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An Adaptive Zone-Division-Based Automatic Voltage Control System With Applications in China Hongbin Sun, Senior Member, IEEE, Qinglai Guo, Member, IEEE, Boming Zhang, Fellow, IEEE, Wenchuan Wu, Member, IEEE, and Bin Wang
Abstract—The power industry in China has grown significantly over the past decade, spurring the adoption of system-wide automatic voltage control (AVC) technology to meet stricter requirements for security and economical power system operation. To cope with the rapidly developing and frequently changing Chinese power grid, an AVC scheme based on adaptive zone division is introduced in this paper. Logically, this type of system has a three-level hierarchical structure, but, here, both secondary voltage control (SVC) and tertiary voltage control (TVC) are implemented via software at the same control center. The control zones are no longer fixed but are reconfigured online and updated in accordance with variations in the grid structure. The technical details of these procedures are presented here. The implementation of TVC (which is based on an online reactive optimal power flow) and SVC are also discussed, together with some technical details on improvements to their reliability and robustness. Some results obtained from field-site applications based on similar-days testing rather than simulations are employed to evaluate the performance and improvements of the AVC system. This system has been installed at over 20 control centers in China. Index Terms—Adaptive network zone division, automatic voltage control (AVC), control center, secondary voltage control (SVC), tertiary voltage control.
I. INTRODUCTION
I
N the previous century, voltage control of electric power systems in China was traditionally accomplished in a decentralized way at the power plant or substation level, with little in the way of system-wide coordination. However, as the scale and operational complexity of a power grid increase, it becomes increasingly infeasible for system operators to manually coordinate a large number of widely distributed voltage/VAR-control devices in real time. As a result, voltage violations and unexpected massive reactive power flows may occur, degrading the reliability and efficiency of the transmission grid. Accordingly, in recent years, a closed-loop, system-wide automatic voltage Manuscript received June 03, 2012; revised September 28, 2012; accepted November 10, 2012. Date of publication December 20, 2012; date of current version April 18, 2013. This work was supported in part by the National Key Basic Research Program of China (973 Program) under Grant 2013CB228203, the National Science Foundation of China under Grant 51277105, and the National Science Fund for Distinguished Young Scholars of China under Grant 51025725. Paper no. TPWRS-00600-2012. The authors are with the Department of Electrical Engineering, State Key Laboratory of Power Systems, Tsinghua University, Beijing 100084, China (e-mail:
[email protected];
[email protected]; zhangbm@mail. tsinghua.edu.cn;
[email protected];
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPWRS.2012.2228013
control (AVC) technique has been developed and implemented in China. The first system-wide AVC system was established in Europe. Three-level hierarchical voltage control architecture, based on the concepts of zone division and pilot buses, was introduced in France [1]–[4]. This type of hierarchical control scheme [5], [6] consists of primary voltage control (PVC), secondary voltage control (SVC) and tertiary voltage control (TVC), defined here. • PVC is a kind of local control, such as an automatic voltage regulator (AVR), which maintains generator terminal voltages at set-point values. PVC protects against rapid voltage variation over a short interval (such as a few seconds). • SVC keeps the pilot buses at the reference voltages by updating the PVC set-point values in a given control zone. Pilot buses are selected to represent the overall voltage profiles in a control zone. SVC is based solely on information in the given control zone and has a time constant of a few minutes. • TVC, which is the highest level in the hierarchical control architecture, is designed to minimize power transfer losses while considering security constraints. It updates the reference values for the pilot bus voltages of all of the secondary control zones. TVC is a kind of system-wide optimization control, with a time constant ranging from 15 min to several hours. SVC has been implemented by Electricité de France [EDF] [1]. Up to 1985, nearly all French power grids were equipped with secondary voltage controllers, including 27 control zones and more than 200 generators, with a reactive power regulation capacity of 30 Gvar. Similar hierarchical control architecture has also been put into operation in Italy [7]–[9]. It consists of one national voltage regulator (NVR), three regional voltage regulators (RVRs), and 35 voltage and reactive-power regulators (REPORTs) installed in power plants, with an overall capability of about 20 Gvar [9]. A reactive optimal-power flow (ROPF) for loss-minimization control (LMC), which was adopted for the NVR, predicts the optimal voltages and reactive levels over the short term (one day ahead) or very short term (minutes ahead) based on foreseen/current state estimation. Furthermore, the tertiary voltage regulator (TVR) minimizes the differences between the actual field measurements and the optimal predicted references and then resets the RVR set-point values [8]. Research and implementation of AVC has also been carried out in many other power grids, including those of Belgium [10], Bonneville Power Administration [BPA] [11], Brazil [12], Spain [13], South Africa [14], and TOKYO Electric Power
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SUN et al.: ADAPTIVE ZONE-DIVISION-BASED AVC SYSTEM WITH APPLICATIONS IN CHINA
