Optimal Fuzzy Logic Based Smart Energy Management System for Real Time Application Integrating RES


Optimal Fuzzy Logic Based Smart Energy Management System for Real Time Application Integrating RES, Grid and Battery
Nesar Uddin1*
Md. Saiful Islam2
1*Institute of Energy Technology
2Dept. of Electronics and Telecommunication Engineering
Chittagong University of Engineering and Technology, Chittagong, Bangladesh.1*[email protected], [email protected]
Abstract-This paper proposes an intelligent power management controller based on the fuzzy logic that integrates wind energy, solar energy, and grid with battery backup. This system is capable of exchanging power with the local grid. The proposed fuzzy control strategy senses the continuous fluctuations in solar, wind power generation and battery state of charge. Then it calculates the load demand in order to make the best use of sources without any exact numerical model and obtain better reliability than that could be obtained by the conventional system. In this paper, a mathematical calculation is provided to evaluate the proposed Optimal Fuzzy Logic based smart Energy Management system which is more cost-effective than the others conventional systems like direct grid connected and renewable energy feeding system.

Keywords-Smart system; fuzzy logic controller; grid-tied hybrid generation system; PV cell; wind turbine;
I INTRODUCTION
Electricity is an important blessing of science that has been gifted to mankind. It is considered as a part and parcel of modern life and no one can imagine a world without it. Despite that, a large number of the world’s populations are still out of electricity. Most of these non-electrified areas are found in developing countries like Bangladesh. These areas can be electrified by expanding the grid or by utilizing alternative energy sources 1. So, there is a huge potentiality for solar and wind resources to provide sustainable and reliable power to these areas. Besides, the production cost of conventional energy in Bangladesh is increasing gradually 2. Moreover, most of the power plant is based on fossil fuel such as petroleum, gases and coals etc. But, Bangladesh has huge renewable energy sources and these available abundant energies can be implemented and converted to clean electricity to provide sustainable and low-cost power for electrification as an alternative source of energy in lieu of usual non-renewable energy sources.

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As a part of global strategy to lessen dependence of fossil fuels and with a view to minimizing greenhouse gas emissions, renewable energy has become a significant issue across the world. So, three reasons have been suggested why renewable energies are rapidly making their way up the energy agenda: 1) Increasing price competitiveness, ii) Long-term certainty, and iii) Energy security 3.

Hybrid Energy Systems accumulate renewable energy sources such as wind, Solar PV, mini/Pico-hydro, fuel cells, and biomass and diesel generators to supply electrical power. Conventional control methods demand a mathematical model for the dynamic system to be controlled. This mathematical model is used to build a controller. Nevertheless, it is not always possible to obtain a proper mathematical model of the controlled system in many practical situations. On the contrary, Artificial intelligent (AI) control suggests a way of dealing with modeling problems by implementing control laws expressed in non-formal linguistic terms which are derived from expert knowledge. Fuzzy logic control is an ideal way for applications where a mathematical model is either known or not known particularly with problems of different parameters and nonlinear models 5.

In this paper, fuzzy logic based smart energy management system is proposed which is integrated with solar and wind power with a storage battery. Wind and solar energy sources are the most potential fields because they are ubiquitous, freely available and environmentally friendly. Integrating renewable energy resources enhances efficiency, ensures power reliability and is cost effective as the different resources complement together 4. Hybrid systems also offer the scope to widen the system in future to cater for growing electricity demand. Again the production and the consumption of electricity are advanced in an effective way in the last decades. Thus, it is necessary to make durability between the production and the consumption of real time electricity. This issue can be solved by using proper energy management techniques.

The main objective of this paper is to develop a fuzzy logic controller that responds to the variations in the renewable resources, storage battery and the load demand. The aim of the fuzzy logic controller is to guarantee load power to consumer end under different conditions while maintaining the state of charge (SOC) of the battery in the desirable range to avoid damage. This system will also provide available excess power to grid after fulfilling the demand of consumer and maintaining suitable state of battery. In the following sections of this paper, the structure of the hybrid system has been explained and energy management for the hybrid energy system is analyzed. Finally, a mathematical calculation is also given to compare this fuzzy logic based control system with conventional system.

II LITERATURE REVIEW
A. PV MODEL
Conversion of light electricity in electric power is primarily based on a phenomenon known as photovoltaic effect. The PV module is the semiconductor materials interface which converts light into electricity. When semiconductor substances consisting of Silicon are exposed to mild, the some of the photons of light ray are absorbed by using the semiconductor crystal which reasons substantial range of free electrons inside the crystal. This is the primary purpose of manufacturing strength because of photovoltaic effect 6. The operation of a photovoltaic (PV) cell requires 3 simple attributes: The absorption of mild, producing both electron-hollow pairs and exactions.

