PSO Based Frequency Controller for Wind- SolarDiesel Hybrid Energy Generation/Energy Storage System Dulal Ch Das, A K Roy, N Sinha, Senior Member, IEEE Abstract— The aim of this paper is the tuning of a PI controller using PSO techniques for autonomous hybrid energy generation/energy storage system. The autonomous hybrid generation system consisting of wind turbine generators (WTG), solar photovoltaic (PV), diesel engine generators (DEG), fuel cells (FC), battery energy storage system (BESS), ultra capacitors ( UC) and aqua electrolyzer (AE) has been considered for simulation studies. The power system frequency deviates for sudden changes in load or generation or the both. The comparative performance of the controllers installed to alleviate this frequency deviation for different hybrid systems, is carried out using time domain simulation. In practice, PI controller is tuned manually which is difficult and time consuming. The computational intelligence has opened paths to a new generation of advanced process control. Here, PSO is used for optimization of controller gains of the proposed hybrid system. The simulation results demonstrate the effectiveness of the PSO based controller in terms of reduced settling time, overshoot and oscillations. The results are compared with conventional controller.
Keywords-Particle Swarm Optimization, Aqua electrolyzer, Fuel cell, Diesel engine generator, Battery energy storage system, Wind turbine generator, Solar photovoltaic (PV). NOMENCLATURE Δf Ksys
System frequency deviation. Frequency characteristic constant of hybrid power system. GSYS(s) Transfer function of hybrid power system. PDEG Output power of diesel generators. GDEG(S) Transfer function of diesel generator. KDEG Gain of diesel generator. TDEG Time constant of diesel generator. Output power of fuel-cell generators. PFC KFC Gain of fuel cell. TFC T ime constant of fuel cell. GFC(S) Transfer function of fuel-cell generators. PBESS Power of battery energy storage system. _________________________ Dulal Ch. Das is Assistant Professor with Electrical Engineering Department, NIT Silchar, Assam, e-mail:
[email protected] A K Roy is Professor with Electrical Engineering Department, NIT Silchar, Assam, e-mail:
[email protected] N Sinha is Professor with Electrical Engineering Department, NIT Silchar, Assam, e-mail:
[email protected]
978-1-4673-0136-7/11/$26.00 ©2011 IEEE
GBESS(S) KBESS TBESS GUC (S) TUC GAE (S) PAE KAE TAE PS PL
Transfer function of battery energy storage system. Gain of battery energy storage system. Time constant of battery energy storage system. Transfer function of ultra cpacitors . Time constant of ultra capacitors. Transfer function of aqua electrolyzers . Output power to aqua electrolyzers. Gain of the aqua electrolyzer. Time constant of the aqua elctrolyzer. Total power generation to the system. Average power absorbed by loads. ΔPe Error in power supply and demand. MS Inertia constant of the hybrid power system. D Damping constant of the hybrid power system. PWTG Power output of wind generator. GWTG(S) Transfer function of wind generator. KWTG Gain of wind generator. TWTG Time constant of wind generator. PPV Output power of solar photovoltaic system GPV(S) Transfer function of solar photovoltaic TPV Time constant of solar photovoltaic Abbreviations or subscripts PSO Particle swarm optimization AE Aqua-electrolyzer DEG Diesel-engine generator WTG Wind-turbine generators FC Fuel-cells BESS Battery energy storage system PS Power system PV Solar photovoltaic
I
I.
