R. Arunkumar

Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos

Over the past decade, several conventional optimization techniques had been developed for the optimization of complex water resources system. To overcome some of the drawbacks of conventional techniques, soft computing techniques were developed based on the principles of natural evolution. The major difference between the conventional optimization techniques and soft computing is that in the former case, the optimal solution is derived where as in the soft computing techniques, it is searched from a randomly generated population of possible solutions. The results of the evolutionary algorithm mainly depend on the randomly generated initial population that is arrived based on the probabilistic theory. Recent research findings proved that most of the water resources variables exhibit chaotic behavior, which is a projection depends upon the initial condition. In the present study, the chaos algorithm is coupled with evolutionary optimization algorithms such as genetic algorithm (GA) and differential evolution (DE) algorithm for generating the initial population and applied for maximizing the hydropower production from a reservoir. The results are then compared with conventional genetic algorithm and differential evolution algorithm. The results show that the chaotic differential evolution (CDE) algorithm performs better than other techniques in terms of total annual power production. This study also shows that the chaos algorithm has enriched the search of general optimization algorithm and thus may be used for optimizing complex non-linear water resources systems.

Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization

The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less.

Evaluation of a multi-reservoir hydropower system using a simulation model

In the present study, the operation of a multi-reservoir hydropower system is evaluated using a monthly simulation model for power production and irrigation releases. The hydropower production is assessed for two operating scenarios, namely, unconstrained and constrained scenarios based on hard bound constraint on powerhouse releases and also for various duration of hydropower plants operation. Further, the behaviour of the system is analysed using the statistical performance indices such as reliability, resilience and vulnerability. The results show that the power production is high for the unconstrained operating scenario due to unrestricted releases to the powerhouses. On the other hand, the constrained scenario has resulted in lesser but reliable steady power production irrespective of duration of operation. It is found that the power production can be increased by 14% without compromising the irrigation releases. The statistical performance analyses show that lesser the duration of operation of powerhouses, higher is the reliability and resilience with less vulnerability.

Performance assessment of storage policies of the Vaigai Reservoir using a simulation model

A comprehensive simulation model was used to assess the performance of the storage-based operating policies of the Vaigai Reservoir system, Tamil Nadu, India. The model first used historical inflow data, and then inflow data generated through an artificial neural network (ANN) model. The modelled releases were then compared with the actual ones. It was found that the existing storage-based policies were violated within the year but not on an annual basis, except in the recent past, when annual deficits occurred due to inadequate water availability from the upstream reservoir.

PERFORMANCE ASSESSMENT OF CANAL IRRIGATION SYSTEM

ABSTRACT Water that seeps through the bed and sides the canal are very significant. The ultimate purpose of performance assessment of an irrigation system is to achieve an efficient and effective use of available water resources. The present study was conducted to estimate the transmission losses in the Sathanur Left Bank canal and one of its distributory. The transmission losses was estimated using inflow—outflow method. The performance of the system is assessed by applying indicators. The transmission losses in the head reach of the canal is found to be higher than that of middle and tail reaches. The performance indicators showed that there was excess supply of water in the distributory.

Improving the Performance of the Optimization Technique Using Chaotic Algorithm

Optimizing the operations of a multi-reservoir systems are complex because of their larger dimension and convexity of the problem. The advancement of soft computing techniques not only overcomes the drawbacks of conventional techniques but also solves the complex problems in a simple manner. However, if the problem is too complex with hardbound variables, the simple evolutionary algorithm results in slower convergence and sub-optimal solutions. In evolutionary algorithms, the search for global optimum starts from the randomly generated initial population. Thus, initializing the algorithm with a better initial population not only results in faster convergence but also results in global optimal solution. Hence in the present study, chaotic algorithm is used to generate the initial population and coupled with genetic algorithm (GA) to optimize the hydropower production from a multi-reservoir system in India. On comparing the results with simple GA, it is found that the chaotic genetic algorithm (CGA) has produced slightly more hydropower than simple GA in fewer generations and also converged quickly.