An effective encoding scheme of obtaining radial topology structures in distribution networks
- Juan Wen^{1, 2}Email author,
- Yanghong Tan^{1, 3} and
- Jianmin Zhang^{3}
Received: 7 January 2016
Accepted: 18 August 2016
Published: 31 August 2016
Abstract
The structure of a distribution network has great effects on economy, power supply reliability and investment of a power system. To obtain an optimal topology from possible topologies, we need to solve an optimisation problem which aims to find a radial structure satisfying operating constraints. As a basis of solving this optimisation problem, the encoding scheme, is to represent the candidate configurations by a series of codes. Numerical candidate topologies and unfeasible codes would lead low efficiency or premature convergence. This paper presents an effective scheme which can rapidly produce all radial configurations of a distribution network. In order to reduce the computational requirement of solution space, initial network is simplified as a topological graph which reserves loop branches and T-nodes. And a loop-branch chain incidence matrix is derived from analyzing the relationship between any two loops. Then the principles of selecting switches of each variable are designed to determine the ranges of the variables. All radial candidate solutions are available rapidly through applying the theory of combination. The scheme presented minimizes the number of solutions and avoids tedious radial checking procedure in view of avoiding any infeasible solutions. The validity of the proposed scheme is verified by illustrative examples.
Keywords
Background
Distribution network topological structure plays a vital role in improving the reliability and efficiency of power system (Narimani et al. 2014). Distribution feeders are connected in a radial structure that is maintained by appropriately altering the status of the switches installed on the distribution networks. There are two types of switches, that is, sectionalizing switches and tie switches. The sectionalizing switches are normally closed switches on connecting the line sections and the tie switches are normally open switches on the tie-lines connecting either two primary feeders. By changing the status of some switches, loads can be transferred from one feeder to an adjacent one to generate a reconfigured network topology. Thus, the switching combinations determine the reconfigured network structures in a distribution system. There are many possible candidate topologies in a distribution network because of the large number of candidate switching combinations. It is a complicated combinatorial optimisation problem to obtain an optimal topology from these candidate topologies. This optimisation aims at finding a radial structure that satisfies operating constraints. The artificial intelligence algorithms have widely used to address the optimisation problem recently (Tang et al. 2014; Rao et al. 2013; Hu et al. 2014; Tolabi et al. 2011; Gupta et al. 2014; Alonso et al. 2015; Golshannavaz et al. 2014). An encoding scheme, as the base of the intelligence optimisation approaches, is to represent the candidate configurations by a series of decimal codes.
The control variables of the encoding scheme are sectionalizing switches and tie switches, which are expressed as a set of decimal number. The encoding space depends on the number of possible solutions generated from coding scheme. The solution space is composed of all possible combinations of control variables. The codes of feasible solutions in solution space are named valid codes and the corresponding topologies satisfy the constraints of network reconfiguration. The size of the solution space will be increased as the increasing of candidate solutions. Moreover, the large scale of solution space would be disturbed the optimisation process if one optimal solution is identified from numerical possible solutions. Furthermore, the validity of solutions ensures that a meaningful new solution in each generation is obtained by combining original codes. Evidently, a good encoding scheme has a significant influence on the efficiency and convergence time of optimisation.
