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Power system congestion management is a hard task, and the electricity industry restructuring process tends to difficult it even more. The competitive electric power market arises new congestion management perspectives and difficulties. This work demonstrates that modal analysis technique can be a powerful tool for defining corrective actions to ov...
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... the analysis of the critical eigenvalue of J near the Saddle-Node bifurcation point with associated left and right eigenvectors, voltage stability critical areas of a power system can be identified. Gao [5] defines the UHDFWLYH (power) SDUWLFLSDWLRQ IDFWRU ( 53) ) from the reactive power reduced matrix J RQV, and Da Silva [4,6] defines the DFWLYH (power) SDUWLFLSDWLRQ IDFWRU ( $3) ) from active power reduced matrix J RP θ . With this, the conventional modal analysis technique is expanded to the active portion of the Jacobian matrix. Similarly to the RPF [5], the APF is defined as the element- by-element product of the left and right eigenvectors of the J RP θ matrix. If λ i is the i th eigenvalue of J RP θ , and μ i and ν i its right and left eigenvectors related to λ i , the participation factor of bus k to mode i is defined as: The APF reveals those buses where active power changes are more detrimental to system voltage stability. They represent the best locations for planning or operation active power based control actions, such as load shedding and generator rescheduling, for improving system power transfer capability. On the other hand, the RPF is related to the reactive power demand at load (PQ) buses, and indicates the best locations for reactive power compensation based control actions. The New England test system (10 generators and 29 load buses) is used in this work. Initially, it is compared the information provided by left and right eigenvectors for the critical mode, and also their combination as participation factors. Figure 1 shows that there are just little differences between the modal shape provided by left and right eigenvectors and by the participation factors. It should be remembered that for identifying critical buses or areas the most important information is the modal shape, and not the eigenvector values. It can be seen from Figure 1 that nearly the same critical buses are indicated by the three indices. This conclusion is confirmed in Table I, which shows 10 critical buses ranked by left and right eigenvectors, and participation factors, respectively. Almost the same set of critical buses is obtained by the three methods. We have confirmed this characteristic of left and right eigenvectors for many power systems, and believe that the participation factor is still the best option, since it combines the two eigenvectors. IV. R ESULTS AND D ISCUSSION Congestion due to static security is the main transmission constraint analyzed so far [1]. But, voltage stability margins contribute significantly to the system $7& ( $YDLODEOH 7UDQVIHU &DSDELOLW\ ) definition. For this reason, this work focuses on the congestion assessment due to system inability to assure minimum voltage stability margin criteria. In this work voltage stability margin is considered as been the maximum load increase that the system could supply from the base case loading until it reaches the voltage stability limit. Voltage stability margin is obtained by using PV-curve methods [7], [10]. PV curves are obtained in this work by considering load increases for all load buses in a proportional way to the base case loading (keeping constant power factor). System generation level is also increased (in proportion to the base case injections) in order to match the load increases during the PV curve construction process. It should be emphasized that all generators respond for an increase in demand, and not only the slack bus. Generators reactive power and tap limits are also properly considered. For each load increase it is solved a load flow problem, and the set of obtained equilibrium points defines the PV curve. The stability margin represents the distance, in MW or percentage, from the base case operation point to the maximum power transfer capability point of the system (PV curve nose point). The New England test system is used to demonstrate the adequacy of participation factors for identifying congested areas. Initially, it is assessed control actions associated with reactive power variations ( ∆ Q), looking for voltage stability margin enlargement. Figure 2 shows the RPF for load buses. Buses with larger RPF represent the best places for applying reactive power based control actions for improving the security level. Large RPF’s indicate congested areas, where there is undersupplied reactive power support. The reactive power support of each bus is changed with the inclusion of 69&¶V ( 6WDWLF 9DU &RPSHQVDWRU ). Figure 3 shows the voltage stability margin with and without the inclusion of the SVC at bus 16. It can be seen that the SVC provides a significant gain at the margin. In other words, the SVC enlarges systems transfer capability. In order to assess the correlation between the RPF ranking and the voltage stability margin, the SVC is connected to each load bus one by one, and the margin gain is checked out. Figure 4 shows the results of this test. It can be noted that the SVC allocation at buses with larger RPF provides the largest margin gains. This test validates the idea of using the RPF for identifying congested areas from a point of view of reactive power support deficiencies. This section investigates control actions associated with active power variations ( ∆ P) at load buses. Figure 5 shows the APF for load buses. Similarly to the RPF, buses with large APF’s represent the best places for applying active power based control actions. Adequate modifications on the active power injections (load shedding) for those places would produce maximum voltage stability margin gain. The test consists of applying a 5MW load shedding for each load bus, and verifying the provided voltage stability margin gain. It can be seen, from Figure 6, that the margin gain can be considered closely correlated to the APF. This