Cost Optimization by Integrating PV-System and Battery Energy Storage System into Microgrid using Particle Swarm Optimization

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1 Volume 114 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Cost Optimization by Integrating PV-System and Battery Energy Storage System into Microgrid using Particle Swarm Optimization Bhukya Ramesh 1, Suhashini 2, Gauri Kalnoor 3, BVVN Manikanta 4, D. Nageswara Rao 5 *Bala Murali Krishna. V EEE Dept, SIET , Scholl of Engineering, CUK , 3, K.LU , EEE Dept, SCET *School of Engineering, CUK murali.kv19@gmail.com * Apr 14-15, 2017 Abstract This paper presents an optimal power management method for grid connected photovoltaic (PV) system with battery energy storage systems (BESS) by particle swarm optimization (PSO) method. The objective of this work is to perform peak cutting, total electricity cost minimization and making the use of dynamic energy pricing model. Energy profile of PV system, load and the price of the energy consumed from the grid per day are the inputs of the system in this work. During the peak hours the battery supplies the energy to the grid and in the normal base condition 45

2 hours the battery gets charged from grid. Particle Swarm optimization (PSO) has been adapted to be optimized the electricity bill in the sense of control the battery charging and discharging conditions. Key Words: Photovoltaic (PV) system, microgrid, particle swarm optimization (PSO), renewable energy (RE), battery energy storage systems (BESS), bidirectional converter. 1. Introduction Renewable energy (RE) based grid power generation systems are becoming popular to avoid the transmission and distribution losses. Among the RE based power generation systems, photovoltaic (PV) systems are more popular because of simplicity in installation, easy maintenance, noiseless, eco-friendly etc. Nowadays, a microgrid system is being considered as one of the best practices to the growth in the electric energy around the world. [1], [2]. Battery energy storage systems (BESS) are becoming popular in various aspects of the power systems [3], [4]. For any large disturbance conditions, by integrating the BESS with an under abnormal frequency conditions, trip action of the BESS can enhance the system performance. Hence, it is reported that BESS is a hasty and easy handled management element for power systems [5] - [8]. At the distribution power generation level, integrating a small/micro scale or residential-level PV-power generation system with BESS into the smart grid or microgrid will provide a better way of utilizing the renewable energy sources [9]. Moein Choobineh et al., 2016 proposed that to optimize the capital investment for expanding the power generation capacity to meet up the future energy demand at the worst case, a better choice is that to integration of smart meters that will transfer power from the grid to a decentralized smart grid [10]. In General, all most of the studies related to the minigrid/micro-grid keep focus on how to manage the flow of electric energy between distributed generation, BESS, connected loads and the grid in order to optimizing bill profit, improving generating stations efficiency, saving 46

3 of electric energy and stabilizing output power and so on can be reached [11],[12]. In this paper a local energy monitoring management system monitors the BESS for charging and discharging conditions. In order to do the business between grid and BESS, that means to buy from the grid or sell to the grid electricity at the correspondent optimum price, a linear programming approach is developed to optimize the BESS operation [13]. In this paper to optimize the cost of electricity bill, the PSO method has been adapted. However, to get the optimization, there are some more methods such as linear programming method (LPM), enumerative method (EM), balanced generation and load demand (BG & LD) method, genetic algorithm (GA), iterative algorithm (IA), particle swarm optimization (PSO) have been reported. In view of [14-16], because of simplicity, high convergence rate, nominal storage requisite and ease of use with PSO method became popular. Over view of this paper is organized as follows. Significance of RE based power systems, integration of microgrid with BESS and optimization methods are discussed in Section 1. System configuration, modeling and optimized operation of BESS is given in the Section 2. MATLAB/Simulink based output responses with proposed method and discussions are given in Section 3. Section 4 is given the Conclusion. 2. System Configuration, Modeling and Optimized Control Approach of Proposed Model To optimize the cost of the grid-tied PV system with BESS by PSO of proposed system is drawn in a single line diagram and is shown in Fig.1. This section deals with problem formulation of proposed system and its solution algorithms has been discussed. 47

4 Fig.1. Single line diagram of proposed PSO based optimized PV-System with BESS. 2.1 Problem Formulation: Min. c k. e g (k) (1) s. t r d e b k + 1 e b k r c (2) α m E m e b k α m (3) e b N = E o (4) The above problem.i.e, from equations (1)-(4) can be converted into cost function as follows: Cost function = C e g + ( Constraints) (5) Constraints = λ 1 C 1 + λ 2 C 2 + λ 3 C 3 + λ 4 C 4 (6) C 1 = e p e l + r d e g (7) C 2 = e p e l r c + e g (8) C 3 = e p e l + e b e g α m E m (9) C 4 = e p e l + e b + e g α M E m (10) 48

