An intelligent energy management system (IEMS) for maintaining the energy sustainability in renewable energy systems (RES) is introduced here. It consists of wind and photovoltaic (PV) solar panels are established and used to test the proposed IEMS. Since the wind and solar sources are not reliable in terms of sustainability and power quality, a management system is required for supplying the load power demand. The power generated by RES is collected on a common DC bus as a renewable green power pool to be used for supplying power to loads. The renewable DC power bus is operated in a way that there is always a base power available for permanent loads. Then the additional power requirement is supplied from either wind or PV or both depending upon the availability of these power sources. The decision about operating these systems is given by an IEMS with fuzzy logic decision maker proposed in this study. Using the generated and required power information from the wind/PV and load sides, the fuzzy reasoning based IEMS determines the amount of power to be supplied from each or both sources. Besides, the IEMS tracks the maximum power operating point of the wind energy system.
In today’s world, the increasing need for energy and the factors, such as increasing energy costs, limited reserves, and environmental pollution, leads the renewable energy to be the most attractive energy source. Since these sources have unlimited supply and they do not cause environmental pollution, they are studied extensively lately and utilized more and more every day. Governments put in new legislations and feed-in-tariffs to encourage the investors to install new renewable energy utilization sites [
Renewable energy sources consist of solar energy, wind energy, geothermal energy, and wave energy which are considered to be endless since they exist naturally and they always renew themselves [
Solar and wind energies have a distinguished place among these energy types. There are wind and sun everywhere on earth; therefore, there is more intense study on these sources. The aim is not only to obtain the energy but also to turn the energy to proper values, manage the existent energy, and terminate the harmonics. While managing all these, lowering the cost of the system in every step is taken into consideration. Today, producing electrical energy from these renewable sources appears to be the main objective [
The combined operation of these systems is far more complex than operating them separately. In a system with only solar or wind energy, just one element is controlled. In a hybrid scheme, both sources are controlled individually and simultaneously depending upon the operating conditions and energy demand. During low sunlight conditions, photovoltaic (PV) solar panel cannot supply consistent power. Similarly, wind turbine will not work in conditions without wind. In this case, the required energy must have the structure to make up the lack of energy in conditions when this system does not work regularly or the composition produces less energy than the requirement. Power management assures that the system works efficiently while preventing the lack of energy in loads. Here it is aimed at obtaining clean and sustainable energy in stable frequency and definite voltage. While or after obtaining the energy, harmonics must be definitely controlled.
Power management is important to assure both economical and efficient work of the system in combined usage of renewable energy sources. Variable weather conditions, day-night conditions, and rapid change in voltages make this necessary. Power management can be achieved by using maximum power point tracking (MPPT) [
Nowadays renewable energy sources are structured in two ways as grid connected and standalone. Renewable energy sources as solar energy and wind energy can be used to feed loads far from the grid especially the home type ones. However, there are problems in these types of systems when there is no sun or wind. Users become fully powerless after the batteries are flat which are used as backup systems. An alternative situation to this is to connect the loads to the grid if they are close to it, in conditions that there is no sun or wind and the batteries are empty [
In literature review, it can be seen that there are many researches which include wind turbine and PV solar panels used together [
Energy sources such as PV solar panels, wind turbines, fuel cell, and diesel generator can be used both as standalone or hybrid. There are many studies and utilizations such as wind/PV [
In addition to this, MPPT is one of the important parts of the work, because IEMS calculates maximum power in WEC system. There are various methods which produce MPPT to obtain maximum power from RES. It is tried to run the system continuously at this point by defining the maximum power point with more efficient controls of power electronics converters. While there are studies for calculation of instantaneous generated power decided according to measurements of the environmental conditions, studies which focus on the efficient controls are conducted for similarly used engines to produce maximum power production. It is aimed that the wind turbines work with maximum efficiency [
In this study, a power management system will feed the loads from a hybrid power generation system consisting of PV solar panels, WES and grid. WES consists of a different and new MPPT method. The hybrid system is connected to a common DC bus, which is used as a power pool for sustainability. PV system is also connected to a backup battery unit to be charged for emergency usage when additional power is needed. In addition to the source side, the load side management is also very important for the renewable energy systems and also considered in this study.
