Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
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A Multi-Agent Approach for Enhancing Transient Stability of Smart Grids with Renewable Energy M. S. Rahman*, Student Memeber, IEEE, H. R. Pota and T. F. Orchi School of Engineering and Information Technology University of New South Wales, Canberra, ACT 2600, Australia Email:
[email protected],
[email protected] and
[email protected]
Abstract—This paper presents the use of an intelligent multiagent system (MAS) approach to investigate the effect of a sudden change in generator load and the impact of integrating renewable energy sources (RESs) on the enhancement of the transient stability of smart power grids. In this proposed approach, an algorithm is developed through which intelligent agents properly coordinate a system’s protective relays using the critical clearing time (CCT) information to avoid loss of synchronism. An IEEE 14-bus test system is used to dynamically evaluate the online performance and effectiveness of the proposed approach. The simulation results show that the individual agents provide a powerful framework to successfully enhance the transient stability of the system for different fault locations under various generator load conditions and increasing penetration of wind power. Index Terms—transient stability, smart grid, multi-agent system, renewable energy, protective relay, CCT
I. I NTRODUCTION As smart electric power grids are quite large, complex and extensively interconnected, due to the difficulty of controlling them using conventional centralized control task, employing decentralized multi-agent system (MAS) technology for their secure and reliable operation has gained significant attention because of its of distributed characteristics, dynamic adaptability and flexibility. An MAS can provide an ideal means of achieving systems integration by wrapping disparate systems as intelligent systems [1]. Recent researches has indicated that MAS technology provides a more flexible way of increasing both the resilience and efficiency of a smart grid by combining top-down and bottom-up intelligent autonomous decisionmaking in its communication and control architecture [2]. Power systems are vulnerable to several disturbances, such as three-phase short circuit faults and sudden generator load changes, which may cause loss of synchronism and a risk of widespread blackout in a power grid. Recent blackouts in different countries have illustrated the vital importance of, and need for investigations into the transient stability of power systems [3]. A smart power grid is a large distributed and complex system in which large-scale wind energy penetration has a significant impact on the dynamic behavior of it as well as directly influencing its transient stability. A system’s capability to maintain the synchronism of its synchronous machines in the presence of several types of disturbances is defined as transient stability. To achieve this for the reliable and safe operation of a future smart grid under large disturbances, it is essential to develop an intelligent decision support system
which provides better coordination of a system’s protection devices to avoid loss of synchronous operation. Motivated by this reason, this paper highlights the use of an intelligent agent-based approach for improving the transient stability by dynamically evaluating a system’s critical clearing time (CCT) for generator load changes and various penetration levels of wind power at different fault locations. Transient instability, which is still one of the largest threats to modern power systems [4], can be avoided by properly coordinating a system’s protective relays with their corresponding CCTs, which are key indices of transient stability, by examining whether the system is able to sustain balanced and normal operation after experiencing a three-phase fault. Many techniques have been proposed for improving the transient stability of a power system, including a few agentbased methods such as in [5] Mohamed et al. have proposed new strategy agents for transient stability improvement by controlling turbine fast valving; in [6] Karady et al. have proposed an agent-based turbine fast valving for improving the transient stability; in [7] the same authors have proposed a multi-agent technique for online transient stability analysis for post fault valve control using robotic ball-catching algorithm; in [8] Hadidi et al. have proposed a multi-agent-based realtime wide-area power system stabilizer which uses reinforcement learning (RL) and MAS technique for transient stability enhancement; in [9], [10] Dou at al. have proposed a multiagent-based decentralized coordinated control for transient stability improvement; in [11] Abood et al. have proposed an agent-based technique for instability prediction and control for transient stability assessment, etc. Although these methods are appropriate for different aspects, to date, transient stability enhancement considering a dynamic evaluation of the system’s CCT for cases of a generator load change and integration of renewable energy sources (RESs) using an intelligent multiagent approach has not been studied. Therefore, in this paper, a MAS framework, in which a MAS platform for real-time agent communication is developed in the Java agent development framework (JADE) [12] in conjunction with MATLAB and integrated with a power system platform developed in MATLAB, is proposed. In this research, a MAS framework consisting of global agents (GAs) and local agents (LAs) which uses an algorithm to provide proper coordination of protection devices with their corresponding CCTs is developed. In an MAS environment,
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Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
as agents simultaneously work together to update system information at each integration step with the CCT computated for the present generator load conditions and wind energy penetration levels during a fault, the system relies on a continual streaming of CCT information to promote the on-line capability and scalability of real-time agent-based protection device coordination to improve the transient stability. An illustrative example of an IEEE 14-bus test system is used to evaluate the effectiveness and performance of the proposed approach, with simulation results showing that this MASs successfully enhances the system’s transient stability during three-phase faults at different locations under changing generator loads and various levels of wind power penetration.
