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Optimal Allocation of STATCOM with Energy Storage to Improve Power System Performance E. Ghahremani and I. Kamwa, Fellow, IEEE Abstract—The production cost of electrical energy has created a need to find reliable, cheap and accessible sources for energy generation. As a result, the use of alternative sources such as wind and solar energy is in rapid growth world-wide. Nowadays these renewable energies are always combined with energy storage systems (ESSs) to save extra energy production and keep the production level below a specific limit. An ESS could also be combined with a STATCOM in a power system. This combination could add the benefits of an ESS to the advantages of a FACTS device such as reduced power flows on overloaded lines, resulting in increased system loadability, lower transmission line losses, improved power system stability and security, lower power production costs, and more secure bus voltage levels. This paper presents a genetic algorithm-based optimization process, for seeking optimal locations and parameters for a STATCOM combined with an ESS in power systems. The optimization process is designed to minimize transmission line losses and maximize the power transmitted by the network. The simulation results show the effectiveness of the proposed optimization process in determining optimal locations for the device in several test networks. Index Terms—STATCOM, Energy Storage Systems (ESSs), Optimal Placement, Power System Loadability, Minimizing Transmission Line Losses, Maximum Loadability.
I. INTRODUCTION
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enewable energy resources still represent only a small part of the current electric energy market and therefore have little impact on the overall quality or reliability of a power system. However, governments around the world are trying to increase the contribution of renewable energies in power systems such as local or regional grids in order to decrease production costs and increase energy independence [1]. For effective use of renewable energy, the sources are combined with energy storage systems (ESSs) in order to effectively control the production rate and improve power quality. The energy storage devices could be Superconducting Magnetic Energy Storage (SMES) systems or batteries to absorb/insert active power from/into the power grid. The SMES system stores energy in the magnetic field created by the flow of direct current in a superconducting coil which has been cryogenically cooled to a temperature below its superconducting critical temperature [2-4]. A typical SMES system includes three parts: superconducting coil, power-conditioning system and a cryogenically cooled refrigerator. Once the superconducting
E. Ghahremani is with R&D Team Opal-RT Technologies Inc., Montreal, QC (email:
[email protected]). I. Kamwa is with the Power Systems and Mathematics Department of Hydro-Québec’s research institute (IREQ), Varennes, QC (e-mail:
[email protected]).
978-1-4799-3656-4/14/$31.00 ©2014 IEEE
coil is charged, the current will not decay and the magnetic energy can be stored indefinitely. The stored energy can be released back to the network by discharging the coil. The power-conditioning system uses an inverter/rectifier to transform alternating current (AC) power to direct current or convert DC back to AC power. Due to the energy requirements of refrigeration and the high cost of superconducting wire, a SMES is currently used for short duration energy storage and consequently commonly devoted to improving power quality [4]. In this paper, a SMES device is combined with a STATCOM in order to include the advantages of both FACTS devices and energy storage. FACTS devices could help to achieve greater flexibility in power system management and control and can play a significant role in this area [3]. Using the STATCOM combined with a SMES device could therefore be a suitable solution for increasing transmission system capacity with power flow controls. From a steady-state point of view, by supplying or absorbing active or reactive power, increasing or reducing voltage and controlling series impedance or phase angle, a STATCOM with a SMES device makes it possible to operate transmission lines close to their thermal limits and also reduce line losses [3]. The effects of FACTS devices such as a STATCOM alone or a STATCOM together with energy storage are highly dependent on their type, size, number and location in the transmission system [4]. Many studies have been done on FACTS device placement to improve power system performances with different placement algorithms such as the Genetic Algorithm (GA) [5-7], Tabu Search (TA) [8]-[9], Simulated Annealing (SA) [9], Particle Swarm Optimization (PSO) [10], Evolutionary Algorithm (EA) [11], Bacterial Swarming Algorithm (BSA) [12], Group Search Optimizer with Multiple Producer (GSOMP) [13], Harmony Search Algorithm (HSA) [14], and Bees Algorithm (BA) [15]. This paper uses the Genetic Algorithm (GA) as its optimization method to perform the optimal placement of the STATCOM device with a SMES in order to maximize power transmitted by the network (system loadability) or minimize transmission line losses. This process will yield the optimal location and value of the device in the network. This paper is organized as follows. The modeling of a STATCOM with the SMES device for use in Matpower [16] is described in section 2 while the influence of this device on power network variables is presented in section 3. Section 4 contains a discussion of the general concepts underlying the genetic algorithm, the definition of the objective function and also details of the optimization process. Simulation and placement results for the proposed method are presented in section 5. Section 6 concludes the paper.