Various encoding schemes are proposed to solve the optimisation problem (Sivanagaraju et al. 2008; Fontan 2008; Zhu 2002; Sawa 2009; Andervazh et al. 2013; Wu and Tsai 2011; Asrari et al. 2015; Jikeng et al. 2013; de Macedo Braz and de Souza 2011; Santos et al. 2010). An attempt to express all switches as a set of configuration variables is presented in Sivanagaraju et al. (2008). Each parameter of solution vector represents a switch. Since the value 0 and 1 denotes that a switch is open or closed, it is named binary coding. Fontan (2008) has proposed an improved characteristic vector which the network topology string stores only the opened switches positions. An encoding strategy based on keeping the number of chords constant has presented in Zhu (2002). Sawa (2009), Andervazh et al. (2013) and Wu and Tsai (2011) employ a decimal integer loop encoding strategy which only one switch is opened in each fundamental loop. A look-up table based on loop cut set is built and each loop is considered as a control variable. The number of each open switch is lined up with the order of the loop number. One group of decimal integers is uniquely corresponding to a candidate solution. Based on the integer loop encoding strategy, a reliability-based encoding scheme is presented in Asrari et al. (2015). For a distribution system with m switches and n tie switches, the possible solutions in Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002) and Sawa (2009) are \(2^{m} ,\, C_{m - l }^{n} ,\,m^{n}\) and \(\prod\nolimits_{i}^{n} {m_{i} } ,\) where \(m_{i}\) is the number of switches in ith fundamental loop and l is the number of switches outside loops. For example, in a 33-bus system with m = 37 and n = 5, the number of candidate switching combinations in Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002) and Sawa (2009) are 1.3744 × 10^{11}, 376,992, 69,343,957 and 242,550, respectively. And the proportion of feasible solutions is only 0.00004, 13.46, 0.0732 and 20.92 %. The computational requirement of these schemes is considerable and they require a tedious radial checking procedure to remove the infeasible solutions. As a result, the performance of optimisation methods is dramatically reduced because the validation of the solutions usually is time consuming. Jikeng et al. (2013) proposes a method using basic tree to obtain the radial topology of the distribution network. The sequential encoding and node-depth encoding are presented in de Macedo Braz and de Souza (2011) and Santos et al. (2010). Although the candidate solutions generated by these approaches are all valid codes, the encoding scheme is complex as they demand optimisation algorithms with dynamic parameters. Furthermore, these schemes in (de Macedo Braz and de Souza 2011; Santos et al. 2010) cannot be used to calculate the feasible solutions in view of their large size of the solution spaces.
This paper proposes an effective encoding strategy which mitigates the computational burden and obtains rapidly all candidate configurations in a distribution network. The initial network is simplified as a topological graph which only reserves T-nodes of loops and corresponding branch chains. A loop-branch chain matrix is established according to the relationship between any two loops of simplified network. Combining with the designed principles of selecting branch variables, all possible solutions that satisfy constraint conditions are obtained by fixing the diagonal element of the matrix. All the unfeasible solutions can be completely avoided, thus it leads to dramatic reduction of the size of solution space. Numerical experiment was executed to evaluate the proposed encoding scheme in comparison with the methods (Sivanagaraju et al. 2008; Fontan 2008; Zhu 2002; Sawa 2009; Asrari et al. 2015; Jikeng et al. 2013; de Macedo Braz and de Souza 2011).
The next section introduces the preprocessed network model and the nomenclature to be used in the following sections. The paper continues proposing encoding criteria and the effective encoding scheme description. We applied the scheme to three test systems and show the results in “Case study” section.
Network modeling and simplification
An adjacency matrix of network topology is defined as \(A = \left( {a_{ij} } \right)_{n \times n} ,\) where n is the number of nodes, \(a_{ij}\) is a binary variable, if \(a_{ij} = 1\) demonstrates i-node is connected to another j-node by a branch. Otherwise, \(a_{ij} = 0.\) The degree of i-node is defined as \(D_{vi} = \sum\nolimits_{j = 1}^{n} {a_{ij} }\). Generally a distribution network incorporates a few kinds of nodes, i.e. terminal nodes, electric T-nodes and junction nodes. So the different sets of degree value \(D_{vi} = 1,\,D_{vi} = 2\) and \(D_{vi} \ge 3\) are corresponding to the terminal nodes, junction nodes and T-nodes.
Effective encoding scheme
Look-up branch chains table for each loop
Loop | Branch chains |
---|---|
Loop ① | L1 L2 L4 |
Loop ② | L2 L3 L5 |
Loop ③ | L4 L5 L6 |
For a simplified network with m branch chains and n loops, the dimension of L matrix is n row and n column. The diagonal parameters appear only once in the matrix, which represents the corresponding branch chains only exist in one loop. Other elements are common branch chains between the two loops. Thus, this matrix is symmetric.