Physical parameter: Power generation and network topology
Measurements and monitoring: Fault current, generator load, wind power
Disturbances: Three-phase faults, sudden generator load change
Computation: Dynamic evaluation of critical clearing time (CCT)
Machine/Devices: Generators and protection devices
Communication: Inter-agent communication through FIPA-ACL
II. MAS A RCHITECTURE FOR S MART G RID P ROTECTION An MAS consists of a group of individual autonomous agents running and co-operating with each other to solve sophisticated problems in dynamic and unpredictable domains, such as smart electric power grids which are nonlinear largescale systems that undergo a wide range of transient conditions. In a smart grid in which faults and outages are likely to occur the real-time operation of its protection devices for fault detection, isolation and reclosing of its circuit breakers (CBs) is indispensable for improving its transient stability. The CCT is widely used as an important transient stability index and is defined as the maximum duration for which a disturbance may continue without the power system losing its capability to recover to normal operating conditions [13]. If the CB’s operating time exceeds the CCT for a three-phase fault occuring in a smart grid, the system will go to a unstable condition and as a result, a cascading blackout may occur. Therefore, it is necessary to properly coordinate a system’s protection devices for the opening and reclosing of its CBs with their corresponding CCTs which can be well achieved by an intelligent MAS. The proposed MAS framework consists of intelligent agents: GA and LA, where GAs are associated with the LAs. The GAs are able to measure and monitor a system’s present status from the physical parameters of its network, and then dynamically evaluate the CCT according to the disturbances occur, while the LAs negotiate and communicate with each other through the agent’s communication language FIPA-ACL to coordinate the system’s protection devices by tripping and auto-reclosing its CBs with their corresponding CCT information to enhance the system’s transient stability. A general architecture for an MAS-based smart grid protection and security scheme is shown in Fig. 1. III. MAS
STRUCTURE FOR TRANSIENT STABILITY ENHANCEMENT
In the proposed MAS architecture for real-time protection device coordination, the GAs are developed in MATLAB and the LAs in JADE. These agents are able to run together to update the system information at each integration for any change in the system’s condition such as a sudden change in the generator load and various wind energy penetration levels,
Multi-Agent System (MAS)
Smart Power Grid
Control Action Relay coordination: CB tripping Auto-reclosing
Fig. 1.
General architecture for agent-based smart grid protection scheme
to reinforce the decisions regarding its secure and reliable operation of a smart grid. In a smart grid, when disturbances such as three-phase faults or sudden load changes occur, the equivalent agents co-operate with each other to decide on the proper real-time coordination of protection devices to enhance the system’s transient stability. The interactions of a MAS and smart grid for proper coordination of protection devices is shown in Fig. 2. A. Proposed MAS-based approach The individual roles of GA and LA in the proposed MASbased approach are discussed in the following subsections. 1) Global agent (GA): In this MAS environment, GA is used to precisely detect a fault and its location, whether near a generator bus or distant from generating stations, and obtain its corresponding CCT. For fault detection, a threshold value of the current (Ith ) is set for the each GA so that, when a threephase fault occurs in the system, the GA can detect that fault when its current flows above the threshold value and identify its location by the magnitude of its current. To obtain the initial conditions of the system, a load flow analysis is performed. Then, at the onset of a fault, the GA checks the generator load condition and, for the present generator load (PG ), obtains the corresponding CCT through a transient stability analysis using the time-domain simulation method which, at the beginning of the simulation, sets the fault’s start and end times, initial values of the estimated maximum and minimum critical fault clearing times and time-domain simulation step size. Then, a transient stability analysis by time-domain simulation is conducted using system’s dynamic data. The system’s stability is checked then based on the simulation results and, the system
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Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
Read the data Bus data: V, δ, P, Q... Line data: R, X, B... Gen. data: X’d, H...
