Niladri Chakraborty

Particle swarm optimization technique based short-term hydrothermal scheduling

Particle swarm optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal power system. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. In the present work, the effects of valve point loading in the fuel cost function of the thermal plants are also taken into consideration. The developed algorithm is illustrated for a test system consisting of four hydro plants and three thermal plants. Cost characteristics of individual thermal units are considered. The test results are compared with those obtained using evolutionary programming and simulated annealing technique. It is found that the convergence characteristic is excellent and the results obtained by the proposed method are superior in terms of fuel cost and computation time.

Short-term combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using particle swarm optimization technique

This paper develops an efficient and reliable particle swarm optimization (PSO) based algorithm for solving combined economic emission scheduling of hydrothermal systems with cascaded reservoirs. A multi-chain cascaded hydrothermal system with non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The water transport delay between connected reservoirs is also considered. The problem is formulated considering both cost and emission as competing objectives. Combined economic emission scheduling (CEES) is a bi-objective problem. A price penalty factor approach is utilized here to convert this bi-objective CEES problem into a single objective one. The effect of valve-point loading is also taken into account in the present problem formulation. The feasibility of the proposed method is demonstrated on a sample test system consisting of four cascaded hydro units and three thermal units. The results of the proposed technique based on PSO are compared with other evolutionary programming method. It is found that the results obtained by the proposed technique are superior in terms of fuel cost, emission output etc. It is also observed that the computation time is considerably reduced by the proposed technique based on PSO.

Daily combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using self organizing hierarchical particle swarm optimization technique

Daily optimum economic emission scheduling of hydrothermal systems is an important task in the operation of power systems. Many heuristic techniques such as differential evolution, and particle swarm optimization have been applied to solve this problem and found to perform better in comparison with classical techniques. But a very common problem with these methods is that they often converge to sub-optimal solution prematurely. A reliable and efficient method termed as self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) is presented in this paper to avoid premature convergence. A multi-chain cascaded hydrothermal system with non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The water transport delay between connected reservoirs is also taken into consideration. The problem is formulated considering both cost and emission as competing objectives. The effect of valve point loading is also taken into account in the present problem formulation. The feasibility of the proposed method is demonstrated on a sample test system. The results of the proposed technique are compared with other heuristic techniques. It is found that the results obtained by the proposed technique are superior in terms of fuel cost, emission output etc.

Effect of Control Parameters on Differential Evolution based Combined Economic Emission Dispatch with Valve-Point Loading and Transmission Loss

Differential evolution (DE) has been proved to be a powerful evolutionary algorithm for global optimization in many engineering problems. The performance of this type of evolutionary algorithms is heavily dependent on the setting of control parameters. Proper selection of the control parameters is very important for the success of the algorithm. Optimal settings of control parameters of differential evolution depend on the specific problem under consideration. In this paper, a study of control parameters on differential evolution based combined economic emission dispatch with valve-point loading and transmission loss is conducted empirically. The problem is formulated considering equality constraints on power balance and inequality constraints on generation capacity limits as well as the transmission losses and effects of valve point loadings. The feasibility of the proposed method is demonstrated on a fourteen-generator system. The results of the effect of the variation of different parameters are presented systematically and it is observed that the search algorithm may fail in finding the optimal value if the parameter selection is not done with proper attention.

Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

Nonconvex emission constrained economic dispatch (NECED) is a complex optimization problem.Many optimization techniques and algorithm have been applied to solve it.A new improved particle swarm optimization technique is proposed for the same.The effectiveness of the proposed method is tested on two practical systems.The results are compared with classical as well as other heuristic technique. This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.

