Intelligent Energy Management for Distributed Power Plants and Battery Storage

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Introduction
Electrifcation of rural areas without access to AC grids or with problematic grid connections is one application of renewable energy system-based grid integrated hybrid power systems (GIHPS). Other applications include powering remote telecommunications and military installations, energy-intensive desalination of water, and irrigation pumping [1]. In most cases, GIHPS systems make use of solar PV systems, WECS, fuel cells, and batteries in their capacity as external levelling agents [2,3]. Te PCC is where the DC-DC converters, which regulate and aggregate the source power, are located. A good energy management system (SEMS) is necessary for an HPS to function properly [4][5][6], despite the fact that it increases both the reliability and efciency.
Te SEMS is responsible for regulating the active power fow from the RES that is required to meet the demand [7]. Because the instantaneous power from the sources does not always meet the demand, SEMS is required for the reliable operation of the system [8]. Tis is one of the many reasons why SEMS is so important. Batteries and supercapacitors have advantages in terms of both their energy density and their reaction times. As a result, they are better able to endure rapid variations in the power demand and give a higher level of transient stability. As a direct consequence of this, the battery will perform the function of an external levelling agent within the stand-alone hybrid system [9][10][11]. For hybrid energy systems to achieve their full potential in terms of energy efciency and long-term viability, efective energy management is essential [12]. Te irregular RES (PV panel, WECS) and many objectives (priority in the exploitation of sources for consumption) that need to be achieved make the SEMS a very complicated system [13]. Standard optimization techniques, in general, are too slow for real-time optimization issues with several objectives and constraints, such as power management challenges [1]. Tis is because real-time optimization problems have numerous objectives and constraints. As a consequence of this, the focus of the most recent research in this feld is on the development of intelligent controllers for power management. Figure 1 depicts the various input and control signals connected to a stand-alone power supply's power management system [14]. Te maximum power output of the fuel cell is used to calculate the instantaneous reference current used to calculate the WECS, SOC of the battery, V ref , i.e., the reference voltage required to be maintained at PCC, demand, and actual output current delivered to SEMS [15]. Te duty cycle of each DC-DC converter and the signals to connect and detach the sources are examples of the parameters produced by the power management controller [16]. When using several DC-DC converters, SEMS controls the duty cycle of each converter individually while regulating the voltage to keep PCC at 300 V [17].
GIHPS is meant to run in parallel with the utility grid. Te RES-based GIHPS gives consumers more options and fexibility while enhancing deployment reliability. State electrical boards enforce obligatory power cuts on companies over some time due to the country's peak power defcit, leading to a drop in output [18]. If there is no access to utility, electricity must be generated by DG sets; this results in high prices and carbon dioxide emissions [19].
Installing RES such as PV systems, WECS, fuel cells, AC grids, and other captive power generation can minimize grid dependency and greenhouse gas emissions [20]. Te gridconnected systems can be confgured in two ways depending on the HPS grid or AC supply, i.e., the grid joined at either end [21]. In a system with grid input, the grid is one of the several sources, with or without battery backup. In the second setup, a grid-tie inverter connects the HPS output to the grid, exporting excess electricity [22]. Combining AC and RES minimizes peak power and energy demand on the utility grid, reducing electricity generation from fossil fuels and thereby reducing greenhouse gas emissions [23]. Te paper is organized as follows: the concept of a power management system in a hybrid power system is discussed in Section 2. Te proposed power management system and algorithm approaches are discussed in Sections 3 and 4, respectively. Section 5 addresses the working of the control strategy in a hybrid power system. Section 6 discusses the diferent modes of operation of the SEMS. Te paper concludes with future research opportunities related to microgrid power management under source and load uncertainties in Section 7.

Energy Management System in Hybrid Power System
SEMS is an integrated system that works to ensure that the LD is being supplied with the necessary amounts of active power by regulating the amount of power generated by RES [24,25]. Many times, the instantaneous power extracted from the sources does not match the LD, making ESS necessary for continuous and reliable operation. High energy density and lightning-fast response times are hallmarks of modern battery and supercapacitor technologies [24,[26][27][28][29][30]. Because of this, they can handle a surge in power demand and maintain a constant output throughout the change [31][32][33][34][35]. To maximize energy efciency and sustainability, hybrid energy systems require careful power management. Furthermore, the SEMS is generally complex and requires fnding solutions very quickly and continuously every second because of the intermittent nature of the RES involved (SPV panel, WTG) and the multiple objectives that need to be satisfed (priority in the utilization of sources for consumption) [29,36]. Multiobjective multiconstraint power management optimization [37][38][39][40][41][42] is notoriously difcult to solve in real-time using traditional optimization methods [29,[37][38][39][40][41][42]. Terefore, intelligent controllers for power management [41,42] have been the focus of recent research.
Here are some of the goals of the SEMS: (i) To maintain the PCC or DC-bus voltage of the HPS at 156 V (ii) To use the available resources by the priority that was established (iii) To control the amount of current that fows to the load according to the amount of instantaneous power that is produced by the sources (iv) To satisfy the LD continuously (v) To control the charging and discharging of the battery within the safe limit as specifed in the battery handbook to extend the lifetime of the battery