Company (TEPCO) [15]. In North America, considering the electricity market issues and voltage/VAR management architecture, a distributed local control mode that provides regulation of high-side voltage of power plants and fast switching of shunt capacitor banks was implemented, which is significantly different with the hierarchical control scheme but also proved to be very effective on improving power system stability and reducing transmission losses [16], [17]. As AVC is a practical technology that directly affects a power grid, each of the above systems has been tailored to the features of the power grid to be controlled. In other words, different power grids are confronted with different challenges, which may ultimately result in different AVC schemes. The European hierarchical voltage control system consists of geographically distributed SVCs, which were typically established on the basis of offline studies [3] and then became fixed entries in the distribution. However, original weak-coupled control zones may be altered if the power grid structure or operational scheme changes dramatically enough to degrade the control performance of the SVCs. In China, the electrical power industry is now developing very rapidly. In 2000, the generating capacity of the whole of China was 300 GW, whereas in 2008 this figure increased to 790 GW. The added generating capacity over the five years from 2002 to 2007 was nearly equal to the total for the 53 years prior to 2002. As a consequence, high-speed construction of power grids has taken place in China during those years, and the power-network structure has changed significantly and frequently. This is why an architecture based on a fixed zone division is not suitable for Chinese power grids. On the other hand, nearly all of the control centers and substations have fiber-optic communication equipment, which ensures the robustness and high quality of real-time data transmission. In view of the strong demand and the advanced communication facilities, a novel hierarchical AVC system based on online adaptive network zone division has been adopted in China. This system was briefly introduced at the IEEE PES General Meeting of 2009 [18], and the details are reported here, including recent developments and additional field results. Logically, such a system has a three-level hierarchical structure, but here, both SVC and TVC are implemented via software at the same control center. Also, the control zones are no longer fixed but are reconfigured online and updated in accordance with variations in grid structure, which is more suitable for the rapidly developing Chinese power grids. The hierarchical AVC system was first implemented in the Jiangsu provincial power system in 2002 [19] and has been extended to more than 20 control centers in China over the past decade. The control architecture based on adaptive zone division is introduced in Section II. Aside from the architecture itself, there are some essential issues to be considered, including: 1) realizing adaptive zone division while power grids are changing; 2) realizing the ROPF-based online TVC module and improving its robustness; 3) realizing the SVC module and making it reliable under all kinds of potential disturbances; and 4) realizing the local voltage controllers on the power plants. Key technologies for resolving these issues are presented in Section III. It is difficult to evaluate the control performance of an online
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Fig. 1. Physical architecture of the hierarchical AVC system.
closed-loop system because we cannot create two identical scenarios online to test pre-control and post-control results, as is done in the simulations. Hence, field-testing methods based on similar days were adopted, and some important results are reported in Section IV. Finally, Section V contains a brief summary of conclusions. Furthermore, to explain the concepts and technologies more clearly, some simulation results are also presented in appendix. Due to space limitations, it is not possible to cover all relevant techniques in this paper, and further details will be presented in future papers. II. CONTROL ARCHITECTURE As shown in Fig. 1, the hierarchical AVC system is physically composed of two parts: the power-plant voltage controllers (PPVCs) and the control-center master system (CCMS), outlined here. • Part 1 is the PPVCs, which are decentralized control devices installed in power plants to track commands from the control center. The details inside the PPVCs will be introduced in Section III-D. In addition, if the capacitors, reactors, and tap changers are also required to be closed-loop controlled by the AVC system (as the situations in some regional power grid in China), the substations will also be involved in the control architecture. In most of these regional grids, the discrete devices are directly controlled by the control center via remote signals to switch on/off the breakers or regulate the tap changers, and no physical decentralized voltage controller is built on substation side. It is different from the power plants. • Part 2 is the CCMS, which is the centralized control system installed in the control center to coordinate the actions of all decentralized PPVCs. Output from the CCMS is sent down to the PPVCs in plants or substations through existing communication channels. The CCMS in this paper consists of the following modules. 1) The adaptive zone division (AZD) module adaptively divides the overall power grid online into weak-coupled control zones using a zone-partition algorithm based on local voltage/Var parameters [20].
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III. SOME KEY TECHNOLOGIES A. Adaptive Zone Division
Fig. 2. Data structure supporting the adaptive hierarchical AVC architecture.
2) The TVC module coordinates and adjusts the reference voltages of the pilot buses in all control zones using are active optimal power-flow algorithm. 3) The SVC module maintains the pilot bus voltages at the reference values by updating the set-point values of PPVCs and the remote control signals for reactors, capacitors, and tap changers at the substations of each zone. Unlike European AVC schemes, there is no actual zone-control center for secondary-voltage regulation here. The SVCs for different control zones are separated only logically, not physically. The SVCs and the TVC are all implemented at the same control center using software modules. The number of SVCs and the control zone covered by each SVC are not fixed but are adaptively adjustable via the AZD module in accordance with the power-system operating status. In Europe, control zone division, pilot bus selection, and control generator selection were all carried out before the SVCs were implemented [1]–[3]. However, in China (as a preliminary study), power plants with the best control effects are usually chosen first and furnished with PPVCs, whereas zone division and pilot bus selection are determined online during CCMS implementation. Once a PPVC has been implemented, it is supposed to be part of the SVC of a certain control zone. This type of relationship between an SVC and a PPVC is specified by the AZD module of the CCMS. It is the most important part of control zone division and is only used for control-strategy calculations on the control-center side. A PPVC never knows, and never needs to know, the specific control zone to which it has been assigned in the CCMS. It simply communicates with the CCMS by sending data upward and receiving commands from the control center. Fig. 2 shows the data structure of the AVC architecture in a Unified Model Language (UML) format, where the parts covered by the colored rectangle are not permanent and are supposed to be regenerated or reconfigured automatically by the AZD module in accordance with the evolution of the power grid. This includes the following three steps indicated in the figure: 1) determining the optimum number of zones;2) clustering the power plants and substations in the correct zones; and 3) selecting the most representative nodes as pilot buses. The details of these procedures are discussed in Section III
