Hari Mohan Dubey

A Biologically Inspired Modified Flower Pollination Algorithm for Solving Economic Dispatch Problems in Modern Power Systems

Abstract Gradient-based traditional algorithms fail to locate optimal solutions for real-world problems with non-differentiable/discontinuous objective functions. But biologically inspired optimization algorithms, due to their unconventional random search capability, provide good solutions within finite time to multimodal and non-convex problems. The search capability of these methods largely depends on their exploration and exploitation potential. This paper presents a modified flower pollination algorithm (MFPA) in which (1) the local pollination of FPA is controlled by a scaling factor and (2) an intensive exploitation phase is added to tune the best solution. The effectiveness of MFPA is tested on some mathematical benchmarks and four large practical power system test cases.

Bio-inspired optimisation for economic load dispatch: a review

Restructuring of power sector in the last decade has introduced competition among the various entities of power system. In this revolutionised competitive environment, day by day increase in fossil fuel cost along with the environment issues, force the electric power producing utilities for a best possible economic real power dispatch with a minimum power production cost. The economic load dispatch ELD is a complex optimisation problem due to non-linear, non-convex type fuel cost characteristics of the fossil fuel operated thermal generators. Although many conventional optimisation approaches have been developed to solve such problem, over the past decade, the bio-inspired optimisation BIO techniques have shown promising performance on such constrained ELD problems. This paper attempts to provide a comprehensive review of application of BIO algorithms to solve most complex practical ELD problems.

Dynamic Energy and Reserve Dispatch Solutions for Electricity Market with Practical Constraints: Intelligent Computing Technique

Continuous power supply is crucial for any developing economy and its cost efficiency and reliability is highly dependent on operating reserve. Globally, the power systems are adopting a market based structure to enhance performance. In conventional electricity markets reserve gets a second place as its dispatch is performed after energy allocation but in the competitive market these two commodities are dispatched simultaneously to maximize efficiency and reliability. This study proposes a technique based on interior point algorithm for optimizing the dynamic combined energy and reserve dispatch problem with practical complex equality and inequality constraints such as power balance, generation and reserve capacity limits, ramp-up/down limits, and reserve-energy coupling constraints. To simulate practical outage conditions generator contingencies and their effect on operating cost and reserve dispatch is observed. The proposed algorithm is simulated on MATLAB platform and tested for three different cases of the IEEE 57 bus system with 17 generating units. It is found that the proposed method converges to optimal solution for all tested cases and produces feasible solutions where all complex constraints are completely satisfied.

Cuckoo Search Algorithm for Short Term Hydrothermal Scheduling

This paper presents a novel nature inspired cuckoo search algorithm (CSA) to solve short term hydrothermal scheduling problems. The effectiveness of CSA algorithm is examined on three different test cases considering quadratic cost with and without prohibited discharge zones (PDZ), quadratic cost with prohibited discharge zones and valve point loading (VPL) effect in thermal unit. The outcome of simulation were compared with other recent reported approaches demonstrates the superiority of CSA algorithm.

Dynamic scheduling of operating energy and reserve in electricity market with ramp rate constraints

Earlier the economic dispatch problem was solved for generation first and subsequently for reserve but nowadays in electricity market these two operations are simultaneously performed to increase the efficiency and reliability of the power system. In this study, MATLAB based optimization solver Sequential quadratic programming (SQP) is used to allocate the static and dynamic dispatch in Electricity market based on price bids submitted by the private and public GENCOS. The idea is implemented on IEEE 17-units system with 57 buses for reserve and energy cost optimization under all operating constraints. The realistic representation of the power system is done where constantly changing load demands, generator outages etc. are included in the model. The SQP algorithm is found to model the practical constraints like ramp-limits, reserve/energy coupling constraints, power-balance and generation capacity limits.

Wind integrated multi area economic dispatch using backtracking search algorithm

This paper presents a solution of multi area economic dispatch (MAED) problem with wind integration using backtracking search algorithm (BSA), a novel optimization technique. BSA is a stochastic search algorithm with only one control parameter and mutation and crossover operators for exploration and exploitation of the search space for problem under consideration. It can easily take care of various complex operating constraints, which is difficult to solve using any mathematical approaches. To show its efficiency, BSA algorithm is applied to two different categories of MAED problems. Category I has a two area network having 40 and 140 power generating units with valve point loading effects (VPL), ramp rate limits (RRL) and prohibited operating zones (POZ) and category II presents simulation study of MAED problem with wind integration. The outcome of simulation results in terms of operating cost are compared with other recently published relevant approaches and are found to be better.

