C I R E D 17 th International Conference on Electricity Distribution Barcelona, May 2003

Transcription

1 METHODOLOGY FOR INTEGRATING LONG AND MEDIUM TERM ELECTRIC POWER DISTRIBUTION SYSTEM PLANNING MODELS C. C. B. OLIVEIRA N. KAGAN A. U. ANTUNES S. ARAUJO M. M. FILHO H. P. OLIVEIRA L.N. SILVA University of São Paulo Brazil AES - SUL barioni@pea.usp.br INTRODUCTION A long-term distribution system planning model [1,2,3] has been developed in Brazil in a collaboration between the University of São Paulo (USP) and the Brazilian Association of Distribution Companies (ABRADEE). It determines investment plans considering distribution substations and the medium voltage network reinforcements. The corresponding software named SISPAI is implemented in many Brazilian utilities, allowing for such companies to evaluate their long range investment plans taking into account quality of service levels, providing directives to expansion plans to be installed along time. On the other hand, USP researchers have been developing for quite some time now models and computational tools to deal with short and medium term distribution planning [4,5]. These models have been implemented in a software named. The application of this tool in specific regions allows for companies to evaluate the capacity, location and installation time for the required system reinforcements. uses an optimization model based on mixed integer and linear programming. This paper presents the methodology and corresponding computational tool that allows the integration of the above models. Results and trends obtained from the long term planning study (SISPAI) are considered for regional expansion reinforcements to be evaluated by the short/medium term planning model (), considering quality of service and financial constraints. This research also shows how topological and load data required for short and medium term planning models can be automatically available for long range planning studies. The data needed for SISPAI long range planning is based on statistical representation of the distribution system, by using specific attributes such as load factor, power factor, network length, cable sizes, number of distribution transformers, load growth rate, action angle and load density. THE LONG TERM DISTRIBUTION SYSTEM PLANNING MODEL Overall Model The main goal of the long term planning model is to determine the necessary investment for the evolution of the distribution system, in medium/long term scenarios, within pre-determined service and quality standards. The investment under consideration regards the construction or expansion of substations, medium and high voltage lines. The model may be used as an overall one, for all of the company s substations and feeders, as well as for specific regions. With the use of representation models for substations and primary distribution systems, simplified through parameters substituting the actual topology, the planning model is able to determine the trends in investment, without considering the geographical base of the distribution system. The study of the short and medium term planning will determine the geographical location of the reinforcements to be introduced in the system. The process starts with the clustering of substations and associated networks (distribution units) into groups with similar characteristics, in order to decrease the complexity of the problem. In a second phase, it simulates the demand growth in such units by using statistical laws that relate technical indices to the cluster attributes. Such statistical laws are determined by a procedure that incorporates the Monte Carlo method followed by a regression analysis that determines the main indices (voltage, losses, reliability indices and so on) as a function of given attributes. A main module evaluates the best measures that meet the technical criteria for each cluster by computing the costbenefit indices (CBI). Such measures, for a given year, are ordered by their corresponding CBIs until the budget is about to be exceeded. Network representation by clusters Given a universe under study, a clustering and grouping process of substations and feeders takes place. Initially, the substations are grouped through a hierarchical classification procedure, which takes into consideration the following attributes: - Rated voltage at sub-transmission level; - Rated voltage at primary level (medium voltage); - Transformer rating; - Number of transformer units; - Number of outcoming primary feeders; - Utilization factor (relation maximum demand / capacity); - Load growth rate; - Load factor. USP_Oliveira_B1 Session 5 Paper No

