Optimal Reactive Power Planning By Using Evolutionary Engineering Essay

Optimal Reactive Power Planning By Using Evolutionary Engineering Essay This paper presents a methodology for solving Optimal Reactive Power Planning (ORPP) problem by using Evolutionary Programming (EP) Optimization Technique in order to improve the voltage stability and minimize the losses in the power system. This study has developed the Evolutionary Programming (EP) Optimization Technique using MATLAB software. The study tested two fitness functions namely total loss minimization and the voltage stability improvement in power system with two different mutation technique. Comparison in the results obtained was made in order to determine the best fitness function and the best mutation technique to be used for solving ORPP and hence the voltage stability is improved. The proposed technique was tested on the IEEE 26 bus reliability test system. Keywords: Optimal Reactive Power Planning (ORPP), Voltage Stability Improvement, Evolutionary Programming (EP) I. INTRODUCTION In general, the problem of optimal reactive power planning (ORPP) can be defined as to determine the amount and location of shunt reactive power compensation devices needed for minimum cost while keeping an adequate voltage profile. The ORPP is one of the most challenging problems since objective functions, the operation cost and the investment cost of new reactive power sources, should be minimized simultaneously [1]. Transmission loss can be minimised by performing reactive power planning which involves optimisation process. The ORPP is a large-scale nonlinear optimization problem with a large number of variables and uncertain parameters. Various mathematical optimization algorithms have been developed for the ORPP, which in most cases; use nonlinear [2], linear [3], or mixed integer programming [4], and decomposition methods [5-8]. However, these conventional techniques are known to converge to a local optimal solution rather than the global one for problems such as ORPP which have many local minima. Recently, evolutionary algorithms (EAs) have been used for optimization; in particular both the genetic algorithm and evo1ution programming have been used in the ORPP problem. The EA is a powerful optimization technique analogous to the natural selection process in genetics. It is useful especially when other optimization methods fail in finding the optimal solution [1]. Evolutionary Programming (EP) optimization technique is recently applied in solving electric power system optimization problems. It is part of the Evolutionary Algorithm (EA) optimization techniques under the artificial intelligence hierarchy. Optimization is an important issue in power system operation and planning particularly in the area of voltage stability studies [9]. In this study, EP engine was initially developed to implement the optimisation process considering two mutation techniques, each with two different objective functions. Comparative studies performed in this study aimed to identify the most suitabl e mutation technique with the best objective function for minimising transmission loss in power system and also improving the voltage stability. The parameters for this problem are: generated reactive power (Qg), injected reactive power (Qinj) and transformer tap (T). Validation on the effectiveness of the proposed technique was conducted on the IEEE-26 reliability test system. Figure 1: The IEEE 26 bus test system II. OBJECTIVES The two objective functions of this study are: To obtain the total loss minimization To improve the voltage stability Where: Total_Loss is total loss minimization LQNmax is voltage stability improvement III. BACKGROUND STUDIES A. Optimal Reactive Power Planning (ORPP) Optimal Reactive Power Planning (ORPP) is a sub-problem of Optimal Power Flow solution which has been widely used in power system operation and planning to determine the optimal control parameter settings, in order to minimize or maximize the desired objective function while satisfying a set of systems constraint. Reactive Power Planning (RPP) involves in optimizing the transformer tap setting, injection of reactive power at generator and load bus so as to fulfill the objective function. Since the OPF approach is commonly concerned with the