One way to increase reliability and quality of electrical power supply in advanced power generation networks is the integration of distributed production, energy storage and energy management on level of microgrids and distribution networks. Benefits of distributed production are reduction in transm Contact online >>
One way to increase reliability and quality of electrical power supply in advanced power generation networks is the integration of distributed production, energy storage and energy management on level of microgrids and distribution networks. Benefits of distributed production are reduction in transmission and distribution losses, improved utilization of energy sources, shorter construction time and possibility of production at all voltage levels.
Hybrid power systems (HPS) are designed for the generation and use of electrical power. They are independent of a large, centralized electricity grid and incorporate more than one type of power source. They may range in size from relatively large island grids of many megawatts to individual household power supplies on the order of one kilowatt. In general, a hybrid system might contain AC diesel generators, DC diesel generators, an AC distribution system, a DC distribution system, loads, renewable power sources (wind turbines, photovoltaic power sources, small hydro power plant), energy storage, power converters, rotary converters, coupled diesel systems, dump loads, load management options or a supervisory control system [1].
Analysis in [2] focuses on the comparative analysis between HPSs on a microgrid and the supply option over the transmission and distribution network. Autonomous HPSs are conceptualized by taking into account storage in the electric vehicles of guests and employees within the treated example of the winter tourist center. In this way, a possible future concept for a sustainable power supply is proposed, focusing on locally available RES utilization and capacity optimization, in accordance with real indicators at the site.
The distribution system energy system needs to evolve to facilitate such access to distributed generation based on renewable energy sources and to establish a communication system that will enable interaction with end users to gain data on the amount of energy required. The presence of distributed sources slowly transforms the distribution network from the passive network into active, resulting in some branches of the network changing the direction of power flows [8]. The active network requires new equipment and services, voltage control, system protection and calculation of power flows, which makes it harder for the job of the system dispatcher. But the main function of such a network is, of course, to equalize production and consumption of electricity in real time.
Bjelimići is community of villages located in the southeastern part of the municipality of Konjic. Konjic is located in northern Herzegovina and is mountainous, heavily wooded area. Bjelimići is wide, hilly area between the mountains Visočica, Treskavica and Crvnja, and is 1000 m above sea level. It has great potential for installing renewable energy resources on this area.
Figure 1 shows the georeferenced scheme of the medium voltage distribution network in analyzed area Bjelimići.
Georeferenced scheme of the area Bjelimići
Besides complete topology and georeferenced scheme, the materials used in this paper consist of real network parameters of components (transformers, lines, loads) in the feeder 10 kV Lađanica, then load profiles of loads (mostly village houses) on TS 10(20)/0.4 kV Odžaci and TS 10(20)/0.4 kV Luka, in 15-min intervals for 1 year (2016) and wind potential and solar insolation measurement data from wind atlas and PVGIS.
According to load profile of this area, data for PV, wind and diesel generator will be taken from PVGIS, wind atlas and HOMER generators catalogue, respectively. After entering this into HOMER software, appropriate HPS configuration will be established, based on least-cost investment optimization.
The load following strategy is a dispatch strategy whereby whenever a generator operates, it produces only enough power to meet the primary load. Lower-priority objectives such as charging the storage bank or serving the deferrable load are left to the renewable power sources. The generator can still ramp up and sell power to the grid if it is economically advantageous [11] (Table 1).
The real network will be modeled in DIgSILENT PowerFactory software, but due to the limited number of buses that are allowed with used software license, the certain parts of network would be implemented as equivalent loads. For modeling microgrid in this software, data obtained from HOMER will be used. Two modes of operation will be analyzed, grid-connected and island mode. For both cases, two scenarios will be obtained, winter and summer scenarios. After this, power flow, voltage profiles, line and transformer loading, and total grid losses will be compared and analyzed.
Results section will be divided into two subsections, one for results from HOMER and another for results from DIgSILENT PowerFactory (Table 2).
In this section, the analysis results will be presented and shortly discussed. The goal was to design hybrid power system which will meet the needs of consumers and get minimal total cost of investment which will be presented through table results and charts. In Table 3 and Fig. 2, net present costs of all elements used in designing hybrid power system and cost summary are shown, respectively. The prices in Table 3 are obtained as a result from HOMER software, and it is important to mention that they depend on the region and are subject to change.
In this section, two modes of operation, grid-connected and island mode, will be presented. The winter and summer scenarios will be shown separately with focus on four important segments of analysis: the power flow in and out of the microgrid, voltage profiles (worst-case scenario), line and transformer loadings (worst-case scenarios) and total grid losses.
The power of every transformer was provided as well as the load characteristics of TS Luka and TS Odžaci. It was necessary for the analysis to enter the load characteristic for each transformer in the observed network, but due to the lack of data, the provided characteristics were used as an approximation on the rest of the transformers taking the relevant maximum power into consideration. It was possible to make these approximations due to the similar load characteristics of households.
For winter scenario, 2 days were chosen, specifically 13th January and 11th February, both working days. After shorter analysis, it was stated that there was no bigger difference between working days and weekends in winter.
There were no changes in the summer scenario regarding the methodology of calculations on the obtained data. The only difference compared to the winter scenario is the days chosen for analysis. The load characteristics for summer include 19th July and 20th August. The first one is a work day and the latter is a day of the weekend, chosen due to the expected household load characteristic. Again, these characteristics were used for approximation on loads for which there was no measurement data provided.
After implementation of the input characteristics explained in Sect. 2, the quasi-dynamic simulation was performed, which is an automated load flow calculation for longer time periods (in this case 1 day) with 5-min step of simulation. In the following subsections, the results will be shown and explained.
Power flow will be observed from the line that is closest to the switch which separates microgrid from the rest of the network. The negative values of active power present the power returned to the network due to the higher production than consumption. The active power flow is shown in Fig. 3.
Active power flow in the microgrid—GC mode, winter scenario
After shorter analysis, it is concluded that the worst-case scenario of voltage profiles belongs to the TS Luka, with minimum value of 0.984 [p.u.] and maximum value 0.994 [p.u.]. The voltage profile of this scenario is shown in Fig. 4.
Voltage profile of the worst-case scenario—GC mode, winter scenario
Analysis has shown that the largest loadings are present on Line (29) and on TS Luka transformer. The maximum loading (%) is 3.81% on the mentioned line, and the complete line loading is shown in Fig. 5. The maximum loading on transformer is 38.76%, and the complete transformer loading is shown in Fig. 6.
Line loading—GC mode, winter scenario
About Microgrid development bosnia and herzegovina
As the photovoltaic (PV) industry continues to evolve, advancements in Microgrid development bosnia and herzegovina have become critical to optimizing the utilization of renewable energy sources. From innovative battery technologies to intelligent energy management systems, these solutions are transforming the way we store and distribute solar-generated electricity.
When you're looking for the latest and most efficient Microgrid development bosnia and herzegovina for your PV project, our website offers a comprehensive selection of cutting-edge products designed to meet your specific requirements. Whether you're a renewable energy developer, utility company, or commercial enterprise looking to reduce your carbon footprint, we have the solutions to help you harness the full potential of solar energy.
By interacting with our online customer service, you'll gain a deep understanding of the various Microgrid development bosnia and herzegovina featured in our extensive catalog, such as high-efficiency storage batteries and intelligent energy management systems, and how they work together to provide a stable and reliable power supply for your PV projects.