There are many methods for dividing a power grid into weakcoupled control zones, online or offline. Basically, the problem involves the following two subproblems: defining an“electrical distance” that describes the degree of “difference” between two nodes, and then merging the nodes into clusters according to “electrical distance.” In the adaptive method introduced here, another important subproblem is to determine the optimum zone division, especially the optimum number of zones. Based on the sensitivity of voltage with respect to Mvar output, the concept of “VAR control space” was introduced in [20]. Suppose that there are reactive power sources (such as generators or dynamic reactive power compensators) and nodes to be partitioned in the power gird, the sensitivity of the node’s voltage with respect to the th reactive power source’s VAR output is denoted as . The “VAR control space” is defined as a -dimensional Euclidean space spanned by the reactive power sources, and each load node can be described by a coordination vector in this space, where is defined as (1) For
two
nodes , the electrical distance
and is
then defined as (2) Based on the above definition, the each component of a node’s coordination vector represents how much the node is coupled with a specified reactive power source. Hence, those nodes that strongly coupled with the same set of reactive power sources are with less distance and shaped as a cluster in the space. Thus, the problem of power-network zone division is transformed into a clustering-analysis problem in a higher dimensional Euclidean space, which is solved via a hierarchical clustering algorithm. The hierarchical clustering method is a process of merging zones step by step [21]. Based on the electrical distance between two nodes, the distance between two zones (marked as zone distance) can also be defined in several different ways [3] that can be customized by operators. Some candidate definitions of the distance between zone and zone includes the following. • Maximum distance: is defined as the maximum distance of the two zones’ members. • Minimum distance: is defined as the minimum distance of the two zones’ members. • Average distance: is defined as the average distance of the two zones’ members. First, each node is a zone, and, in each step, the closest two zones (in terms of zone distance) are merged into one until all nodes have finally been merged into a single zone. Of course, zone divisions resulting in different numbers of zones are provided during such a merging process. It is interesting to note that
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Fig. 3. Determining the optimum number of zones based on hierarchical clustering.
a reasonable number of control zones can be determined automatically according to the variation in the average zone distance , which is defined as (3) where is the current number of zones. The larger is, the weaker the coupling is. Hence, the number of control zones with the largest average zone distance is generally adopted as the optimum choice. In the example shown in Fig. 3, six zones will be the autoselected result. If is determined to be the optimum number of control zones, then the zone division result with zones is chosen. All generators with PPVCs are assigned to a certain control zone with the largest regulating sensitivity. From all the nodes in a control zone, several dominant nodes are selected as pilot buses based on short circuit capability and control sensitivity [3], [8]. First, the node with the strongest short circuit capability in this zone is selected as the first pilot bus. Second, other nodes that are most closely coupled with this pilot bus (the electrical distance between them is less than a threshold) will be marked. Then, such a process will be carried out recursively within the remained unmarked nodes to find out the next pilot bus until all the nodes in this zone are marked. In a commercial AVC system, operators are also able to select pilot buses manually according to their own experience and the measurement configurations. In this way, the control model (including information on control-zone division, pilot-bus selection, and control-generator assignment) is established and saved in a real-time database. There are two independent loops with different time-period parameters, as shown in Fig. 4. The left-hand loop is the AZD loop for evaluating and reconfiguring the control model, and the right-hand loop is the AVC loop for calculating the voltage control strategy based on the control model. At the beginning of each AVC loop, the current control model is loaded from the real-time database and incorporated into the control-strategy calculations. Thus, once the control model has been reconfigured by AZD and saved in the database, the latest version will take effect adaptively. Fig. 4 provides a mechanism to implement voltage control based on the adaptive reconfiguration of control model. The AZD loop can be carried out in a predefined period, or triggered by some significant topology change, or manually started by operators. Generally, the threshold is set to an appropriate value to avoid frequent zone reconfiguration
Fig. 4. Diagram of the online AZD-based voltage control.
in actual practice. A zone-change warning is transmitted to operators when necessary, and a new control model is not saved into the database or put into effect until the operators have confirmed it. B. Implementation of the ROPF-Based TVC The optimal reference voltage values for the pilot buses in all control zones are computed by an online ROPF module, with the goal of minimizing transmission loss while considering security constraints. Typically, the objective function of a ROPF model has the form (4) with the constraints
(5) (6) (7) (8) and the following nomenclature: Set of branches. Active power of the branch node (from to ).
on the start
Active power on the branch node (from to ).
on the end
All of the nodes that are connected to the node , including . Phase angle of the slack bus. Number of buses. Number of control generators.
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Number of transformer tap changers. ,
Active and reactive power of the generator on bus .
,
Active and reactive power of the load on bus .