security and economic operation of the power system, Economic Dispatch (ED) technique is also adopted in RPP scheme. The value of active power generated by the generator is also adjusted in the approach. [11] ORPP is a nonlinear programming problem which has the following mathematical formulation: Maximize or minimize f(x, u) (3) subject to g(x, u) =0 (4) hmin à ¢Ã¢â‚¬ °Ã‚ ¤ h(x, u) à ¢Ã¢â‚¬ °Ã‚ ¥ hmax (5) where u is the vector of control variables and x is the vector of dependent variables. f(x, u) is the objective function, while g(x, u) is the nodal power constraints with hmin à ¢Ã¢â‚¬ °Ã‚ ¤ h(x, u) à ¢Ã¢â‚¬ °Ã‚ ¥ hmax are the inequality constraints of the dependent and independent variables. B. Evolutionary Programming (EP) EP is one of the popular techniques which fall under the Evolutionary Computation in Artificial Intelligence (AI) hierarchy and increasingly applied for solving power system optimization problem in recent years. A new population is formed from an existing population through the use of a mutation operator. This operator produces a new solution by perturbing each component of an existing solution by a random amount. The degree of optimality is measured by the fitness, which can be defined as the objective function of the problem [12]. Through the use of a ranking scheme, the candidate solutions in each population were sorted in ascending order according to the number of the best population. The best population form a resultant population is referred as the next generation. The ranking scheme must have more optimal solutions which has a greater chance of survival than the poorer solutions. It is a stochastic optimization strategy, which based on the mechanics of natural selections-mutation, competition and evolution. This technique stressed on the behavioural linkage between parents and their offspring. In general, EP consists of 3 major steps which briefly discussed as follow [12], [13]: i. Initialization The initial population of ÃŽÂ ¼ individuals consists of (xi, ÆÅ ¾i), ˆ¦i à ¢Ã¢â‚¬Å¡Ã‚ ¬ {1, 2,à ¢Ã¢â€š ¬Ã‚ ¦ÃƒÅ½Ã‚ ¼} are generated randomly based on its limit, whereby xi denotes the control variable and ÆÅ ¾i is the strategic parameter with respect to xi. The fitness is calculated for each individual based on its objective function, f(xi). ii. Mutation a) First Mutation Technique Each parent (xi, ÆÅ ¾i), i=1,à ¢Ã¢â€š ¬Ã‚ ¦, ÃŽÂ ¼, creates a single offspring (xi, ÆÅ ¾i), where xi and ÆÅ ¾i are given by: xi (j) = xi (j) + ÆÅ ¾i (j) Nj (0, 1) (6) ÆÅ ¾i (j) = ÆÅ ¾i (j) exp (à Ã¢â‚¬Å¾ N (0, 1) + à Ã¢â‚¬Å¾ Nj (0, 1)) (7) and à Ã¢â‚¬Å¾ = ((2(n)  ½)  ½)-1 (8) à Ã¢â‚¬Å¾= ((2n)  ½)-1 (9) xi (j), xi'(j), ÆÅ ¾i(j) and ÆÅ ¾i'(j) are the j-th component of the vectors xi, xi, ÆÅ ¾i and ÆÅ ¾i respectively. N(0,1) represents a normally distributed one-dimensional random number with mean zero and standard deviation 1. Nj(0,1) denotes that the random number is generated anew for each value of j. Subsequently, the fitness is calculated for each offspring. b) Proposed Mutation Technique The proposed mutation rule was inspired by neural network back propagation learning. The following three equations are employed for perturbing the parents to generate their offspring: In these equations, xij [k] [k] is the jth variable of an ith individual at the kth generation. The learning rate, ÆÅ ¾, and the momentum rate, ÃŽÂ ±, are real-valued constants that are determined empirically. |.| denotes an absolute value and N represents the normal distribution. Άxij [k] is the amount of change in an individual, which is proportional to the temporal error, and it drives the individual to evolve close to the best individual at the next generation. It may be viewed as a tendency of the other individuals to take after or emulate the best individual in the current generation. sxij [k] is the evolution tendency or momentum of previous evolution. It accumulates evolution information and tends to accelerate convergence when the evolution trajectory is moving in a consistent direction [14]. The best individual is mutated only by the momentum. This expands the exploitation range and increases the possibility for escaping from local minima. acci[k] in (10) is defined as follows. acci[k] = 1; if the current update has improved cost, 0; otherwise. (10) iii. Combination