Proposed Power Management System
A dedicated controller is required for power management to prioritize energy sources for use and to govern the fow of energy from generators to consumers in response to variations in heat dissipation requirements. According to the instantaneous power given by the sources and the demand, as well as the capacity of the grid, the proposed SEMS coordinates the allocation of electricity among sources such as solar PV systems, wind turbines, fuel cells, batteries, and the grid (which acts as one of the inputs).
To fulfl any level of demand, the suggested SEMS manages the sharing of electricity among RES, such as solar PV panels, wind turbines, fuel cells, and batteries, in addition to the AC grid (which acts as one of the inputs). Additionally, in addition to the input and output signals used in the SAHPS, parameters such as the input and output voltages of the DC-DC converter connected to the AC supply, control pulses that connect and disconnect the AC supply from the PCC, and the duty cycle to deliver the I ac_ref are taken into consideration. Figure 1 depicts the diferent input and control signals related to a GIHPS's SEMS, as well as the GIHPS's SEMS itself.
Priority is given to using energy produced by solar photovoltaic and wind systems, followed by the battery and fuel cell. In case of an excess demand that the RES could not supply, the controller connects the AC supply along with the RES to meet the LD.

Algorithm of Power Management System
Te algorithm of control logic developed for instantaneous power fow control in the GIHPS as shown in Figure 2 is listed below: Step 1. During step one, the controller measures various voltages and currents at the output of the DC-DC converters that are connected to the PV system, wind turbine, fuel cell, and battery. As well as measuring and calculating the P s and P W provided at the output of the MPPT system, the controller also monitors and calculates the battery SOC and the instantaneous reference currents I PV_ref and I W ref .
Step 2. Te controller optimizes the duty cycle for all DC-DC converters to generate 300 V at their output, i.e., at the PCC, to generate 220 V RMS at the output of the inverter.
Step 3. When the controller detects that the PCC voltage is 300 V, it either continues to the next step or recalculates the duty cycle.
Step 4. Te instantaneous load current I L is compared to the sum of the current delivered by the SPV panel and WECS, resulting in I PV ref + I W ref > I L , which indicates that the instantaneous load current I D is higher.
Step 5. If the answer is yes, the SEMS links the PV system and WECS to satisfy the load demand requirement. As long as there is surplus power at PCC, the SEMS computes the battery charging reference current of the battery using

International Transactions on Electrical Energy Systems
Step 6. Continuation of Step 4. If no, then the SEMS checks to see if the power delivered by the PV system and WECS exceeds the total power delivered by the panel and the WECS, which is represented by the expression I L > I PV ref + I W ref .
Step 7. If not, then I L � I PV ref + I W ref ; as a result, the SEMS links the PV system and WECS to the PCC, optimizes the duty cycle D PV , and D W supplies the demand as necessary.
Step 8. To re-evaluate the situation (continuation of Step 6), if the answer is yes, the SEMS checks to see if the battery's state of charge (SOC) is greater than 40%. After determining whether the answer is yes, the SEMS calculates the reference discharge current of the battery using the formula Step 9. Te SEMS then checks to see if the IBD reference is less than 10% of its maximum capacity. If so, the SEMS discharges the battery, as well as the PV system and the  WECS, to fulfl the demand. Also, the SEMS responds quickly to the changes in irradiance, wind speed, and load demand by adjusting the duty cycle of the DC-DC converter that is powered by the batteries.
Step 10 Step 11. If the answer is true, the SEMS optimizes the duty cycle D Fuel for the fuel cell supplied DC-DC converter to release I Fuel ref , as well as the PV system, WECS, and battery, to meet the load demand requirements. Adjusting the duty ratio D Fuel of the H 2 cell (fuel cell) powered DC-DC converter in response to changes in solar irradiation, wind velocity, and demand is how any variances are managed.
Step 12. In this case, the SEMS connects the AC grid with other sources to supply the power defcit (this is a continuation of Step 10). If the answer is no, i.e., the nature of the demand is such that Step 13. Organize the information (continuation of Step 8).
If the answer is no, i.e., if the SOC is less than 40% and the demand is such that I L > I Step 14. If so, the SEMS links the fuel cell, as well as the SPV panel and the WECS, to supply the demand.