,
TABLE I AVAILABILITY RATIO OF ROPF IN SOME TYPICAL POWER GRIDS
Voltage magnitude and phase angle of bus . Tap ratio of the transformer . ,
Real and imaginary parts of the bus admittance matrix element on the th row and th column, the tap ratio is considered when calculating and during iteration. ,
Reactive power limits for the generator on bus . Tap ratio limits for the transformer
, ,
Voltage magnitude limits for bus
In (4)–(8), the actual control variables include the terminal voltage of generators and the tap ratio of transformers. A tap ratio is considered as continuous variable when computing and will be rounded to the nearest integral tap position finally. If the shunt compensators are also supposed to be optimized (such as in some regional power grid in China), they will be transformed into additional virtual generators to be taken into account in the ROPF model as continuous variables and finally discretized into appropriate banks. It should be noted that the generator reactive power limitations and are not constant but are dependent variables with respect to the current active power , as defined by the reactive capability curves [22] and taking into account the armature current limit, field current limit, end-region heating limit, and so on. This constraint is also adopted in the SVC model. This is a typical nonlinear OPF problem and there have been many algorithms proved to be effective to solve it. Here, an efficient hybrid decoupled optimal power-flow approach is adopted to take advantage of the inherent weak coupling characteristics between the real and reactive power flows [23].The solution to the original optimization problem can be approximated iteratively by alternately solving the real and reactive subproblems. The reactive subproblem, based on sparse quadratic programming, is solved iteratively while the active subproblem is held constant. Some detailed description about the hybrid decoupled method can be found in the appendix, and more information can be referred from [24]. The ROPF result can be computed in about three to five times the CPU time required for the fast decoupled load flow[23]. An optimized power flow can be solved by the above method, in which the optimal voltage magnitude for bus is denoted as . From the current control model created by the AZD module, the TVC module will know where the pilot buses are. Thus, the voltage of these pilot buses will be pick out from and denoted as a vector , which will be outputted to the corresponding SVCs as reference values. The ROPF cycle of execution varies from 15 min
to 1 h and is adaptive according to the predicted daily load-demand curve, which means that the time interval between two ROPFs will be shortened when there is a dramatic change in the load demand. In present-day China, the communication and measurement infrastructure is still not strong enough to eliminate all data-acquisition problems leading to less-than-ideal data quality. Hence, on occasion, the ROPF-based TVC may not find a convergent solution if a degraded state estimation result is used as input. According to the statistics on history operation data, the availability ratios of ROPF in some typical power grids are shown as Table I. The availability ratio is defined as a percentage to evaluate that how many times the ROPF is successfully calculated of all the ROPF calculations. The unavailable of ROPF may be caused by three reasons: 1) the SE is not converge; 2) the SE is converge but its average or residuals are over a predefined threshold; 3) the ROPF itself is not converge. As our experience, the situations 1) and 2) are more possible to happen. To ensure 24-h uninterrupted operation, substitute methods must be provided if the optimal reference voltages are not available. For this purpose, there is a history database in which the optimal reference voltage curves for all the pilot buses are saved every day. If an exception occurs, AVC will check the database and select a day whose operational features are similar to those of today. The optimal reference curves for that day are then adopted as alternative output and sent to the SVCs. Furthermore, for each pilot bus, a manual reference curve can be predefined by operators according to their own experience, and this can be used as a backup alternative reference if a similar day cannot be found. These alternative reference curves are essential for improving the robustness of TVC based on solutions that are not optimal, but satisfactory. When the optimal reference is replaced by an alternative curve, a maximum control step is necessary to avoid a large difference between the current control reference and the alternative objective during the next control loop. We typically set the maximum control step at 1.0 kV. C. Implementation of SVC A quadratic programming model similar to the EDF CSVC model [24] is adopted to compute the SVC strategies [25]. The primary goal of this model is to control the voltages at the pilot buses to follow the optimal reference values as updated by the TVC module, and the secondary goal (with lower priority) is to equilibrate the Mvar reserve distribution among all the control generators in each control zone to enhance the security of
SUN et al.: ADAPTIVE ZONE-DIVISION-BASED AVC SYSTEM WITH APPLICATIONS IN CHINA
the power system. In each SVC module, the two goals are coordinated in a single objective function based on a series of voltage/Var constraints, which include (9) (10) (11) denotes the current reactive power output vector of Here, control generators and denotes the regulation amount to be determined by this round of control. and denote the voltage of pilot buses and the power plants’ high side voltage respectively. and are the sensitivity matrix. Equations (9) and (10) are the voltage operation limits of the pilot buses and the high-side buses in power plants, which can be customized for peak hours and off-peak hours. Equation (11) shows the reactive power operation limits of the control generators, which are dependent variables with respect to the active power output as mentioned in Section III-B. The SVC module for each zone is just a small-scale optimization problem and can be solved easily and efficiently. The sensitivity is calculated online via a module called SENS. A systematic quasi-steady sensitivity analysis method is adopted here to enhance the accuracy of the sensitivity calculations [26]. Rather than using state-estimation (SE) results, raw data from SCADA are adopted as SVC input. Not depending on the SE results is a key feature of SVC to ensure its reliability, just like AGC does not depend on the SE results either. The SE results are the optimized value depending on all the related measurements, so sometimes a topology error, an incorrect parameter or some unimportant measurements with low accuracy may result in large residuals on the important points after estimation. In East China Power Grid, for example, on February 11, 2011, the status error of a breaker finally