and Selection In combination stage, the union of parents and offspring are ranked in ascending or descending order according to its fitness and purpose of the optimisation. Hence, the top ÃŽÂ ¼ individuals are selected to be parents for the next generation. The process of mutation, combination and selection are repeated until the stopping criterion is met. In this paper, the stopping criterion is taken to be the convergence of fitness value. IV. METHODOLOGY Figure 3 explained the overall methodology for the evolutionary programming optimization technique to solve ORPP. The produced offspring vector must satisfy and consider the constraints as at the initialization. The main concept of EP is the mutation process. Then continues with learning about the MATLAB software and tested the IEEE 26-Bus Test System to observe initial values which are total power loss, initial minimum and maximum voltages and the initial line stability index (LQP LQN). These initial values have been taken by considering the unstable transmission lines in the test system (IEEE 26-BUS). The unstable line means the line stability index value is close to 1.00. The unstable voltage is when the value is not within the range of (0.90à ¢Ã¢â‚¬ °Ã‚ ¤Và ¢Ã¢â‚¬ °Ã‚ ¤1.10). Figure 3: The flow chart for the EP optimization technique The EP program was developed and the analysis of the result is tested based on objective function of the project such as minimize total loss and the voltage stability improvement. Then, the program has been run for five times for each type of objective function. Finally, this project has been concluded and the report has been written. A. Development of EP for Optimal Reactive Power Planning The optimal reactive power planning problems has been tested on the IEEE 26 bus test system. The two objective functions tested are: Fitness1 = Total_Loss; Fitness2 = LQNmax; To find the solution of the problem, the parameters d were decided. The parameters were: Reactive Power of Generator Bus Table 1 shows the parameters and size of reactive power of generation bus. There are five generator buses in IEEE 26-bus test system: Bus 2, 3, 4, 5 and 26. The size of each bus is as below. Table 1: Parameters and size of reactive power of generator bus Parameter Bus Size (MVar) Qg2 2 0 to 50 Qg3 3 0 to 40 Qg4 4 0 to 35 Qg5 5 0 to 30 Qg26 26 0 to 20 2. Injected Reactive Power to the Bus Table 2 shows the parameters and size of injected reactive power to the bus. It shows that there is nine buses have been injected by reactive power. The buses are as below. The unit of the injected reactive power is in MVar. Table 2: Parameters and size of injected reactive power to the bus Parameter Bus Size (MVar) C1 1 0 to 9 C4 4 0 to 9 C5 5 0 to 9 C6 6 0 to 9 C9 9 0 to 9 C11 11 0 to 9 C12 12 0 to 9 C15 15 0 to 9 C19 19 0 to 9 3. Transformer Tap at the Transmission Line Table 3 shows the parameters and size of transformer tap at transmission line. It shows that there is seven transformer tap change at transmission line in IEEE 26-bus test system. The size of each transformer tap is (0.9 to 1.2). Table 3: Parameters and size of transformer tap at the transmission line Parameter Line Size (p.u) T1 2-3 0.9 to 1.2 T2 2-13 0.9 to 1.2 T3 3-13 0.9 to 1.2 T4 4-8 0.9 to 1.2 T5 4-12 0.9 to 1.2 T6 6-19 0.9 to 1.2 T7 7-9 0.9 to 1.2 The EP process is initialization, mutation, rank and selection and convergence test. 4.1.1 Initialization Initial population of size 20 is formed by a set of randomly generated actual value. Each member is tested using equation (12) (17) as below. Equations (12) (16) are the generation constraints. The bus voltage limits in equation (17) are stated in order to avoid any violation to the system operation. The total loss limit in equation (18) is stated in order to avoid the losses greater than the initial values. 0MVar à ¢Ã¢â‚¬ °Ã‚ ¤ Qg2 à ¢Ã¢â‚¬ °Ã‚ ¤ 50MVar (12) 0MVar à ¢Ã¢â‚¬ °Ã‚ ¤ Qg3 à ¢Ã¢â‚¬ °Ã‚ ¤ 40MVar (13) 0MVar à ¢Ã¢â‚¬ °Ã‚ ¤ Qg4 à ¢Ã¢â‚¬ °Ã‚ ¤ 35MVar (14) 0MVar à ¢Ã¢â‚¬ °Ã‚ ¤ Qg5 à ¢Ã¢â‚¬ °Ã‚ ¤ 30MVar (15) 0MVar à ¢Ã¢â‚¬ °Ã‚ ¤ Qg26 à ¢Ã¢â‚¬ °Ã‚ ¤ 20MVar (16) 0.90V à ¢Ã¢â‚¬ °Ã‚ ¤Ãƒ ¢Ã¢â‚¬ Ã¢â‚¬Å¡Và ¢Ã¢â‚¬ Ã¢â‚¬Å¡Ãƒ ¢Ã¢â‚¬ °Ã‚ ¤ 1.10V (17) Total Losses à ¢Ã¢â‚¬ °Ã‚ ¤ 16 (18) The generated random numbers must be smaller than the initial solution set in order to make sure that fitness will be improved. Only the member that satisfy the constraints are included in the initial population set. 4.1.2 Mutation Mutation is a method to execute the random number to produce offspring. An offspring vector Li is created from each parent vector by adding Gaussian random with zero mean and standard deviation. 