Smart Micro-Grid Performance using Renewable Energy

The aim of this research is to determine the performance of PV panels, wind turbines, battery storage and power imported from the grid to the system which will ensure a reliable energy supply, as well as the technical feasibility of a smart microgrid system. Indonesia's renewable energy potential for electricity reaches 443 GW, where solar energy is the largest potential, namely 4.8 KWh m or equivalent to 112 000 GWp, but only 10 MWp has been utilized. The most basic problem in this system, namely, the uncertainty of wind energy and solar energy, one of the most vital factors in the optimal size of a renewable energy-based smart microgrid system is the reliability of the system being built. The method used in this research is to collect data on the availability of wind energy and solar energy as well as load analysis on the smart microgrid system. As a result, the resulting power output was 6.2 MWh during the experiment. The highest average Performance Ratio (PR) of the solar energy power generation system, namely 77 % in February 2020. Optimized with Battery Life (OBL) model produces a power output of 102.4 kWh and has an overall system efficiency of 81.92 %.


Introduction
Indonesia has high renewable energy potential. Based on data from the Ministry of Energy and Mineral Resources (KESDM), Indonesia's renewable energy potential for electricity

Smart microgrid in Indonesia
Studies on renewable energy-based smart microgrids in Indonesia have been carried out by several researchers [3], where the system used is to combine energy from solar PV and generators with diesel fuel. Diesel power used is 1 kW and solar PV 1 kW. The diesel fuel used consists of coconut oil and palm oil [5]. This simulation was carried out using HOMER and MATLAB for the installation of a smart microgrid to be installed in Pesisir Selatan Regency. From the final report on technological innovation [1], the Center for Energy Conversion Technology reported the installation and testing of a smart microgrid in the form of the installation of 10 kW roof top solar PV installed on the roof of an energy building in the Puspipek (Pusat Penelitian Ilmu Pengetahuan dan Teknologi -Research Center for Science and Technology) area, Serpong, Tangerang, Indonesia to represent the smart micro grid system in urban areas. The excess power generated can be supplied to the State Electricity Company (PLN -Perusahaan Listrik Negara) network. This smart grid system balances the energy supply and the energy load generated by using software management.
For smart microgrid activities for rural areas, installation was carried out in Bilacenge village, West Sumba Regency, Sumba Island, Indonesia in collaboration with Japanese company Kyukendo using an Energy Management System (EMS) which is integrated with the SMG (Smart Microgrid) system. For the Sumba island, 500 kWp is installed, which is connected directly to the State Electricity Company (PLN) network. The EMS that is implemented consists of three components of the Smart Meter Control System (SMCS), the Smart Power Management System (SPMS), and the Battery Monitoring Unit (BMU). EMS accepts the input electrical power generated by four groups of solar panels with a capacity of 100 kWp each.
Several researchers plan to install a renewable energy-based smart microgrid on the STT PLN campus, West Jakarta, Indonesia, which combines the power from a 14.5 kWp solar power plant (solar PV), a 20 kW wind power plant, a 16 kVa Biomass power plant consisting of from a wad14 kVa waste gasifier and a 2 kVA waste digester plus a kWH meter extraction system from the State Electricity Company (PLN) network and batteries [7].

Research methods
The smart microgrid system installation has been completed, which will then be followed by a performance test of the system, including the performance efficiency and performance of the control system. The results of this performance test study will be used as the basis for the feasibility techno-economic calculation of a system that can be used in areas that require electricity based on solar and wind energy. The schematic of the installed smart microgrid system is shown in Figure 1.

