Application of GIS Technology in Electrical Distribution

Application of GIS Technology in galvanic automobile automobileal dispersalABSTRACTElectric utilities have a need to keep a all-round(prenominal) and accurate inventory of their physical assets, both as a business office of normal answer provision (ext remnant the vane, undertaking maintenance, etc.) and as a part of their obligation to in chance variable third parties about their facilities. Complexity of electrical distribution bureau arrangement is a good reason for introducing new culture technology GIS (Geo intense study trunk) that carries out entangled power carcass analyses (e.g., fault analytic thinking, optimization of ne 2rks, point prodigy) in accept able-bodied amount of time. By using modern GIS, in conjunction with his own in-ho expend real softw be, in less time and more accurately, the utility engineer is able to role and to analyze electrical distribution net give. This paper presents the idea of the project CADDiN (Computer Aided Design of Dist ribution Net make up) currently under development at the Power Systems section of the Faculty of Electrical Engineering, University of Zagreb.INTRODUCTION immenseness of Distribution Network in Energy SupplyOne of the elementary contribution to the advancements and improvements in mans modus vivendi over the years has been the ability to use and control energy. Mans use of energy wad be seen in everyday operations such as mechanical motion and the production of heat and light.Large amounts of power be generated at power plants and sent to a vane of high-voltage (400, 220 or 110 kV) contagious disease lines. These transmission arranging lines supply power to medium voltage (e.g. 10 or 20 kV) distribution profitss (distribution old organisation), which supply power to still lower voltage (0.4 kV) distribution networks (distribution thirdhand carcass). Both distribution network lines supply power to guests directly. Thus, the total network is a complex grid of intercommitt ed lines. This network has the dish up of transmitting power from the points of generation to the points of exercise.The distribution system is particularly important to an electrical utility for two reasons its proximity to the ultimate client and its high investment terms. The objective of distribution system planning is to promise that the growing demand for electricity, with growing rates and high charge densities, apprize be satisfied in an optimum way, mainly to achieve minimum of total live of the distribution system expansion. Therefore, the distribution system planner breakdowns the total distribution system planning problem into a set of subproblems that can be handled by using available, usually heuristic methods and techniques T.G matchlessn, 1986.The design of electrical distribution networks is an everyday task for electric utility engineers, specially in RD department. Such design was carried out few years past manually. This undefiled climax usually result in overdesign distribution system, which is now considered as a waste of dexterity that can be utilize instead of investing in system expansion. Four years ago a PC program package (CADDiN) for optimal planning of distribution network was put in operation in Elektra Zagreb (Electric Utility of City of Zagreb). It is a result of joint RD of Power System Department of Faculty of Electrical Engineering andElektra Zagreb. Based on the experience or PC-CADDiN, at the end or 1992. the prototype of new project CADDiN was started conceptually organized as a part of the geographical Information System.The role of GIS in Distribution NetworksDatabase plays a central role in the operation of planning, where analysis programs form a part of the system supported by a selective informationbase management system which stores, retrieves, and modifies various entropy on the distribution systems. The thing that distinguishes an electrical utility information system from an other information system such as those used in banking, stock control, or payroll systems is needed to record geographical information in the entropybase. Electrical utility companies need two types of geographical information details on the location of facilities, and information on the spatial interrelations mingled with them. The integration of geographically referenced informationbase, analytic tools and in-house essential softw are tools will allow the system to be knowing more stintingally and to be operated ofttimes closer to its limits resulting in more efficient, low-cost power distribution systems. Additional benefits such as meliorate material management, inventory control, preventive maintenance and system setance can be accomplished in a systematic and cost-effective manner (Z.Sumic, et al, 1993). Before graphical workstations were developed, many electric utilities have create technical information systems based on relational selective informationbase management systems (E .Jorum, et al, 1993.). Technical information system is designed to cover the requirements of power supply utilities considering network expansion and operation planning, maintenance management and system documentation. In advanced utilities all information systems are built around same RDBMS and constantly updated. Establishing links between these information systems and geographical information system is only in defining relationship between objects in the two systems. The problem that has risen is in a number of different information systems in the same utility (technical information system, customer information system, etc.) or even several overlapping technical information systems and some of these are not updated.ObjectiveThe objective of the distribution network design process can be divided into troika independent parts. These parts areLoad forecastingload return of the geographical area served by substationdetermination of load magnitude and its geographic locationcustome r load characteristics Design of utility(prenominal) system (low voltage distribution network)optimal substation allocation and transformer sizing secondary circuitry routing and sizingDesign of primary system (medium voltage distribution network)optimal substation allocationprimary circuitry routing and sizingTo bring down a problem complexity each part of the design process is divided in operable subproblems. Each of these subproblems can be then much easier to manage. Although only independent some parts of design process interact, i.e. placement of substation will influence secondary routing which in turn will influence primary routing. The number of possible design settlements that might