Voltage Controller.
Te PCC collects the power output from all of the RES and battery and aggregates it. To prevent circulating current between sources when all sources are linked in parallel at the PCC, the output of all converters coupled to the point of common coupling should remain constant regardless of changes in the source voltage or load variations. Consequently, when a load is added to a variety of sources connected to PCC, the voltage at the output of the converter changes due to diferences in the characteristics of each source and variations in the regulation of the load. As a result, the controller is programmed to maintain a fxed output voltage by altering the duty cycle continually. On turn-on (when the controller is activated), it creates a duty cycle "D" to develop ref voltage V ref � 300 V at the PCC, as shown in equation (1), for all of the sources, and to provide an inverter output of 110 V RMS sine wave.

Instantaneous Current Controller.
Te power generated by the RES and the batteries is aggregated by the PCC. To prevent circulating current from the sources when all sources are linked in parallel at the PCC, it is important that the output voltage of all converters connected to the PCC be the same regardless of changes in the source voltage or load variations. Hence, due to diferences in the characteristics of each source and variations in the regulation of the load, the voltage at the output of the IBCs changes when a load is introduced to a set of sources linked to PCC. Tis is why the controller is set to continuously vary the duty cycle in order to keep the output voltage stable. During power-on (when the controller is enabled), it generates a duty cycle of " " to produce V ref � 300 V at the PCC for all sources, as given in equation (1), and to generate an RMS sine wave of 110 V at the inverter output. where PV system power output (P PV ) is equal to the WECS power output (P W ) at the MPPT converter's output. Te instantaneous reference current is generated by adjusting the duty cycle of the DC-DC converter linked to the SPV panel and WECS according to equation (4) when the load current I L is determined by equation (2).

International Transactions on Electrical Energy Systems
Also, the wind speed is erratic and continuously varying; hence, I WO , i.e., the actual output current of WECS is made equal to I W ref , by adjusting the duty cycle of the converter using the following characteristic equation: where N is the scaling factor that determines I PVO to I PV_ref convergence. A variable scaling factor was used because a constant scaling factor might create more extreme dynamic oscillations. Tis is seen in equations (6)- (9) where the scaling factor "N" of each source is changed to refect the contribution of the other sources to the total power delivered to the load (9). where Using equation (9), it is possible to calculate the reference charging current of the battery proportionate to the extra power available at the PCC if the load current follows equation (2).
Te AC supply is connected to the additional sources to meet load demand when the HPS is connected to the grid (grid as input) and the LD is more than the combined PV system, WECS, and fuel cell plus the peak dischargeable power of the battery equation.
I B DH ref is the rated discharge current of the battery, which is 10 percent of the battery's ampacity, and n is the number of cells in the battery. It is constructed in such a way that the immediate power produced by the PV system, WECS, and fuel cell as well as the battery discharging at its rated discharge limit if the battery is completely charged are all fully utilized by the controller. With the use of equation (12), the IG ref can be determined based on the power defcit.
Adjusting the grid supply's duty cycle so that the actual discharging current equals the reference discharging current, i.e., I Grid_act � I Grid_ref , as follows: Te grid supply scaling factor N Grid(k) is automatically determined based on the source (PV system, wind turbine, or fuel cell) contributing the majority of the load demand at the time as shown in the following equation: When the battery's state of charge (SOC) is less than 40%, the alternating current supply satisfes the load requirement in conjunction with the other sources and charges the batteries as well. Assuming the battery's state of charge is below 40%, the SEMS will connect the fuel cell (P Fuel max ) to the load if the load's power need is less than the fuel cell's maximum power deliverable (P Fuel max ). In this scenario, there are two possible controller settings when the fuel cell is meeting the load requirement. To fulfl the load requirement, either (1) the fuel cell is operated at or above its rated power and the surplus power is utilized to charge the battery or (2) the controller appropriately changes H 2 fow rate. Te cell is utilized to its maximum potential to create electricity, and any surplus is used to charge the battery since the fuel cell's dynamics are low. Te controller computes "I B_CH_ref " from "I B_CH_ref � I Fuel_ref − I L ," calculates "D B_CH " about "I B_CH_ref " and charges the battery as shown in Figure 3. Te power values of various sources and loads are shown in Table 1.