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Fig. 2.
Interactions of smart grid and MAS
is stable for the minimum critical fault clearing time and unstable for the maximum critical fault clearing time, the CCT calculation is successful. The GA is also able to monitor and observe the generator’s load condition and, if it has changed, dynamically adapt to it and obtain the corresponding CCT for the new generator load (PG′ ), i.e., PG = PGo (for normal operation) if PG ≷ PGo (PGo ± % load change) PG′ = PG (for new load) else PG = PGo (for given load) o On the other hand, if any wind energy (PWG ) is integrated in the grid, the GA also checks the wind power penetration level by comparing the total and previous wind generation and ′ obtains the CCT for new wind power (PWG ), i.e., o PWG = PWG (for normal operation) o o if PWG ≷ PWG (PWG ± % penetration level change) ′ PWG = PWG (for new penetration level) o else PWG = PWG (for normal penetration level) where PGo is the total generator input mechanical power and PWG is the total wind power extracted from the wind turbine. Once information on the fault location and CCT is obtained, the GA sends it to the corresponding LA. 2) Local agent (LA): In JADE environment, several LAs are established to conform with the protection devices in a smart grid and used properly to coordinate them. After getting the CCT information from GAs, the LAs begin to communicate and cooperate with their neighboring agents to coordinate the protection devices to open the corresponding CBs before the corresponding CCT and reclose the CBs quickly after the fault is cleared. In this way, LAs cooperate with the GAs to enhance the system’s transient stability.
Start Run simulation
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Fig. 3.
Flow-chart of proposed algorithm
B. Flow-chart of proposed approach In the proposed MAS-based power system for transient stability enhancement, the agents use an algorithm developed through a combination of MATLAB and Java which provides them with the capability to simultaneously monitor and measure the system’s present status to store the values of voltage, current and power. A flow-chart of the proposed method is shown in Fig. 3. When a disturbance occurs in the system, the agents detect the fault by measuring the fault current (when If > Ith ) whereas, if there is no disturbance, they take the decision to maintain normal operation. After fault detection, the generator’s load condition is checked and the corresponding CCT is
Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
function, is considered as a test case. The nonlinear dynamic model of the system can be represented by the following equations,
MATLAB Server (S-function) Communication Middleware
Java Agent Development Framework
∆ω˙ i = Fig. 4.
MATLAB-JADE integration
calculated. At the same time inter-agent communication takes place to decide to trip the corresponding CBs to isolate the faulted portion from the rest of the system according to the corresponding CCT information. When the fault is cleared, the agents decide to reclose the CBs as soon as possible for the resumption of normal operation while the agents can still performing their individual tasks within their own environments. The proposed method is successfully implemented and validated by a comparison of it with a conventional approach in [2] demonstrating the effectiveness of the proposed approach. C. On-line performance of proposed approach The proposed method provides an on-line capability and scalability for the real-time coordination of protection devices under variable system conditions. In many cases, for the assessment of transient stability, the CCT, which plays a vital role in maintaining the synchronism of synchronous machines, is assumed to be fixed and, in most centralized and decentralized approaches, the generator load is assumed to be constant, whereas a sudden change in this load can adversely affect the CCT. As the proposed method deals with variable load conditions when updating the measured system information at each integration step using CCT computations of the present load conditions, thus the system relies on a continual stream of on-line CCT information which enhances the on-line capability and scalability of the real-time operation of agent-based protection devices for the secure operation of a smart grid. D. Implementation of developed MAS on power system The simulated power system and proposed MAS reside on two different software platforms: the former in MATLAB and the latter a combination of MATLAB and JADE, in which agents can communicate and cooperate with each other in realtime. JADE is selected as an agent platform for developing MAS due to its strong support from the industrial sector, and its open source status [14]. In order to link them, they are integrated using the MATLAB control server through the MATLAB S-function, as shown in Fig. 4.
