B. Tudu

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.

Efficient and Automatic Reconfiguration and Service Restoration in Radial Distribution System Using Differential Evolution

This paper addresses two complex optimization problems in the form of radial distribution system reconfiguration and service restoration using a novel optimization technique called differential evolution. For distribution feeder reconfiguration (DFR) problem, the close and open statuses of sectionalizing and tie switches are changed to find minimum loss configuration. During any sudden outage of any section of the distribution system, the quickness of the restoration is checked with the help of basic optimization technique while feeding all the load points. A standard IEEE 3 feeder, 16 bus distribution system is chosen to simulate the dual problem of optimization. The feasibility and novelty of the optimization is also checked in a comparatively more complex IEEE 33 bus distribution system. Differential Evolution is chosen to find alternative topologies for feeder system and simplified forward Dist-Flow Equation is implemented to do power flow study and it is seen that differential evolution is quite capable of solving this type of complex, non-linear optimization problem with less time which is a basic requirement for the service restoration (SR) of the network system.

Techno-Economic feasibility analysis of hybrid renewable energy system using improved version of particle swarm optimization

The present paper presents an improved version of particle swarm optimization method for obtaining unit sizing and techno-economic feasibility analysis of off-grid hybrid energy system for Sundarban region, worlds largest mangrove forest located partly in West Bengal, India considering the real load data and other meteorological parameters. Initially the hybrid energy system is designed keeping in mind the load pattern and the availability of the renewable sources of that specific location. The hybrid system is designed with the combination of different renewable energy sources like wind turbines, solar panels along with battery and diesel generator to meet the localized load demand in different hours. Net present cost (NPC) and cost of energy (COE) for power generation have been considered to obtain the optimal unit sizing of the system. Emission from the hybrid system is also considered and compared with a conventional energy system in terms of emission per unit of generation and it is seen that with the implementation of this hybrid system, 0.688Kg of CO2 emission per unit generation of electricity can be reduced while meeting the local demand. It is also seen that the improved version of particle swarm optimization technique is quite capable of solving this complex non-linear optimization problem quite efficiently.

A new hybrid particle swarm optimization technique for optimal capacitor placement in radial distribution systems

A new hybrid particle swarm optimization algorithm based on black-hole theory called modified black-hole particle swarm optimization (MBHPSO) is introduced in the present paper. Placement of capacitor of optimal sizes and at optimal locations not only reduces the power losses, but also improves the voltage stability of the electric power systems. Several meta-heuristic techniques have been used by Scientists and researchers over the years to address the problems of capacitor placements. They are very effective and powerful in comparison with conventional methods in solving complex nonlinear constrained optimization problems. But one of the major difficulties for these methods is the premature convergence. A new improved hybrid technique is introduced in this paper that addresses the issues of premature convergence successfully for the present problem. The accuracy, performance and effectiveness are authenticated by testing the algorithm proposed in the present paper on a test system. The present paper also compares the results with those obtained by applying several other modern techniques such as fuzzy reasoning, plant growth simulation algorithm, opposition based differential evolution. The outcomes of the experiment show that high quality solutions can be obtained by the proposed method.

Logistic map adaptive differential evolution for optimal capacitor placement and sizing

This paper presents a new adaptive differential evolution technique based on logistic map for optimal distribution placement and sizing. The parameters of differential evolution that need to be selected by the user are the key factors for successful operation DE. Choosing suitable values of parameters are difficult for DE, which is usually a problem-dependent task. Unfortunately, there is no fix rule for selection of parameters. The trial-and-error method adopted generally for tuning the parameters in DE requires multiple optimization runs. Even this method can not guarantee optimal results every time and sometimes it may lead to premature convergence. The proposed method combines differential evolution with chaos theory for self adaptation of DE parameters. The performance of the proposed method is demonstrated on a sample test system. It is seen that the proposed method can avoid premature convergence and provides better convergence characteristics. 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.

A New Improved Particle Swarm Optimization Technique for Daily Economic Generation Scheduling of Cascaded Hydrothermal Systems

Optimum scheduling of hydrothermal plants is an important task for economic operation of power systems. 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 solving daily economic generation scheduling of hydrothermal systems to avoid premature convergence. The performance of the proposed method is demonstrated on a sample test system comprising of cascaded reservoirs. 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.

Stand-alone Hybrid Renewable Energy System-An Alternative to Increased Energy Demand

This paper presents the feasibility and technical insights of hybrid renewable energy system as an alternative energy to the existing conventional energy for the isolated load demand. The system is initially modeled with solar PV, battery and diesel generator and then optimized for a certain local load demand with HOMER simulation tool. Cycle charging dispatch strategy is adopted for the simulation and the feasibility analysis is also carried out with respect to its net present cost and levelized cost of energy. The cost of energy is comparable with the existing cost of energy and it is feasible to use the hybrid system keeping in mind the fact of detrimental effect of coal based energy to the environment and cost of electrification through the extension of existing grid.