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II. STRUCTURE OF STATCOM WITH THE SMES DEVICE
III. MODELING OF STATCOM WITH SMES DEVICE
The first SVC with a voltage source converter, the socalled STATCOM (Static Synchronous Compensator), went into operation in 1999 [2]. The advantage of a STATCOM is that the reactive-power provision is independent from the actual voltage at the connection point. This can be seen in the diagram (Fig. 1(b)) for the maximum currents being independent of the voltage in comparison to the SVC [2-3]. With regard to the modeling of a STATCOM in this paper, the optimization method used is based on the power flow calculation in steady-state condition using Matpower [16] so the STATCOM is modeled as shunt susceptance, which includes two ideal switched elements in parallel: a capacitor for capacitive compensation and reactor for inductive compensation [3]. The STATCOM with a SMES forms a device that could be used to absorb or produce both active and reactive power. The STATCOM part, presented in above, is related to the reactive power while the SMES part is for active power. The SMES system stores energy in the magnetic field created by the flow of direct current in a superconducting coil which has been cryogenically cooled to a temperature below its superconducting critical temperature. The schematic and the characteristics of this device are presented in Figs. 1(a) and (b) respectively [2-3].
As mentioned before, the STATCOM with a SMES can be used to produce or absorb both active and reactive power. As a result, this device can be modeled as an injected pair of active and reactive power at an allocated bus, as presented in Fig. 3.
Fig. 3. (a) Transmission line with shunt FACTS device located at bus. (b) Equivalent injected model of shunt FACTS device.
The reactive power injected or absorbed by the STATCOM part at a voltage of 1 p.u. (rated system voltage) could vary between the following values: (1) 300 d QSVC d 300 MVar Meanwhile, the range of the SMES part for injected (produced) or absorbed active power is: (2) 300 d PSMES d 300 MW. By inserting the STATCOM with a SMES in the branches of the lines, the parameters of the classic equivalent π-model (Fig. 5 (a)) will be modified as rikc , xikc , y kk c yiic and bcc . In this case, the line is split into two equal parts and the STATCOM with a SMES is inserted in the middle.
(a)
(b)
Fig. 1. (a) STATCOM with energy storage device. (b) V-I characteristics.
The symbol and model of a STATCOM with a SMES are also presented in Figs. 2(a) and (b) respectively. As can be seen in Fig. 2(b), the model of this device comprises two parts: a variable inductance for absorbing or producing reactive power (STATCOM part) and a variable resistance for absorbing or producing the active power (SMES part). The SMES resistance has two operating modes: negative resistance (-rSMES) for modeling active-power production and positive resistance (+rSMES) for modeling active-power consumption.
The influence of PSMES and QSTATCOM with the ranges presented in (1) and (2) on the modified parameters of branch ( rikc , xikc , y kk c yiic and bcc ) is presented in Figs. 4 and 5. For example, it could be observed that, by increasing the value of QSTATCOM in capacitive mode in Fig. 4, the value of bcc also increases.
c y kk yiic and bcc To take another example, it is shown in Fig. 5 that in SMES absorption mode (+PSMES) in the network, the value of rikc decreases while in production mode (-PSMES) the value of rikc increases. Fig. 4. Effects of PSMES and QSTATCOM on branch parameters
(a) Fig. 2. (a) Modeling of the STATCOM alone. STATCOM with energy storage device.
(b) (b) Modeling of the
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(a)
Fig. 5. Effects of PSMES and QSTATCOM on branch parameters on
rikc , xikc .
IV. INFLUENCE ON POWER NETWORK VARIABLES After modeling a STATCOM with a SMES device, in this section we consider its influence on the power transmitted over a line between two buses. The impact of a STATCOM with a SMES device could be analyzed by inserting it in a small network such as the nine-bus test system presented in Fig. 6.
(b) Fig. 8. Effect of STATCOM with SMES on power flow of lines in two modes: (a) Active-power absorption. (b) Active-power production.
The results are presented in two modes: active-power absorption and active-power production. In the active-power production mode, PSMES changes from -300 MW to 0 and for absorption from 0 to +300 MW. The results for voltage magnitude and active power flow of the branches are presented in Figs. 7 and 8. V. OBJECTIVE FUNCTION DEFINITION The goal of the optimization process, which is done using the Genetic Algorithm, is to maximize system loadability on the network without any bus voltage violation or branch loading [17]-[18]. In order to achieve this goal, the network load factor (λ) is increased in an iterative optimization process as follows: First, the generating powers in generation buses (PG buses) are modified as in (3): (3) P OP
Fig. 6. Nine-bus test system.
Gi
(a)
G0i
where PG0i is the initial power generation at bus i and PGi is the modified power generation. Then, for the load buses (PQ buses) the active and reactive demands (PL and QL) are modified as (4):
PLi Q Li
O PL0i O Q L0i
(4)
where PL0i and QL0i are the initial active and reactive load power respectively at bus i and PLi and QLi are the modified values. The corresponding objective function which maximizes the power system loadability (λ) could be formalized as follows: (5) J = Max ^O` (b) Fig. 7. Effect of STATCOM with SMES SVC on voltage magnitudes of buses in two modes: (a) Active-power absorption. (b) Active-power production.
