Design and Development of a GUI for an Optimal Hybrid Energy System

Transcription

1 Design and Development of a GUI for an Optimal Hybrid Energy System Sree Ramya A, Nirushini Periyasamy, Vidhya A and Sangeetha Sundrapandian Department of EEE National Institute of Technology Tiruchirappalli, Tamil Nadu, India P. Raja, Member, IEEE Department of EEE National Institute of Technology Tiruchirappalli, Tamil Nadu, India praja@nitt.edu Abstract In this paper user-friendly Graphical User Interface in MATLAB has been developed to cater the need of residential customers who opt for installation of renewable energy sources for their electrical demand. The developed GUI will display the optimal number of wind generators and solar PV panels with their optimal cost of installation and the savings for a year based on the input from the customer. Also the customer can have an idea of daily energy production of the installed system for any day of the year through another GUI portal. A simple optimization algorithm has been developed and executed in MATLAB for finding the number of wind generators and solar PV panels required for installation in any system ensuring that the total cost of the system is minimized with the specified demand being met. A comprehensive data collection for wind speed, solar irradiation and temperature was done for all the districts of Tamil Nadu, India. Keywords Graphical User Interface; Optimization Algorithm; Solar PV panels; Wind Generators I. INTRODUCTION There is a current global need for clean and renewable energy sources. The use of fossil fuels raises serious environmental concerns. The burning of fossil fuels produces around 21.3 billion tonnes of carbon dioxide (CO 2 ) per year, but it is estimated that natural processes can only absorb about half of that amount, so there is a net increase of billion tonnes of atmospheric carbon dioxide per year (one tonne of atmospheric carbon is equivalent to 3.7 tonnes of carbon dioxide). Carbon dioxide is one of the greenhouse gases that enhances radioactive forcing and contributes to global warming, causing the average surface temperature of the Earth to rise in response, which the vast majority of climate scientists agree will cause major adverse effects. A global movement towards the generation of renewable energy is therefore necessary to help reduce global greenhouse gas emissions [1]. Solar power, a clean renewable resource with zero emission, has got tremendous potential of energy which can be harnessed using a variety of devices. With recent development, solar energy systems are easily available for industrial and domestic use with the added advantage of minimum maintenance. Solar energy could be made financially viable with government tax incentives and rebates. Small solar systems of Rs. 45,000/- promise to run three lights, a couple of fans and a TV. Newer products in the sub Rs. 25,000/- range and an intermediate system at Rs. 75,000/- are soon to be announced [2]. Most of the developed countries are switching over to solar energy as one of the prime renewable energy source. The current architectural designs make provision for photovoltaic cells and necessary circuitry while making building plans. Wind power is yet another efficient alternative energy sources. There has been good deal of development in wind turbine technology over the past few years with many new companies joining the fray. The concept of wind farm has become popular with improvement in their efficiency and availability. It could be combined with solar, especially for a total selfsustainability project. The economics of wind energy is already strong, despite the relative immaturity of the industry. The downward trend in wind energy costs is predicted to continue. As of 31 March 2014 the installed capacity of wind power in India was MW [3]. With the Tamil Nadu government in India issuing guidelines on Generation-Based Incentive (GBI) regarding installation of rooftop Solar Photo-Voltaic (SPV) plants, domestic customers in Tamil Nadu can get good savings/year. In some districts like Nagercoil and Tirunelveli where there is very high potential for wind energy, domestic customers can install home wind generators. Hence installation of solar PV panels and wind generators for domestic load is a liable option for meeting out increasing load demand. Though the resources available are plenty in nature, it is a challenging task to design and operate in a feasible manner. Hence in this paper a vast data and literature survey have been carried out to meet two important objectives of identifying the number of PV panels and wind generators in a given location for a specified demand pattern and development of userfriendly interactive tool for the proposed system. Faisal et al. [4] proposed a methodology for system modeling and online optimal management of micro-grid with battery storage. Hongxing Yang et al. [5] recommended an optimal sizing method to optimize the configuration of a hybrid windsolar system under varying weather conditions, and this method was applied to design the hybrid system which supplied power for a telecommunication relay station. Matlab /14/$ IEEE

