Abstract
This chapter studies the prospects of energy complexes on the basis of renewable energy sources to supply electricity to the stand-alone consumers in different regions of Myanmar. In order to do that, the territory of Myanmar is divided into regions according to their amount of renewable energy sources. The developed methods are for determining the optimum parameters and operation of the energy complex on the basis of renewable energy sources and the cost-effectiveness of those energy complexes was examined. This was for the purpose of a mathematical formulation of the problem of optimization of the energy complex on the basis of renewable energy sources for autonomous rural consumers of the Republic of Myanmar.
TopBackground
Currently, one of the biggest problems of the Republic of Myanmar, which is a developing country, is the need to raise the social standard of living of a large rural population, which is largely determined by the level of consumer supply of cheap electricity. In 2017, Myanmar produced 18 billion kWh of electricity, and the demand for electricity amounted to 20 billion kWh (Aung & Shestopalova, 2016). The specific energy consumption per person was only 200 kWh / year (Aung & Shestopalova, 2016). The level of energy consumption in Myanmar is the lowest in comparison with neighboring developing countries. The installed capacity of power plants operating in the Unified Energy System (UES) of Myanmar was 5,390 MW in 2017.
In Myanmar, there is a shortage of electricity in all sectors, including the municipal sector (Hla, 2015). Currently, Myanmar’s national electricity grid does not cover the entire territory of the country.
The National Grid (NG) covers only 38% of the country's population. NG does not cover mountain areas due to the high cost of transmission lines. Mountain and remote regions have only local networks of autonomous power supply.
This means that 62% of the population of Myanmar live in a decentralized area with an unguaranteed energy supply. Of the 64907 rural settlements, only 7% are connected to the National Grid. Most autonomous consumers in rural areas use diesel or gasoline generators. The cost of electricity for a centralized consumer is 3.5-7.5 US cents / kWh. The cost of electricity in the villages is much higher (about 50-90 US cents / kWh) (Aung & Shestopalova, 2016; Aung, 2015; Aung, Malinin & Shestopalova, 2016).
Thus, the most pressing problem of Myanmar is the lack of power supply to the population. Another important issue is the heating and air conditioning of residential buildings.
Key Terms in this Chapter
Solar Energy: Is radiant light and heat from the Sun that is harnessed using a range of ever-evolving technologies such as solar heating, photovoltaics, solar thermal energy, solar architecture, molten salt power plants and artificial photosynthesis.
Small Hydro: Is the development of hydroelectric generation facilities on a scale corresponding to river discharge and potential, and which is suitable for local community and industry, or to contribute to distributed generation in a regional electricity grid.
Renewable Energy: Is energy that is collected from renewable resources, which are naturally replenished on a human timescale, such as sunlight, wind, rain, tides, waves, and geothermal heat.
Wind Energy: Wind energy (or wind power) describes the process by which wind is used to generate electricity. Wind turbines convert the kinetic energy in the wind into mechanical power. A generator can convert mechanical power into electricity.
Geographic Information System (GIS): Is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. The key word to this technology is Geography – this means that some portion of the data is spatial.
Criteria: Are generally categorized into the three groups: energy, environmental and economic criteria. Determination of optimal energy mix comes down to determination of the percentage share of each component of renewable energy supply in defined boundary of the observed problem.
Optimization Parameter: (Or a decision variable, in the terms of optimization) is a model parameter to be optimized. For example, the number of nurses to employ during the morning shift in an emergency room may be an optimization parameter in a model of a hospital. The OptQuest Engine searches through possible values of optimization parameters to find optimal parameters. It is possible to have more than one optimization parameter.