Fig. 1 Equivalent circuit of PV cell 7.

Based on the equivalent circuit of figure 01, the current to the load can be given as 7:
I=Iph- ISexpqv+RsiNKT-1-(v+RsI)Rsh………….(1)
In this equation, Iph, is the photocurrent, Is, is the reverse saturation current of the diode, q is the electron charge, V is the voltage across the diode, K is the Boltzmann’s constant, T is the junction temperature, N is the ideality factor of the diode, and Rs and Rsh are the series and shunt resistors of the cell, respectively. As a result, the complete physical behavior of the PV cell depends on Iph, Is, Rs and Rsh from one hand and with three environmental parameters as the temperature, the solar radiation and wind speed on the other hand.

B. WIND MODEL
A wind turbine is a tool that converts kinetic strength from the wind into energy. The blades of a wind turbine flip among thirteen and twenty revolutions in keeping with minute, relying on their era, at a consistent or variable pace, where the rate of the rotor varies in terms of the speed of the wind on the way to reach a more efficiency. The components for the way to calculate produced mechanical electricity is 8:
P=kCp2?AV3……………………………………………..….(2)Where P=Power output, in kilowatts; Cp = Maximum power coefficient, ranging from 0.25 to 0.45, dimension less (theoretical maximum = 0.59); ?=Air density, in kg/m3; A = Rotor swept area, A = ? D2/4 (D is the rotor diameter in m, ? = 3.1416); V=Wind speed, in m/s; k = 0.000133  a constant to yield power in kilowatts. (Multiplying the above kilowatt answer by 1.340 converts it to horse- power i.e., 1 kW = 1.340 horsepower)9.

C. BATTERY MODEL

The majority of new home energy storage technologies, such as the, use some form of lithium ion chemical composition. Lithium ion batteries are lighter and more compact than lead acid batteries. They also have a higher DoD(depth of discharge) and longer life span when compared to lead acid batteries. The equivalent circuit of Lithium-ion batteries is shown in figure 02.

Fig. 02 Equivalent circuit of Lithium-ion battery 9

The open voltage source is calculated with a non- linear equation based on the actual SOC of the battery as follows 9:
Vbatt=Ebatt-R…………………………………….(3)
During discharge9:
Ebatt=Ee-K{Q÷Q-itit-K{Q÷Q-it}i*+Aexp-Bit……………………………….….….(4)
During charge9:
Ebatt=Ee-K{Q÷Q-itit-K{Q÷it+0.1Q}i*+Aexp-Bit…………………….……(5)Where Ebatt is the no-load voltage, Eo is the battery constant voltage, K is the polarization constant, Q is the battery capacity, it is the actual battery charge, i* is the low frequency current dynamics, A is the exponential zone amplitude, B is the exponential zone time constant inverse (Ah)-1, Vbatt is the battery voltage, and i is the battery current. In this study, we will use 12V, 200AH Lithium ion Battery features an automatic built in battery protection system (BPS) that keeps the battery running at peak performance and protects the cells for thousands of cycles. Lithium-ion batteries are common in home electronics. They are one of the most popular types of rechargeable batteries for portable electronics, with a high energy density, tiny memory effect and low self-discharge 9.In these energy management system two batteries has been connected in series according to Table I.

TABLE I. DATA SHEET OF BATTERY 9.

Specifications Details
Normal voltage 12.8V
Charge voltage 14.4V-14.6V
Peak discharge(5 sec) 2000A
Continuous discharge/discharge rate 100A
Capacity (amp hours) 200AH
Capacity (watts) 2560W
D. FUZZY LOGIC CONCEPT
Fuzzy logic is widely utilized in machine management. The term “fuzzy” refers to the actual fact that the logic concerned will touch upon ideas that can’t be expressed as the “true” or “false” but rather as “partially true”10.Though various approaches such as genetic algorithms and neural networks will perform even as well as fuzzy logic in several cases 10. Fuzzy logic has the advantage that the solution to the matter is solid in terms that human operators can perceive, so that their expertise is utilized in the design of the control 10. This makes it easier to mechanize tasks that are already successfully performed by humans. Fuzzy controllers are very straight forward conceptually. They contains an inputs stage, a process stage and an output stage which is shown in figure 03.The input stage maps sensing element or alternative inputs, such as switches, thumbwheel, and so on, to the suitable membership functions and truth values which is understood as fuzzification. The process stage invokes every acceptable rule and generates a result for each, then combines the results of the rules. This process step is completed under fuzzy interference engine. Finally, the output stage converts the combined result back to a specific management output value as a defuzzification process which is shown in figure 3.

Fig. 3 Block diagram of basic fuzzy logic system 10.