satisfy a given set of spatial, technical and economic constraints is quite numerous. Multiple, interdependent goals and constraints make conventional procedural optimization methods inappropriate for distribution network design. collec remit to the complexity of the design process, heuris tic methods and AI techniques must be utilize to find near optimal S.Krajcar, 1988 or satisfying dissolvents Z.Sumic, 1993. The main reason for this simplification is regarding work-force and computer time for finding optimal solution that in high percentage could not be applicable in real situation.End Page 1858 common DESCRIPTION OF GEOGRAPHIC INFORMATION SYSTEM OF PROJECT CADDINPi pickle-project CADDiN was started at the beginning of 1993 as a inquiry project inside the main research project Research and Development of Electric Power System supported by the Ministry of Science and Technology of the Republic of Croatia. The development of optimization and design procedures of electric distribution network is a parallel process with building database by Cadastral Office of the City of Zagreb, and wherefore some other available examples of basic map databases are used for research purposes (see judge 1). The outline busy emphasized only the data composed of basic map databas es for technical applications (scales of 1 five hundred to 15000).There is no unique definition for Geographic Information System (GIS) but a commonly accepted one is that it is a system with computer hardware and software functions for the spatial data input, storage, analysis, and output T. Bernhardsen 1992. Many textbook definitions go further and identify analysis as the one activeness which differentiates GIS from other computer-based systems for handling geographic data, such as automated cartography.Modern GIS, stores information on the geometry, attributes and regional anatomy of geographic traces in one relational database management system. SYSTEM 9 used in the pilot-project CADDiN is a feature-oriented GIS which organizes geography-related information into a topology-structured, object-oriented, relational database system.A project is the highest level of data organization of GIS used in CADDiN Computervision, 1992. It represents the entire database that has been set u p for a particular geographic area for example, a town, a municipality, or a service district. It comprises two components a data store that contains all the geographic and attribute data relating to features and a database definition that specifies the structure of the project through feature classes and themes.Theme definition determines which features and attributes are to be used and the ways in which are to be displayed. Independently stored geometry of a feature, and its graphic representation enables shoes and representational data to be changed without reference to each other. The link between the geometry and the representation is raised by the theme. It comprises a list of feature classes, feature class attributes, and a link to a separate list of graphic transforms.An important safety aspect of used GIS is that it does not allow substance abusers to make changes to the database at project level. A user may only query it. The database is created and updated by intend o f the next lower level of data structure the sectionalization. This is a copied, working subset, or portion of a project. It is at this level that a user interacts with the system to enter, edit, update and manipulate data. Partitions are extracted from a project based on the type of work to be done and the data that will be required to perform that work. When editing is completed, the partition is co-ordinated into the project database, effecting the update. Partitions are created by means of a partition definition that describes the spatial extent, the contents, and the representation. The system uses the partition definition to extract the required nonrepresentational and attribute data and then allocates them into the required partition. The merit of the partition structure is that it allows different departments within an organization to work safely on the data from the same project. any geometrical features in the data model are built up from geometric primitives, referred to as leaf nodes, lines, surfaces and spaghetti. A node is stored as a set of X, Y, and optionally Z coordinates in 3D database, and might be used to represent e.g. transformers, switchgears, MV LV buses, etc. A line primitive is a geometric element outlined by two end-nodes (allowing negociate points), and might be used to describe transmission lines, cables, etc. A surface consists of one or more line segments that together form a closed polygon. A forest, lakes, parks, a portion of network, or area covered by a lot of buildings could be described by this kind of polygon. Spaghetti enables to model features where no topological structure is required. Nodes are the only geometric primitives that have coordinate information directly associated with them. Lines are not defined in terms of geographic coordinates, but by pointers to their topological nodes. Surfaces are defined by pointers to the lines surrounding the surface. exclusively these pointers are created and maintained a utomatically.Geographic objects are stored as collections of nodes, lines, surfaces or spaghetti, but they can be referred to as geometric primitives as strong as some group of objects which can be set and named in the real world roads. cables, transformers, buildings, and so on. These categories are represented by feature classes, and the individual instances of geographic objects as features. Such features at pop off consist of one or more geometric primitives. All features within a particular feature class will have the same topological structure, and the same set of attributes.Feature classes could be also identified as objects in groupings of related objects that may be established on the basis of location, spatial relationships or common attributes. These logical groupings of features are called complex features. They are defined as features that contain other features. All complex features of particular type, comprise a complex feature class. A useful application of compl ex feature classification would be in forming logical groupings such as MV bus, transformer, LV bus, security devices into substation. Complex features can also have attributes associated with them (for example name, number). It would eliminate duplicating of feature attributes which properly relate to the substation. commentary of complex feature is not restricted to include only simple features as constituent components. For