- 1.
The number of opened branches is equal to the number of independent loops in a distribution network.
- 2.
Diagonal set of branch chains should be at least one opened branch. It is important to prevent the emergence of loop structure.
- 3.
For avoiding the isolated nodes, only one branch is opened in the same set of branch chains.
- 4.
There must be at least one opened branch in each column of L matrix.
- 5.
Each branch chain could not be repeated in a set of branches.
Initial encoding space of example network
Fixed loop | Encoding form |
---|---|
Loop ① | Subspace 1: [{L1} {L2 L3 L5} {L4 L5 L6}] |
Loop ② | Subspace 2: [{L2 L4} {L3} {L4 L5 L6}] |
Loop ③ | Subspace 3: [{L2 L4} {L2 L5} {L6}] |
Loop constructed branches
Subspace number | Encoding scheme | Constructed branches | ||
---|---|---|---|---|
Loop ① | Loop ② | Loop ③ | ||
Subspace 1 | [{L1} {L2 L3 L5} {L4 L6}] | e1 | e2, e3, e10, e15, e11 | e5, e14, e9, e7, e4, e6, e16, e13, e12 |
Subspace 2 | [{L1} {L2 L3} {L5}] | e1 | e2, e3 | e10, e15, e11 |
Subspace 3 | [{L2 L4} {L3} {L5 L6}] | e2, e5, e14, e9, e7 | e3 | e10, e15, e11, e4, e6, e16, e13, e12 |
Subspace 4 | [{L2} {L3} {L4}] | e2 | e3 | e5, e14, e9, e7 |
Subspace 5 | [{L2 L4} {L5} {L6}] | e2, e5, e14, e9, e7 | e10, e15, e11 | e4, e6, e16, e13, e12 |
Subspace 6 | [{L4} {L2} {L6}] | e5, e14, e9, e7 | e2 | e4, e6, e16, e13, e12 |
A set of solutions is formed through extracting a random branch each loop in the subspace. For example, the number of solution combinations in subspace 1 is \(C_{1}^{1} \times C_{5}^{1} \times C_{9}^{1} = 45.\) The solution combinations of subspace 2 through subspace 6 have 6, 40, 4, 75 and 20, respectively. It is obviously that all the constructed branches of each subspace are not duplicated. The sum of solution combinations is equal to the number of radial topologies in the system. Therefore, the trial solutions are validity and non-redundancy.
Cases study
The performance of the proposed encoding scheme is demonstrated by testing on 16-bus, 33-bus and 69-bus radial distribution systems. For all these systems, all tie and sectionalizing branches are normal. The experimental results are obtained to evaluate its effectiveness. All scenarios have been programmed in MATLAB software. The simulations are implemented on a computer with the processor of Intel core i7 CPU and windows 10 operating system.
Test case 1
Computational results of 16-bus system
Method | Solution space | The ratio of feasible solution (%) | Run time of obtaining radial solutions (s) | The time of obtaining the first feasible solution (s) |
---|---|---|---|---|
Sivanagaraju et al. (2008) | 2^{16} = 65,536 | 0.29 | 17.7024 | 0.172562 |
Fontan (2008) | \(C_{15}^{3}\) = 455 | 41.76 | 0.1954 | 0.006554 |
Zhu (2002) | 16^{3} = 4096 | 4.64 | 0.8145 | 0.078451 |
Sawa (2009) | 7 × 6 × 5 = 210 | 90.48 | 0.0943 | 0.002125 |
Asrari et al. (2015) | 210 | 90.48 | 0.0930 | 0.002037 |
Jikeng et al. (2013) | 190 | 100 | 0.0917 | 0.001078 |
de Macedo Braz and de Souza (2011) | – | – | – | 0.000990 |
Proposed scheme | 190 | 100 | 0.0826 | 0.000945 |
For this system, almost all the schemes can get a solution space except to sequential encoding of de Macedo Braz and de Souza (2011). The number of radial configurations is given by using basic tree search method (Jikeng et al. 2013). The size of solution space by the proposed scheme is 190, which is consistent with (Jikeng et al. 2013) but less than other schemes (Sivanagaraju et al. 2008; Fontan 2008; Zhu 2002; Sawa 2009). Due to a large number of solutions, the ratio of feasible solution in Sivanagaraju et al. (2008), Fontan (2008) and Zhu (2002) is 0.29, 41.76 and 4.64 %, respectively. Although the methods of Sawa (2009) and Asrari et al. (2015) are improved to 90.48 %, some unfeasible codes still exist in the solution space. So the number of analyzing solutions’ validity computer memory required for the proposed method is less than method in Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002), Sawa (2009) and Asrari et al. (2015). To obtain the radial solutions, the branch-to-node incidence matrix method is used to check the radiality and remove the infeasible solutions in this paper (Alonso et al. 2015). The least time identified the first feasible solution by referenced methods is 0.000990 s (de Macedo Braz and de Souza 2011). Our method is able to find the first radial configuration in 0.000945 s which is less than de Macedo Braz and de Souza (2011). The average run time of obtaining all radial solutions for the schemes are 17.7024, 0.1954, 0.8145, 0.0943, 0.8145 0.0930, 0.0917 and 0.0826 s, respectively. It illustrates the our encoding strategy requires less time to get all feasible solutions if the basic tree search method obtained valid codes are the same. The results presented demonstrate that the proposed scheme is much faster and more efficient than those by conventional methods. Hence, this scheme is able to present more benefits to intelligent reconfiguration algorithms.
Test case 2
Search space of different encodings for systems
Reference | 33-bus system | 69-bus system | m-bus system | ||||||
---|---|---|---|---|---|---|---|---|---|
Size of solution space | Feasible solution (%) | Rt1 (s) | Rt2 (s) | Size of solution space | Feasible solution (%) | Rt1 | Rt2 (s) | Size of solution space | |
Sivanagaraju et al. (2008) | 2^{37} = 1.3744 × 10^{11} | 0.00004 | – | 197.04 | 2^{73} = 5.9030 × 10^{20} | 3.4714 × 10^{−15} | – | 1018.34 | 2 ^{ m } |
Fontan (2008) | \({\text{C}}_{ 3 6}^{ 5}\) = 376,992 | 13.46 | 465.94 | 2.5131 | \({\text{C}}_{ 7 1}^{ 5}\) = 4,187,106 | 7.83 | – | 2.6507 | \(C_{m }^{n}\) |
Zhu (2002) | 37^{5} = 69,343,957 | 0.0732 | – | 125.9735 | 73^{5} = 2.0731 × 10^{9} | 0.0158 | – | 541.0476 | m ^{ n } |
Sawa (2009) | 10 × 15 × 7 × 21 × 11 = 242,550 | 20.92 | 294.76 | 0.0154 | 17 × 8 × 24 × 32 × 17 = 1,775,616 | 18.47 | 8.1 h | 0.0482 | \(\prod\nolimits_{i}^{n} {m_{i} }\) |
Asrari et al. (2015) | 242,550 | 20.92 | 253.72 | 0.0143 | 1,775,616 | 18.47 | 7.6 h | 0.0445 | – |
Jikeng et al. (2013) | 50,751 | 100 | 79.35 | 0.0049 | 327,868 | 100 | 292.02 s | 0.0088 | – |
de Macedo Braz and de Souza (2011) | – | – | – | 0.0032 | – | – | – | 0.0059 | – |
Proposed scheme | 50,751 | 100 | 0.1092 | 0.0024 | 327,868 | 100 | 0.2068 s | 0.0034 | \(\sum\nolimits_{j = 1}^{j} {\prod\nolimits_{n = 1}^{n} {m_{jn} } }\) |
From the Table 5, the solution space for this system is significantly greater than the 16-bus system. Among the references consulted, only the sequential encoding (Jikeng et al. 2013) is unable to calculate the solution space. The minimum solution space obtained by the methods of Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002) and Sawa (2009) is 242,550, but the proportion of feasible solutions is only 20.92 %. It would take a very long time to obtain all feasible solutions and sometimes is impossible. Using the proposed scheme, the number of branch combinations is 50,751, which is the same as the result of Jikeng et al. (2013) but much less than other coding strategies. The least run time consumed to reach the first feasible solution in conventional methods is sequential encoding (Asrari et al. 2015), spending 0.0143 s. And the corresponding time is 0.0024 s in our scheme. Based on the run time of obtaining all feasible solutions, our method is the fastest to achieve the goal with Rt2 = 0.1092 s on average. Therefore, the proposed scheme can identify the first feasible solution and obtain all the valid solutions rapidly.
