Smart Service Restoration of Electric Power Systems

Transcription

1 Smart Service Restoration of lectric Systems Leonardo H. T. Ferreira Neto lectrical ngineering Dept. scola de ngenharia de São Carlos, Brazil Benvindo R. Pereira Júnior lectrical ngineering Dept. scola de ngenharia de São Carlos, Brazil Index Terms lectric power systems, heuristic restoration problem, system optimization, Tabu Search. I. INTRODUCTION ditional optimization technique. Although, the results present that for general model, the processing time is considerable for solving the restoration problem. In order to deal with the processing time, heuristic techniques and expert systems have been developed for hastily determining restoration plans. In [], the authors present a guided heuristic optimization technique on a binary decision tree using a depth-first search. A genetic algorithm, Nondominated Sorting Genetic Algorithm-II (NSGA-II), is proposed in []. A service restoration problem including a load curtailment heuristic of in-service customers proposed in [] and [] and the heuristic proposed embodies procedures based on an operator's experience. The PSR essential objective is to minimize the of out-ofservice area affected by an outages on the power system. Although, other concerns are also introduced to increase the result quality, such as: Abstract This paper presents a smart service restoration method for electric power systems. system restoration (PSR) is the procedure of restoring the power supply after a power outage. Its objective is to restore the power system rapidly while satisfying all the operation constraints. Although it has been studied and applied most only to electrical distribution systems. The proposed methodology takes account the subtransmission and distribution systems combined, and considers the objective of minimizing of out-of-service area. The PSR is formulated as a constrained objective optimization problem. Therefore, to solve this problem and obtain feasible solutions of valuable quality, with suitable computational effort, a Tabu Search approach is proposed. Tests were performed on an extended distribution test system, and the results show that the model and methodology are robust and efficient for practical size restoration problem applications. Geraldo R.. da Costa lectrical ngineering Dept. scola de ngenharia de São Carlos, Brazi rules, Nowadays, along with the development of the Smart Grids concept, the utilities are focused on attending the increasing customer demand as well as improving the reliability of the power system. Although, in the traditional power industry, power system restoration process was generally designated by a control center, most only based on the operator s experience. any researches have been undertaken to solve the service restoration problem, which is extremely important for operation efficiency []. The PSR is the process of, after a fault occurrence and isolation of the affected area, transferring as much as possible de-energized loads from out of service areas through the distribution system while satisfying all the operation constraints. The operational configuration target is achieved by switch maneuvers. It is an essential process to utilities in both operation (on-line) and planning (off-line) areas in order to determine the system state and identify schemes to restore/attend the secure system operation, attending the operating constraints. Therefore, determining the feasible load transfer (and load curtail if necessary) solutions represents an important tool to reduce the effects of outages on the power system. i. number of switching operations; ii. type of switches (manual or remote operation); iii. especial loads prioritization; iv. loss minimization; v. feeders load balancing; vi. distribution system radial topology; vii. power system operational constrains; viii. service restoration time. As establish in [] and [], loss minimization and feeders load balancing results minor benefit and may conflict with the essential objectives. Thereby, this paper presents a smart service restoration method for electric power systems with distributed generation, extending the electric power system, combining subtransmission and distribution systems, and considers all the essential objectives and constrains. II. ATHATICAL ODL Due to PSR combinatorial nature, complete mathematical models are challenging to establish. A complete mathematical modeling of the restoration problem is presented in [], where the authors propose the conversion of the original model into a problem of second-order cone programming, solved using tra- The service restoration problem is formulated as a mixed integer non-linear programming problem. The objective is to restore the power supply to the maximum possible out-ofservice area and the objective function represents the operational costs. The constraints are related to the power system operational constrains such as source capacity (supply, substations and distributed generation), feeder loading, branch loading, nodal voltage, connectivity of the sub-transmission systems and radiality configuration of the distribution system. The authors gratefully acknowledge Daimon ngenharia e Sistemas for the support and permitting the use of Interplan framework. The proposed mathematical model for the PSR is described as follows:

2 Distribution system reconfiguration: transference of sections between substations; () subject to: Sub-transmission reconfiguration: transference of substations between sub- transmission systems; Sub-transmission () () () () () () () where sub- The selection of these parameters makes it possible to differentiate between operators and employ preference options of the restoration problem, such as favoring sub-transmission over distribution system operations, or other combination. The problem constraints are upper and lower voltage limits (), branch conductors capacity (), the active and reactive balance equations () and (), distributed generation capacity (), substation operational capacity () and distribution system radiality conditions () () () []. III. SOLUTION TCHNIQU The proposed solution technique is based on [] enhanced to solve a broader power system network. The radial distribution system is represented as a forest graph (system switches as graph arcs and the set of loads in between two or more switches as s, called section) and modeled using the depth encoding (ND) representation [], where the s represent the sectors and the edges connecting the bars represent the switchgear. Figure. shows a distribution system with two feeders. ach feeder is represented by a tree formed by solid lines and dashed lines. The edges represented by solid lines symbolize normally closed switches NC, and dashed edges symbolize normally open switches NO. Load of section aneuver operator weight Decision to maneuver () or not (0) distribution switch Set of sections out-of-service in a system configuration Set of network buses Set of network branches Set of lines connected to substation n Set of distribution network sectors Set of distribution network buses Set of substations Number of distributed generators inimum voltage level of the feeder Voltage at bus aximum voltage level of the feeder Current of branch aximum current allowed through branch Active power demand at bus Active power generated at bus i Active power demand at bus i Reactive power injection at bus i Reactive power generated at bus i Reactive power demand at bus i output of the n -th DG support: closing transmission normally open switches. Nominal capacity of the n -th DG Load at bus i connected to substation n Losses in branch ij connected to substation n Capacity of substation n The objective function considered () is to minimize the of out-of-service area (modeled as section ) when an outage occurs in the sub-transmission system, than the search space for this problem is the set of all post-fault network configurations, including the sub-transmission and distribution network configurations. The operational cost function applies weights,, to each operator applied, as follow: The ND representation is implemented in a vector containing the s, and the corresponding depths, of a graph tree. The order in which the pairs are placed in this list is necessary and can be achieved by a depth-first search algorithm, inserting the pair in the list each time the is visited by the algorithm. (a) (b) Figure. a) Feeders modeled on graphs with two dispersion trees; b) ND vector of feeders 0 and 0 of Figure. Representing the distribution system by ND improves the computational efficient of switches maneuvers operations in the distribution system and guarantees the radiality conditions. Complementing the ND, the adjacency vector is established for each section of the full electric system. Figure. shows the neighborhood of section and its adjacency vector. This representation enables to identify easily the open

3 (a) (b) Figure. a) Section sub-graph; b) Section adjacency vector The proposed model is solved by a tabu search algorithm [0] and the problem is codified with a binary base. The codification structure is illustrated in Figure. Figure. Codification structure The neighborhood is defined through switches maneuvers operations, considering the connectivity of sectors and radiality of the distribution network. Therefore, the metaheuristic attempts to change the switches status systematically, generating new solutions by sections transfers between substations, substations transfers between sub-transmission systems and closing loop between sub-transmission systems. The neighborhood generation is defined using a branch exchange technique []. The neighborhood is generated from a current solution (seed) and the search starts from the initial network with the faulted section of a sub-transmission line isolated. In the classical tabu search, the tabu list (TL) stores forbidden (tabu) attributes during k iterations, to prevent the searching processes from cycling (endlessly execution of the same sequence of movements by revisiting the same set of solutions) during the search. The TL proposed stores the last k switches maneuvers operations applied. It is assumed that the location and capacity of DGs are already in the optimal conditions according to previous planning and the DGs are featured with voltage and frequency controllers (such as co-generation systems). In order to evaluate the system operation state the Newton Raphson power flow algorithm is applied. The convergence criteria adopted is either a maximum number of iterations or until the best solution found does not modify for a number of iterations. TSTS AND RSULTS The proposed algorithm has been tested on the power system network represented in Figure., adapted from []. The test system had a distribution system and a subtransmission system. The distribution system had -, substations, load s, active power demand of,. kw and a reactive power demand of,. kvar, distributed generation s with active power supply of W, reactive power supply of VAr and nominal voltage was. kv. The sub-transmission system had -, load s, active power demand of, kw, reactive power demand of, kvar and nominal voltage was kv. Its data is presented in TABL I. and in Appendix A. TABL I. 0 0 Active power (kw) Reactive (kvar) ach part of the codification structure represents separately the distribution and sub-transmission systems. All the systems switches are represented in the codification structure along with its status (0 open, closed) for each solution generated. IV. NOD DATA 0 0 B B B B B B B0 and closed switches during the generation of new network topologies. Active power (kw) Reactive (kvar) Figure. shows the normal operation of the system with a configuration of open (dashed lines) and closed switches (solid lines), forming a radial topology for the distribution system, a radial topology for sub-transmission systems with the sources at bar B and B and a meshed topology for the sub-transmission system with the source at bar B. The nominal capacity of the substations at 0 is, kva, 0 is,000 kva, and 0 is 0,000 kva. The lower and upper voltage limits were 0. p.u. and.00 p.u., respectively. For the following tests, any switch could be operated, all of the switches were of the same type and there were no priority loads.