4.1.3 Rank and Selection The offspring populations generated form mutation process is merged with the parent populations. The selection process is to generate a new 20 populations based on the objective function of total losses minimization and the voltage stability improvement. All of members were sorted in ascending order to produce the best twenty or the strongest twenty populations for next generation. 4.1.4 Convergence test The stopping criteria in order to obtain the optimal solution are by looking at the difference in maximum fitness and minimum fitness which must less then certain values. If not achieved, the process will be repeated until it converged. Where: Total_Lossmax- Total_Lossmin LQNmax LQNmin V. RESULTS AND DISCUSSION An EP optimization technique has been developed in this study and tested on IEEE 26-bus test system. The objective function is to minimize the total power loss and to improve the voltage stability in power system. The program has been developed to find the optimal value of control variables based on each objective functions. However, before this program was run, load flow solution for the IEEE 26-bus test system was obtained to determine the initial values. The initial total power loss and stability index is 18.986 MW and 0.754 respectively. For each objective function the program was run five times and the results were tabulated in tables according to the objective function. Then the best result for each objective function was selected and tabulated in Table 1 in the Appendix A in order to make a comparison between the two objective functions. According to the result which tabulated in Table 1 in the Appendix A, it was found that EP optimization technique with voltage stability improvement as the objective function give the best result which is total power loss of 14.462 MW and stability index of 0.717. The EP optimization technique with total power loss minimization as the objective function give results 14.987 MW. The EP optimization technique using proposed mutation rule with voltage stability improvement as objective function, the result MW and for the total power loss and stability index respectively. According Table 4, the total power loss and stability index is 15.534 MW and 0.734 respectively. The result after solve the Optimal Reactive Power Planning (ORPP) is 13.019 MW and 0.699. The percentage reduction for total power loss and stability index after solves the ORPP is 16.19 % and 4.77 %. Table 4: Comparison results before and after solves the Optimal Reactive Power Planning Terms Before Solve ORPP After Solve ORPP Total Power Loss (MW) 15.534 13.019 Stability Index, LQNmax 0.734 0.699 VI. CONCLUSION An evolutionary programming optimization technique has been developed to optimize the real power of generator bus, the reactive power and transformer tap control variables for minimal total cost of generation, total power loss and voltage stability improvement. In this paper, the total cost minimization is the best objective function for minimization of total cost, total power loss and stability index is reduced. The percentage reduction for the total cost and total power loss is acceptable. The percentage reduction of stability index is the highest. The percentage reduces for the total cost, total power loss and stability index is 7.77 %, 16.19 % and 4.77 % respectively. This is the acceptable and reasonable percentage reduction as compared to other objective functions. Therefore voltage stability improvement may not have to be the objective function in order to improve the voltage stability condition of a power system in solving the OPF. VII. FUTURE DEVELOPMENT For future development, the other optimization techniques are proposed to be implemented in solving the ORPP in order to minimize the total power system losses and especially to improve the voltage stability in larger power system. Further modification should be included to get more accurate results for example using different