test shows that the APF can easily identify congested areas from a point of view of extreme active power demand. From the modal information a congestion cost could be attributed to the active power demand of each bus, which allows the identification of areas in which the increase of demand are acceptable (buses with sm all APF ’ s), and also areas where interruptible load should receive incentives (buses with large APF ’ s). The APF could also be used for defining preventive load shedding schemes for relieving congestion. This section evaluates control actions associated with active power variations ( ∆ P) at generator buses, such as generator active power rescheduling. Generator with large APF ’ s can provide additional active power to the system without severely depleting system reactive reserves [6]. The ones with small APF ’ s are more contributing for system congestion. It means that system reactive reserves are relieved if these units inject less active power. In other words, by increasing the active power generation in large APF ’ s units, and reducing it in buses with small APF ’ s, a congested situation could be relieved. It has an impact, of course, on system dispatch cost, and also on the energy price. Figure 7 shows the APF for generator buses. Generator 2 is the slack bus and has no APF. Generator 3 has the smallest APF and must be encouraged to generate less active power for improving voltage stability margin. The test consists of a 20MW generation increase at the buses 1 and 4 to 10 individually, while reducing the same 20MW generation at Bus 3. The test results are shown at Figure 8. It can be seen that the generation increases at buses 1, 8 and 10 provide larger voltage stability margin gain. It should be emphasized that the voltage stability margin gains are related to the generation reduction at Bus 3, and because of this there is not a gain for the Generator 3. This test demonstrates that the generators APF ’ s could be used for active power re-scheduling purposes with the objective of relieving congested situations, since it indicates which generators should inject more, and which generators should inject less active power. Sections A, B and C demonstrates that the modal participation factors can provide the best candidate buses for control actions related to active and reactive power changes at generator and load buses for improving voltage stability margin. In this section we introduce the participation factor information at the optimal power flow problem. The idea is iteratively manage active and reactive power for generators and loads following the participation factors optimal direction, in order to improve system margin, and relieve congested situations. The modal analysis technique provides voltage stability critical areas and gives information about the best actions for improving system stability margins. Modal participation factors indicate which generators should inject more active (or reactive) power to improve the voltage stability margin, and which generators should inject less [6]. This information is added to the system dispatch problem, so that the final solution leads to an optimized reactive power injection for each generator and synchronous condensers, from a perspective of improving voltage stability margin. Figure 9 illustrates the voltage stability margin against the iterations of such a MVAR optimization methodology. It can be noted that the method leads to a margin improvement of 7.5%. It is significant, since there is no modification on generators active power injections. In other words, the stability margin is improved with no cost deviation compared with ...
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Citations
... So management of congestion due to voltage stability is too important in foreseen operating states (established after the day-ahead market clearing). In recent years voltage stability is paid much more attention under electric market environment [1,[15][16][17][18][19][20][21][22]. ...
... One of the congestion management methods is using of FACTs devices that with changing the lines parameters provide effective usage of transmission line capacity [2][3][4][5][6][7]. In some literature [8,10,21,22], by cutting the some transactions that cause congestion, try to reduce overloading of the lines. This means load-shedding is one of the congestion relief methods. ...
... Load margin reflects the distance of the currently operation point to voltage collapse point, with which the system can be indicated to be stable or not. The expanded modal analysis [22][23][24][25] technique allows the best choice of control actions to get over congestion problems, such as the identification of key areas for interruptible load incentives, the best generator re-dispatch scheme for eliminating congestion, critical areas for investment on reactive power ancillary services and others. Da Silva at [22] applied Active Participation Factor (APF) to identify those buses where active power changes are more detrimental to system voltage stability. ...
One of the main issues that threaten the system security in deregulated electricity
market is congestion and its management. Congestion Management is one of the most
challenging tasks System Operator. It may not be always possible to dispatch all of the
contracted power transactions due to congestion of the transmission corridors. System operators
try to manage congestion, which otherwise increases the cost of the electricity and also threatens
the system security and stability. This paper presents a comparison of the Cluster/ Zone based
approach and Relative Electrical Distance approach. In Cluster/Zone based approach
congestion management is carried out by using congestion relief and based on associated pricing.
In congestion relief process, rescheduling the generation using Transmission Congestion
Distribution Factors is done so as to minimize the rescheduling cost. In re-schedule method,
some of the generators are required to increase/decrease their actual market schedule which
causes to increase/decrease transmission losses. The proposed method is tested on IEEE 24-bus
Reliability test system, the simulation results shows the effectiveness of the method.