INTRODUCTION
n recent years the increasing concerns about the limited fossil fuel resources, their impact on the environment, especially the global warming and the harmful effects of carbon emissions have created a new demand for clean and sustainable energy sources. Wind and solar power generation are two of the most promising renewable power generation technologies. FCs also have potential to be considered as one of the green power sources of the future. However, each of the aforesaid technologies has its own drawbacks. For instance, wind and solar power are highly dependent on climate while FCs need hydrogen-rich fuel. Nevertheless, because different alternative energy sources can complement each other to some extent, multisource hybrid alternative energy systems (with proper control) have great potential to provide higher quality and more reliable power to customers than a system based on a single resource. Because of this
feature, hybrid energy systems have caught worldwide research attention [1].A hybrid system can supply power AC or DC or both [2]. Component or system control or both is used to regulate the overall system operation. In this paper, an autonomous hybrid generation system consisting of WTG, PV, DEG, FC, AE, UC, and battery energy storage system is proposed. The hybrid system will accrue the full benefit of WTG and PV. The FC–electrolyzer combination is used as a backup and a long-term storage system. UC and battery bank are used in the system for shorttime backup to supply transient power. The power system frequency deviates for sudden changes in load or generation or the both. The controllers are installed before the sources to alleviate this frequency deviation. Various optimization approaches, such as, genetic algorithm, particle swam optimization, artificial neural network, are applied to optimize the gains of the controllers used in automatic generation control. These techniques have not yet been reported to apply in the field of hybrid energy systems for optimization of controller gains. In[3]-[5] hybrid system studies proportional plus integral (PI) controller is used to regulate the output powers from distributed generation system to achieve power balance condition due to sudden variations in generation and load. The gain values of PI controller are chosen by trial and error method. In [6] the conventional PI controller has traditionally been tuned by the method described in Ziegler and Nichols. The controller gains once tuned for a given operating point are only suitable for limited operating point changes. Therefore, the use of the conventional PI controller does not meet the requirements of the robust performance [6]. Moreover, when the number of parameters to be optimized is large, conventional technique for optimization is certainly not preferred one. In this article, PI controllers are tuned using PSO technique. The results of applying the PSO based PI controller to the hybrid-power system are compared to those obtained by the application of a conventional PI controller. Simulated results show that the PSO based controllers provide improved dynamic performance than fixed gain conventional controllers. The PSO based controllers also show better transient performance for load disturbances. II. PROPOSED HYBRID SYSTEM The general block diagram of the proposed hybrid system is shown in Fig.1. The system consists of wind turbine generators, diesel generator, fuel cell, aqua electrolyzer, solar thermal and battery energy storage system. The power supplied to the load is the sum of output powers from wind turbine generators, diesel generator, fuel cell and battery energy storage system. The aqua electrolyzer is used to absorb the fluctuations of wind turbine generator and produce the hydrogen gas which is used as input to fuel cell generator. The mathematical models with first order transfer functions for wind turbine system, fuel cell, aqua electrolyzer, PV system, diesel engine generator are shown in this section. A. Wind Turbine Model The output of wind turbine generator depends on the wind speed at that instant. The characteristic of wind turbine generator is illustrated in [3].
The wind turbine system contains several nonlinearities. When a wind turbine uses its pitch controller to counteract utility grid frequency oscillations, its output power varies between maximum, or rated power, and zero power. Hence, WTG PS AE
FC ±
±
PS
DEG
Δf
BESS
Fig.1. Block diagram of hybrid system the pitch angle setpoint is nonlinearly limited by these boundaries. The pitch system, which turns the pitch angle according to wind speed, introduces a nonlinearity. The wind turbine can be simplified to a first order system. The transfer function of the WTG is represented by a first-order lag [7] as
GWTG ( S ) =
KWTG sTWTG + 1
(1)
B. Photovoltaic power system PV power supplied to the utility grid is gaining more and more visibility, while the world’s energy demand is steadily increasing. With reduction in the system cost (PV modules, dc/ac inverters, cables, fittings and manpower), the PV technology has the potential to become one of the main renewable energy sources for the future electricity supply [8]. The characteristic of PV system is illustrated in [9]. Large PV system generates dc voltage that is converted into ac using dc-ac converter.For extracting maximum power, under a given irradiance and cell-surface temperature, a PV array should operate near at the peak point of the V−P curve. Various MPPT techniques have been discussed in [10]. Power output (in Watts) of a PV array which varies with irradiance and cell-surface temperature, of a PV system is illustrated in [7] For low frequency domain analysis it is represented by a first order lag transfer function model given as [7]
GPV ( S ) =
K PV TPV s + 1
(2)
C. Diesel generator A diesel generator is a nonlinear system because of presence of a nonlinear, time-varying dead time between the injection and production of the mechanical torque. The diesel generator is modeled by a simple first order transfer function given by [7]