2 Subsequently, a clustering of existing distribution feeders, that form each group of substations, is carried out. Each one of such groups will constitute a universe, for which representative feeders will be determined. A statistic process, based on Euclidian distance, deals with feeder clustering. The following attributes are considered, to determine groups of feeders: - Network length; - Number of load points served; - Action angle; - Maximum demand; - Load factor; - Load growth rate; - Load density; - Power factor. Figure 1 presents an example of the use of the model, showing, on a yearly basis, the investment and main expansion works to be introduced in the system,throughout a period of time. building and operational data regarding substations, configuration of primary feeders, data regarding load, costs, etc.; - Study the overall and espacial load forecast by using the load growth estimate for all the years being considered; - Make a diagnosis of the system for the target year by applying the load estimated for the target year in the present network. This way, we may identify the areas and components of the system, that will not comply with technical criteria. - Suggest alternatives for the reinforcements (expansion of existing substations, new substations, new feeders, recabling, new switches); - Select the best alternative for expansion of the system; - Optimize the chosen alternative for system expansion, through the study of reactive power allocation, capacitor and voltage regulator banks, contingency analysis, and protection studies. In order to select the best alternative for system expansion, the software uses a pseudo-dynamic optimization model, with a Mixed Integer and Linear Programming. The goal is to minimize total costs (investment and cost due to losses). The authors have also researched other approaches [6-8]. INTEGRATION OF LONG AND SHORT/MEDIUM TERM MODELS Steps in the integration process Figure 1 Reports for the long-term model THE SHORT/MEDIUM TERM DISTRIBUTION SYSTEM PLANNING MODEL Several computer modules, which are part of the software, have been developed, in order to study the short and medium term planning of substations and primary networks. Such modules are also used to analyze the performance of the existing system and to select the best alternatives for reinforcement, to be commissioned in the next few years, through the use of optimization models. Topological changes in the network are also taken into consideration, to improve technical performance (voltage levels, loading, losses). Their introduction in each specific region of the utility s concession area (e.g. in the regional offices) creates a expansion plan that determines capacity, geographic location and year in which the commissioned reinforcements will be introduced. In accordance with the adopted methodology, a planning study is developed, which follows the steps below: - Extract data from corporate base (geographical location, The goal of the project, resulting from a partnership between USP and AES-SUL, was to develop a methodology and computational support tool to integrate and make compatible the following two items: on one hand, the results of plans, obtained by means of the long term planning model (SISPAI) and on the other, the regional expansion needs to be set by the medium term planning (), bearing in mind the investment as well as the parameters for quality of supply. The schematic diagram in Figure 2 shows the different stages in the integration process of the planning models. They may be divided in three phases: Phase 1: Obtain data, from the company s database, regarding substations and feeders, by means of the software; process the load flow model and generate the above mentioned attributes to represent the substations and feeders in the long- term model. Phase 2: Read the attributes of the substations and feeders, by means of the SISPAI software; group the substations and feeders with similar attributes and process the module to obtain statistical laws. Validate such laws by comparing the values obtained through their use and those obtained through the modules of load flow and reliability. In this stage, a feedback of data to the SISPAI is possible, in order to improve the statistical laws for a more accurate representation of network performance. USP_Oliveira_B1 Session 5 Paper No

3 Phase 3: Process the main module of SISPAI, to obtain the investment needs for system expansion as well as the plan for the corresponding reinforcements. The indicated reinforcements are transferred to as proposed ones in the stage in which alternatives are suggested for the short/medium term planning model. Finally, the optimization module sets the year for the introduction of the reinforcement (new substations, expansion of the capacity of existing substations, new feeders, etc.), as well as its capacity and location. It also proposes changes in the topology of the network, so as to supply the load within the established technical criteria, with the lowest overall costs (investment and losses). - Feeders: network length and number of load points served. 2. Figures calculated by the load flow and load forecasting modules: - Substations: utilization factor, load growth rate and load factor. - Feeders: maximum demand, load factor, power factor and load growth rate. 3. Figures obtained from specific calculation: - Feeders: action angle and load density. Company Data Base These two attributes are obtained for each feeder, based on its topology and the geographical location of the load points. Figure 3 presents the view of a feeder and the way to determine its action angle. Atributes generation Load flow Phase 1 SISPAI Statistical laws Statist. Laws validation Phase 2 SISPAI Long term plan Figure 3 Determination of feeder action angle Generation and validation of statistical laws Short/medium term plan Phase 3 Figure 2 Integration of planning models Attributes for the representation of the system The attributes to represent substations and feeders were divided into three categories: 1. Figures obtained directly from filed data: - Substations: rated voltage, transformer rating, number of transformer units and number of outcoming primary feeders. In this stage of the process, the SISPAI software generates several groups of statistical laws by varying one of the most important attributes to evaluate the network performance indicators. Such attribute is the load density along the network, as defined by the following expression: D r = D 0 r (1) where: D r : load density for distance r from the initial point of the feeder, in kva/km 2 ; D 0 : load density at the initial point of the feeder, kva/km 2 ; : exponent that defines load density (e.g. = 0 for uniform load density and = 1for decreasing density). The software validates the generated laws and USP_Oliveira_B1 Session 5 Paper No

