International Journal of Trend in Research and Development, Volume 3(3), ISSN: 2394-9333 www.ijtrd.com IJTRD | May-Jun 2016 Available Online@www.ijtrd.com 145 An Optimization Model For Sustainable Energy Scheduling Planning Irvan Mathematics Doctorate Program, Univ. of Sumatera Utara/ University of Muhammadiyah Sumatera Utara Medan, Indonesia Herman Mawengkang Department of Mathematics University of Sumatera Utara Medan, Indonesia Abstract: The world is plaqued with global warming problem due to energy inefficiency. In this paper we address a model based on energy flow optimisation model for evaluating the contribution of distributed-generation (DG) production and energy-efficiency actions. The proposed methodology details exploitation of primary energy sources, power output scheduling, emissions level and end-use sectors. The model framework has been enhanced to include a description of DG contributions and energy-efficiency improvements. The framework considers the capacity of outflow power from generator of non-renewable energy type. If the outflow power has reached the capacity, the power plant should make a change over. The problem is then formulated as a mixed integer programming model. A direct neighborhood search approach is developed to solve the model. Keywords—Optimization, Energy Planning, Distributed Generation, EFOM, Direct search. I. INTRODUCTION It is widely acknowledged that energy, for the modern society, is the most important part of supporting people’s daily lives. Without energy, there can be little economic development, clean water, refrigerated foods and medicines, there would be no telephones, radios, televisions, or the most basic forms of sustainability. In many developing countries the population growth far exceeds planned rates of grid connection, which implies that many will either remain without energy, or be forced to migrate to urban areas where the infrastructure is already over burdened. Nevertheless, the fullfilment of energy for only to support people economic welfare would be out of question if the environmental degredation caused by the energy is not considered. People awareness of environmental impact caused by large conventional power plants is growing, together with a greater interest in distributed-generation (DG) technologies based upon renewable energy sources (RES) and cogeneration. Distributed generation is an approach that employs distributed energy resources to produce electricity close to the end users of power. DG technologies often consist of modular (and sometimes renewable –energy) generators, and they offer benefits to the electric power system and the total energy system [7]. For this reason DG facilities are often located close to load centres ([1], [2], [8]). Energy sources employed in DG are the combination of non-renewable fuels and RES. In such a case the environmental degredation can be minimized. Non-renewable fuels are mainly used in co- generation appliances and micro-turbines systems, and include fossil fuels such as natural gas, diesel oil, process-by products (blast furnace gas, cokery gas, refinery gas). A distinction is made between non-combustible RES and renewable fuels. The former include wind energy, water flow energy, geothermal sources, solar energy, tide and wave energy. Solid biomass, biogas, liquid bio-fuels and refuse-derived fuels (RDF) fall into the category of renewable fuels. In contrast to the use of a few large-scale generating stations located far from load centers-the approach used in the traditional electric power paradigm-DG systems employ numerous, but small plants and can provide power onsite with little reliance on the distribution and transmission grid. DG technologies yield power in capacities that range from a fraction of a Kilowatt[kW] to about 100 megawatts [MW]. Energy planning has to be carried out by studying all primary energy sources, from fossil fuels to RES, to determine optimal exploitation. It may seem that energy planning in the electricity sector has been superceded in a liberalised energy- production context where the electricity market is present. However, energy planning is a powerful tool for showing the consequences of certain energy policies, which helps decision-makers to choose the most suitable strategies to promote the spread of cleaner DG-technologies that take into account environmental impact and costs to the community. The distributed energy system is a challenging and interesting field therefore there is a large body of literature have been written. ([10], [11], [12], [13]) have addressed a comprehensive review of the model formulation and methods that are used for analyzing and solving the DG system planning. Energy planning involves finding a set of sources and conversion devices in order to satisfy the energy demands of all the tasks in an optimal pattern [14]. Within the set of optimization models, a mixed integer linear programming (MILP) model has been the favoured approach, due to structure of the problem and the robustness of the model to manage the trade-off involved. [15] propose a geographical information sysyem coupled with MILP, called EnerGIS, to solve the designing of district heating and cooling networks. [9] construct a MILP model for the optimal design of distributed energy resource (DER) systems coupled with heating, cooling, and power distribution networks. The target of their model is to acheive simultaneous optimiation of synthesis and operation strategies of the entire system. Another MILP approach is proposed by [8] to design optimally the DER systems in which energy is produced outside energy consuming buildings and sent to the buildings through the energy distribution networks. [16] use MILP for the integration plan and evaluation of DER systems. The objective of their model is minimize the total energy cost while guaranteering reliable system operation. They apply their model to the distributed energy system at Greek residential sector. [17] create a MILP model to solve the design of a distributed energy system that satisfies the electricity and heating demands of a cluster of commercial and residential buildings. The objective of their model is to minimize annual investment and operatiing cost.