mutation rules and selection strategies. VIII. REFERENCES [1] Kwang. Y. Lee and Frank F. Yang Department of Electrical Engineering The Pennsylvania State University University Park, PA 16802, Optimal Reactive Power Planning Using Evolutionalry Algorithms: A Comparative Study for Evolutionary Programming, Evolutionary Strategy, Genetic Algorithm, and Linear Programming; IEEE Transactions on Power Systems, Vol. 13, No. 1, February 1998 [2] R. Billington and S. S. Sachdev, Optimum network VAR planning by nonlinear programming IEEE Trans. on Power Appar. and Syst., Vol. PAS-92, pp. 6 [3] T. Heydt and W. M. Grady, Optimal Var siting using linear load flow formulation, IEEE Trans. on Power Appar. and Syst., pp. 1214-1222. Vol. PAS-102, No. 5, May 1983. [3] K. Aoki, M. Fan, and A. Nishkori, Optimal Var planning by approximation method for recursive mixed integer linear planning, IEEE Trans. on Power Syst.,Vol. PWRS-3, No. 4, pp. 1741-1747, November 1988. [4] K. Y. Lee, Y. M. Park, and J. L. Oritz, A united approach to optimal real and reactive power dispatch, IEEE Trans. on Power Appar. and Syst., Vol. PAS-104, pp. 1147-1153, May 1985. [SI K. Y. Lee, J. L. Ortiz, Y. M. Park, andL. G. Pond, An optimization technique for reactive power planning of subtransmission network under normal operation, IEEE Trans. on Power Syst., Vol. PWRS-1, pp. 153-159, May 1986. [6] M. K. Mangoli, K. Y. Lee, and Y. M. Park, Operational real and reactive power control using linear programming, Electric Power Systems Research,[7] M. K. Mangoli, K. Y. Lee, andY. M. Park, Optimal long-term reactive power planning using decomposition techniques, Electric Power System Research, Vol. 26, Rana Mukerji, Wendell Neugebauer, Richard P. Ludorf and Armand Catelli, Evaluation of Wheeling and Non-Utility Generation (NUG) Options using Optimal Power Flows, IEEE Transaction on Power Systems, Vol. 7, No. 1, February 1992. [3] Kessel and Glavitsch> Estimating the Voltage Stability of a Power System, IEEE Transaction on Power Delivery, Vol. PWRD-1, NO. 3, July 1986, pp 346-352. [4] Jason Yuryevich and Kit Po Wong, Evolutionary Programming Based Optimal Power Flow Algorithm, IEEE Transaction on Power Systems, Vol. 14, No. 4, November 1999, pp 1245-1250. [5] A.M. Chebbo, M.R. Irving. M.J.H Collapse Proximity Indicator: Behaviour and Implications, IEE Proceedings -C, Vol. 139, No. 3, May 1992. [6] Mahmoud Moghavvemi, New Method for Indicating Voltage Stability Condition in Power System, Proceedings of IEE International Power Engineering Conference, IPEC97, Singapore, pp. 223-227. [7] Jason Yuryevich, Student Member IEEE, Evolutionary Programming Based Optimal Power Flow Algorithm, IEEE Transactions on Power Systems, Vol. 14, No. 4, November 1999. [8] Salami, M. and Cain, G., Multiple Genetic Algorithm Processor for The Economic Power Dispatch Problem, Proceeding of the genetic algorithm in engineering systems: Innovations and Applications, Conference Publication No. 414, IEE, 1995, pp 188-193. [9] I Musirin and T K Abdul Rahman, On-Line Voltage Stability Index for Voltage Collapse Prediction in Power System, presented at Brunei International Conference on Engineering and Technology 2001 (BICET2001), Brunei. October 2001. [10] Toro, V.D., Electric Power System, Prentice Hall, Englewood Cliffs, New Jersey, 1992. [11] Whats Evolutionary Programming, http://www.faqs.org/faqs/ai-faq/genetic/part2/section-3.html. [12] Leandro Nunes de Castro and Fernando Jose Von Zuben, Artificial Immune System:Part 1- Basic Theory and Applications, Technical report TR-DCA 01/99. 1999. [13] Slobodan Pajic, Dr Kevin A. Clements, Dr. Paul W. Davis and Dr Alexander E. Emanuel, Sequential Quadratic Programming Base Contingency Constraint Optimal Power Flow, Degree of Master of Science in Electrical and Computer Engineering, Worcester Polytechnic Institute, April, 29 2003. [14] Fogel , D.B. : A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation, June,1995,pp.397-404. [15] Yao, X., Liu, Y. and Lin, G., (1999) Evolutionary programming made faster, IEEE Trans. Evolutionary Computation. vol. 3, no. 2, pp. 82-102. [16] Miller, R.H. and Malinnowski, J.H., Power System Operation, McGraw-Hill, Inc., 1994. Appendix A Table 1: Results of EP Optimization Technique Objective Function Control Variables/ Parameters of OPF Total Cost ($/h) Total Power Loss (MW) Stability Index, LQNmax Time Taken (s) Real Power of Generator Bus (MW) Injected Reactive Power (Mvar) Transformer Tap (p.u) Pg2 Pg3 Pg4 Pg5 Pg26 C1 C4 C5 C6 C9 C11 C12 C15 C19 T1 T2 T3 T4 T5 T6 T7 Total Power Loss Minimization 163.12 281.14 146.07 147.94 92.11 5.95 4.79 0.39 5.39 5.23 1.40 4.23 5.43 3.83 0.96 0.99 1.04 0.96 0.96 0.96 0.91 15449.1 12.132 0.767 11843 Voltage Stability Improvement 110.35 287.28 128.95 163.42 97.14 1.24 1.28 2.36 1.05 2.58 0.76 4.50 2.19 2.39 0.94 1.00 0.96 1.11 1.05 0.90 0.98 15523.1 14.461 0.713 6358