Cost and profit optimization of integrated wind-thermal system by dynamic dispatch using swarm intelligence

Particle swarm optimization (PSO) algorithm is applied to find optimal dispatch for minimizing cost and maximizing profit of conventional units integrated with renewable power. Non-conventional energy sources, such as wind, are highly uncertain in nature. Generation of wind power depends on the wind speed. The uncertainty of wind power in cost model is considered by taking a dynamic dispatch model. This paper considers six conventional thermal generator units taken from IEEE 118-bus test system with wind turbines. The model includes all practical constraints like ramp rate limits and valve point loading effects of thermal generating units. Min-max limits are modified by ramp rate limits of generating systems. Optimization problem includes practical constraints with nonlinearity and non-convexity. For convex cost case, the results are validated using the sequential quadratic programming (SQP) algorithm but unfortunately, the conventional gradient based optimization methods are unsuitable for non-convex cases. On the other hand the swarm intelligence based models guarantee stable convergence to near best solution for such cases.

A Novel Swarm Intelligence Based Gravitational Search Algorithm for Combined Economic and Emission Dispatch Problems

In this article swarm Intelligence based gravitational search algorithm PSOGSA is used to solve combined economic and emission dispatch CEED problems. The CEED problem is modeled with the objective of minimizing fuel cost as well as emission level while satisfying associated operational constraints. Here the multi-objective function is converted into single objective function using price penalty method. The performance of PSOGSA approach is investigated on standard 10 unit system, 6 unit system and 40 unit system .The results obtained by simulation are compared with the recent reported results. The simulation result shows the fast convergence and its potential to solve complicated problems in power system.

An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch

Abstract Over the last few decades, bio-inspired (BI) evolutionary optimization techniques have experienced overwhelming popularity, extraordinary growth and large number of applications, particularly, in the field of engineering and technology. These techniques present a tough competition to traditional numerical methods which suffer from convexity and continuity assumptions and which normally employ a gradient based search that is sensitive to the initial solution. While initial BI techniques suffered from limitations such as premature convergence and dependence on control parameters, eventually, these issues were specifically addressed by improved variants and many novel BI methods. The population based computing methods are particularly attractive for solving multi-objective (MO) problems due to their capability of producing a large number of Pareto-optimal solutions in one run. In this paper, an integrated ranking index (IRI) composed of TOPSIS and fuzzy-min concept is proposed as a performance metrics to aggregate the different objectives. The performance of eight handpicked recent BI techniques is compared for the solution of wind integrated multi-objective optimal power dispatch (MOOD) problem for simultaneous minimization of fuel cost and emission. Due to the uncertain nature of wind power (WP), the effect of its over and underestimation on both economic as well as environmental aspects, has also been considered. Six standard test cases having non-convex, multi-modal and discontinuous objective functions, dynamic operation and complex equality/inequality constraints, are selected for testing Flower Pollination Algorithm (FPA), Mine Blast Algorithm (MBA), Backtracking Search Algorithm (BSA), Symbiotic Organisms Search (SOS), Ant Lion Optimizer (ALO), Moth-Flame Optimization (MFO), Stochastic Fractal Search (SFS) and Lightning Search Algorithm (LSA).

Optimization of benchmark functions using a nature inspired bird swarm algorithm

This paper presents a new powerful Bird Swarm Algorithm (BSA) for optimization. BSA basically works on the swarm intelligence and interactions among the birds. The concept behind this algorithm is the exploitation and exploration of optimum solution for a given problem based on foraging, vigilance and flight behavior. Formulation of BSA includes four search strategies associated with five simplified rules. Mathematically models the behavior of bird swarm is utilized for solution of various mathematical functions. To validate the effectiveness of BSA simulations have been performed on various numerical functions and ELD problems. The results obtained by BSA have been also compared with other Nature-Inspired algorithms. The performance of BSA on the convergence rate to obtain the optimal result on changing the parameter is also observed. Statistical comparison of results affirms the superiority of BSA over other algorithms reported in recent literatures.