4 adapts them, for an improved representation of the actual networks. This is done by means of a comparison between the results obtained by the laws and those obtained by processing the load flow and reliability modules. Figure 4 shows the influence of exponent in the result obtained from the statistical law to calculate the maximum voltage drop in a specific feeder, and the result obtained by the software. module of the long-term model, SISPAI, as alternative reinforcements to be introduced in the system per region, are transferred to the optimization module in the short/medium term model,. Figure 5 shows the diagnosis of a region under study during year 5 (research limit); Figure 5b shows the system with the suggested reinforcements, based on the plans obtained from the long term model. Maximum voltage drop (%) ,5 0 2 Sispai Sisplan Figure 4 Influence of exponent Table 1 presents the results of voltage drop obtained for several feeders, by applying the statistical laws and by processing the load flow module. Table 1 Comparison of results Maximum voltage drop SISPAI Feeder (%) (%) 1-0, , As can be seen from the table above, the results obtained from the statistical laws are extremely good, since they show very accurately the situation in the feeders. We may see, for example, that feeders 2 and 6 present high levels of voltage drop, which indicates the need for corrective measures to be taken in the years ahead. On the other hand, feeders 1,3,4,9 and 10 do not present voltage drop problems. Finally, feeders 5,7 and 8 show intermediate values and may present problems further in the future. Use of the SISPAI results in The third and last phase of the process that integrates the planning models takes place by means of the following process. The results obtained from processing the main Figure 5a Network diagnosis Figure 5b Suggested reinforcements Figure 5 System diagnosis and suggested reinforcements CONCLUSION This paper shows the way in which long term and short/medium term planning models may be integrated, in order to obtain more appropriate and sensible plans for investment in distribution systems, as well as for their expansion. The long-term model is extremely efficient to determine the trends, which help defining the reinforcements, to be commissioned in the system, for the short/medium term scenarios. Furthermore, it turns the expansion plan compatible USP_Oliveira_B1 Session 5 Paper No

5 with the company s annual budget, by setting acceptable and homogeneous quality levels between the different regions of concession. However, the long-term model may not be used, by itself, to set the expansion plan, since it is not tied to the geographical basis but rather to aggregated information. It considers only the global expansion investments. This model does not take into consideration key elements for the expansion of the network; therefore, it prevents the observation of the technical characteristics unique to each system. On the other hand, with the short/medium term planning it is impossible to visualize the company s global investments, throughout the concession area. Therefore, an detailed expansion plan, that meets the system needs and is compatible with available resources, may become unfeasible when the trends obtained by the long term planning model are not taken into account. The above mentioned considerations highlight the importance of the two planning categories, which essentially complement each other. BIBLIOGRAPHY [1] Gouvêa, M.R.; Kagan, N.; Arango, H. A methodology for planning electricity supply systems on aggregated basis. In: Proceedings of 13th CIRED International Conference on Electricity Distribution, vol. 1, Session 6, pp , [2] Kagan, N.; Oliveira, C.C.B.; Gouvêa, M.R.; Tahan, C.M.V.; Arango, H. A quality driven software for expansion planning in an open regulated electricity market. In: Proceedings of 14th CIRED International Conference on Electricity Distribution, Nice, France, [3] Oliveira, C. C. B.; Kagan, N. Long Range Investment Planning in Distribution Companies Considering Quality Indices and Uncertainty. In: PMAPS 2000 Probabilistic Methods Applied to Power Systems Funchal, Ilha da Madeira, [4] Kagan, N.; Oliveira, C.C.B.; Schmidt, H.P.; Robba, E.J.. Methodology for automatic allocation of new facilities in distribution systems expansion planning. In: International Conference on Electricity Distribution, Buenos Aires, 1996, CIRED Argentina'96 [5] Kagan, N.; Robba, E.J.; Oliveira, C.C.B. A computational tool for electrical distribution planning studies. In: 12 0 National Seminar on Electric Energy Distribution, Recife, Brasil, Disq.II, seção D, n.112. [6] Kagan, N. Electrical power distribution systems planning using multiobjective and fuzzy mathematical programming. London, p. Thesis (Ph.D.). Queen Mary & Westfield College, University of London. [7] Oliveira, C. C. B.; Kagan, N. Distribution expansion planning under uncertainty by a best first search technique. In: PSCC - Power Systems Computation Conference, Trodheim, Norwegian, [8] Kagan, N.; Oliveira, C. C. B. Fuzzy decision model for the reconfiguration of distribution networks using genetic algorithms. In: PSCC - Power Systems Computation Conference, Trodheim, Norwegian, USP_Oliveira_B1 Session 5 Paper No