III SMART ENERGY MANAGEMENT SYSTEM
Smart Energy Management System has been designed for four or five members of each family of a home in where two families live and solar and wind energy available for use. Each family has 100m2 of living space. To provide comfort, the usual electrical appliances such as refrigerator, electric oven, electric fan, air conditioner, washing machine, electric motor and electronic device charging facility etc. are installed in each family. Additionally, the devices require the sector’s standard voltage (220V AC, 50Hz). Table II presents the electric load characteristics of a house that have 2 living rooms, 1 kitchen, 2 bathrooms and 1 hallway.

TABLE II. ELECTRIC APPLIANCES OF HOUSE
Domestic Appliances Number Watt rating(W) Total watt Ratting(W) Watt hours /day
Lighting 10 24 240 12
Refrigerator 1 150 150 8
Ceiling Fan 5 50 250 10
Electric oven 1 1 900 4
Electric motor 1 1 750 1
Washing machine (WW90K6410QX/TL) 1 1 800 1
Air conditioner (YQ-1000-AN01) 1 1 900 8
TV 1 1 100 8
For the above house, proposed Smart Energy Management System deals with produced energy from solar PV array, wind generator, two battery banks (for serving one hour) plus maximum load demand of 4kW as shown in fig. 4. The solar and wind generator are the primary sources of power and the surplus is stored in batteries to meet the power demand during periods of low or no generation from the renewable sources or any other situation according to set rules. DC output voltage from solar PV can vary due to variation of solar radiation or unpredictable environmental effects. Again, AC output voltage from wind generator can vary with respect to wind speed .So DC to DC converter is used to provide desired output voltage and rectifier is used to convert AC to DC. Not only the power from renewable energy can be used to serve the load directly but also the system can convert DC to AC for serving the load. This DC energy flow is controlled with fuzzy controller for dealing with load demand and state of charge in the battery. Fuzzy logic controller is also connected to the grid for controlling energy inlet and outlet as well as to reduce the effect of frequent change of load demand.

– Controlling the status of charging and discharging of battery via taking load demand and available power into account.
– Selling of energy, excess after serving the load will get priority.

– Buying energy is highly avoided in order to minimize power production costs.
The system consists of three inputs and three outputs in figure 05. The three inputs are total generated power (Pnet), State of Charge (SOC) and Load demand (LD). Pnet is the summation of solar and wind energy. All the inputs have three triangular membership functions representing low, medium and high for 24 hours shown in figure 6. The three output membership function are use_of_Pnet denoted as On (S2) and Off (S1), Battery_NU_C_DC(B) denoted as not use (S1) charge (S2) discharge (S3) and Grid_NU_I_OL(G) is divided into three classes described as not use (S1), inlet (S2) and outlet (S3).

Fig.4 Block diagram of hybrid system.

Fig. 5 Block diagram of proposed fuzzy logic controller

Fig. 6(a) Membership functions of generated power, Pnet

IV FUZZY LOGIC CONTROLLER DESIGN
Smart Energy Management System has been developed based on FL technique using a Mamdani interface system. The proposed system aims to fulfill the conditions noted below-
– The main sources are the PV and wind generators and Battery acts as an auxiliary energy source.

– The load demand must be fulfilled irrespective of the time.

Fig. 6(b) Membership functions of state of charge, SOC

Fig. 6(c) Membership functions of load demand, LD

The FLC relates the outputs to the inputs using a list of if-then statements known as rules. The if-part of the rules describes the input parts and at the same time the then parts describes the outputs. We have set total 14 rules to construct this energy management system considering different operational conditions. Third bracket of right hand side of rules denoted hours. The rules are given below-
1.When Pnet High, SOC High, LD Medium Then Use of Pnet ON S2, Battery not use S1, Grid outlet S3 9,14,15,16
2.When Pnet Low, SOC Low, LD Low Then Use of Pnet ON S2, Battery not use S1, Grid not use S11
3.When Pnet Medium, SOC Low, LD Low Then Use of Pnet ON S2, Battery Charge S2, Grid not use S1 2
4.When Pnet Low, SOC Low, LD High Then Use of Pnet ON S2, Battery not use S1, Grid inlet S2 20,21
5.When Pnet High, SOC High, LD High Then Use of Pnet ON S2, Battery not use S1, Grid outlet S3 10-13
6. When Pnet Medium, SOC High, LD High Then Use of Pnet ON S2, Battery discharge S3, Grid not use S1 18
7.When Pnet Low, SOC Low, LD Medium Then Use of Pnet ON S2, Battery not use S1, Grid inlet S2 24
8.When Pnet Low, SOC Medium, LD Low Then Use of Pnet ON S2, Battery charge S2, Grid not use S1 4
9.When Pnet Medium, SOC Medium, LD Low Then Use of Pnet ON S2, Battery Charge S2, Grid not use S13,5
10.When Pnet Medium, SOC High, LD Medium Then Use of Pnet ON S2, Battery not use S1, Grid outlet S3 7-8,17
11.When Pnet Medium, SOC Low, LD Medium Then Use of Pnet ON S2, Battery not use S1, Grid inlet S2 23
12.When Pnet Medium, SOC High, LD Low Then Use of Pnet ON S2, Battery charge S2, Grid outlet S3 6
13.When Pnet Medium, SOC Low, LD High Then Use of Pnet ON S2, Battery not use S1, Grid inlet S2 22
14.When Pnet Low, SOC Medium, LD High Then Use of Pnet ON S2, Battery discharge S3, Grid not use S1 19
V RESULT & ANALYSIS