led to a residual of 6.0 kV on some 220-kV buses. If such a result was directly adopted by the closed-loop control, it would have caused unacceptable results. Using the raw data instead, only a few key measurements from each zone (such as pilot bus voltages, voltages of high-side buses in plants, and reactive outputs of controlled generators) are necessary, and the field experiences show that these key measurements are more credible than the estimated results after promotion of measuring accuracy. This technique has proven to be robust against upward-communication interruptions in which data from some plants, substations, or control areas become unavailable. If an affected site is not a key point (where pilot buses or control generators are located), it will not influence SVC calculations, which are based solely on key measurements. On the other hand, the SVC for a control area with a communication problem will be out of operation, and all PPVCs in that area will be degraded to local control according to the predefined voltage curves. However, the problem will not affect any of the other control areas, which require only their own data. To avoid the influence of abnormal measurements, data must be passed through a filter module before being input to the control loop. One kind of filter focuses on each individual frame, taking into account the electrical constraints of different measurements. This is a local state estimator for a single substation or control zone, used to evaluate measurement quality
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rather than directly providing input data. If bad measurements are found, an alarm is triggered, and an “abnormal” flag is attached to data targeted for later control. Another kind of filter focuses on a single measurement carried out over several continuous samples (for instance, the last five data-acquisition periods). For example, suppose a voltage measurement , the last five samples at time step n are respectively denoted as , , , , and . A weighted average for these samples, rather than only, is inputted to the SVC. is A typical calculation formula of (12) where ject to
are the weighted coefficients that sub-
(13) All of these coefficients can be configured by the operators. In this way, variational trends are taken into account, and stability is provided by filtering out sudden changes and high-frequency fluctuations. Basically, SVC modules only regard generators as control devices. However, in some Chinese power grids, capacitors, reactors, and tap changers in substations must also be remotely controlled by the AVC system. A practical guideline is adopted for coordinating power plants (continuous control variables) and substations (discrete control variables), in which discrete variables are first adjusted to meet the basic reactive power demands, and continuous devices are held as dynamic reserves [27]. D. Implementation of PPVCs As mentioned above, the AVC system is composed of a CCMS at the control center and PPVCs in power plants. The PPVCs are in charge of local control at the power plant level, which entails receiving the set-point values from the control center and allocating reactive power among the generating units in the plant to track the set-point commands. In general, a set-point value from the CCMS is one of the following two choices: the high-side bus voltage or the total reactive power of all the generators in the plant. In China, the former is used in most situations, based on the following considerations. First, using the high-side bus voltage creates a clear interface between the CCMS and the PPVCs. The CCMS, which is more concerned with the security and economical operation of the power grids, sends its objective for the high-side bus voltage. The PPVCs regulate the reactive outputs of the generating units to follow the control objective. Such a separation is consistent with equipment proprietor ship among power grid companies and power generation companies in China. Second, compared with reactive power, the voltage variation over a single day is much more stable and predictable. Thus, power plant operators can easily predefine set-point curves for the high-side bus voltage as a backup. If something goes wrong with the communication between the CCMS and the PPVCs, the PPVCs are still able to operate in local-control mode by
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TABLE II COMPUTATIONAL INDICES OF TYPICAL POWER GRIDS
Fig. 5. Sketch map of the VQR module in PPVC.
tracking the predefined curves, thereby ensuring greater control reliability. Third, comparing with the conventional AVR that keeps the terminal bus of generator to be constant, PPVCs can be regarded as a kind of high-side voltage control [28] in power plant, which makes the high-side bus of the setup transformer to be a new “constant” voltage node under disturbance and reduces the electrical distance between the load center and the “constant” voltage node. It is proved to be positive to enhance the power grid security [16]. The detailed function of the VQR (Voltage/VAR Regulator) module in Fig. 1 is shown in Fig. 5, which takes a two-generator (named as and ) power plant (named as ) for an example. There are three steps in the VQR. In step I, based on the – sensitivity on the high-side bus, the VQR transfers the bias of the high-side bus voltage into the total reactive power demands for the power plant , which is calculated as . Since the Thevinin’s equivalent circuit of the external power cannot be accurately calculated from grid connecting with the side, a practical method is adopted to estimate online using the measured data from the latest regulation on the previous control round. In step II, will be allocated to all of the generator units that connected with the same high-side bus via step-up transformers. Several allocating methods are provided, such as with equal outputs, with equal reservation ratios or with equal power factors. The reactive power consumed by the step-up transformers will be considered and removed before allocation. Once the reactive power regulating amount of each generator unit is known, it will be transferred into the terminal voltage regulating signal in step III via a PI controller. In general, steps I and II are implemented by an industrial control computer located in the power plant control room, and step III is implemented by a programmable logic controller (PLC) for each generator unit. For downward communication between the CCMS and the PPVCs, two-way communication with a main channel and a spare channel is adopted. The spare channel is automatically put into operation when the main channel is out of service. In the worst case scenario in which both channels have failed, the PPVCs are not able to receive control commands from the CCMS. In this case, after a specified timeout (e.g., 20 min), the PPVCs automatically switch to local control. As soon as communication has been restored, the PPVCs return to remote-control mode.