Principles of Diversity, Equality and Inclusion in Adult Social Care Settings

Understand the importance of diversity, equality and inclusionDiversity means understanding that each individual is unique and that we recognise our individual differences this can include race, gender, religious beliefs, age, sex orientation or any other differences. Equality means equal rights for people no matter what differences they may have and that they should all be treated fairly and that nothing should be lacking in their care and all care plans should be followed.Inclusion is based on the idea of allowing everyone to access to any service regardless their gender, religion or age and what they want and be included. Discrimination means when someone is taking less care of one person or is in less favour then someone else due to probably a disability or religion etc.Direct discrimination can occur in the work setting if the service you provide for one service user is less then the other due to their gender, age, religion, race or mental health or could be they are told they c ant do anything as they are in a wheelchair or not fit enough.Indirect discrimination can occur if someone has learning difficulty or English is not their first language and you leave them notes, which they cannot understand, or when the local authority sends leaflets for their services and they are not easy to read. 1.3Practises that support diversity, equality and inclusion to reduce the likelihood of discrimination may include encouraging everyone to say what there views are having them to listen to so that it can help to respect and help to resolve peoples differences and it helps an individual to be fully involved not only in their lives but in everyone’s else’s lives too.Know how to work in an inclusive wayThese are some of the key legislation and codes of practice relating to diversity, equality, inclusion and discrimination in adult social care settings:The Disability Discrimination Act 2005The Special Educational Needs and Disability Act 2001The Race Relations (Amendment) Act 2000Convention on the Rights of the Child (UN, 1989)The Human Rights Act 1998The Sex Discrimination Act 1975 (as amended)Employment Equality Regulations 2003You can interact with an individual in an inclusive way by making them feel comfortable and asking general questions about them. Treating them equally, be respectful and listen to them carefully, not making them feel small and encourage to ask questions and give suggestions.The ways in which discrimination may be challenged by gathering evidence, and if I witnessed discrimination is going on I would report it to the office to my supervisor or manager, I would also speak to the person that is being discriminating that they will try to sort everything out and they are not alone. Outcome 3 Know how to access information, advice and support about diversity, equality, inclusion and discrimination.Sources of information, advice and support about diversity, equality, inclusion and discrimination on government web sites that list all the current legislations in place to protect the rights of an individual. You can obtain leaflets to show service users about their rights, read books, or even can contact certain age groups, such as AGE UK who can supply materials to hand out to service user’s on discrimination, equality etc..If I think someone is being a victim of discrimination, I would report this straightaway to my manager or supervisor, even if I was in this situation I would do the same, I would also look through my policy and procedures which should include policies related to inclusion and diversity, if I encounter problems I would refer to my own policies, as to the actions to take as an employee.