5 To solve the considered model to optimize the cost of electricity bill, PSO has been adapted and nomenclature is given by Table.1. Table.1. Nomenclature of the proposed model Term Defined function C Cost e g Grid energy e p PV Energy e l Energy drawn by load e b Battery Energy LaGrange s coefficient Term Defined function r c Rate of charge of Battery r d Rate of discharge of Battery E m Energy rating of Battery E o Initial Energy of Battery α m Minimum percentage of Battery α M Maximum percentage of Battery 2.2 Flowchart of Particle Swarm Optimization (PSO) Fig.2 shows the PSO Algorithm Flow chart diagram, which is used in this paper to optimize the electricity bill. 49

6 Fig.2. PSO Algorithm Flow chart diagram. 2.3 Proposed System Controlling Algorithm Step 1: Read Inputs (dynamic pricing, load, PV schedule) Step 2: Initialize parameters Step 3: Initialize loop variable to 1 Step 4: Define the cost function as given in the equation ( 5 ) Step 5: Use PSO to optimize the cost function to get optimized grid power Step 6: Now update battery energy for next iteration Step 7: Increment the loop variable by 1 Step 8: If the value of loop variable is less than the total no. of samples of input go to Step-4 Step 9: Calculate the total electricity bill Step 10: Plot the graphs 3. Results and Discussions This section deals with the MATLAB/Simulink 2016b software based results and discussion of the proposed system. The simulation carried out per 24 hours of a day. Fig.3. shows the Dynamic Pricing variation of considered grid connected system per 24 hours per day of the considered microgrid. Fig.3. Dynamic Pricing variation of considered grid connected system. Fig.4. Shows the PV-system, load and grid profile of base system, i.e., without any optimization of considered model without BESS and Fig.5 shows the same considered model with BESS. 50

7 Fig.4. With BESS of base model. Fig.5. Without BESS of base model. Fig.6. Shows the PV-system, load and grid profile of the proposed system with PSO base system, i.e., with optimization without BESS and Fig.7. shows the with optimized system with BESS. Fig.6. With BESS of proposed model. Fig.7. Without BESS of proposed model. Table.II shows the performance of the proposed integrated PV System and BESS with PSO and without PSO method. Table.II. Comparison table of proposed system with and without; BESS and PSO Method. With PSO With Without BESS BESS (c) (d) % 0 % Without PSO With Without BESS BESS (b) (a) % 0 % % Savings of electricity bill in of proposed system compared to other models a % % 0 % % b % 0 % % 0 % c 0 % % % % d 51

8 4. Conclusion Thus to optimize the electricity bill of a considered model has been developed by integrating grid-tied PV system, BESS with bi-directional power converter and micro grid. Particle Swarm Optimization (PSO) method has been adapted and solved by using MATLAB/ Simulation based coding. The results and discussions section (Fig.3-Fig.7 and Table. II) clearly shows that the saving of electricity bill of the proposed model per day by percentage of Through the proposed algorithms the dynamic characteristics of battery energy storage system decides the system either give the electric energy to the grid or take the electric energy from the grid. For further future optimization of the considered model, other optimization techniques, like Model Predictive Controller (MPC) can be adapted and also for more accuracy in battery energy management system Artificial Intelligence (AI) based methods can be used. 5. REFERENCES [1] Muhammad Khalid, Abdollah Ahmadi, Andrey V. Savkin and Vassilios G. Agelidis, Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage, Elsevier publication, Renewable Energy 97 (2016) pp [2] X. Fang, S. Misra, G. Xue, D. Yang, Smart grid e the new and improved power grid: a survey, IEEE Commun. Surv. Tutor. 14 (4) (2012) pp [3] Bala Murali Krishna.V, A. Sri Hari Babu, J. Jithendranath and Ch. Uma Maheswara Rao, An Isolated Wind Hydro Hybrid System with Two Back-to- Back Power Converters and a Battery Energy Storage System Using Neural Network Compensator, IEEE International Conference on Circuit, Power and Computing Technologies 2014, pp [4] Bala Murali Krishna & Manikanta. B, Low voltage ride through of PV based grid connected system with efficient energy storage system and MPPT, presented in International Conference on Recent Trends in Engineering Science and Technology-2016 (Elsevier Energy Procedia publication). 52

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