Besides, energy management software can rapidly and continuously respond without being bounded to environmental conditions, which keep continuously some amount of power in reserve and during instantaneous load changes control the system efficiently. This study is different from the others in its being efficient management approach and having different, cheaper, and simpler peak power point tracking.
The overall scheme of the proposed hybrid renewable power management system is given in Figure
Renewable power generation system and energy management.
The power management algorithm (PMA) is developed to operate both photovoltaic energy system (PVES) and wind energy system (WES) at the maximum power they can generate under various environmental conditions while maintaining power supply demand of the load side at required amount. Therefore the power management includes maximum power point tracing of PVES and WES, energy storage, utility connection, and load switching. Besides, the power quality issues such as harmonic elimination, voltage sags, voltage increments, frequency deviations, and voltage magnitude are included in the management system. It is obvious that there are too many inputs and parameters into the management decision algorithms. A fuzzy logic based decision making algorithm is developed and used for this multi-input, multiobjective system.
PVES is modeled using the well-known characteristic equation of PV cell. The voltage-current equation of a PV cell is based on photocurrent of a P-N junction semiconductor. The photocurrent is a function of solar irradiation and changes with the sunlight. The voltage across the connection terminal of the P-N device varies as a function of the photocurrent. When there is a path across the terminals of the P-N device, which is the photovoltaic cell, external current flows through this path, if there is a load on the path then through the load. The maximum value of this external current, or, in other words, load current, is the short circuit current and assumed to be equal to the generated photocurrent. It is observed that as the cell current increased, the cell gets heated resulting in a decreased terminal voltage. Therefore, considering this voltage decrement and reverse saturation current of the P-N diode, the terminal voltage of a single PV cell is written as in
PV solar cells are connected in series and parallel combinations and manufactured as PV modules to be used more effectively in commercial applications. The voltage of a PV module is determined by the PV cells connected in series and the current of a PV module is determined by the number of parallel connected series branches. The required load power is then obtained by connecting the PV modules in series and in parallel yielding the PV arrays [
The power flow path from PV power generation unit to the load is shown in Figure
The PV power generation part of the system.
PV array and battery groups are connected to each other with a device including a battery charging regulator and maximum power point tracker. When the sunlight is not sufficient, the batteries step in and supply the necessary power to the loads. The MPPT is used to transfer the maximum generated power from the PV array and charge the batteries if any power is left after feeding the load. This unit is MOTECH PV4830 MPPT charge controller. The batteries are also charged from the DC power bus when the sunlight is insufficient. A battery charge regulator with 12/24/36/48 V and 30 A is used as a charging interface device. Total peak power generated by the PV array under good weather conditions is about 320 Wp. Since the value of the generated voltage from the PV array changes depending upon sunlight, a DC chopper (19~72 V DC input voltage) is used to keep the DC voltage from the PV panels at 48 V. This chopper is boost converter. The diode that is used after the chopper is located in order to protect the chopper. It is a kind of electronic fuse. It is a diode that prevents reverse current flow with a high current. This is the magnitude of the DC bus voltage, which is inverted to 220 V, 50 Hz AC voltage by a boost-up inverter. In this scheme, the current and voltage data from the loads are measured and transferred to the computer, besides the input voltage and output current of the chopper to be used in decision making process.
The wind turbines convert the mechanical energy that is produced by the wind to electrical energy. To use this electrical energy a voltage and a frequency regulation has been needed. The model of the wind turbine is developed by the basis of the steady state power characteristics of the turbine.