Test case 3
Table 5 presents the comparison of the proposed scheme and approaches presented in Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002), Sawa (2009), Asrari et al. (2015), Jikeng et al. (2013) and de Macedo Braz and de Souza (2011). Under the viewpoint of quality of encoding, only sequential encoding (Jikeng et al. 2013) is unable to calculate the solution space. Using the mentioned methods in Sivanagaraju et al. (2008), Fontan (2008), Zhu (2002), Sawa (2009) and Asrari et al. (2015), there are many trail solutions because large number of infeasible solutions exist in the solution space. And the ratio of valid codes in entire encoding space is lower than 5 %. It is almost impossible to identify all the possible radial solutions in a short time. The number of feasible solutions obtained by the proposed method is 327,868, which less than above the approaches. The method are presented in Jikeng et al. (2013) can accomplish the searching result of solution space, but the run time of obtaining the all the possible solutions takes 292.02 s. Referring to Table 5, in the proposed scheme, it takes only 0.2068 s to obtain all feasible configurations. Evaluation running time Rt2, our scheme achieves the first feasible solution faster than other methods, i.e. 0.0034 s against 0.0059 s in de Macedo Braz and de Souza (2011), 0.0034 s compared to 0.0088 s in Jikeng et al. (2013), and 0.0034 s instead of 0.0445 s in Asrari et al. (2015).
From the above results, the number of possible solutions of a distribution network is related to the common branches and tie branches. The proposed method is dramatically reduced the number of possible configurations as it avoids the unfeasible solutions. The computational requirement for a network with m branches and n tie branches is \(\sum\nolimits_{j = 1}^{j} {\prod\nolimits_{n = 1}^{n} {m_{jn} } } ,\) where j represents the number of subspaces, and \(m_{jn}\) is the number of branches in nth loop of each subspace. Furthermore, the run time of achieving the first feasible solution by our scheme remains at the forefront. In direct comparison the time (Rt1), an apparent advantage of the proposed scheme is that could obtain the all valid solutions fast, i.e., 0.1092 s in 33-bus system and 0.2068 s in 69-bus system.
Conclusions
Due to the extremely large number of unfeasible solutions, the size of solution space of traditional encoding schemes is very considerable and would lead time consuming before selecting an optimal solution. This work proposes an effective encoding scheme which can rapidly produce trail solutions of a distribution network. The advantage of this method is only generated the radial configurations. It is possible that the encoding strategy avoids the unfeasible solutions and thus reduces the solution space dramatically. The computational burden may be alleviated as it does not need to verify the validity of the trial solutions. The validation tests are carried on 16-bus, 33-bus and 69-bus systems, and the test results are compared with other conventional schemes. Test results show the proposed encoding scheme can ensure each trial solution to be feasible. Thus, it has been able to get all feasible solutions with a few calculations. And this scheme minimizes significantly the solution space. Furthermore, we can obtain the first feasible solution quickly. In future, it is useful to improve the intelligence reconfiguration algorithms in terms of computational speed and memory requirements.
