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1. INTRODUCTION

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Electrification plays a prominent role in maintaining the standard of living. Modern civilization differs from earlier civilizations in the enormous use of inanimate sources of energy and mechanization of day-to-day activities. The results are a diminution of mechanical drudgery, a shorter working day, a higher standard of living, a healthier and more balanced diet and freedom to a large extent from local famines. Energy demand has been increasing with burgeoning population coupled with intensive agricultural activities, industrialisation and changes in living standards. Renewable energy technologies can meet much of the growing demand through adequate support and interventions from the Government¹ . The market for renewable energy depends in part on the future demand for energy services like heating, lighting, cooking so forth. This demand in turn, depends on economic and population growth and so on efficiency of energy use . Energy supply requirements need to be estimated by taking such considerations into account.

A DSS can assist to analyse and estimate energy requirements and consumptions. DSS is defined as “interactive computer-based systems, which help DMs to utilize data and models to solve unstructured problems". A “what if” is an important feature of DSS that enables us to find what happens to certain conclusions or results if changes are being made in the assumptions or in the input information.

DSS is designed to support the solution of complex problems, provide fast response to unexpected situations that result in changed conditions. It is versatile with an ability to try several different strategies under different configurations quickly and effectively. The basic characteristics of DSS are:

• New insights and learning.
• Facilitated communication.
• Improved management control and performance.
• Cost savings.
• Objective decisions.
• Improving managerial effectiveness.

DSS play a major role in pre-feasibility investigations, generation, planning of transmission and distribution systems, and management of load and energy (at feeder level etc.). Owing to the limited resources on generation and transmission as well as high rates of growth has resulted in many distribution systems in the less developed countries being in a poor technical state.

A multi-attribute DSS was developed for evaluating energy resources to enable the selection of a suitable electricity generation alternative in Turkey² . It also provides an integrated decision framework for the selection of the most suitable multi-attribute method and presents ranking of alternatives and robustness analysis as recommendation to the authorities. Preference Ranking Organization Method for Enrichment Evaluation is used to analyse decision problem. According to the partial ordering, wind power is ranked first followed by hydropower and photovoltaic (PV) for electricity generation.

A DSS was developed to solve complex Electrical Power Districting Problem (EPDP), which renders the solution using a visualization tool, known as Pareto Rank Scatter Plot (PRSP)³ . The PRSP is useful in helping the DMs judge alternative districting plans relative to others in the solution space. The PRSP organizes the solutions into a "soft efficient frontier" comprised of equivalence class layers. Each solution in an equivalence class layer is displayed with a marker corresponding to the legend in PRSP. This DSS was found to effectively support DMs at the World Bank in solving an EPDP in the context of a case study for the Republic of Ghana .

A time-series-based decision-support system was developed to integrate data management, model base management, simulation, graphic display and statistical analysis to provide near-optimal forecasting models⁴ . The model base includes a variety of time-series techniques, such as exponential smoothing, Box-Jenkins and dynamic regression. The system produces short-term forecasts (one year ahead) by analysing the behaviour of monthly peak loads. The performance of the DSS is validated through a comparison with results suggested by econometricians.

Modular Energy System Analysis and Planning (MESAP) was developed to aid as a DSS for energy and environmental management on a local, regional or global scale⁵ . In addition to this, MESAP can be used to set up statistical energy and environmental information system to produce regular reports such as energy balances and emission inventories. It consists of a general information system based on relation database theory, which is linked to different modelling tools. It supports every phase of the structured analysis procedure to assist the decision making process in a pragmatic way. It offers tools for demand analysis, integrated resource planning, demand side management and simulation and optimisation of supply system.

1.1 Transmission and distribution of electricity

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Electricity generated at a power station is distributed to consumers through a network of transmission and distribution systems. Transmission systems have large power handling capability; relatively long lines that connect generator sites to load centres or one utility to another. Distribution systems branch out from the transmission systems; they have lower power levels and relatively short lines. Transmission and distribution systems are again subdivided into primary and secondary transmission, and primary and secondary distribution. The distribution systems can be subdivided into feeders, distributors and service mains. The feeders are the conductors, which connect the sub-stations or the generating stations to the areas served by these stations. The distributors consist of numerous tappings from which the supply to consumers is taken.

A transmission system can be used to carry electrical energy from generating station to a series of substations. The transmission system carries large quantities of energy, which can be most economically transported on thin conductors at high voltage over long distances. Then at the areas of energy consumption the transmission lines deliver energy to substations, which reduce the voltage to safer values for circuits running through inhabited localities. The distribution systems in turn feed transformers, carried on poles or located in underground manholes that finally reduce the voltage to the magnitude at which the consumers will use it. Power system energy loss occurs in the process of delivering energy from the point of generation to consumption. These are broadly grouped as transmission losses, substation losses and distribution losses.

Transmission loss: This is the electrical energy lost owing to the electrical characteristics of transmission lines. The total amount of loss within the transmission network is calculated by two different approaches.

• By subtracting the sum total of the energy taken by all the loads from the sum total of the energy fed into the network by all sources.
• By calculating and summarizing the losses of the individual lines within the network.

Substation loss: This is the electrical energy spent in transforming voltage level step-up or step-down at the grid or distribution substation.

Distribution loss: This is the electrical energy lost by the distribution electrical networks, which is categorized as:

• Technical loss: These are line losses caused by the resistance of the conductor, transformer and regulator loss, corona loss in extra high voltage or ultra high voltage, dielectric loss especially in under ground cables, energy meter loading loss, power factor loss, voltage drop loss and overload loss.
• Non-technical loss: They are theft or pilferage by metered and unmetered customers, illegal connection, incorrect operation of the meter and illegal use in collaboration with the utility's personnel and others.

Energy losses cannot be eliminated fully, but can be minimized by strengthening of electrical facilities. In this regard, spatial decision support system helps in identifying the nodes (with unreasonable losses), which in turn helps in lowering the losses⁶ . Nodes of the network represent energy activities or processes. The DSS processes a representative network of all energy production, conservation, transportation, distribution and utilization activities in a region.

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