Calculations of induction machines are completed in
Phase depiction of components of
Here we can obtain
After the necessary conversions are completed,
Here
Here
The relation between flux and current in
The state-space model according to
Consider the following symbols:
The WES emulator is established by coupling two squirrel cage asynchronous machines together as seen in Figure
Feeding the loads with power generated from WES model.
The machine on the left is a prime mover representing the wind turbine and is controlled with a
The reactive power to generate energy at this point is supplied with a condenser group. Later on, this three-phase voltage which is rectified is sent through a chopper and it is brought to 48 V value and connected to common DC bus. Then an inverter is used to convert this 48 V voltage to 230 V/50 Hz alternating voltage and the loads are fed. Here the current and voltage information on the loads and copper input voltage and output current is measured and these values are transferred to the computer.
The proposed renewable energy scheme consists of two-type operating conditions. The first case described in previous sections is the one supplying power to individual loads where the utility grid is not available or not connected. The second case deals with utility connected renewable energy scheme as shown in Figure
Grid connection structure in the system.
As shown in Figure
Data collected from various parts of the system is transferred to the computer and used for control and decision making processes. The interfacing between the real system and the computer is established by NI USB 6259 data acquisition card.
The electrical power generated by renewable sources such as wind and solar power is affected by environmental conditions resulting in problems in load part. When there is no sun or the weather is cloudy, the power amount to be generated by solar energy changes. Accordingly, wind does not blow at the same speed all the time; it is discontinuous. Henceforth, energy amount to be generated from these sources are variable.
In particular in low powered applications, for instance, while supplying energy to a house from these sources, it is a problematic situation to have a power cutoff while watching a TV. Power sources must be efficiently operated in order to avoid such situations.
Proposed PMA and decision making process are developed to prevent problems like voltage sags and discontinuities that occur due to either weather changes or sudden load changes. The intelligent decision making algorithm manages the energy storage and usage switching patterns so that energy sustainability is guaranteed.
General block diagram of the power management scheme is given in Figure
Basic block diagram of power management system.
In order to solve sustainability and power quality problems, the power transfer from the renewable sources to load must be managed in a proper way. Therefore a PMA system has been designed to prevent power discontinuity and overvoltage and undervoltage operations so that the loads operate properly. The power management system is automated in an efficient way by switching on or off the sources and backup units. For example, if the wind power is sufficient enough to feed the load, then there is no need for the auxiliary sources of PV, backup batteries, and the utility. If the wind power decreases, the gap is filled by PV first, then batteries, and then the utility. The overgenerated power is stored and used only when needed.
The overall energy generation system established experimentally can be seen in Figure
PV solar panels-WES-grid system.
The main objective of employing a power management algorithm in power systems where the renewable energy is the priority supply is to have the power ready to be used and feed the load continuously. For this reason, the peak power value from both WES and PV solar panels must be calculated. MPPT device used for PV solar panels handles this duty on its own. Therefore, there is no need to calculate the peak power calculation in PV solar panels. MOTECH PV4830 MPPT charge controller calculates MPP by itself. However, the peak power value to be obtained from WES must be defined. The priority supply is WES in this system. At the same time, since the changing environmental conditions affect the amount of energy to be produced, power management is planned to avoid this effect by leaving a base power in the system. The base power is the power that must be supplied all the time for the loads with nonstop operating behaviors. The base power in the proposed system is defined as 300 W, which can be easily changed inside the software if desired. The system is designed to feed maximum 1 kW, which is more than three times the base power. If the environmental conditions are sufficient, which means there is enough sun and wind, wind energy generation system and PV solar panels generation system produce the maximum power they can, and the installed power can rise over 2 kW value. The main operational principle of the system is summarized as follows.
the operating time of each unit, turnoff time of each unit, the amount of load at present conditions.
Since only the wind energy will operate constantly, these lists of rules are processed by taking the measurements from wind energy system into consideration.
Flow diagram of power management program is given in Figure
Simplified flow diagram of power management program.