In order to analyze the impact of a STATCOM with a SMES on the network variables, the device is located on branch 5, between buses 6 and 7.
subject to the following security constraints: Sl ≤ Slmax : for all branches of the network. (6) : for all buses of the network. (7) | ΔVbi | ≤ 0.05 Pgi min ≤ Pgi ≤ Pgi max : for all generation buses. (8) where Slmax is the maximum value for the apparent power of line l, Sl is the current apparent power of line l and ΔVbi is the difference between the nominal voltage at bus i and the
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current voltage, Pgi is the generation at bus i, Pgi min and Pgi max are minimum and maximum bounds on Pgi respectively. This optimal process will increase the capacity of the existing transmission system. On the other hand, the goal is to maximize power transmission by the network without violating the voltage levels of buses or overloading any branches. After choosing the numbers of STATCOMs with SMES devices, the genetic algorithm will find their optimal locations and values. At each iteration, according to (3) to (5), the load factor is increased and the optimization constraints, namely bus voltage violation and branch loading, are verified. In the initial condition, λ is equal to 1 (λ0=1). When it is no longer possible to satisfy the constraints, it is concluded that the maximum loadability has been reached [17]-[18].
After finding the optimum locations and values for the current generation, the new generation starts with new values for the network load as presented in (3) to (5). This loop will continue until we reach the maximum possible network loadability with the constraints in (6)-(8) met. In this case, the corresponding load factor will be chosen as λmax. The optimization algorithm was performed on a 57-bus test system (Fig. 10) with one STATCOM with a SMES device. The results are presented in Table I, which shows a 14% improvement in system loadability (λmax =1.14) which is equal to 175 MW by installing a device at bus 38. Table I: Device allocation results in the 57-bus test system.
VI. RESULTS OF THE ALLOCATION PROCESS A typical individual (population) of the genetic algorithm for allocation process of STATCOM with a SMES is presented in Fig. 9 in which the number of device is three. Each individual corresponds a configuration of device in the network with device location and its value.
After allocation of the STATCOM with a SMES, its influence on the voltage of buses can be analyzed. The bus voltages for networks with and without a STATCOM with an SMES for the maximum load factor (λmax =1.14) are presented in Fig. 11. As may be seen from this figure, under the same loadability conditions, the network without any devices has a greater voltage drop in the buses. This clearly shows the significant influence of a STATCOM with a SMES device on keeping bus voltages in the acceptable range.
Fig. 9. Typical example of an individual in genetic algorithm for allocation of STATCOM with SMES for three devices.
As can been seen in Fig. 9, the first section of each individual is related to the device locations. Each device has its own location. Repeated locations are not allowed; each line or bus can appear only once in the string. The second section contains the values of the devices, which are normalized between 0 and 1, with 0 corresponding to the minimum value and 1 to the maximum. By defining randomly the locations and values in an initial populations with this structure, for all individuals in initial populations which are different configurations in the network, the genetic-algorithm calculates the fitness function to verify the security constraints for all of them to rank them based on the value of fitness function. Then, the genetic algorithm reports the best individual for the optimal locations and optimal values of selected desired number of device.
Fig. 10. 57-bus test system: case study for STATCOM with SMES placement.
Fig. 11. Effects of STATCOM with SMES allocation on bus voltage.
It is also possible to monitor the total system losses for different load factors. For the 57-bus test system, the graph in Fig.12 shows the total system losses. For three examples, at λ=1.00 the loss reduction is 12 MW, at λ=1.12 it is 16.8 MW and at λ=1.40 it is 29 MW.
Fig. 12. Effects of a STATCOM with a SMES device on total line losses.
5 Table II: Comparison of the STATCOM with a SMES device and a STATCOM alone in different power networks.
To better understand the effect of the SMES (associated with STATCOM), this device is compared with a STATCOM alone. The analysis for a STATCOM alone and a STATCOM with a SMES was done for different power systems. The results are presented in Table II. As is clear from these tabulated results, when the STATCOM device is associated with energy storage (SMES), there is greater improvement in loss reduction. Also, higher values are obtained for the maximum load factor when using a STATCOM associated with a SMES. For example, in the 57-bus test system, with a STATCOM alone there is an 11% improvement in system loadability, whereas with one STATCOM associated with a SMES the rate is 14%.
[6]
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[8]
[9]
[10]
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VII. CONCLUSION In order to find the optimal location and value of a STATCOM with a SMES device to maximize power system loadability and/or minimize transmission line losses an optimization process based on the genetic algorithm is proposed. The simulation results show the effectiveness of this optimization process in finding the optimal location for the device. To show the influence of energy storage in loss reduction and loadability, the device is compared with a STATCOM used alone in different networks.
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