GDEG ( S ) =
K DEG TDEG s + 1
(3)
D. Aqua electrolyzer Aqua electrolyzers are used to absorb the rapidly fluctuating output power from wind turbine generators and solar photovoltaic power system and generate hydrogen. The generated hydrogen is stored in the hydrogen tank and used as fuel for fuel. The decomposition of water into hydrogen and oxygen can be achieved by passing the electric current between the two electrodes separated by aqueous electrolyte. The transfer function model of aqua electrolyzer can be expressed by [7]
G AE ( S ) =
K AE TAE s + 1
(4)
E. Fuel cell Fuel cell generates power through the electrochemical reaction between hydrogen and oxygen. Fuel cell is discussed in [21] Fuel cell generator is a higher order model and has non linearity. For low frequency domain analysis it is represented by a first order lag transfer function model given as [7]
K FC GFC ( S ) = TFC s + 1
(5)
F. Battery energy storage system The short time power fluctuation from wind, solar causes large problems for power systems operation. A possible solution is storage of wind energy. Due to very good technical characteristics (large energy density, fast access time) the battery energy storage system has been an effective energy storage technology to store large amount of wind energy [11]. They can supply the system with a large amount of the power in a short time, or large amount of energy for a longer period. A higher power capacity can be achieved by connecting more modules. The transfer function model of battery energy storage system expressed by first order as [7]
GBESS =
K BESS TBESS s + 1
(6)
G. Ultracapacitor Ultracapacitor or supercapacitor also called double layer capacitor, is an emerging device for energy storage. It stores charge in a double layer formed on a large surface area of micro-porous material such as activated carbon [12] It has specific energy in the range of 1-10Wh/Kg and high specific power in the range of 1000-5000 W/Kg. The charge /discharge efficiency (85%-98%) and rate of discharge (0.330s) very high. It offers large capacitances in the order of thousands of farads but at a low voltage of about 2.5V [13]. The combination of ultracapacitors and batteries can take the advantage of each kind of device to yield a power source of high power capability and longer run time. Neglecting all the non-linearities, the transfer function of ultracapacitor is given by first order lag:
GUC ( S ) =
KUC TUC s + 1
(7)
H. Power, frequency deviationsand control strategy In order to provide good quality of supply to the consumers it is very important maintain the scheduled frequency under varying demand and supply conditions. Frequency can be maintained at desired level by maintaing the active power balance between the generation and demand. A hybrid system with wind/solar /solar thermal as one of the generating unit requires special control strategies because of highly fluctuating nature of wind. The strategies to be adopted to alleviate mismatch between generation and demand can either be by controlling the fuel to diesel electric power-generating unit or by rescheduling. The conventional approach normally uses a PI or PID controller. The use of PSO based frequency control is more efficient method. In this paper power control strategy is obtained by difference between the power demand reference PL and total power generation PS. ΔPe = PS − PL (8) Because system frequency is changed with net power variation, the system frequency variation Δf is calculated by
Δf =
ΔPe Ksys + D
(9)
Since an inherent time delay exists between system frequency variation and power deviation, the transfer function for system frequency variation to per unit power deviation can be expressed by [7]
Gsys ( s ) =
1 1 Δf = = ΔPe Ksys (1 + sTsys ) Ms + D
(10)
In this paper PI controllers are equipped for each generator, aqua electrolyzer, ultracapacitor and battery energy storage device. Input to each controller is the sum of the error in supply demand and product of frequency deviation of power system and the gain. By controlling ∆Pe and ∆f, the system can supply high quality power to the load. TABLE I PARAMETERS OF PROPOSED HYBRID SYSTEM Gains
KWTG=1.0
KAE =-1/500 KDEG=1/300
Time constants(sec) TWTG=1.5 TAE=0.5 TDEG=2
Gains
KBESS=-1/300 KUC =-7/10 KPV=1
Time constants(sec) TBESS=1.0 TUC=0.9 TPV=1.8
KFC=1/100
TFC=4
-
-
I. Particle Swarm Optimization Particle Swarm Optimization (PSO) is a population based optimization technique developed by Russell Eberhart and James Kennedy in 1995, [14], [15], [16] inspired by the swarming or collaborative behavior of bird flocking and fish schooling. A number of papers have been published in the past few years that focus on the application of PSO [15]-[20]. In this paper the following PSO parameters are used to verify the performance of the PSO-PI controller. Population size: 50; ωmax =0.99; ωmin=0.85; C1 =0.12; C2 =1.2; Iteration: 50; In PI controller design methods, the most common objective function are integrated absolute error (IAE), the integrated of
time weight square error (ITSE) and integrated of squared error (ISE) etc. These three integral performance criteria have their own advantages and disadvantages. In this paper, we consider integral of the square of frequency deviation as the objective function. ISE = ∫ (∆f)2dt (11) The Fitness function is the objective function we want to minimize. By minimizing the fitness function we get the optimal parameters of PI controllers. III. SIMULATION RESULTS AND ANALYSIS In this section, dynamic performances of the proposed hybrid generation systems are analyzed using time-domain simulation. The responses of different combinations under various operating points and disturbance conditions are presented with optimum gain settings of classical and PSO based PI controllers respectively. This has been carried out under the following two cases as shown in table II.