Built-in smart microgrid system
The Photovoltaic (PV) array consists of 90 modules with a capacity of 330 Wp per module, which is divided into four series, namely 2 × 30 PV for on grid systems and 2 × 15 PV for battery charges. The system is supported by a 20 kW inverter connected directly to the grid, three units of 48/10 000/140-100/10 Victron QUATTRO inverters with one phase each, 13.8 kWh lithium battery, MPPT Victron Smart Solar 150/100, cables and other supporting structures. The total area of the array is approximately 180 m 2 . Moreover, the PV Module is tilted at 7º facing towards the North.
The installed wind turbine with a capacity of 6 kW has three blades and a wind guide. This turbine is supported by the Wind turbine tower P-750. This turbine also has an inverter which is connected to the smart microgrid system.

Weather station
Measurement of the potential for solar energy and wind energy in the Darma Persada University, East Jakarta campus environment using weather station instruments. The amount of solar radiation and wind speed can be read in real time using Davis Instruments Weatherlink software.

Solar energy and wind energy
This section discusses basic computation theory, including defining a series of concepts, terms and variables used in evaluation. The number of obstacles that affect the wind speed and cause the wind turbine to not rotate, hence the wind energy in this system is neglected.

Power
The electric power generated by solar cells when exposed to light is calculated from the ability to produce voltage when given a load and current through the load at the same time. This is as represented on the current-voltage (I-V) curve in Figure 3.
The power (Wpeak) generated by the Solar Power Plant to meet energy needs is calculated by Equation 1 [6] : Where A is the area of the PV array, Speak is the Peak Solar Insulation (1 000 W m -2 ) and ηPV is the efficiency of the solar panels. The curve shows when the current and voltage are at the maximum power point, it will produce the maximum output power (PMPP). The voltage at the Maximum Power Point (VMPP) is less than the open circuit voltage (VOC) and the current at IMPP is lower than the short circuit current (ISC).

Solar cell efficiency
The efficiency of daily (ηPV, d), monthly (ηPV, m), and annual (ηPV, y) of the PV arrays, respectively, is given in Equation 2: EPV, d is the daily energy (kWh) produced by the PV module and is given by Equation 3 : where VDC and IDC are DC voltage (V) and DC current (A), ti and tf are the start time and end time (h), Er,d are the daily available energy (kWh), which are defined as the daily radiation in the surface plane of the array when the inverter is connected to the grid and is calculated using Equation 4 : where C = 0 if the inverter is outgoing and C = 1 if the inverter is operating, n is the total number of values recorded daily, Ei is the total energy received on the surface plane of the array during one measured interval (kWh), S is the solar radiation (W m -2 ), and A is the area of the PV array (m 2 ). Therefore, if C = 0 for Ei < Inverter threshold, then the inverter is disconnected from the grid.

Inverter efficiency
The efficiency of the daily (ηinv, d), monthly (ηinv, m), and annual (ηinv, y) of the inverter is calculated by the Equation 5 : where Egrid is the output of AC energy (kWh) obtained from the receiving and sending measures (E1-E2), where E1 is the reading of the measuring instrument at the start of daily data acquisition and E2 is the reading of the measuring instrument at the end of the daily data acquisition, Egrid, d is also the daily active energy at the output of the inverter (kWh), and it can be calculated using the Equation 6 : Where, E1 = Total power of each phase (initial time) × (tend-tinitial), (kWh). E2 = Total power of each phase (end time) × (tend-tinitial), (kWh)

System efficiency
The efficiency of the daily (ηsys, d), monthly (ηsys, m) and annual (ηsys, y) of the systems can be evaluated using the Equation 7 : In order to compare the results of different PV systems, two other parameters are needed, namely: final result (Yf) and performance ratio (PR). The final result (Yf) is defined as the annual or daily system output energy divided by Wp,c, where Wp,c represents the previous peak power estimate of the array under the Standard Testing Conditions (STC). Thus the daily, monthly and yearly final results are calculated using the Equation 8: The daily performance ratio (PRd) is defined as the ratio between the daily final result and the daily global irradiation in the surface plane of the array Ei,d given in the Equation 9 : where GSTC is the radiation below the STC. PR allows a comparison between the performance of different PV systems taking into account all parameters such as: location, tilt angle, orientation, nominal power, shading dust, etc.