2 GUI tutorial gives a clear picture on how to use GUIDE for developing GUI [6]. A grid-connected residential hybrid wind-solar system is considered in this paper. Monthly average wind speed, monthly average global solar irradiation and temperature data were collected for different districts of Tamil Nadu, India [7-10]. Using this data, a simple optimization technique was developed for calculating the solar PV panels and wind generators for a minimum cost configuration for any residential load in Tamil Nadu, India. This was framed as a GUI in MATLAB for different cases. II. OPTIMIZATION FOR A HYBRID RENEWABLE ENERGY SYSTEM Optimizing the number of solar PV panels and wind generators required to meet a particular domestic load is an important task in a design of hybrid renewable energy system. Several combinations would meet out the demand, nevertheless the chosen configuration will be the one corresponding to the minimum cost. An iterative technique is proposed in this paper for calculating the optimal number of wind generators and solar PV panels based on the minimum cost configuration. This includes calculation of the number of wind generators and solar PV panels that can meet out the maximum demand independently as well as together. Among the various configurations (combination of solar PV panels and wind generators), the least cost configuration is finally proposed as a solution. The entire iterative technique is illustrated as a flowchart in Fig. 1. One of the required data to complete the entire process of optimization is load data. In this paper, the realistic residential consumer load pattern is obtained from the energy consumption data. The calculation of wind power and solar power for a given location is performed. The mathematical equations used in this paper for these calculations are as follows : The average power obtained from wind is calculated using the wind speed data collected for that location using equation 1. P t = (1/2) C p ρ A T V 3 (1) capacity of the panels or the capacity of the generators individually. An iterative process is carried out by checking if at least 70% of the demand is met for each of the possible configurations of number of wind generators and number of solar PV panels. All the configurations which meet out the demand are stored and the cost is calculated for each configuration. The one which corresponds to the minimum cost is the optimal solution for the renewable hybrid system. No Start Read Temperature, Wind Speed, Solar Irradiance at a location for each month Compute the average power produced by wind generator and solar panel over one year Compute the maximum number of wind generators and solar PV panels that can meet the maximum average demand independently Set iteration Count, i = 1 Compute the units generated by the combination of wind generators and solar PV panels Units generated in hybrid system > 0.7 (average demand) Yes Store the combination Increment iteration count The power coefficient, C p, of the wind turbine is defined as the actual power delivered divided by the available power in the wind. Ideally, C p is taken as 0.4. Likewise, the average power obtained from solar is calculated using the temperature and irradiance data collected for that location in equation 2 and 3. T i c (t) = T i A (t) + {(NOCT - 20 O C) /800 } * G i (t,β) (2) (3.2) P S = (P stc *I rr ) / G stc *(1+k (T c -T r )) (3) Fig. 1 Optimization Algorithm (3.3) Next, the maximum number of solar PV panels and wind generators is calculated by dividing the total demand by the I=I max The units produced by wind and that by solar is calculated for the optimized solution. This calculation is carried out for all months of the year. For a given location it is identified that in No Select minimum cost combination End Yes

3 certain months power could be given to the grid and in some months power needs to be drawn from the grid. The numerical solution is given in section IV. The savings obtained by the installation of the solar PV panels and wind generators is also calculated taking into account the residential tariff (low tension tariff 1-A) of Tamil Nadu [11] and the subsidies given by the state government. III. GRAPHICAL USER INTERFACE (GUI) Using the wind speed, temperature and solar irradiation data collected for different districts of Tamil Nadu, India a GUI has been designed for installation of a Hybrid Wind-Solar system for residential customers of Tamil Nadu. Two GUI portals have been designed. In the first GUI portal, by inputting the location and the number of units consumed during the previous year (bimonthly), the customer can get the desired hybrid system for installation in his/her house. The customer could select various combinations like only solar system, only wind system or the combination of wind and solar by feeding the load data alone. This number of solar PV panels and wind generators for installation displayed in the portal is obtained by the optimization algorithm explained in last section. In the second GUI portal, the customer can give the location, day and month of the year, the number of solar PV panels and wind generators to be installed(can make use of GUI Portal I), unit consumption(from the energy meter) for each hour in his/her house. Based on these inputs, the number of units contributed by the proposed number of solar, wind systems and the units given or taken from the grid are displayed. A. GUI Portal I The GUI portal I gets the name of the customer and the district in which he lives in Tamil Nadu, India. In order to obtain accurate results, data has been collected for 29 districts in Tamil Nadu. Once the customer clicks on the location, GUI will produce the list of districts from which the customer can select the required one. Since the tariff is calculated bimonthly (LT 1-A) for residential load in Tamil Nadu, the customer enters the units consumed as shown in the Energy meter reading card as shown in Fig. 2. Once the customer selects the desired configuration of renewable system and clicks Run button, the necessary calculations are carried in the m-file correponding to GUI and the results are displayed in the GUI portal. The customer can click Generate Report for getting the results in pdf format. B. GUI Portal II Fig. 2 GUI Portal I