120
PWTG=0.5 p.u.at 040s PPV=0.2 p.u. at 0< t≤40s =0.1 p.u at t>40s PL=1 p.u. at0< t≤40s
=1.3p.u. at t>80s 2.
WTG, PV,AE,FC DEG,BESS , UC & Load
120
The power system frequency fluctuates due to these sudden changes in power generation by wind and solar photovoltaic and load demand. This deviation in frequency is controlled by PI controllers and the outputs of system components are automatically adjusted to corresponding values such that the error in supply demand and the deviation in frequency are minimum. Fig. 2(d) shows that when wind power decreased from 0.5 p.u to 0.4 p.u., solar photo voltaic power reduced from 0.2 p.u to 0.1 p.u and the load increased from 1 p.u to 1.3 p.u suddenly, the power outputs of the fuel cell, battery storage energy, and diesel generators are also increased to different values for different controllers, as shown in Fig. 2(c). This in turn alleviates the mismatch in generation and demand. Frequency deviation of system and the total power generation are shown in Fig. 2(a) and Fig.2 (b) respectively. Finally the frequency settles to a steady state value. Fig. 2(e) shows the ISE for classical and PSO technique. The gain values of the controllers obtained through classical and PSO technique and are given in TABLE III. These results shows that the PSO based tuning is better than the classical one and capable of successfully adapt the controller to physical plant dynamic characteristics changes. Frequency deviation (Hz)
WTG, PV, AE ,FC, DEG, BESS, UC & Load
Operating conditions
Simulation Time (s)
1.
Subsystems
Case
TABLE II SIMULATION CONDITIONS FOR EACH CASE
demand ultracapacitor is incorporated in the hybrid system. The net power generation in this case may be expressed as PS = PDEG + PWTG + PPV + PFC − PAE ± PBESS ± PUC (12)
Randomly variable WTG, PV & Load
-1 -1.5 -2
0
20
40
60
80
100
120
X: 80.09 Y: 0.08056
0.1 0 -0.1
PI PSO
-0.2 -0.3 -0.4
79
J. Time-Domain Analysis :case1 In order to examine the fact that ultra capacitor delivers high power within a short duration of time during peak load
79.5
80
80.5 Time(s)
81
81.5
82
82.5
Fig.2. (a)
Case2
1.5
1
S
PSO -460.5 -245.4 1701.7 -83.4 619.2 148.8 -1042.8 -1025.0 -940 5.9
PI Classical
X: 80.05 Y: -0.5592
-0.5
P (p.u)
Gains KpAE KiAE KpFC KiFC KpDG KiDG KpBESS KiBESS KpUC KiUC
Case1 PI controller Classical PSO -290.399 -440.3 -15.4018 -306.9 69.348 1027.8 33.285 115.2 338.348 311.6 71.285 447.8 -284.351 -1155.4 -112.684 -299.9 -22.28 -178.9 -0.341 -3.4
0 -0.5
0.2
TABLE III. GAINS OF PI CONTROLLERS Case
0.5
0.5
0
0
20
40
60 P S (p.u)
80
100
120
1.4 PI PSO 1.2 PI Classical
1 79.8
80
80.2
80.4 Time (s)
Fig.2. (b)
80.6
80.8
81
PI PSO
0.2
PI Classical
0 0 0.2
20
40
60
80
100
120
P
FC
0 -0.2
20
40
60
0.5
80
PI PSO
P (p.u)
0 0 0.5
20
40
60
100
120
PI Classical
BESS
(p.u)
PI Classical
PI PSO (solid line)
-0.4 0
It may be observed that there is a very small difference between the frequency responses obtained under two different operating conditions. This in fact shows the robustness of the controller.