Declarations
Authors’ contributions
JW designed and performed the study, acquired and analyzed data, and drafted the manuscript. YHT reviewed the study design, helped analyze the data, and revised the manuscript text. JMZ revised the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by National Natural Science Foundation of China (61102039, 51577046), National hi-tech research and development plan (2014AA052600), and Open Foundation of first level zhejiang key in key discipline of Control Science and Engineering.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- Alonso FR, Oliveira DQ, Zambroni de Souza AC (2015) Artificial immune system optimization approach for multiobjective distribution system reconfiguration. IEEE Trans Power Syst 30(2):840–847View ArticleGoogle Scholar
- Andervazh MR, Olamaei J, Haghifam MR (2013) Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particle swarm optimization algorithm and graph theory. IET Gener Transm Distrib 7(12):1367–1382View ArticleGoogle Scholar
- Asrari A, Lotfifard S, Payam MS (2015) Pareto dominance-based multiobjective optimization method for distribution network reconfiguration. IEEE Trans Smart Grid 1(99):1–10Google Scholar
- de Macedo Braz HD, de Souza BA (2011) Distribution network reconfiguration using algorithm with sequential encoding: subtractive and additive approaches. IEEE Trans Power Syst 26(2):582–593View ArticleGoogle Scholar
- Fontan DMS (2008) Reconfiguração de Sistemas de Distribuição usandoum Algoritmo Evolutivo, Master’s thesis, Electrical Engineering Department, Federal University of Campina Grande, Paraıacute, pp 17–22Google Scholar
- Golshannavaz S, Afsharnia S, Aminifar F (2014) Smart distribution grid: optimal day-ahead scheduling with reconfigurable topology. IEEE Trans. Smart Grid 5(5):2402–2411View ArticleGoogle Scholar
- Gupta N, Swarnkar A, Niazi KR (2014) Distribution network reconfiguration for power quality and reliability improvement using genetic algorithms. Int J Electr Power Energy Syst 54(54):664–671View ArticleGoogle Scholar
- Hu W, Chen Z, Bak-Jensen B et al (2014) Fuzzy adaptive particle swarm optimisation for loss minimisation in distribution systems using optimal load response. IET Gener Transm Distrib 8(1):1–4View ArticleGoogle Scholar
- Jikeng L, Guang P, Yunpeng L et al (2013) A fast determination method of topological radial property of distribution network based on basic-tree and distribution network reconfiguration. Proc CSEE 33(25):156–165Google Scholar
- Narimani MR, Vahed AA, Azizipanah-Abarghooee R et al (2014) Enhanced gravitational search algorithm for multi-objective distribution feeder reconfiguration considering reliability, loss and operational cost. IET Gener Transm Distrib 8(1):55–69View ArticleGoogle Scholar
- Rao RS, Ravindra K, Satish K et al (2013) Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans Power Syst 28(1):317–325View ArticleGoogle Scholar
- Santos AC, Delbem ACB, London JBA et al (2010) Node-depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration. IEEE Trans Power Syst 25(3):1254–1265View ArticleGoogle Scholar
- Sawa T (2009) Radial network reconfiguration method in distribution system using mutation particle swarm optimization. In: IEEE Bucharest power tech conference, pp 1–6Google Scholar
- Sivanagaraju S, Rao JV, Raju PS (2008) Discrete particle swarm optimization to network reconfiguration for loss reduction and load balancing. Electr Power Compon Syst 36(5):513–524View ArticleGoogle Scholar
- Tang L, Yang F, Ma J (2014) A survey on distribution system feeder reconfiguration: objectives and solutions. In: IEEE conference on innovative smart grid technologies, Asia, pp 62–67Google Scholar
- Tolabi HB, Gandomkar M, Borujeni MB (2011) Reconfiguration and load balancing by software simulation in a real distribution network for loss reduction. Can J Electr Electron Eng 2(8):386–391Google Scholar
- Werho T, Vittal V, Kolluri S et al (2016) Power system connectivity monitoring using a theory network flow algorithm. IEEE Trans Power Syst 1(99):1–8View ArticleGoogle Scholar
- Wu WC, Tsai MS (2011) Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE Trans Power Syst 26(3):1591–1599View ArticleGoogle Scholar
- Zhu JZ (2002) Optimal reconfiguration of distribution network using the refined genetic algorithm. Electr Power Syst Res 62:37–42View ArticleGoogle Scholar