Mode 2.
Te sum of the PV power and wind power is less than the load demand, while the battery is fully charged. Based on the priority of source utilization battery supplies to the load controller optimizes the duty cycle for the I B_CH to harness all PV power and wind power to supply the demand. Also, the controller calculates "I B_DH_ref " using "I L − (I PV_ref + I W ref )" and optimizes "D B_DH " to supply the power defcit from the battery as shown in Figure 4. Also, the controller endlessly monitors PV power, wind power, and demand and accommodates the instantaneous variations in the P wind , and if there were any drastic variations the controller shifts to the other modes of operation. Te power values of various sources and loads are shown in Table 2.

Mode 3.
In this mode, the sum of the PV power and wind power is higher than LD, and also the SOC ≤ 90%. Te proposed SEMS connects the PV system and WECS to the PCC to supply the demand. Also, the controller estimates "I B_CH_ref " from "(I PV_ref + I W ref ) − I L " and computes "D B_CH " about "I BC_ref " to charge the battery with the surplus power, which is shown in Figure 5. Te controller continuously monitors the variations in P PV , P W , and LD and correspondingly adjusts the duty cycle to charge the battery and also changes the mode of operation if needed. Te power values of various sources and loads are shown in Table 3.

Mode 4.
In mode 4 PV system and WECS power, while the battery SOC ≤ 40%. When the LD > P PV + P W , the LD is met by the controller's use of the fuel cell, PV array, and WECS at full capacity. Te SEMS calculates the "I B_CH_ref " from "(I PV_ref + I W ref + I F_ref ) − I L ," and optimizes the duty cycle to charge the battery. Te controller response with such source and load condition is given in Figure 6, where the battery is charged at 5% ampacity rate based on the excess power. Te power values of various sources and loads are shown in Table 4.

Mode 5.
In this mode, P PV + P W � LD, the SEMS optimizes the duty cycle of both the converters to extract all the P PV and P W to meet the LD and is shown in Figure 7. Te power values of various sources and loads are shown in Table 5.
6.1.6. Mode 6. Assuming the PV is plenty, while the P W � 0 in this mode. Presume the battery SOC < 40%. For any LD > P PV , executing the priority in utilization, the SEMS combines photovoltaic (PV) panels with fuel cells to generate the LD power. If LD > P PV and LD ≤ P S + P Fuel max , the SEMS optimizes the duty cycle for WECS fedDC-DC converter to harvest all of P W and suitably controls the fuel cell to deliver P Fuel max if LD � P PV + P Fuel max and to deliver aptly if LD < P PV + P Fuel max the fuel cell to meet the LD as in Figure 8. Te power values of various sources and loads are shown in Table 6.

Mode 7.
Te SEMS in an HPS should have a good dynamic response, i.e., a faster response speed to accommodate the sudden source and load variations in the PM. For all stability difculties, it is shown that the SEMS based on the instantaneous current reference scheme is stable, and the controller's reaction to a rapid load rejection is shown in Figure 9. For a fxed LD, i.e., "I L � I PV_ref + I W ref + I B_DH_ref ," the SEMS connects the PV panel, WECS, and fuel cell to the PCC and adjusts the duty cycle to extract all of P PV and P W for supplying the LD, and for the remaining power defcit, it suitably discharges the battery. At 6 ms into the simulation, when a sudden load rejection is established, the controller switches the linked converter from discharging to charging mode and does the necessary calculations. "I B_CH_ref � I PV_ref + I W ref " uses all of the P PV and P W to charge the battery immediately as shown in Figure 9. Te proposed system has a good dynamic response and it is very much mandatory to manage the power fow under sudden load and source variations. Te power values of various sources and loads are shown in Table 7.               In this mode, the PV system produces power while P W is zero, which also presumes the battery SOC < 40%; however, the fuel cell can deliver its maximum deliverable power. Under these circumstances, when LD > P PV + P Fuel max , the controller duly optimizes the duty cycle to harvest all the P PV and to deliver P Fuel max . As the power scarcity still prevails, the SEMS estimates "I Grid_ref " from "(I B_CH_ref + I L ) − (I W ref + I Fuel_ref )" to deliver the sum of power required to satisfy the residual LD and to charge the battery at its rated charging current which is "I BC_ref � (I ac_ref + I S_ref + I F_ref(max) ) − I L " as shown in Figure 10. Te power values of various sources and loads are shown in Table 8.

Mode 9.
In this mode, P Fuel � 0, P PV + P W < LD and presume that the battery SOC < 40%, while the fuel cell cannot produce power. When the LD > P PV + P W , the controller utilizes all of P PV and P W to meet the LD. Also, the SEMS connects the AC grid along with PV, and WECS and optimizes the duty cycle about "I Grid_ref " using "  Table 9.

