This chapter proposes a mixed-integer optimization using genetic algorithm (MIOGA) for determining the optimum sizes and placements of battery-sourced solar photovoltaic (B-SSPV) plants to reduce the total energy losses in distribution networks. Total energy loss index (TELI) is formulated as the main objective function and meanwhile bus voltage deviations and PV penetrations of B-SSPV plants are calculated. To deal the stochastic behavior of solar irradiance, 15 years of weather data is modeled by using beta probability density function (Beta-PDF). The proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired for different time varying voltage dependent load models. From the results, it is known that, compared to PV only, the integration of B-SSPV plants in the distribution networks resulted in higher penetration levels in distribution networks. The proposed algorithm was very effective in terms of determining the sizes of the PV plant and the battery storage, and for the charging and discharging of the battery storage.
TopIntroduction
Due to the inadequate fuel reserves, the power producers are considering to use renewable energy sources for the development of new power plants. Unlike the conventional large scale power stations, the new small and medium sized power stations are considered suitable to be installed at nearer to distribution stations, also known as distribution generation (DG) (Liu, Wu, Tu, Huang, & Lou, 2008). The concept of using renewable energy resources for producing the electricity, has been globally accepted and according to reports (Lee et al., 2012), electricity production in coming years will mainly depend on the renewable sources such as solar and wind energy. The major advantages of using renewable energy sources over the traditional power producing technologies are reflected as the environment friendly and the fuel free sources. Some of these sources especially wind and solar energy, are rapidly growing and have become more competitive, because per unit cost of electricity produced through wind turbines and solar photovoltaic (PV) modules, is much cheaper than the cost of electricity produced by the fossil-fuel based power plants. During the recent years, several studies have covered the technical possibility of an electrical network that can be powered through renewable energy sources (Gökçek, Bayülken, & Bekdemir, 2007; Plebmann, Erdmann, Hlusiak, & Breyer, 2014). Furthermore, the research also suggests that in 2050, 80% of total U.S. electricity demand could be supplied by using existing renewable electricity technologies (Bazilian et al., 2014).
Among the other renewable energy sources, solar PV technology is getting more mature and popular (Tyagi, Rahim, Rahim, Jeyraj, & Selvaraj, 2013). This is mainly due to the availability of good solar irradiance levels in many countries, ease of operation & maintenance (O&M), environmental benefits, increasing efficiency and reduced cost of PV panels (Aman et al., 2015; Devabhaktuni et al., 2013). The Photovoltaic (PV) technology converts the sunlight directly into the electricity without using any fuel. The upper surface of the atmosphere of the earth receives 174 Peta Watts (PW) of solar energy and it is naturally available across many parts of the world. Most PV modules come with 15 - 25 years warranties on their rated power outputs and these modules require virtually no maintenance during their life. The efficiency of commercial silicon modules has dramatically seen improvement during the last decade and in recent days, some manufacturers are claiming to have prototypes of PV modules, which can convert the sunlight into the electricity with an efficiency of 22.5% (Panasonic, 2015). The good thing with utilizing the PV technology is that the maintenance required for solar PV systems is mostly the cleaning of the PV modules.
The output of the PV modules mainly depends on the solar irradiations, but however, this is also affected by the temperature. Surface irradiations of the solar lights are measured as Watt/m2. The ideal irradiation required for a PV module to produce the maximum power output is 1000 Watt/m2 at temperature of 25 0C (Al Riza & Gilani, 2014). A 1 kWp solar PV system installed in a location with good solar irradiations can produce about 5-6 kilowatt/m2/day. Besides the technical systems constraints, the output power of the solar PV modules, is a function of sun light and mainly depends on the levels of solar irradiation and temperature, therefore, the changes in weather conditions will affect the power output of the PV modules. In order to cope with the uncertainties associated with solar irradiations, the solar weather data is modeled by using Beta Probability Density Function (Beta-PDF). (Atwa, El-Saadany, Salama, & Seethapathy, 2010; Fan, Vittal, Heydt, & Ayyanar, 2012; Hung, Mithulananthan, & Lee, 2014; Khatod, Pant, & Sharma, 2013; Tan, Hassan, Majid, & Rahman, 2013)