4 Fig. 3 GUI Portal II The GUI portal II gets the name of the customer and the district in which he lives in Tamil Nadu along with the day and month of the year. The customer has the provision to enter the number of PV panels and wind generators according to the ratings. The customer keys in the last three digits of unit consumption for each hour. In all these cases the number of wind generators and solar PV panels are obtained from the proposed algorithm for the same units consumed. Once the customer clicks the Run button, the total number of units consumed by the customer, the number of units produced by the solar PV panels and wind generators are displayed. The net units given to the grid in case of surplus or the net units taken from the grid in case of shortage are also displayed. IV. RESULTS A. Optimization Results The optimization results show the distribution of Energy from the wind and solar systems for all the months of the year. These optimal numbers of wind generators and solar PV panels that can meet 70% of the average demand is obtained from the algorithm discussed in Chapter II. Fig. 4 shows the distribution of energy over a year by 1 wind generator of 600 W capacity and 4 solar PV panels of 250 W p rating for a residential customer of Chennai. Fig. 5 shows the distribution of energy over a year by 3 wind generator of 600 W capacity and 4 solar PV panels of 250 W p rating for a residential customer of Tiruchirappalli. Fig. 4 Monthly Distribution of Energy for a Residential customer in Chennai Fig. 5 Monthly Distribution of Energy for a Residential customer in Tiruchirappalli

5 B. GUI Results In Fig. 6 the demand is met using a combination of wind and solar systems for Nagercoil. The customer is presented with two Combo Packs namely 250 W p solar PV panels and 600 W wind generators and 100 W p solar PV panels and 600 W wind generators. The savings for a year corresponding to both the combinations are also shown. These results give an idea of the cost involved in installation of renewable energy systems for residential customers. Moreover the savings are also calculated over a year based on the units produced by solar and wind systems. The second GUI portal validates the results obtained in the first GUI portal. Fig. 6 GUI for Solar and Wind system in Nagercoil Fig. 7 Savings from a Hybrid Energy System for a Residential Load on December 22 nd in Tiruchirappalli

6 Fig. 7 shows the results for Tiruchirappalli on Dec 22 nd. It is observed that the output of solar PV panels has drastically decreased from June and also the number of units produced by wind generators is very less. V. CONCLUSION In order to utilize renewable energy sources of both solar and wind energy efficiently and economically, a simple optimization algorithm has been developed in this paper to calculate the optimal number of solar PV panels and wind generators for a given location and demand. The proposed methodology has been used as a backend tool for developing GUI which calculates the number of solar panels and wind generators based on the location and the customer's load pattern. It is also observed that for the proposed two different configurations as combo packs in the portal, the customer can select by trading off between the spacing available and cost. The second GUI portal takes 24 hours unit consumption along with the location, day and month from the customer and displays the accurate value of units supplied by wind and solar systems and by the grid if there is any requirement is obtained. [7] "SolarInsolation", [8] "Wind Speed Data", [9] "Weather forecast for Tamil Nadu", [10] "Wind Speed", [11] "Determination of tariff for Generation and Distribution", June 20, VI. NOMENCLATURE GUI : Graphical User Interface C p : Power coefficient ρ : Density, kg/m 3 A T : Area of air parcel interacting with the rotor, m 2 V : Wind speed, m/s T c : Cell Temperature, o C T a : Ambient Temperature, o C NOCT : Normal Operating Cell Temperature, o C G i : Global Irradiation incident on PV module, W/m 2 β : Angle of inclination of PV module, degree P s : Power from solar panel, W STC : Standard Operating Conditions P stc : Power at standard operating conditions, W I rr : Irradiance, W/m 2 G stc : Global Irradiation at STC, W/m 2 T r : Reference temperature, o C k : Temperature coefficient of P max, %/ o C V oc : Open-circuit voltage of PV module, V I sc : Short-circuit current of PV module, A : Maximum power at STC, W P max VII. REFERENCES [1] "Fossil fuel", June [2] "What it costs to go solar today", The Hindu, Chennai, May 11, [3] "Wind power in India",, April [4] Faisal A. Mohamed, Heikki N. Koivo, "Online Management of Microgrid with Battery Storage using Multiobjective Optimization",IEEE, Proc. of POWERENG 2007, pp , April [5] Hongxing Yang, Wei Zhou, Lin Lu, Zhaojong Fang, "Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using Genetic Algorithm", Solar Energy 01/2008, Vol 82, pp , Jan [6] "GUI Building Basics", Jan 2014.