... In [12], a congestion alleviation method ensuring voltage stability, using loadability limits in pool electricity markets, is proposed. In [13], modal analysis and modal participation factors are used for saving voltage stability within a congestion management framework. The research work of [14] introduces a new measure for transient stability margin (TSM) and incorporates it into a congestion management framework to mitigate congestion while enhancing the transient stability of the power system. ...
... Constraint (12) limits voltage magnitude of every bus within its allowable limits. Constraint (13) limits apparent power flow of branches where SB is the set of branches of the power system. In (14), G j P is final active power of j th generating bus after congestion management, which consists of three parts. ...
Transmission congestion management plays a key role in deregulated energy markets. To correctly model and solve this problem, power system voltage and transient stability limits should be considered to avoid obtaining a vulnerable power system with low stability margins. Congestion management is modeled as a multi-objective optimization problem in this paper. The proposed scheme includes the cost of congestion management, voltage stability margin and transient stability margin as its multiple competing objectives. Moreover, a new effective Multi-objective Mathematical Programming (MMP) solution approach based on normalized normal constraint (NNC) method is presented to solve the multi-objective optimization problem of the congestion management, which can generate a well-distributed and efficient Pareto frontier. The proposed congestion management model and MMP solution approach are implemented on the New-England's test system and the obtained results are compared with the results of several other congestion management methods. These comparisons verify the superiority of the proposed approach.
... The linearized power flow equations can be formulized as follows[22][23][24][25]: ...
... Information about system voltage stability can be obtained from these matrices in both perspectives: reactive and active power conditions. Modal analysis applied to reduced or unreduced Jacobian matrices results in[22]: ...
... where: Φ-Contain the right eigenvectors of matrix J; Γ-Contain the left eigenvectors of matrix J; Λ-Contain the eigenvalues of matrix J. Gao[24]defines the Reactive Participation Factor (RPF) from the reactive power reduced matrix JRQV, and Da Silva[22]defines the Active Participation Factor from active power reduced matrix JRPθ. The RPF is defined as the element by-element product of the left and right eigenvectors of the JRQV matrix. ...
Congestion in the transmission lines is one of the technical problems that appear
particularly in the deregulated environment. The voltage stability issue gets more important
due to heavy loading in this environment. The main factor causing instability is the inability
of the power system to meet the demand for reactive power. This paper presents a new
approach for alleviation congestion relieving cost by feeding required reactive power of
system in addition to re-dispatching active power of generators and load shedding.
Furthermore with considering different static load models in congestion management
problem with both thermal and voltage instability criteria, tries to the evaluated congestion
management cost become more real, accurate and acceptable. The voltage stability is a
dynamic phenomenon but often static tools are used for investigating the stability
conditions, so this work offers new method that considers two snapshots after contingency
to consider voltage stability phenomena more accurate. This algorithm uses different
preventive and corrective actions to improve unsuitable voltage stability margin after
contingency. The proposed method is tested on IEEE 24-bus Reliability test system, the
simulation results shows the effectiveness of the method
... Polarization and depolarization current (PDC) analyses can easily identify whether the cause of insulation trouble is due to conduction such as that in free water or in an ageing process due to temperature effects. The dielectric properties of an insulating material change with moisture, ageing, and contamination. 1) For typical measurement conditions, the conductivity of oil affects the polarization current mainly in a time range t < 100 s. 2,3) The PDC measurement technique has already been applied as a quality assurance tool for the assessment of the refurbishment efficiency of power transformers. [1][2][3][4][5][6][7][8][9][10][11][12][13] Researchers [14][15][16][17][18] have applied such a technique to the insulation assessment of power and cross-linked polyethylene (XLPE) cables. ...
... The dielectric properties of an insulating material change with moisture, ageing, and contamination. 1) For typical measurement conditions, the conductivity of oil affects the polarization current mainly in a time range t < 100 s. 2,3) The PDC measurement technique has already been applied as a quality assurance tool for the assessment of the refurbishment efficiency of power transformers. [1][2][3][4][5][6][7][8][9][10][11][12][13] Researchers [14][15][16][17][18] have applied such a technique to the insulation assessment of power and cross-linked polyethylene (XLPE) cables. ...
Nondestructive and time domain dielectric measurement techniques such as polarization and depolarization current (PDC) measurements have recently been widely used as a potential tool for determining high-voltage insulation conditions by analyzing the insulation conductivity. The variation in the conductivity of an insulator was found to depend on several parameters: the difference between the polarization and depolarization currents, geometric capacitance, and the relative permittivity of the insulation material. In this paper the conductivities of different types of oilpaper insulation material are presented. The insulation conductivities of several types of electrical apparatus were simulated using MATLAB. Conductivity insulation was found to be high at high polarizations and at the lowest depolarization current. It was also found to increase with increasing relative permittivity as well as with decreasing geometric capacitance of the insulating material. © 2012 The Japan Society of Applied Physics.