4 Figure. Topology of the test system All the study cases, the voltage was above the lower limit, substations and distributed generators operated within their limits. ) Fault at line between B and B: In this case, a fault at line between B and B was analyzed. As such, the switch between these bars was isolated, disconecting the substation T from supply. The operators weights applied where, and, favoring distribution system reconfiguration over sub-transmission support and sub-transmission reconfiguration. The following results were obtained: the switches in circuits B- where closed restoring the substation, totalizing,w and 0,VAr restored. Despite the fact of operatos weighting, the solution was obtained in the first iteration, when the neighbor created by the sub-transmission support solved the fault, restoring all the out-of-service area. ) Fault at line between B and B: In this case, a fault at line between B and B was analyzed. As such, the switch between these bars was isolated, overloadind the branch between bars B and B to.0%. The operators wheights applied where, and, favoring distribution system reconfiguration over sub-transmission support and sub-transmission reconfiguration. The following results were obtained: the switches in circuits B-, 0- where closed and the switches in circuits - where opend. Restoring,W and 0, VAr from T to T and restoring, W and, VAr from T to T. In this test, the operatos weighting affected the convergence, applying first the distribution reconfiguration and, than, in the second iteration, applying the sub-transmission reconfiguration, restoring all the out-of-service area. V. CONCLUSION In this paper PSR is formulated as a constrained objective optimization problem taking account the electric the subtransmission and distribution systems combined and considering the minimization of the out-of-service area. The proposed TS algorithm is very efficient treating the proposed problem, solving the optimization with flexibility of which operation is preferable, allowing the PSR study to be adjustable for the utility needs. The simulation results show excellent performance with regard to the quality of the solution and demonstrate that the proposed approach is flexible, robust and computationally efficient. APPNDIX A TST SYST DATA TABL II. Initial CIRCUIT DATA Final Resistence Reactance (Ω) (Ω) Current limit (A)

5 TABL III. DG DG CAPACITIS Capacity (W) TABL IV. Transformers T T T Initial B B B factor TRANSFORRS Final Capacity X Voltage (VA) (pu) (kv) / / /. RFRNCS [] S. S. Ćurčić, C. S. Ozveren, L. Crowe, and P. K. L. Lo, lectric power distribution network restoration: A survey of papers and a review of the restoration problem, lect. Syst. Res., vol., pp.,. [] R. Romero, J. F. Franco, F. B. Leão,. J. Rider and. S. Sousa, "A New athematical odel for the Restoration Problem in Balanced Radial Distribution Systems," I Trans. On Systems, in press. [] A. L. orelato and A. onticelli, Heuristic search approach to distribution system restoration, I Trans. Del., vol., no., pp., Oct.. [] Y. Kumar, B. Das, and J. Sharma, ultiobjective, multiconstraint service restoration of electric power distribution system with priority customers, I Trans. Del., vol., no., pp., Jan. 00. []. R. Kleinberg, K. iu, and H. D. Chiang, Improving service restoration of power distribution systems through load curtailment of in-service customers, I Trans. Syst., vol., no., pp. 0, Aug. 0. [] Y. Y. Hsu, H.. Huang, H. C. Kuo, S. K. Peng, C. W. Chang, K. J. Chan, H. S. Y, C.. Chow. And R. T. Kuo. Distribution system service restoration using a heuristic search approach. I Transactions on Delivery, New York, Vol., No., pp. -0, Apr.. []. Lavorato, J. F. Franco,. J. Rider, and R. Romero, Imposing radiality constraints in distribution systems optimization problems, I Trans. Syst., vol., pp. 0, 0. [] B. Pereira, A.. Cossi, and J. R. S. antovani, Proposta de uma metodologia baseada em busca tabu para restauração automática de sistemas de distribuição de energia elétrica, in Anais do XIX Congresso Brasileiro de Automática, CBA 0, 0, pp. 0. [] A. Delben, A. Carvalho, C. Policastro, A. Pinto, K. Honda, and A. Garcia, -depth encoding for evolutionary algorithms applied to network design, Proc. GCCO (), pp., 00 [0] Glover, F.: Tabu search fundamentals and uses (University of Colorado, Bolder, CO, ) [] S. K. Goswami, Distribution system planning using branch exchange technique, I Trans. Syst., vol., pp. -, B B B B B B B B B0 B B B B B B B B B B B B B B B B B0 B B B B B