Mode 10.
In the daytime, solar insolation naturally prevails, while the wind speed may be negligible. Hence, P PV is predominant, while the P W � 0. Also, presume the battery SOC < 40% and the fuel cell cannot produce power. Under these conditions, if the LD > P PV , the SEMS optimizes the duty cycle to extract all the P PV to meet the LD. As the power defcit still prevails, following the hierarchy in utilization, the SEMS connects the AC grid (as other sources are not available) along with the PV panel to the PCC and optimizes the duty cycle to deliver "I Grid_ref " using "(I B_CH_ref + I L ) − I PV_ref " to meet the remaining power defcit and to charge the battery at its rated charging current calculated from "I B_CH_ref � (I Grid_ref + I PV_ref ) − I L ." Te response of the SEMS is shown in Figure 12. Te power values of various sources and loads are shown in Table 10.

Mode 11.
Te P PV � 0 and WECS generate an ample amount of power, also battery SOC < 40%, while the fuel cell can deliver its maximum deliverable power. Under these situations, when LD > P W + P Fuel max , the controller optimizes the duty cycle to deliver all of P W and P Fuel max to meet the demand and the controller connects the AC grid to deliver "I Grid_ref " equal to "(I L + I B_CH_ref ) −  )," i.e., the sum of power required to meet the remaining LD and for battery charging with the reference charging current "I B_CH_ref ." Te simulation response to such input and LD is shown in Figure 13. Te power values of various sources and loads are shown in Table 11.

Mode 12.
A copious amount of P PV and P W is available while the fuel cell can generate its maximum deliverable power. Presume the battery SOC < 40%. At this stage, when the LD > P PV + P W + P Fuel max , the controller duly optimizes the duty cycle to extract all of P PV P W , and P Fuel max to meet the LD. As the power insufciency still   prevails, the SEMS connects the AC grid to the PCC to deliver "I Grid_ref " equal to "(I L + I B_CH_ref ) − (I PV_ref + I W ref + I Fuel_ref(max) )" to satisfy the residual LD and to charge the battery with its rated charging current "I B_CH_ref ." Te controller response in managing the power fow under such constrained source and load conditions is shown in Figure 14. Te power values of various sources and loads are shown in Table 12. Grid 1200 w 1300 w 1200 w  Figure 13: SEMS harness all of P WIND and P Fuel max , connects the AC grid to supply the defcit, and charges the battery.

Conclusions
Tis study presented a novel energy management control technique that can quickly regulate power fow in a hybrid power supply comprised of a photovoltaic (PV) system, a fuel cell, a battery, and the grid. Tese results were obtained by creating a comprehensive model of a dynamic hybrid power supply for simulation. Te data show that the proposed hybrid system performs wonderfully in several contexts. Te SEMS is in charge of overall control of the hybrid power supply, both independently and while engaging with the grid. Te efciency and power generation of the hybrid power supply are both improved as a result of using the recommended approach. Furthermore, utilising the proposed voltage control and voltage-dependent current control technique, the real current fow can be promptly stabilised at the reference current magnitude. Because it is designed to operate instantaneously, the suggested SEMS has exceptionally high levels of dynamic performance. Te SEMS improves the steady-state performance of the boost converter by coordinating the converter's output current with the reference current. Moreover, the suggested SEMS has improved transient stability.

Nomenclature
SEMC: Smart energy management controller LD: Load demand SAHPS: Stand-alone hybrid power system GIHPS: Grid-interactive hybrid power system EMS: Energy management system PEMFC: Proton exchange membrane fuel cell SPV: Solar photovoltaic WTG: Wind turbine generator.

Data Availability
Te data used to support the fndings of this study are available from the corresponding author upon request (head.research@bluecrest.edu.lr).

Conflicts of Interest
Te authors declare that they have no conficts of interest.

Authors' Contributions
Prakash RBR proposed the methodology, Srinivasa Varma P conceptualized the study and provided the software, Ravikumar CV performed data curation and formal analysis, Vijay Muni T supervised the study and reviewed and edited the article. Asadi Srinivasulu provided the software and wrote the original draft. Kalapraveen Bagadi visualized and investigated the study, Rajesh A was responsible for the resources and performed the formal analysis, and Sathish K performed the data curation.  Figure 14: SEMS harvests all of P PV , P Wind , and P Fuel Max , connects the AC grid to supply the defcit, and charges the battery.