Fig.3.

Fig.3 Three typical load curves of resident,commercial,and industrial loads

The residential load mainly consists of the load on household appliances,which is closely related to daily life.As shown in Fig.3,the residential load ratio was distributed between 40 and 100%.The peak value is at 21:00,and the lowest value is shown at 5:00.The commercial load primarily included the illumination and power supply employed for business purposes.A typical commercial load exhibits a peak period from 13:00 to 19:00.Industrial load reflects the electricity consumption for industrial production.It has a large base value,and its fluctuation is relatively small compared with the resident and commercial loads.The industrial load keeps at peak during daytime.

1.4 Establishment of optimization operation model

1.4.1 Objective function

The intermittence and fluctuation of DG can cause an out-of-limit node voltage.This leads to poor power quality and restricts DG consumption.Thus,the voltage deviation(F1) was selected as the objective function.To improve the economic benefits,network loss (F2) was selected as another objective function.In addition,PV consumption represents the utilization of clean energy,which contributes to the development mode of low-carbon and environmental friendliness.To increase PV consumption,the ratio of photovoltaic abandonment (F3) was selected as the objective function.All three objectives were considered in the optimal operational model.The objective function is defined as follows:

1) Objective Function 1: The lower the voltage deviation for all nodes,the better the power quality.A lower voltage deviation also provides the potential for the integration of DG.The voltage deviation is defined as follows:

whereVi and ViN are the actual and rated voltages of node i,respectively,n is the quantity of nodes.

2) Objective Function 2: The lower the network loss,the more the economic benefit.The network loss is defined as

where Pij and Qij are the active and reactive power flowing through branch ij,respectively.Rijdenotes the resistance of branch ij.Ω0 is the set of all branches. represents the loss of all SOP ports.PBase is the network loss base value.

3) Objective Function 3: The smaller the ratio of PV abandonment,the better the consumption capability of the power system for PV.The ratio of PV abandonment is defined as:

wherePmaxrepresents the installed capacity of PV.P is the actual power output obtained from the solution.

1.4.2 Constraints

Three constraints were set to ensure the stable operation of the system and equipment security.

1) Power flow constraints [26]

Pi,t and Qi,t are the active power and reactive power injected into Node i,respectively.θij is the phase angle difference between nodes i and j.Gij and Bijis the conductance and susceptance between nodes i and j,respectively.Ωirepresents the set of nodes adjacent to Node i.

Considering the injection of PV,SOP,and load,Pi and Qi can be expressed as

where are the active and reactive loads of Node i,respectively and η is the efficiency.

2) Voltage constraints of nodes

The voltage of any node should be limited to ensure system security.Thus,the voltage constraint can be defined as:

whereVi,min and Vi,max are the allowed minimum and maximum voltages of Node i.

3) Operation constraints of equipment

During operation,the SOP should satisfy the capacity and active power balance constraints of each VSC.The constraints are expressed in Equations (1) -(3).

For the optimization of the PV,the maximum power point tracking and capacity constraints should be satisfied,as shown in Equations (4) and (5).For the different reactive power output modes of the PV inverter,the constraints were set as listed in Table 1.

In the optimization operation model,the decision variables include the active power output and reactive power output of each PVi,the active power flowing among the SOP ports,and the reactive power output of each SOP port.To satisfy these constraints,the decision variables were synergistically optimized to improve the voltage quality and PV consumption rate.

2 Solution Strategy to optimization model

The solution to the proposed optimal operation model is a multi-objective optimization problem.Three multiobjective optimization algorithms were used and compared.The optimal compromise solution is comprehensively evaluated and selected from a Pareto solution set.

2.1 NSGA-II algorithm

NSGA-Ⅱ (non-dominated sorting genetic algorithm-Ⅱ)is a popular multi-objective optimization algorithm based on genetic algorithm.NSGA-Ⅱ achieves individual’s selection and evolution according to non-dominated sorting and crowding distance.Thus,a set of Pareto-optimal solutions was obtained.The convergence and search efficiency are improved by maintaining the diversity and distribution of the population.

The solving process of the optimization model based on NSGA-Ⅱ is listed as follows.

1) Initialization: Randomly generate N initial solutions as the first population.

2) Calculate the objective function value and power flow of each individual in the population.Check whether the individual satisfies the constraints.Make amendments to individuals who do not satisfy the constraints.Calculate the objective function value according to the power flow calculation results.