E. Computational Indices Some computational indices for the above key technologies are presented in Table II. Two typical power grids are selected: the Jiangsu power grid (as an example of a provincial power grid) and the North China power grid (as an example of a regional power grid). It can be concluded that the computational speeds for all key technologies are sufficiently fast to satisfy real-time requirements. IV. FIELD-SITE APPLICATIONS The regional power girds and the provincial power girds are two important level of the hierarchical operation architecture in China. A regional power gird is composed of several provincial power grids geographically, for example, the East China Power Gird involves five provincial grids on map: Jiangsu, Zhejiang, Anhui, Fujian, and Shanghai. However, the operation duties of a regional grid and a provincial grid are not overlapped. Generally, a regional power grid mainly operates the transmission grids over 500-kV voltage level, while a provincial power grid operates the grids of the 220-kV voltage level. The AVC system presented here has been applied to five regional grids (of only six regional grids in all of China), with generating capacities totaling more than 700 GW. It is also in use in 11 provincial grids (of 31 provincial grids) and several distribution grids. A partial list of these grids is compiled in Table III. As an example, some field results from the Jiangsu Power11 Grid (a typical provincial grid) are reported here. The Jiangsu Provincial Power Grid, which is the largest power grid of State Grid in China, has been closed-loop controlled since 2002 by the AVC system presented in this paper. During construction, many field tests were carried out to evaluate the performance of the system. The results of these tests show that significant improvements in voltage quality, system security, and cost effectiveness have been achieved. A typical man machine interface (MMI) of the Jiangsu AVC system is shown in Fig. 6. Some field-application results are presented below. First is the AZD field test result. Table IV presents the statistics data on the adaptive re-configuration times of the control zones in Jiangsu power gird from 2005 to 2011. From 2005 to 2008, the re-configurations are mainly caused by the construction of new substations or new transmission lines, while from 2009 to 2011 the operation scheme readjustment became the main reason.Figs. 7 and 8 show adaptive zone division results for the Jiangsu power grid on March 16, 2010, when several 220-kV transmission lines (SI_HUANG_LN,
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SUMMARY
OF
TABLE III AVC APPLICATIONS
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IN
CHINA
Fig. 6. Typical MMI of the Jiangsu AVC system.
ZHONG_SHEN_LN, LU_XIANG_LN and QI_TONG_LN) were disconnected to open the 500 220-kV electromagnetic loops. As a result, one of the original control zones (before regulation, labeled Zone 7 in Fig. 7) was automatically divided into two zones (Zones 7 and 8 in Fig. 8) by the AZD module. All of the pilot buses and power plants involved in the SVCs are shown in Figs. 7 and 8, which also provide an overview of the Jiangsu AVC system. To evaluate the effects of the SVCs and the TVC, 2 days with similar operational statuses and load demands are selected. One of these is November 17, 2003 (the day AVC was put into operation), and the other is November 18, 2003 (when AVC was suspended). Fig. 9 shows the voltage curves of the LI_YUAN-220 kV bus, which is a typical pilot bus in the Jiangsu power grid. The blue dotted curve and red solid curve indicate the voltage profiles for the 2 similar days, without and with AVC, respectively. It is obvious from the figure that
Fig. 7. Adaptive zone-division results before operation-scheme regulation.
the voltage profile becomes flatter with AVC. The maximum peak-to-valley difference for the day without AVC was 3.9 kV, which was reduced to 2.0 kV with AVC. Thanks to the pilot-bus-based control scheme, the voltage profiles of non-pilot buses were also improved. As an example, the voltage curves of the SU_QIAN-220 kV bus (a nonpilot node in the same control zone as the LI_YUAN-220 kV pilot bus) are shown in Fig. 10, where much smaller voltage profile fluctuations are also observed with AVC. The load-margin index, used to evaluate the improvement in static voltage stability, is calculated based on continuation power flow (CPF) for a specified district. The evaluation is made for Wu xi (one of the heaviest load districts in Jiangsu) by comparing two margins, respectively calculated before and after every round of voltage control on March 15, 2006, and shown
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Fig. 11. Load-margin index comparison before and after AVC.
Fig. 12. Long-distance reactive power transmission with and without AVC. Fig. 8. Adaptive zone-division results after operation-scheme regulation.
STATISTICS
OF
TABLE IV CONTROL ZONES RE-CONFIGURATION TIMES POWER GRIDS
IN JIANGSU
Fig. 13. System loss-ratio curve with several control steps after AVC was put into operation.
Fig. 9. Voltage profiles of a pilot node with and without AVC.
Fig. 10. Voltage profiles of a non-pilot bus with and without AVC.
in Fig. 11. An average load-margin increase of nearly 80 MW is observed after AVC. Considering the control-step limitation in every round, the improvement in static voltage stability is satisfactory. The voltage distribution is also more reasonable after control, so that reactive power transmission between different zones is greatly reduced. PAI_DU_LN, a long-distance ac line providing the interface between two control zones, was frequently
observed to carry large amounts of reactive power before AVC construction. This is indicated by the blue dotted line in Fig. 12, which represents the reactive power transmission on August 25, 2004 (with an average of 170.28 Mvar). On the other hand, on September 2, when the load level and the active power schedule of PAI_DU_LN were very similar to that of August 25, and AVC was put into operation, the average reactive power amount was reduced nearly 28% (to 122.04 Mvar), as indicated by the red solid line in Fig. 12. Two kinds of tests were adopted to evaluate the effect of AVC on reducing transmission losses. The first of these was focused on short-term effects. We selected a specified time from 15 to 16 pm on September 8, 2004, during which the system load was supposed to be stable and even. To filter the influence of differing load demands even further, loss ratio rather than loss itself was selected for comparison. Loss ratio is an index expressed as a percentage and defined as the transmission loss (in MW) divided by the total load demand of the grid (in MW). At 15:15, AVC was put into operation. A decreasing trend is very obvious in the resulting loss-ratio curve, as shown in Fig. 13. Transmission losses for the 2 similar days were also saved and compared as a long-term test. The loss-ratio curves for the two days are compared in Fig. 14, which shows a significant reduction (from 1.29% to 1.24%). A longer term field test (over several months) has shown that about 5% transmission loss reduction can be achieved by using AVC. This amounts to a saving of more than 5 million U.S. dollars per year for Jiangsu Electrical Power Company.