πf0 (Pmi − Pei − Di ωi ) Hi
(2)
δ˙i = ∆ωi
(3)
(−Eqi + Efdi ) E˙qi′ = ′ Td0i
(4)
−Efdi + Ka (Vrefi − Vti ) E˙fdi = Ta
(5)
for i = 1, 2, . . . , n, where n is the total number of machines, ωi the rotor speed, Pmi the mechanical power input, Pei the electrical power output, Di the damping coefficient, Hi the inertia constant, f0 the frequency of the system, δi the rotor angle, Eqi′ the internal voltage behind Xdi′ , Efdi the equivalent ′ the time constant of the excitation excitation voltage, Td0i circuit, Ka the regulator gain, Ta the regulator time constant, Vrefi the reference voltage and Vti the terminal voltage. B. Wind model In this research, a fixed-speed wind turbine (FSWT) model, in which the wind energy is transformed into mechanical energy through a mechanical drive train, is considered. The wind speed and mechanical power extracted from the wind are related as [15] ρi Pwti = Awti cpi (λi , θi )Vw3i 2 for i = 1, 2, . . . , n, where n is the total number of generators, Pwti the power extracted from the wind (W), ρi the air density (kg/m3 ), cpi the performance/power coefficient, ω i Ri λi = wt the tip speed ratio, Ri the wind turbine radius Vwi (m), ωwti the wind turbine rotational speed (rad/s), Vwi the wind speed (m/s), θi the pitch angle (degree) and Awti the area covered by the wind turbine rotor (m2 ). In this paper, a wind farm with asynchronous generators is modeled as a P-Q bus for the load flow analysis with the real power calculated as a function of the wind speed for the first iteration of the power flow analysis and then remaining constant. As the reactive power depends on both the real power and bus voltage, the bus voltage is assumed to be a constant value, and the specified real and reactive powers known from the first iteration remain unchanged [16]. V. C ASE STUDIES AND RESULTS
IV. M ATHEMATICAL MODELING A. Dynamic model of system In this paper, a multimachine power system, an IEEE 14-bus test system represented by a set of differential and algebraic equations given as, x˙ = f (x, y, z)
4
(1)
where, x is the state vector, y vector of algebraic variables, z a vector of system parameters and f a nonlinear algebraic
In this paper, an IEEE 14-bus test system is used to evaluate the performance of the proposed method using the system data according to the IEEE standards [17]. As wind power now accounts for an increasing percentage of power generation, more attention has been directed to the impacts of large wind farms on power system stability [18]. This wind farm model consists of 10 wind generators for 1.5 MW each i.e., 15 MW wind turbines are connected at bus 5. A single-line diagram of the modified IEEE 14-bus system is
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Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
Gen.2
IG
Rotor angle (deg)
0
Single-line diagram of IEEE 14-bus system
shown in Fig. 5. To evaluate the system’s performance for different contingencies, the following two scenarios of a fault at different locations under sudden load changes and different wind power penetration levels are considered.
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Fig. 6. Generator relative rotor angles for faults at (a) bus 1 and (b) bus 4 for given load condition; and (c) bus 1 and (d) bus 4 for 15% load change TABLE I E FFECT OF GENERATOR LOAD CHANGE ON CCT Fault 1 2
Location
Line trip
1 4
1−5 2−4
Critical clearing time (ms) Given load 15% load change 82 76 168 159
A. Effect of generator load change Under the generator’s given load conditions, a 3-phase short circuit fault is applied at t = 1.0 s at two different locations, one at the generator bus 1 and another at bus 4, which is distant from the generating station. The GA detects the fault and its location, and calculates the corresponding CCT and sends this CCT information to the corresponding LA which coordinate the protection devices to open and reclose the corresponding CBs using this information. Fig. 6(a) and Fig. 6(b) show the relative rotor angles when a fault occurs at bus 1 and bus 4, respectively, in which it can be seen that, they are transiently stable following the removal of the fault from the rest of the system by tripping lines 1 − 5 and 2 − 4, respectively, before the corresponding CCT, and reclosing the CBs as soon as the fault is cleared. At a 15% load change, i.e., a 15% increase in load in the generators, a 3-phase fault is applied at t = 1.0 s at bus 1 and bus 4. The GAs dynamically conform this change and obtain the corresponding new CCT using which the LAs coordinate the protection devices to open and reclose the corresponding CBs. Fig. 6(c) and Fig. 6(d) show the relative rotor angles for a fault at bus 1 and bus 4, respectively, in which it can be seen that, they are transiently stable following the removal of the fault by the tripping of lines 1 − 5 and 2 − 4, respectively, before the new CCTs. A summary of the above analysis for the effect of generator’s load change on the evaluation of the CCT information is provided in Table I in which it is clear that the CCTs vary with a change in generator load and decrease under a heavier load, which means that increasing a system’s generator load will decrease its stability margin. The simulation results show that the agents successfully coordinate their CB operations with the corresponding CCT information to enhance the system’s