3) Non-dominated sorting: Non-dominated sorting according to the individual fitness.

4) Selection of crossover and mutation: Calculate the degree of crowding.Filter out the dominant individuals.Complete crossover and mutations in selected individuals.Thus,a subpopulation is obtained.

5) Population combination: Combine the subpopulation with the initial population.Calculate the objective function value,non-dominated sorting,and degree of crowding of the synthetic population.

6) According to the calculation results,select the former N individuals of the synthetic population as the next generation.

7) Repeat steps (2-6) until the stopping condition is satisfied.

2.2 NSGA-III algorithm

The non-dominated sorting genetic algorithm-Ⅲ(NSGA-Ⅲ) is a modified version of NSGA-Ⅱ.By setting the position of the reference point,NSGA-Ⅲ can generate a uniformly distributed solution set,thereby avoiding the problem of excessive crowding.Environmental selection factors were used to maintain population diversity.Hierarchical cluster sorting was used to address highdimensional problems.Thus,the convergence and solving efficiency of the algorithm were improved.

The application of NSGA-Ⅲ is similar to that of NSGA-Ⅱ.This difference exists in the setting of the reference points.A reference point was set at the beginning of the solution process.After the population combination,the next generation was selected according to the reference point set.

2.3 MOPSO algorithm

The MOPSO was developed from PSO.The solution space was searched by updating the particle swarm.A single particle is a potential solution to this problem.The particles search for the target space during the optimization process.The core scheme of MOPSO uses a non-dominated set to update the frontier solution,avoiding falling into a local optimum.Compared to PSO,MOPSO improves the selection method of pbest and gbest for multi-objective problems.The application process is described in Ref.[22].

2.4 Comprehensive evaluation of solutions

Using the above three multi-objective optimization algorithms,the Pareto optimal solutions for the model can be obtained.A comprehensive evaluation was conducted to determine the optimal compromised solution.The promotion ratio was used for the weight setting,leading to a weighted average for a compromised optimal solution.The procedure is as follows:

1) Calculate the promotion ratio for each solution as follows.

whereµ is the promotion ratio.finitial is the initial objective function value before optimization.fsolution is the objective function value obtained after optimization.The promotion ratio of each solution to each objective function was calculated and listed in a promotion ratio matrix.

2) Calculate the normalized promotion ratio.The normalization is defined as

whereµnormal is the normalized promotion ratio.µmax is the maximum promotion ratio in the promotion ratio matrix.µminis the minimum promotion ratio.

3) For all the optimal solutions,the normalized promotion ratios of each objective function at each solution position were calculated.The promotion ratio for each objective function is obtained by summing and averaging.The weight of each objective function can be set according to subjective decisions or objective weighting methods.A compromised optimal solution is obtained using a weighted calculation.

3 Test Case

3.1 Setting of cases

The IEEE 33-node model is used as the initial system in this study [27].The voltage level was 12.66 kV.The total active load was 3715 kW.The total reactive load was 2300 kVar.Five cases were considered to analyze the performance of the proposed method.The five cases are illustrated in Fig.4.The schemes for the five cases are listed in Table 2.The load types and connection locations are listed in Table 3.The simulations were performed using MATLAB on the PlatEMO [28].Except for the particularly mentioned part,NSGA-Ⅱ is utilized for solving the optimization model.

Table 2 Schemes of five cases

Table 3 Connection locations of different load types

Fig.4 System structure of five cases

3.2 Analysis of distribution network without optimization

With the increasing access of PVs to the distribution network,the voltage distribution changes.Case 2 was simulated to evaluate the influence of the access of PVs.The voltage results at certain time points are shown in Fig.5.

Fig.5 Voltage distribution with the access of different PV capacities.The PV capacities are 0 (a),500 kW (b),1000 kW (c),1500 kW (d) and 2000 kW (e)

As shown in Fig.5,the node voltage increases with the connection of PV.Because the PV power is injected at Node 18,the voltage shows a maximum increase at Node 18.With an increase in the PV capacity,the node voltage increases continually and shows the possibility of an outof-limit voltage.An increase in voltage deviation lowers the power quality and destroys the secure operation of the power system.Therefore,it is important to develop an optimized operation method.