example, distribution network could be defined as a complex feature containing a number of substations, cables, which are themselves complex features.A strength of this approach is that it can be used to minimize the level of data redundancy of both attribute and geometric information. Users interact with the database via an object handler, and they are assisted in that fundamental interaction by a structured query language that incorporates extended spatial and reference operators.Behind analytical tools available inside GIS surroundings, a set of standalo ne functions is available from UNIX scramble. This set of functions is called Application Tool Box (ATB). ATB offers an environment in which data can be managed directly, without first-class honours degree having to extract meaning from map representations of those data. Under this approach a user can develop analytical models according to specific requirements by integration of ATB functions, in-house developed software (C and FORTRAN programs) and shell programming. To speed up complex analysis by Development Libraries of ATB new treat functions of ATB could be developed. Applications of project CADDiN are developing by ATB functions in conjunction with C and Corn shell programs.ATB data management and viewing comprise processing functions, dataflow management and graphics viewing system. Processing functions perform the actual analysis operations on sets of data called data flows, each of which corresponds to a relational table in the database. All manipulation of data flows t akes place in a special temporary work area called a clipboard. Processing functions involve the following operations information management (i.e. selecting information from database and placing it into a dataflow, communicating with external software packages), attribute processing (i.e. generating values for attributes based on classification rules or formula), geometry processing (spatial functions union, adjacent, etc.) and arithmetic processing (i.e. calculating the area of surface entities, or length of linear entities). Dataflow management is used to create, display and delete data flows and views. Graphic viewing system allows user to see the intermediate or final results and generate a plot of those results.Compatible to ATB functions are standalone functions of Network Trace depth psychology mental faculty. By those functions network tracing can be carried out using the information on network connectivity and component characteristics that are already stored in database. Special function is used for network generation that is stored as dataflow on the clipboard. On this dataflow several networks tracing functions can be performed( track optimization, range finding, path finding) or can be used by external software. As a result of that analysis a dataflow is produced on the clipboard. Original and resultant networks can be queried simultaneously. The user can keep or delete resulting data flow on the clipboard or retrieved in database.OPTIMIZATION OF DISTRIBUTION NETWORKS IN GISOptimal Location of TS x/0.4 kV in Secondary Distribution NetworkThe procedure for finding optimal configuration of secondary system consists of two possible optimization traveloptimization of new area secondary system andoptimal connexion of the particular customer(s) to actual secondary system.Regarding urbanistic plans, ecological and esthetic constraints as healthful as previous load fruit analyses possible locations of substations are known in advance. These assumpt ions make planning of secondary system more simple because only routing process must be applied for several locations of substations and unbending locations of customers.The first step of routing process begins by connecting customer to the nearest routing corridor. After that procedure, the secondary system network is generated by network module. On this network any path analysis is applied and as results of analysis there are all possible lodges between substation and customers. These results are used as input for external, CADDiN module of optimization of radial structured networks. During this process of optimization the set of rules is used to satisfy standard practices employed by designers. The optimized network is then saved on clipboard in dataflow and can be graphically viewed. The cost for the secondary system is mainly the capital investment cost consisting of cable laying cost and cost of cables. For each location of substation optimization process must be repeated. S olution with minimal investment cost and satisfactory technical constraints is the exceed regarding secondary network. All solutions that are technically satisfied must be taken into account during the primary network optimization. It is requirement because the local optimum of secondary system does not imply the optimum of primary system, and global optimum of distribution network.The optimal connection of the particular customer to animated secondary system must fulfill next two technical as well as economical constraintsthe pettyest possible length of connection due to voltage drop that may be permittedreserve in load capacity of substation due to customer load.The new customer must be connected to the nearest neighbor customer satisfying previously mentioned constraints. The few nearest customers are build in a buffer zone with new customer as a center of this zone. The shortest path between new customer and possible connection node is found in two steps both nodes are conn ected to the nearest routing corridor, and after that by GIS network function find best path analysis shortest path between nodes is found.Optimal Structuring of Predefined Primary Distribution Network Configuration delinquent to the load characteristics, requested availability and quality of energy supply two main configurations of secondary system are used in optimal planning There is a ring structure (starting and ending node is the same HV/MV substation and routing nodes are MV/LV substations) and a link structure (starting node is one HV/MV substation routing nodes are MV/LV substations and ending node is other HV/MV substation). Regarding the utilisation of GIS technology the optimization procedure of these two network configurations is very similar. In optimization process three different problems are consideredoptimization of the new primary systemreconfiguration of the existing primary system regarding predefined structure, and livelihood of the existing primary system wit h defined structure by installing additional capacity in demand nodes or including the new MV/LV substation in the network.The first