Teen Pregnancy The Mom And Child s Well Being, And The...

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Theme Of Love In Merchant Of Venice - 897 Words

Love, to enter or escape In the Merchant of Venice, love plays an important role among the whole story. Different people give â€Å"true love† new definitions, and making the real love as a tool for them to reach their goal. Love for everyone in this play, is a tool to escape from their real life that makes them suffering and also an escape from their past. Love between men and women are often begin with the seek for rich and fame. Take Portia and Bassanio as an example, as Bassanio first mentioned Portia, he was trying to persuade his friend Antonio to lend him money in order to go to Belmont to reach Portia: â€Å"Bassanio:In Belmont is a lady richly left,And she is fair and—fairer than that word—Of wondrous virtues. Sometimes from her eyes.I did†¦show more content†¦O me, the word ‘choose!’ I may neither choose whom I would nor refuse whom I dislike—so is the will of a living daughter curbed by the will of a dead father. Is it not hard, Nerissa, that I cannot choose one nor refuse one?†( Merchant of Venice 1.2.20-25). She didn’t have a chance to determine who she liked and what she loved, though her father has died, she didn’t have her chance of choosing her life but instead, follow what her father wanted. She didn’t want to spend the rest of her life as a property of other people. Marriage seemed to be the only chance for her to get at least mentally No matter Love between father and daughter is more obvious to see on Shylock and her daughter Jessica. Shylock loved his money, and his daughter. For Shylock, the christian dominated world is unfair to him and also he was hated by most of the people in this world. In the whole play, he had nobody to love except his daughter, and nobody loved him. He loved her wife as he described the ring: â€Å"I would not have given it for a wilderness of monkeys† (3.1.101–102). His daughter stole the ring and made Shylock sad. Jessica, as the daughter of Shylock, met Lorenzo and stated â€Å"O Lorenzo/ If thou keep promise, I shall end this strife/ Become a Christian and thy loving wife!† (2.3.19 – 21). Jessica was shame to be Shylock’s daughter and want to be a Christian. She found an accurate way to reach her goal, find Lorenzo, marry him and escape physically and mentally with him. Stealing all herShow MoreRelatedThe Theme Of Revenge In The Merchant Of Venice1118 Words   |  5 PagesRevenge is the central theme of The Merchant of Venice as the plot is centred around the theme of revenge, vengefulness is one of the main character traits present in a sizable number of characters and the main moral message is that revenge is a poor choice. While other themes in the play are present in the plot and moral message and the themes of love and friendship are more prevalent in the majority of the characters, revenge plays a more significant role in the play. 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Although human beings ostensibly love and care for each other, in reality they expect some sort of benefit from every relationship. Both William Shakespeare and Arthur Miller depict the role of self-interest in human relationships in their literary works: The Merchant of Venice and The Crucible respectively. They emphasize this thesis through examples of love, friendship, power and Christianity. Even though love and friendship are regarded as two forms of relationships whereRead MoreThe Merchant of Venice: Self-Interest versus Love Essay1016 Words   |  5 PagesWhile there are many fundamental themes in Shakespeare’s The Merchant of Venice, only one seems to drive the play to its inevitable conclusion. There is a constant theme of self-interest versus love. On the surface, this seems to be the dividing factors between the Christians and the Jew. Shylock is portrayed to only care about money and profits, while the Christians are shown as people who value human relationships more. Examples of this theme are shown through Shylock’s behaviour, the manyRead MoreMerchant of Venice - Plot Structure1714 Words   |  7 Pagesof ‘The Merchant of Venice is apparently fanciful but in reality exactingly structured./b/center br brThe Merchant of Venice is a fairy tale. There is no more reality in Shylocks bond and the Lord of Belmonts will than in Jack and the Beanstalk. brH. Granville-Barker, in Prefaces to Shakespeare. br brThis is one way of looking at the play, reading it or enjoying the performance. But it can be a contradiction to our actual feelings about this complex play. ‘The Merchant of Venice