3.3 Analysis of SOP optimization

Case 3 was used to test the performance of the SOP optimization.A certain time point was then analyzed.The active power of a PV system is the capacity.The reactive power of the PV system was not considered.Thus,the value of Objective Function 3 is zero.Only the voltage deviation and network loss were calculated as objective functions.The simulation results of active power optimization of the SOP are shown in Fig.6.The color of the section reflects the relative value of the section loss.For example,the color of Section 1-2 represents the loss of Section 1-2.The color of Section 18-19 represents the loss of Section 2-19.

Fig.6 Voltage distribution before (a) and after (b) SOP active power optimization

Fig.7 Voltage distribution of before (a) and after (b) SOP reactive power optimization

In the optimization of only active power of the SOP,the optimization result is that 248.9918 kW of active power flow from Node 33 to Node 18.The voltage deviation decreases from 1.7009 to 1.0029.The reduction ratio of the voltage deviation is 41.04%.The network loss decreases from 0.2027 to 0.1264.The reduction ratio of network loss was 37.64%.The optimization of the SOP active power yielded a comprehensive improvement of 0.5889.Both voltage deviation and network loss were reduced.

Figure 7 shows the simulation results for the optimization of the SOP active and reactive power.The active power optimization result was 24.4013 kW from nodes 18 to 33.The reactive power optimization results were 499.3878 kVar and 401.8632 kVar for the two ports of SOP.The capacity of the SOP ports was fully utilized.Compared to the results obtained using only active power optimization,the voltage deviation decreased from 1.0029 to 0.7036.The network loss decreased from 0.1264 to 0.0892.This indicates that optimization of the reactive power further improves the power quality.

3.4 Analysis of synergetic optimization with SOP and PV

In addition to the SOP,a PV inverter can provide reactive power compensation.Mode 1 is used as the reactive power output mode of the PV inverter.To satisfy the maximum power point tracking (MPPT) limitation,capacity constraints,and power-factor constraints,the active power and reactive power of the PV are both set as optimal variables.A synergetic optimization of the SOP and PV for Case 3 was performed.The weight of the comprehensive solution is set to [1/3,1/3,1/3].The synergetic optimization results are listed in Table 4.The results for the voltage and network losses are shown in Fig.8.

Table 4 Synergetic optimization results of SOP and PV

Fig.8 Voltage distribution with SOP optimization (a) and synergetic optimization (b)

Compared to the SOP optimization results,the voltage deviation decreased from 0.7036 to 0.6775.The network loss decreased from 0.0892 to 0.0852.The ratio of PV abandonment increased from 0 to 0.0117.Although the active power output of the PV system was reduced,the reduction was small.Simultaneously,the active and reactive power outputs of the SOP did not reach the capacity limitation of each port.This indicates that synergetic optimization holds further regulatory ability for optimal operations.Thus,the proposed synergetic optimization method improved the control flexibility of the distribution network.Thus,the power quality improved.

3.5 Analysis of synergetic optimization considering continuous power flow

The optimization at a certain time point only shows the optimization effect of a moment.An optimization operation under continuous power flow was performed to evaluate the optimization effect during a period.Owing to the fluctuations and uncertainties of the PV system and load,the operating conditions are generally different during a period.Optimization under continuous power flow will better reflect the actual operation.Simulation of a typical day was performed for Case 3.The PV maximum power point data are shown in Fig.1.The curves and connecting locations of the loads are presented in Fig.3 and Table 3.

To fully utilize the capacity of the PV system,the three reactive power output modes of the PV inverter were compared and discussed.A comparison of the results is presented in Table 5.With the release of the reactive power output limitation,voltage deviation and network loss are reduced.Although the ratio of PV abandonment has increased,this increase has been limited.A comprehensive improvement was achieved.Thus,Mode 3 of the reactive power output was used in this study.

Table 5 Comparison of three PV reactive power output modes.The values are the average value of 24 hours

The simulation results for Mode 3 are shown in Fig.9,10,11,and 12.

Fig.9 Voltage amplitude of the initial system

The voltage amplitude of the initial system is shown in Fig.9.The integration of PVs causes large voltage fluctuations.The voltage is outside the limit at a certain time and node.This leads to poor power quality and limits the PV consumption rate.With the integration of the SOP and synergetic optimization,the voltage deviation was evidently reduced,as shown in Fig.10.The frequency of the out-oflimit voltage was reduced.