Generation performance
The overall performance of the smart microgrid system must also be measured from the performance of the energy source generated. A summary of the results of the generation performance from solar energy is summarized in Table 1. Installation and testing of each component of the renewable energy-based smart microgrid system was carried out in September 2019. As a result, the power generated by the PV module is recorded to determine the system performance daily, monthly, and yearly. The experiments in October and November had a lower total power output than in December, January and February, namely 792.9 kWh and 614.7 kWh, respectively. This is because some components experience errors and system testing. In the last 3 mo, the system is quite stable, because all the components are working properly. The error in the smart microgrid system can be seen in Figure 4. On the other hand, the influence of weather is the rainy season in the area where the research starts from early November to January, hence it affects the average system output energy (Yf) daily, which is (2.7 to 2.8) kWh kWp -1 . The influence of weather also affects the Performance Ratio (PR) of the average generation system, namely between 53 % to 56 %. The highest Performance Ratio (PR) was 77 % in February 2020.

Technical feasibility analysis of smart microgrid system
The smart microgrid system was tested using two optimization models. The first model is Optimized with Battery Life (OBL) and the second model is Optimized without Battery Life (OWBL). The results, as shown in Table 2, that the Total Production of the Optimized without Battery Life (OWBL) model is greater than the Optimized with Battery Life (OBL) model, namely 102.40 kWh and 86.49 kWh, respectively.  Figure 5 -A1 & A2, describes the smart microgrid system that is set in the Optimized with Battery Life model (OBL). This causes the PV production generated in a day to be less productive. In this optimization model, the production of the 2 × 15 PV circuit is only focused on charging the battery, the electric current is cut off by the Solar Charger when the battery is 100 %. As shown in the graphs in Figures 5 -A1 & A2, the current is cut off when the radiation approaches peak solar radiation (Speak), hence 2 × 15 PV only produces 11 kWh per day as shown in Table 2. Figure 5 -B1 & B2, describes the smart microgrid system that is set in the Optimized without Battery Life (OWBL) model. In this model, PV works to charge the battery and transmit directly to the grid. When the battery is in 100 % state, the Solar Charger continues to drain the power generated by the PV. This causes the production of PV produced in a day to be more productive compared to the system mode in Figures 5 -A1 & A2. The power generated by 2 × 15 PV in this system mode is 32 kWh per day with an overall system efficiency of 81.92 % as shown in Table 2.
The total power generated during testing from October 2019 to February 2020 resulted in a PV power output of 6.2 MWh which was used for the water pump load of 55 % of the total PV production power. Due to the limited capacity of the battery installed in the smart microgrid system, the consumption of electrical energy for loads (Consumers) at night relies on electrical energy from the grid. During the data collection period, the energy used by Grid for loads was 37 %, which was mostly used at night. Meanwhile, during the day, the electricity consumption for loads by electricity comes from solar energy, which is 63 % of the total load.  Information : Grid = National Electricity Company (PLN) electricity network.
The excess of PV production during the day, due to the load of the water pump which has a power of only 3 kW, it is set by the system to be automatically transmitted to the grid network. The amount of power transmitted to the grid is 2.3 MWh. Meanwhile, the control room of the smart microgrid system supplied by Grid to operate system components at night is 147 kW or 6 % of the power sent by the smart microgrid system to the Grid (State Electricity Company (PLN) network) [13].

Conclusion
The highest average Performance Ratio (PR) of the solar power generation system, which is 77 % in February 2020. Optimized with Battery Life (OBL) models produces a power output of 102.4 kWh and has a system efficiency of 81.92 %, greater than Optimized without Battery Life (OWBL) model that produces 86.49 kWh of power with an overall system efficiency of 69.19 %. Overall, the smart microgrid system can operate properly. An error in the system that occurs once in January and February for only a few seconds can automatically return to normal immediately, due to the power transmission system regulated by Smart Microgrid.