Optimal design and performance evaluation of a grid independent hybrid micro hydro-solar-wind-fuel cell energy system using meta-heuristic techniques

This paper presents the design and optimal sizing of a grid independent hybrid energy system consisting of micro hydro, solar, wind and fuel cell for catering a specific load. The optimal sizing is obtained using a comparatively new optimization technique called Bees algorithm (BA) and the performance of the algorithm is compared with an established meta-heuristic techniques called particle swarm optimization (PSO) and also the system performance is evaluated in terms of the cost of the system. For obtaining the optimal sizing, net present cost (NPC) of the system has been considered. The system is designed such a way that the maximum utilization of the resources and carbon free electricity can be achieved. Keeping in mind this aspect, apart from renewable resources, electrolyser is introduced for production of hydrogen utilizing the excess power. It is observed that the system is quite feasible in meeting the load and in terms of cost of energy and also observed that though both the algorithms are capable of giving global solution, but Particle swarm optimization is fast in reaching optimal solution and takes less CPU time as compared to Bees algorithm.

Comparative performance study of genetic algorithm and particle swarm optimization applied on off-grid renewable hybrid energy system

This paper focuses on unit sizing of stand-alone hybrid energy system using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and comparative performance study of these two meta-heuristic techniques on hybrid energy system. The hybrid system is designed focusing on the viability and combining different renewable energy sources like wind turbines, solar panels along with micro-hydro plant as well as fuel cells to compensate the deficit generation in different hours. Apart from the non-conventional sources, the system has been optimized with converters, electrolyzers and hydrogen tanks. Net present cost (NPC), cost of energy (COE) and generation cost (GC) for power generation have been considered while optimal unit sizing of the system are performed. Feasibility of the system is made based on net present cost (NPC). The performances of two algorithms have been checked for different values of variants of the respective algorithms and a comparative study has been carried out based on number of iterations taken to find optimal solution, CPU utilization time and also quality of solutions. The comparative analysis shows that the Particle Swarm Optimization technique performs better than Genetic Algorithm when applied for the sizing problem.

Optimal unit sizing of stand-alone renewable hybrid energy system using bees algorithm

This paper focuses on unit sizing of stand-alone hybrid energy systems using bees algorithm. With different combination of renewable sources, two types of hybrid energy systems are taken as test systems. Apart from the non-conventional sources like wind turbines, solar panels along with micro hydro plant, the systems have been optimized with fuel cell, converters, electrolyzers and hydrogen tanks. Different performance parameters such as net present cost (NPC), cost of energy (COE) and generation cost (GC) of power production have been considered. Out of the two systems studied, it is seen that the combination of hydro-wind-fuel cell is the most feasible hybrid energy system in respect to their net present cost and cost of energy. Again it is seen that, the bees algorithm is quite efficient in providing a global solution and makes the choice more easier.

A novel population-based optimization algorithm for optimal distribution capacitor planning

Optimal distribution capacitor planning is an important task for economic operation of power systems. The optimal distribution capacitor planning is a combinatorial optimization problem which considers both power losses and cost of capacitor installation subject to bus voltage constraints. Discrete nature of capacitors, different load are considered in the problem formulation. This paper presents a novel reliable and efficient algorithm based on biogeography-based optimization technique for the solution of capacitor placement problem. Biogeography-based optimization technique is inspired by geographical distribution of biological organisms. The performance of the proposed method is demonstrated on a sample test system. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing comparable results.

Analytical evaluation of magnetic field by planar micro-electromagnet spirals for MEMS applications

A computationally efficient analytical technique is adopted and modified to evaluate the magnetic and magnetostatic force fields produced by MEMS-scale planar electromagnetic spirals. The predicted magnetic field distribution is validated experimentally at a scaled-up geometry. Except the regions very close to the spiral, the predicted field is found to match well with the measured values. The force field established by a standard configuration of a spiral-shaped microelectromagnet, which is suitable for MEMS-based actuators and sensors, is investigated. The analysis shows that the zones of high magnetic field, its gradient and force magnitude occur right above the center of the spiral, while the regions of high force fields exist along the diagonals of the spiral. Parametric investigation is performed to evaluate the optimum number of turns in the spiral, which indicates that the inner loops of the spiral influences the magnetic and force fields more pronouncedly than the outer loops. The method offers an easy tool for comparing different design alternatives of spiral electromagnets for use in magnetic MEMS devices.