Fig.10 Voltage amplitude with synergetic optimization of SOP and PV

The optimization results of the PVs systems are shown in Fig.11.During daytime,the main function of the PV system is to output active power to support the load.Because of the large amount of active power injection,the voltage level increases.To decrease the voltage deviation,reactive power is output by the PV.The reactive power output reduces active power output.However,because PV abandonment was set as an objective function,the active power output was only slightly smaller than the active power of the MPPT.At night,the PV inverters are operated in the SVG mode,which provides reactive power compensation for the network.Thus,the PVs reactive power compensation stabilizes the node voltage and lowers the network loss.Thus,the comprehensive power quality was improved.

Fig.11 Optimal scheduling scheme of three PVs

The SOP dispatching is shown in Fig.12.As shown in Fig.12(a),from 0:00-9:00 and 17:00-24:00,active power flows from Node 33 to Node 18.During the daytime,the active power gradually flowed from Node 18 to Node 33.This can be explained by the injection of three PVs.The active power imbalance caused by the PV generation is relieved by the SOP through active power control.This indicates that the SOP can dynamically adjust according to changes in the PV output.

Fig.12 Optimal scheduling scheme of SOP

The reactive power compensation of the SOP is shown in Fig.12(b).The reactive power output of the VSC at Node 33 approaches the capacity constraints of the port.The reactive power compensation of the VSC at Node 18 was lower than that at Node 33.The capacity of the VSC at Node 18 was abundant.Because of the PV active power output increase at 12:00-14:00,the reactive power output of the VSC at Node 18 increases.This indicates that the SOP and PVs cooperate and complement each other.Thus,synergetic optimization results in improved operational dispatching and power quality.

3.6 Comparison of SOP with different port numbers

The influence of the SOP port number was simulated for cases 3,4,and 5.The comparison is displayed in Table 6.With an increase in port number,the voltage deviation decreases.This indicates that an increase in the number of ports improves the flexibility of power control,which contributes to the promotion of power quality.However,the network loss reaches a minimum for an SOP with three ports.When the number of ports increases continuously,the network loss increases.This is because more ports result in greater SOP loss.When considering the loss of SOP,the entire SOP loss is the summation of loss in each port.The loss in each port was proportional to its power output.Thus,an increase in the number of ports adds to the network loss.The ratio of PV abandonment remained constant.However,owing to the high investment and operational costs of SOP,the actual selection of the SOP port number requires concrete consideration.

Table 6 Comparison of different SOP port numbers

3.7 Comparison of different optimization algorithms

Three multi-objective optimization algorithms were used to compare the applicable algorithms for the model solution.The optimization results for Case 3 are listed in Table 7.

Table 7 Comparison of using different algorithms

It can be observed that the performance of NSGA-Ⅱ and NSGA-Ⅲ is evidently better than that of MOPSO.Owing to the synergetic optimization of the SOP and PV,the search space of the algorithm is larger.The global searching ability of MOPSO is limited.The optimization effect worsens with an increase in optimization variables.The NSGA-Ⅱ and NSGA-Ⅲ shows very similar optimization effect.Thus,NSGA-Ⅱ and NSGA-Ⅲ are more applicable to the cases in this study.

4 Conclusion

In this study,a synergetic optimization method for SOP and PV in a distribution network is proposed.A multiobjective optimization model was developed.The multiobjective was set to minimize the voltage deviation,network loss,and ratio of PV abandonment.Power flow constraints,voltage constraints,and SOP and PV operation constraints were considered.In particular,the PV model was improved by considering three reactive power output modes of the PV inverter.Three multi-objective optimization algorithms were used and compared to obtain the Pareto optimal solution set.The optimal compromise solution is selected according to the promotion ratio.Both cases were simulated at one time point for a continuous period.Comparisons of port numbers and algorithms were discussed.The synergetic optimization of the SOP and PV enhances the power control capability and flexibility,leading to improved power quality and PV consumption rate.Although a small amount of the active power of the PV system is sacrificed for reactive power compensation,the voltage deviation and network loss are significantly reduced.The proposed method shows the potential for flexible power control in DG-integrated distribution networks.

Acknowledgments

This work was supported by the Science and Technology Project of SGCC (kj2022-075).

Declaration of Competing Interest

We declare that we have no conflicts of interest.