transient stability. B. Impact of wind energy penetration As the stability and security of a smart grid with a large amount of wind power have recently become the major concerns, especially in terms of transient stability issues, the impact of wind power generation on transient stability is evaluated in this section through applying a 3-phase fault at t = 1.0 s at bus 1 and bus 4. To evaluate the impact of this renewable energy penetration on the system’s transient stability, the wind energy penetration level is increased from 15MW, i.e., 5.2% penetration to 35MW, i.e., 11.4% penetration with an interval of 5MW. Table II presents a summary of the effect of increasing wind power on the CCT. The relationship between CCTs and penetration levels is shown in Fig. 7 in which it can be seen that increasing the wind power penetration level deteriorates the transient stability margin, i.e., the CCT decreases, where CCT1 and CCT2 stand for the CCTs for the fault at bus 1 and bus 4, respectively. In this paper, the system’s angular stability for wind power penetration levels of 5.2% and 11.4% are considered. For each penetration level, the GAs adapt to the change and obtain the new CCT which the LAs use to coordinate the protection devices to open and reclose the corresponding CBs. Fig. 8(a) and Fig. 8(b) show the relative rotor angles for a fault at bus 1 TABLE II E FFECT OF INCREASING WIND POWER ON CCT Fault 1 2
Critical clearing time (ms) for wind power (MW) 15MW 20MW 25MW 30MW 35MW 66 62 59 56 52 136 129 123 117 111
Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, TAS, Australia, 29 September - 3 October 2013
CCT vs Wind Power
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understand a disturbance and choose appropriate action which provides a powerful framework for the real-time coordination of protection devices to open and reclose their breakers using the corresponding CCT information to enhance a system’s transient stability. From the simulation results, it is seen that, although the transient stability margin decreases, i.e., a CCT decreases as the generator load and wind power penetration level increase, the proposed MAS framework successfully adapts to this situation through on-line measurements, thereby successfully enhancing the system’s transient stability. A future approach related to this work will focus on enhancing the transient stability of a large-scale power system with hundreds of nodes with renewable energy sources.
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Fig. 8. Generator relative rotor angles for faults at (a) bus 1 and (b) bus 4 for 5.2% wind penetration; and (c) bus 1 and (d) bus 4 for 11.4% wind penetration
and bus 4, respectively, for a wind power penetration level of 5.2% in which it can be seen that they are transiently stable by the tripping of lines 1 − 5 and 2 − 4, respectively, before the corresponding new CCTs. Again, when the wind power penetration level is increased from 5.2% to 11.4%, GAs adapt to this change and obtain the corresponding CCT which the LAs coordinate the protection devices to open and reclose the corresponding CBs. The relative rotor angles are shown in Fig. 8(c) and Fig. 8(d) for fault at bus 1 and bus 4 for a wind power penetration level of 11.4%. From this figure, it can be seen that the rotor angles are transiently stable by tripping the lines 1 − 5 and 2 − 4, respectively, before the corresponding CCTs. From the above analyses, it can be seen that, with gradual increases in the wind power penetration level into the grid, the CCT of the system decreases, i.e., the system’s stability margin decreases, but the agents successfully adapt to this situation and coordinate their CB operations with the corresponding CCT information. VI. C ONCLUSIONS AND FUTURE WORK In this paper, the effect of changes in a generator load and the impact of increasing wind power generation on a system’s transient stability for faults at different locations are investigated. The individual intelligent agents in the MAS cooperate and communicate with each other to analyze and
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