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Fig. 14. System losses before and after AVC was put into operation.
V. CONCLUSION AVC techniques widely applied throughout China are described in this paper. The following concluding remarks are appropriate. 1) A novel VCS based on AZD is introduced, in which the control zones are not fixed but are updated online in accordance with variations in the grid structure. The SVC and TVC modules are logically hierarchical but are implemented at the same control center. Such a scheme is suitable for a rapidly developing power grid. 2) Thanks to the methods presented here for improving the robustness, a ROPF-based TVC has been implemented and shown to be essential for security and economical operation. 3) Some key techniques are introduced for making the overall AVC system reliable and practical, facilitating 24/7 operation despite poor measurements or communication interruptions. The AVC system has been applied to more than 20 control centers in China, and significant improvements in power system security and economy have been demonstrated by field tests based on similar days. APPENDIX Simulation Results: Here, some simulation results based on IEEE 39-bus system are presented to verify the control performance of AZD based automatic voltage control. Adaptive Zone Division: Based on the original power network structure, the IEEE 39-bus system is divided into four zones, as shown in Fig. 15 where the pilot buses of each zone are ringed. An operation scheme readjustment is simulated to disconnect Line 4–14 and Line 6–11. The optimum number of zones before and after the readjustment is shown in Fig. 16. Before the operation-scheme readjustment, four zones will be the autoselected result, while the optimum number changes to five after the operation. The new division result with five zones provide by AZD is shown as Fig. 17, where zone 4 in Fig. 15 is divided into two zones (zone 4A and zone 4B) in Fig. 17. The pilot buses are also automatically selected as bus 5 and bus 12, for zones 4A and 4B, respectively. Two AVC scenarios are compared in the following sections: the first is based on the fixed zone division results in Fig. 15 (now the blue lines 4–14 and 6–11 are removed), which means without AZD, though the power grid has changed, and the other is based on the adaptive control model (including the new control zone division and new pilot bus selection) with AZD as shown in Fig. 17.
Fig. 15. Zone division result before the operation scheme changed.
Fig. 16. Determining the optimum number of zones before and after operation scheme readjustment.
Comparison Under Normal Condition: On the power grid after operation scheme changed, as shown in Fig. 17, suppose the system load demand has a 25% increase in one hour as Fig. 18. The two scenarios without and with AZD are compared. Fig. 19 shows the voltage of pilot bus without AZD (bus 6 in Zone 4 in Fig. 15) and pilot bus with AZD (bus 12 in Zone 4B in Fig. 17). Here, a control dead band of 0.002 p.u. is simulated as the commercial AVC system does. If the voltage bias with the reference value of the pilot bus is beyond the dead band, the SVC will take effect and hold the pilot bus voltage back. As Fig. 19 shows, both of the pilot buses’ voltages in the two scenarios can track the reference values by SVC. A nonpilot bus (bus 14) voltage is selected to be compared with its voltage curves under the two scenarios, with and without AZD, as shown in Fig. 20. It is obvious that the control performance with AZD is much better. When the load keeps increasing, the voltage of bus 14 can still be hold on a high and flat profile with AZD, as the red solid curve shows, while if the control model is not adaptively reconfigured, the voltages of bus 14 will dropped with the load increases. Fig. 21, which records the average voltage of all of the buses in zone 4B in Fig. 17, shows a similar result. After the operation scheme readjustment, zones 4A and 4B (in Fig. 17) are actually
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Fig. 20. Comparison of voltage of bus 14.
Fig. 21. Comparison of zone average voltage.
Fig. 17. AZD result after the operation scheme changed.
Fig. 18. System load curve to be simulated.
Fig. 22. Voltage curve of the pilot bus with and without AZD. (a) Voltage curve of the pilot bus without AZD. (b) Voltage curve of the pilot bus with AZD.
Fig. 19. Voltage curve of the pilot bus without and with AZD. (a) Voltage curve of the pilot bus without AZD. (b) Voltage curve of the pilot bus with AZD.
weakly coupled. If the control model is not adaptively reconfigured, though the old pilot bus (bus 6) is still well controlled [as shown in Fig. 19(a)], the control performances for the buses in
zone 4B of Fig. 17 are degraded, because their voltages cannot be represented by bus 6 now. Comparison Under Disturbance Condition: Supposing that a disturbance in Line 10–11 is tripped by a three-circuit grounding fault taking place on the power grid shown in Fig. 17, the two AVC scenarios as the former section are compared again. Fig. 22 shows the voltage deviation of the pilot bus without AZD (bus 6 in Zone 4 in Fig. 15) and the pilot bus with AZD (bus 12 in Zone 4B in Fig. 17). After the operation scheme readjustment with the outage of Line 4–14 and Line 6–11, bus 6 is now weakly coupled with zone 4B in Fig. 17, so there is almost no change of the voltage on bus 6 when the disturbance happens on Line 10–11, which means that the old pilot bus is unable to represent the voltage trends of the whole zone 4 if the control model is still fixed as shown in Fig. 15 after operation
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can be equivalently transformed into two partial duality subproblems
(15) and Fig. 23. Comparison of voltage of bus 11 after the disturbance.