Fig.7 FLC rules view of a particular moment.

The output of FLC depends on which rule is executed at any instant of time. The surface view of outputs is given in figure 8(a) shows the results usage of generated power with load demand based on the developed Inference engine. Figure 08(b) shows the results with battery operation of charge and discharge condition based on the developed Inference engine. Figure 08(c) shows the rules surface of the Grid output which proves good efficiency by presenting more selling energy to the grid than buying.

Fig.8(a)

Fig.8(b)

Fig.8(c)

Figure 9 shows total generated power from solar and wind energy and figure 10 indicates load demand for 24 hours for a typical day. Desired load demand will remain approximately equal for next 25 years. It can be varied ±10%.

Figure13 shows extra energy outlet to grid from renewable sources in watt with respect to time. Total outlet power from renewable sources to the grid is 11.5 kWh within 6 o’clock to 17 o’clock in that’s day. However, for that day extra 3.5 unit electricity outlet flows to the grid. For a month, average extra income is about 3.5 unit*30day*6 BDT=630 BDT If 1 kWh = 6 BDT. Averagely it can be calculated for 25 years is 630 BDT*300months=189000 BDT. Figure 14 shows total energy utilization of renewable to load, renewable to battery charge, renewable to the grid, battery discharge and grid to load in watt with respect to time for 24 hours. Total use of renewable energy is 46.5 units for that ‘s day. It can be calculated average for 25 years 46.5units*30days*300months = 418500 unit * 6 BDTIf 1 kWh = 6 BDT = 2511000 BDT. Without renewable energy 418500 unit energy must be bought from the grid which is costlier than implementation of whole renewable energy system with fuzzy management. The energy buying and selling cycle are controlled by the Fuzzy Logic EMS and prove great efficiency in selling more energy to the grid and thus gaining money from the network provider which generates direct economic benefits then helps for more reduction in power production cost.

Fig. 09 Total Power Generation (Pnet).

Fig. 10 Load Demand (LD).

Figure 11represents battery charge and discharge in percent with respect to time for 24 hours. Figure12. shows energy inlet from grid in watt with respect to time. At 20nd&21sthour’s electricity inlet is 6kWh, 22nd hour electricity inlet is 1kWh and 23rd, 24th hour’s electricity inlet is 1 kWh. Total inlet power is 8kWh from grid to load in that’s day

Fig.11 State of Charge (SOC)

Fig. 12 Use of Grid inlet

Fig. 13 Use of Grid outlet

Fig. 14 Total use of energy

The result shows that the PV and wind generator are capable of feeding the load with the required energy and charging the battery according to its demand during first nineteen hours. As high power generated from the PV and wind generator system, excess power is provided to the utility grid after battery is being fully charged. When PV and wind generator are not capable of feeding the load with its demand, then shortage of power is mitigated by taking power as inlet from the grid. Overall effective utilization of energy is achieved and FLC based energy management system sells more energy than buying from the grid.

VI CONCLUSIONS
The proposed Fuzzy Logic based smart Energy Management System is designed for residential area integrating RES, Grid and Battery which has several advantages over other previously available EMS systems and practical application of FLC is rapidly growing because of its robust performance regardless of complex situations. Firstly, there is no restriction for the number of sources and loads i.e. it can be used as a universal system. Secondly, more intelligent rules are adopted to get optimized system in terms of efficiency and cost. Thirdly, this management system is more effective in where coal, oil and gas are not available and develops meaningful strategy under the set of constraints through rule-base Fuzzy Scheduler. It has the advantages of operating with fuzzy inputs which does not require an exact mathematical or numerical model and can handle nonlinearity. A major problem common to solar and wind generation is their intermittent nature which is dependent on weather and climatic changes. The variations of their output may not match the time distribution of the load demand resulting in reduced system’s energy performance. Significant improvements of the performance of hybrid systems can be achieved through use of proper energy management techniques.

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