Fuzzy logic has so many applications in industry. Fuzzy logic reasoning (FLR) algorithms are generally used in fuzzy decision makers (FDM), which find applications in systems that require a conclusion from uncertain input data. Since the wind conditions are not certain and are not easily predictable, the power generated by the wind energy system becomes uncertain including the maximum generated power as well. Therefore a fuzzy reasoning algorithm is developed to determine the maximum power generated by WES. The fuzzy reasoning applied to determine the maximum power of the WES is represented in Figure
Fuzzy decision maker.
Fuzzy logic unit.
A FDM usually gets fuzzy inputs and evaluates them in the rule base system, which is set up earlier representing the input-output relations of the uncertain system in terms of fuzzy membership functions and fuzzy rules (FR). The FR is the evaluation of the rules to yield fuzzy conclusions from fuzzy inputs-fuzzy rules interactions, if the input variables are crisp. Certain input values are rated here. Thus, these values can be included in a value range that can be used by the controller and then it can be expressed verbally. In addition, it becomes a member of a group that has clear boundaries. Then they are fuzzified to fuzzy values. Similarly if the output is required as crisp value, then the concluded fuzzy outputs are converted to crisp values by the process called defuzzification.
In this study, the input values to the FDM are the current and voltage measured from the WES. The generated rules are used to relate the input voltage and current with the power of the WES depending upon the wind speed conditions. FR algorithm in the FDM is used to obtain maximum power generation from WES for uncertain and unpredictable speed conditions. The designed FR based FDM is modeled in Matlab/Simulink environment as shown in Figure
All of the MPPT calculations are based on the values of
The online data collected and transferred to computer is used to determine the amount of load power demand that supplied from the PV/wind sources. In the meantime the data representing the WES quantities are used by FDM to determine the maximum power generated by the WES for the instant the measurements made. The maximum power values of both WES and PV panels are used in power management part of the study. As seen in Figure
FLR inputs.
Fuzzy subsets of current
Fuzzy subsets of voltages
Fuzzy subset of power output spaces.
Experimental test system is seen in Figures
Appearance of experimental system on one side.
Appearance of experimental system from above.
In WES, MPTT is implemented as software. There is not any electronic intervention to the generator. In addition to this, there is not any added hardware. For this reason, it is different from other MPTT systems.
In Figure
Uncontrolled wind-solar energy generation system.
As the first operating case, the system is analyzed when both wind and PV solar panels are on without applying any power management. For this case, chopper input voltage,
Voltage, current, and power variations of WES for case 1.
Voltage, current, and power change of PV solar panels for case 1.
Voltage, current, and power change of loads for case 1.
It can be seen that when a load power of 800 W is on, WES and PV system together cannot feed the loads from 40 s to 70 s and from 86 s to 89 s, since they could not operate properly. Besides, the load voltage decreases down to 0 sometimes instead of remaining at the required value of 220 V. Although there is enough power generated in the system, power discontinuities occur. Actually the power generated PV solar panels can be used to eliminate the power discontinuities due to changes in wind speed by applying proper power management algorithms. In this case, a decision making system is needed to control the system and make decisions in order to avoid lack of power discontinuity on loads.
The proposed power management algorithm is realized in MATLAB/Simulink environment using dynamic operational blocks library as shown in Figure
Simulink model of the proposed power management system.
The Simulink diagram in Figure Grid_Switch: grid system switch (on/off), PV_Switch: PV system switch (on/off).
Figure
Current, voltage, and power change in WES for case 2.
Current, voltage, and power changes for PV system during case 2 can be seen in Figure
Current, voltage, and power change of PV system in case 2.
The changes of current, voltage, and power from the utility grid are given in Figure
Variations of current, voltage, and power from the utility grid in case 2.
Current, voltage and power changes on the load terminals can be seen in Figure
Current, voltage, and power change on the load in case 2.
In Figure
Peak power changes obtained from WES using FLR algorithm.
In Figures
Switching instants of PV system when wind energy is not sufficient.