problem is similar to the problems in optimization of secondary system. There must be known all possible connections and distances between HV/LV substation (source node) and MV/LV substations(demand nodes) as well as themselves. Therefore, all network nodes must be connected to the nearest routing corridor. By any path analysis and heuristic algorithms (presently genetic algorithms are tested) initial solution or zero-iteration is generated. After that by the union of GIS network function find best path analysis and other heuristic methods optimal solution is found.The second problem is more complicated than the first one because existing connections in network must be considered in optimization procedure. Otherwise, same procedures are used as in the first problem. Example of this optimization procedure can be shown in the Figure 3.In the third proble m, optimization procedure is similar to the procedure of adding the new customer to the second system. exquisite differences are in a way of connecting new substation to the existing network. Inthe primary system, regarding the constraint of reliableness of supply of energy to the customer, each MV/LV substation must have a possibility to be supplied from two sides. Therefore, the nearest existing cable between two substations must be found for the connection of the new station, or the nearest routing corridor by which the new station could be connected to the nearest substations that are found in a buffer zone around it. When a better type of connection is found, solution is tested on several technical constraints (voltage drop, cable and route load, investment costs, etc.).Load forecasting of TS x/0.4 kVSmall area or spatial, forecasting is the prediction of both the amounts and locations of future electric load growth in a manner suitable for distribution planning which really m eans with geographic resolution adequate for planning a new distribution network or extensions to the existing one. The procedure is based on dividing a utility service area into a number of sufficiently wasted areas and projecting the future load in each one. This is usually accomplished by dividing a utility service area into either a grid of uniformly sized rectangular cells, or into equipment oriented areas corresponding to feeder or substation areas (H.L. Willis, 1983,1992).Methods for computerized small area load forecasting, regarding their data requirements and analysis methods, run into into three categoriestrendingmultivariate (multivariable)simulation.Essentially these methods analyze past and present load growth to identify trends, patterns, or information about the process of load growth that is then used to project future load growth.Trending methods require minimal data (they work only with historical load data, usually annual breaker point load) and computer resou rces, and are relatively straightforward in use. Because of their simplicity and generally the lowest expenses, they were the most astray used techniques in the past.Multivariate methods require considerably more data (historical loads, geographic and demographic data on customers and usage) and much more extensive computer resources, but in return they generally provide more accurate forecasts.Simulation methods in addition to historical loads require extensive and comprehensive data that include land use type, geographic and demographic data on a small area basis, transportation and other diverse factors that may affect load growth. They also require large computer resources and work-force. On the other hand they offer advantages in accuracy and analysis of load growth under changing conditions. Because of their complexity and requirements simulation models have been beyond the scope of many electric utilities.So far one can see that the nature of small area forecasting requires heavy use of computerized analyses and manipulation of large quantity of data.With its possibilities GIS is an excellent mean for developing and applying simulation forecast models. Of course, there is no limitation to use GIS for trending methods, at least for some very fast qualitative review, or for short range (less than five years ahead) predictions.A service zone of a substation may be defined as a complex feature which comprises parcels, buildings on those parcels, electrical connections for every building or customer, existing interconnections between customers hookups and associated substation etc. Parcels, buildings and streets are modeled as polygons, and cadastral lot code is attached to them as one of the attributes. Statistical and census districts based on approximately equal number of inhabitants and cadastral districts are polygons, too. Second very important information is address, modeled as complex feature class comprising a street name and number. Polygonal anal ysis and polygon processing, which is possible in GIS, and address as a common link enables the planner to determine a substation service zone and calculate its area. Via features attributes all necessary customers data (annual electricity consumption, annual peak loads, type of customers, some special requests and interfering factors, etc.) are obtainable. In that way it is possible to track amounts and sort of energy used by individual customer, or substation service area or some other region. Upon these information load densities (kWh/m) or kWh sales per customer can be computed.Procedure with built-in clustering algorithm detects groups (classes, clusters) of customers with similar past energy consumption behavior. For distribution load forecasting K-means algorithm Hartigan, 1986 isrecommended, with a minimum of 6-year load history H.L. Willis, 1983. The K-means algorithm searches for a partition, that is, a set of clusters that minimizes the total difference between small area s and their assigned clusters (the delusion of the partition). It works by moving small areas from one cluster to another. The search ends when no such movements of small areas reduce the error value.CONCLUSIONThis paper presents the concept of the pilot project CADDiN for optimization of electric distribution networks based on GIS technology. The architecture of CADDiN consists of the heuristic methods implemented within GIS and procedural programs. In such a hybrid environment, the GIS star task is to model real world, perform spatial analyses and ensure the high accuracy of optimization procedures. The first results obtained by the prototype database and developed procedures encourage that concepts and ideas established in this paper can be applied on the real problems that exist in the distribution system planning.