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Abstract The energy planning endeavours for a particular region involves the finding of a set of sources and conversion devices so as to meet the energy requirement/demand of all the tasks in an optimal manner. This optimality depends on the objective to minimise the total annual cost of energy andthe dependence on non-local resources or maximise the overall system efficiency. Factors such as availability of resources in the region and task energy requirements impose constraints on the regional energy planning exercise. Thus, regional energy planning turns out to be a constrained optimisation problem. This paper describes an optimum energy allocation using integrated energy planning approaches for Uttara Kannada district and makes a satisfying energy allocation plan for the years 2005, 2010 and 2015. Integrated energy planning gives an optimal mix of new/conventional energy sources and is developed based on decision support systems (DSS) approach.
The central theme of the energy planning at decentralised level would be to prepare regional energy plans to meet energy needs and development of alternate energy sources at least- cost to the economy and environment. Regional integrated energy planning (RIEP) mechanism takes into account various available resources and demands in a region. This implies that the assessment of the demand supply and its intervention in the energy system, which may appear desirable due to such exercises, must be at a similar geographic scale. Regional energy planning exercises need to be flexible (to cope with rapidly changing energy systems) and easy to use. The application of DSS is a new approach to this problem. Towards the goal of implementing analytical methods for integrated planning, computerised decision-system provides useful assistance in the analyses of available information, the projection of future conditions, and the evaluation of alternative scenarios. Some of the features of DSS found particularly useful in regional energy planning are: (i) flexible structure—allows appropriate feasible levels of disaggregation, (ii) integrated nature—promotes a better overall understanding of many processes and concepts involved in planning, allowing planners to concentrate on specific energy subsectors, and (iii) iterative nature and easy scenario testing features—provide guidance in optimising data collection activities. Regional integrated energy plan (RIEP) is a computer-assisted accounting and simulation tool being developed to assist policy makers and planners at district and state level in evaluating energy policies and develop ecologically sound, sustainable energy plans. Energy availability and demand situation are projected for various scenarios (base case scenario, high-energy intensity, and transformation, state-growth scenarios) in order to get a glimpse of future patterns and assess the likely impacts of energy policies.
The
application of DSS for Uttara Kananda district energy planning focuses
on renewable resources that could be harnessed for energy, land use
database, sectorwise energy demand database and optimal allocation of
energy resources for various tasks, and then explore the energy use
consequences of alternative scenarios, such as, base case scenarios,
high-energy intensity and improved end use efficiency options. Linear
programming formulation for optimum allocation based on the cost
minimisation objective shows that there is substantial savings of about
19.19% in energy and 36.24% cost reduction in overall energy system.
Cost per unit (kWh) of energy with optimal allocation of energy is Rs.
0.31/kWh (as against Rs. 0.39/kWh without optimisation). Optimisation
carried out with the objective of maximisation of efficiency of ‘ijk’
combination for all combinations shows energy saving of 19.98% and cost
of energy as Rs. 0.34/kWh. The scenario analyses reveal that relatively
vigorous growth in energy demand in Uttara Kannada district can be
accomplished without exceeding available resources.
Keywords :
Integrated energy planning; Decentralised energy plans; Decision
support system (DSS); Energy efficiency; Optimisation; Base case
scenario; Transformation scenario
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