Switching instants of utility grid when wind and solar energy are not sufficient.
In Figure
Wave shape of the voltage on loads.
Between Figures
Current, voltage, and power changes of wind energy system.
The current, voltage, and power value changes from WES can be seen in Figure
Time variations of voltage, current, and power of PV system are given in Figure
Current, voltage, and power change of PV energy system.
Figure
Current, voltage, and power change of grid system.
Current, voltage, and power changes on the load can be seen in Figure
Current, voltage, and power change on the loads.
The maximum power drawn from the WES is given in Figure
The maximum power change tracking of the WES by FLR.
The on and off switching instances of PV solar panels and utility grid system are shown Figures
On and off switching pulses of PV panels.
On and off switching pulses of utility grid.
Wave shape details of voltage on the load are given in Figure
Voltage wave shape on the load.
The variations of power from wind, PV, and load busses are given in Figure
Power changes in the system without power management.
Power changes under only resistive loads condition in developed power management program in solar-wind-grid system.
Variations of the power from WES, PV, and utility grid along with load power and available maximum value of the wind power are shown in Figure
An intelligent energy management system (IEMS) for maintaining the energy sustainability in renewable energy systems is presented in this study. A renewable energy system consisting of wind and PV panels is established and used to test the proposed IEMS. Since the wind and PV sources are not reliable in terms of sustainability and power quality, a management system is required for sustainability on the load side. The proposed PMA is used to collect power from renewable sources and utility grid at a common DC bus and feed the loads preventing any power discontinuity. By employing PMA, a base power is always supplied to DC bus to be used by the loads that are operating permanently. Besides, PMA handles the effects of the changes in wind speed, solar irradiation, and amount of the load by operating wind, PV, and utility grid accordingly using intelligent decision making abilities. The proposed intelligent PMA is also used to determine and track the generated and available maximum wind power from WES so that the efficiency of the installed units is increased. Using the generated and required power information from the wind/PV and load sides, the fuzzy reasoning based PMA generates the required operating sequences to manage the overall system power with the minimum requirement from the utility. The IPMS is also designed to operate the renewable energy systems as a part of power utility. Therefore the IEMS can also be considered as a smart grid operator in the proposed RES application. Proposed IPMS can be extended to be used in distributed power systems for providing decisions on power management during critical peak power instances and fast power demand changes.
Cell output current
Photocurrent function of irradiation level and junction of temperature
Reverse saturation of current of diode
Cell output voltage
Series resistance of cell
Electron charge
Boltzmann constant
Reference cell operating temperature
Curve fitting factor
Chopper input voltage
Chopper output current
WES chopper input voltage
WES chopper output current
PV solar panels system chopper input voltage
PV solar panels system chopper output current
Load voltage
Load current
Wind system current
Wind system voltage
Wind system power
PV system voltage
PV system current
Loads power
Calculated maximum wind power
Grid system switch (on/off)
PV system switch (on/off)
Load power
WEC system power
Stator winding inductance (H)
Rotor winding inductance (H)
Maximum mutual inductance between rotor and stator (H)
Stator phase resistance (Ω)
Circle peace resistance between two strips (Ω)
Strip resistance (Ω)
Converse inductance between stator phase windings (H)
Mutual inductance between rotor strips (H)
Air gap (m)
Air gap segment
Number of pole pairs
Stator angular frequency
Rotor angular frequency
Synchronous speed
Stator frequency
Stator flux vector
Rotor flux vector
Machine axis rotation angle
Viscosity moment
Viscosity friction coefficient.
Intelligent energy management system
Power management system
Power management algorithm
Renewable energy systems
Photovoltaic
Photovoltaic energy system
Maximum power point tracking
Wind energy system
Direct current
Alternative current
Fuzzy logic reasoning
Fuzzy decision maker.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This study was supported by Karadeniz Technical University Scientific Research Projects Unit, Project no. 2008.112.004.2.