... Polarization and depolarization current (PDC) analyses can easily identify whether the cause of insulation trouble is due to conduction such as that in free water or in an ageing process due to temperature effects. The dielectric properties of an insulating material change with moisture, ageing, and contamination. 1) For typical measurement conditions, the conductivity of oil affects the polarization current mainly in a time range t < 100 s. 2,3) The PDC measurement technique has already been applied as a quality assurance tool for the assessment of the refurbishment efficiency of power transformers. [1][2][3][4][5][6][7][8][9][10][11][12][13] Researchers [14][15][16][17][18] have applied such a technique to the insulation assessment of power and cross-linked polyethylene (XLPE) cables. ...
... The dielectric properties of an insulating material change with moisture, ageing, and contamination. 1) For typical measurement conditions, the conductivity of oil affects the polarization current mainly in a time range t < 100 s. 2,3) The PDC measurement technique has already been applied as a quality assurance tool for the assessment of the refurbishment efficiency of power transformers. [1][2][3][4][5][6][7][8][9][10][11][12][13] Researchers [14][15][16][17][18] have applied such a technique to the insulation assessment of power and cross-linked polyethylene (XLPE) cables. ...
Nondestructive and time domain dielectric measurement techniques such as polarization and depolarization current (PDC) measurements have recently been widely used as a potential tool for determining high-voltage insulation conditions by analyzing the insulation conductivity. The variation in the conductivity of an insulator was found to depend on several parameters: the difference between the polarization and depolarization currents, geometric capacitance, and the relative permittivity of the insulation material. In this paper the conductivities of different types of oil-paper insulation material are presented. The insulation conductivities of several types of electrical apparatus were simulated using MATLAB. Conductivity insulation was found to be high at high polarizations and at the lowest depolarization current. It was also found to increase with increasing relative permittivity as well as with decreasing geometric capacitance of the insulating material. (C) 2012 The Japan Society of Applied Physics
... The formulation of objective function penalizes the deviation of dispatch generators like load shedding. Some literatures [8][9][10] are based on Direct Current (DC) power flow approximation to gain the advantages of a linear model. In these references, authors have formulated the OLC problem as a Linear Programming (LP) problem. ...
For the restoration of market equilibrium in contingent power networks and simultaneous optimization of generation cost, congestion management cost and load curtailment, this paper proposes swarm intelligence based multi-objective optimization technique. As the modern power market operates under optimum stress, along with generation cost, congestion management cost and the load curtailment should be given equal importance in optimization to ensure both economy and reliability. The methodology adopted in this paper includes aforesaid considerations by performance indices namely VOLL (Value of Lost Load), VOCC (Value of Congestion Cost), and VOEL (Value of Excess Loss). It has been demonstrated that the proposed method effectively reduces the operating cost volatility in spot power market with respect to the conventional method. The applicability of the developed method has been tested on the IEEE 30 bus system. The results obtained by the proposed method are found to be quite encouraging when compared with the other two conventional methods, suggesting the imperativeness of the method in electrical restructuring.
... In[13],[14]load curtailment based congestion management has been proposed, but the value of lost load (VOLL) may restrict its practical implementation. In[15]a generator and load participation factor based congestion management technique has been proposed which curtails the specific loads contributing more to congestion. But sustained load curtailment may again be prohibitive in many systems. ...
This paper presents a Swarm intelligence based Optimization technique to manage congestion in power system networks with transmission line overload. To maintain all the power transaction requests, line congestion in deregulated systems is almost inevitable which may degrade the system stability, security and reliability by additional line outages. In practice System Operators (SO) charge additional price known as congestion management charge against line limit violation. Thus, restricting power flows within the safe limits is important from stability as well as economy point of view. The algorithm proposed in the present paper uses a standard congestion sensitivity Index to identify the congested lines in a large power network and optimizes 'congestion management charge' without any load curtailment and installation of FACTS devices. The operating conditions with the proposed methodology have been demonstrated to be subjected to an improvement with reference to conventional method. The applicability of the proposed methodology has been presented on IEEE 30 bus benchmark system.
This chapter discusses the analysis of the impact of high levels of microgrid (MG) penetration on power system stability from various points of view such as frequency stability, small‐signal stability and voltage performance. Methods for interpreting system dynamics in terms of simplified system representations are developed, and criteria to determine maximum penetration levels are given using deterministic and statistical sensitivity analyzes. A systematic methodology for the analysis of the impact of distributed MGs on power system dynamic behavior is proposed. It is well known that MGs are realized in the low and medium voltage levels. Therefore, following the calculation of maximum penetration levels of MGs, the chapter tries to determine the maximum active power that MGs can inject into each bus of the distribution system without causing steady‐state voltage violations.
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