80
100
120
Frequency deviation (Hz)
(p.u)
DEG
P (p.u)
0.4
With gains optimized under varying wind, solar and load (solid line)
0.2 0 -0.2
With gains optimized using step change in wind, solar and load
-0.4 -0.6 -0.8 0
0.5
1
1.5
2
2.5
3
3.5
4
0
PI PSO PI Classical
P
UC
0.2
P
AE
(p.u)
-0.5
0 -0.2
0
20
40
60
80
100
120
0.2
-0.4
0
PI Classical
PI PSO
0.1 20
40
60 Time (s)
80
100
60 Time (s)
80
100
120
1 P UC (p.u)
1.2
With gains optimized under varying wind, solar and load
PAE (p.u)
0.6
Wind
0.4
20
40
60 Time (s)
80
100
120
Fig.2. (d) PI Classical
0.2
P FC (p.u)
ISE
PI PSO
60 Time (s)
80
120
100
20
40
60
80
100
120
0
20
40
60
80
100
120
With gains optimized under varying wind, solar and load
0 -0.5
40
100
With gains optimized under varying wind, solar and load
0.5
0.05
20
80
With gains optimized using step change in wind, solar and load
0
0.2 0.15
0
60
With gains optimized using step change in wind, solar and load 0 0.4 0
0.25
0.1
40
With gains optimized under varying wind, solar and load
PDEG (p.u)
0
20
0.05
Solar photovoltaic
0.2
0
0.1
Load demand
0.8
With gains optimized using step change in wind, solar and load
0.5
0
1 Power (p.u)
40
120
1.4
0
20
Fig. 3. (a) 0
Fig.2. (c)
0
0
With gains optimized using step change in wind, solar and load 0
20
40
120
60 Time (s)
80
100
120
Fig. 3. (b)
Fig.2. (e) 1
Load demand
Power (p.u)
0.8 0.6
Wind
0.4 Solar
0.2 0
0
20
40
60 Time (s)
80
100
120
Fig.3. (c) 0.06
0.055
ISE
K. Time-Domain Analysis :case 2 In the first case study it has been observed that PSO based controller better than classical based controllers. Therefore, in second case study PSO based PI controllers are employed. In order to examine the effects of practical variable wind, solar photovoltaic and load power on dynamic performance of the hybrid system, the variable models of these generating systems are employed. These models could not discuss in this paper. The power system frequency fluctuates due to changes in power generation by wind and solar photovoltaic and load demand. This deviation in frequency is controlled by PSO based PI controllers and the outputs of system components are automatically adjusted to corresponding values such that the error in supply demand and the deviation in frequency are minimum. In order to examine the robustness of the controllers, their performance with their gains optimized using step change load, wind power, solar photovoltaic power are compared with the controllers with their gains optimized with variable load, wind and solar photovoltaic power. Simulation results under varying load, wind and solar photovoltaic power condition are shown in Fig.3. (a)-Fig.(d).
With gains optimize using step change in wind, solar and load X: 7.891 Y: 0.0481
0.05
0.045
0.04
X: 7.619 Y: 0.04647
0
20
With gains optimized under varying wind, solar and load
40
60 Time (s)
80
100
120
Fig.3. (d) IV. CONCLUSION The autonomous hybrid generation/energy storage system requires an automatic generation control system to eliminate
the mismatch in supply and demand under varying condition of load and generation. In this paper, in order to reduce the frequency deviation i.e., eliminate the mismatch in supply and demand under varying condition of load and generation, the output power from the sources is regulated using PI controllers. The gains of these controllers are designed by using classical method and PSO. Performance of each controller is examined from dynamic behaviour in timedomain simulations of the system in isolated mode of operation. The simulation results show that PSO-based optimization technique is much better to enable automatic generation control. It may be noted that for real time tuning a simulator may be used to simulate the power system. Because of the nature of the power systems, online tuning of the PI controller is not possible. IV. ACKNOWLEDGMENT Authors wish to thank Electrical Engineering Department, NIT Silchar, for providing the necessary facilities for completing this work. V. REFERENCES [1] [2]
[3]
[4]
[5] [6] [7]
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[11]
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VI.
BIOGRAPHIES
Anjan Kumar Roy received the Ph. D. degree in Electrical Engineering from Indian Institute of Technology (IIT), Kharagpur, India, in 1991. He is currently professor in the Department Electrical Engineering, National Institute of Technology Silchar, India. His research interests include Electrical Machines, Electronics, Electric Drives and FACTS. Nidul Sinha received his B.E. degree in electrical engineering from Calcutta University, in 1984 and M.Tech. degree in power apparatus and systems from Indian Institute of Technology, New Delhi, in 1989. He received his Ph.D. degree in electrical engineering from Jadavpur University. His research interests include application of soft computing techniques to operation, control and economics of electrical power systems, deregulation and optimization. He is a reviewer of IEEE PWRS, PWRD, PESL, IEE part-c, EPSR, and DSP (Elsevier). Dulal Ch. Das received the M.Tech degree in Power and Energy System Engineering from National Institute of Technology Silchar, Assam, India, in 2008. He is currently Assistant Professor in the Department Electrical Engineering, NIT Silchar, India. He is pursuing PhD Degree in the Department Electrical Engineering, NIT Silchar, India. His research interests include distributed generations, and application of soft computing techniques in power system control and operation.