scheme readjustment. However, the new pilot bus 12 with AZD is strongly coupled with zone 4B, whose voltage deviation is obvious when the disturbance happens. Bus 11, which is the nearest bus to the disturbance, is selected to be compared with its voltage curves under the two scenarios, with and without AZD, as shown in Fig. 23. Without AZD, the SVC in Zone 4 (in Fig. 15) is still carried out according to the voltage deviation of old pilot bus (bus 6), which is almost with no change after the disturbance. Therefore, the voltage recovery effect of the bus 11, shown as the bluedotted curve in Fig. 23, is very poor. On the contrary, with the reconfigured control model by AZD after the operation-scheme readjustment, the new pilot bus 12 is still close coupled with bus 11. When the voltage of pilot bus (bus 12) has been recovered to the set-point again by SVC, as shown in Fig. 22(b), the voltage recovery effect of bus 11 (nonpilot bus), shown as the red solid curve in Fig. 23, is much better. Hybrid Decoupled OPF Approach [23]: The coupling relationship between active powers and voltage magnitudes is relatively weak; similarly the relationship between the reactive powers and voltage phase angles is also weak. This is regarded decoupled feature, which has been widely adopted as the and applied in power system analysis. A successful example is the fast decoupled power flow method. This feature is also adopted here to solve optimal power flow problem. Vector denotes all variables, including both control and state variables. Subscripts and are used to represent variables related to active and reactive powers, respectively. Subscripts and are used to represent constraints related to equalities and inequalities, respectively. Thus, a typical OPF problem can be rewritten as
(16) where , , , and are dual variables corresponding to the constraints of (14). Problem (15) only contains the constraints relevant to real power flows, and the reactive power constraints are mainly involved in (16). Hence the two partial duality subproblems have fewer constraints and easier to be solved than the original OPF problem. Since the dual variables are unknown before (14) has been solved, an iterative solution process to solve the partial duality is carried out until both problems conand . verge to the same , and Given initial values of the primary variables the dual variables , , , and , the hybrid decoupled approach solves the two subproblems alternately until the Kuhn–Tucker conditions are satisfied. There is no constraint that closely related to the reactive power in (15), so the -related variables can be treated as constraints that subproblem iteration when solving is given by the latest (15). It is similar when solving (16) to treat as constants from the results of the last subproblem iteration. Different programming algorithms are adopted when solving the two subproblems. The subproblem is transformed into a linear programming model to take advantage of the good linear features of active power problems. Similarly, the subproblem is transformed into a quadratic programming model and is solved by a recursive quadratic programming method. The hybrid decoupled approach is also proved to be flexible in applications. For example, the active power subproblem can be solved independently and used for power flow corrective control. The reactive power subproblem can also be solved independently and used for voltage corrections or for minimizing system loss, which is utilized as ROPF in this paper. REFERENCES
(14)
where and are active and reactive power flow equations, and are inequality constraints that are respectively, and closely related to active and reactive powers. According to the theories of convex and partial dualities [29], if the initial solution of (14) is sufficiently close to the optimal solution and satisfying the local convexity condition, then (14)
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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 28, NO. 2, MAY 2013
Hongbin Sun (SM’12) received B.S. and Ph.D. degrees from Tsinghua University, Beijing, China, in 1992 and 1997, respectively. He is now a Full Professor with the Department of Electrical Engineering, Tsinghua University, Beijing, China, , and an Assistant Director of the State Key Laboratory of Power Systems in China. From 2007 to 2008, he was a Visiting Professor with the School of Electrical Engineering and Computer Science, the Washington State University, Pullman. His research interests include energy management system, voltage optimization and control, applications of information theory and intelligent technology in power systems. Prof. Sun is a member of the IEEE Power and Engineering Society CAMS Cascading Failure Task Force and CIGRE C2.13 Task Force on Voltage/Var support in System Operations.
Qinglai Guo (M’09) was born in Jilin City, China, on March 6, 1979. He received the B.S. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2000 and 2005, respectively. He is currently an Associate Professor with Tsinghua University, Beijing, China. His special fields of interest include the EMS advanced applications, especially the automatic voltage control. Prof. Guo is a member of CIGRE C2.13 Task Force on Voltage/Var support in System Operations.
Boming Zhang (F’10) received the Ph.D. degree in electrical engineering from Tsinghua University, Beijing, China, in 1985. Since 1985, he has progressed from a Lecturer to an Associate Professor and, finally, to a Professor with the Department of Electrical Engineering, Tsinghua University, Beijing, China. His research interests include power system analysis and control, especially the EMS advanced applications in EPCC. Prof. Zhang is a steering member of CIGRE China State Committee and the International Workshop of EPCC.
WenchuanWu (M’06) was born in Jinhua, Zhejiang, China, on November 26, 1973. received the B.Sc. and Ph.D. degrees from Tsinghua University, Beijing, China, in 1997 and 2003, respectively. He is currently an Associate Professor with Tsinghua University, Beijing, China. His special fields of interest include the EMS/DMS advanced applications, especially online security and risk assessment.
Bin Wang was born in Xingtai, Hebei, China. He received the Ph.D. degree from Tsinghua University, Beijing, China, in 2011. He currently holds a postdoctoral position with Tsinghua University, Beijing, China. His special fields of interest include EMS/DMS advanced applications.
