Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

Abstract

This paper presents a modified and novel form of the conventional short-term hydrothermal scheduling problem by incorporating the effects of adding the photovoltaic energy source to the conventional grid. A photovoltaic energy source is intermittent in nature, therefore, to determine the optimal power contribution of the photovoltaic source towards the economic dispatch problem, a detailed strategy is presented in this paper. The proposed design method includes the forecasting of the photovoltaic system's parameters using the Auto-Regressive Integrated Moving Average (ARIMA) model. The analytical model is developed based on the fractional integral polynomials for studying the characteristics of the single photovoltaic module. The optimization of power allocation in the system consisting of conventional and non-conventional energy sources is highly non-linear and classical deterministic methods can not be guaranteed to determine the optimal solution for economic power dispatch. The global optima of such non-linear and non-convex problems can be determined using swarm-based intelligence techniques. This paper presents accelerated particle swarm optimization and the firefly algorithm to determine a solution for short-term non-linear scheduling problems. The multiple test cases have been presented to demonstrate the effectiveness of the proposed solution over classical methods. The overall generation cost of the selected hybrid system is reduced using the proposed methods while meeting the generation constraints of each energy source. Moreover, due to the stochastic nature of the meta-heuristic techniques, a comprehensive statistical comparison, based on the independent T-test results, is also presented to highlight the algorithm which performs better for selected scheduling problems. It has been demonstrated that accelerated particle swarm optimization gives lower mean generation cost of the system whereas the execution time of the firefly algorithm is better compared to its counterpart. INDEX TERMS Mathematical modeling, auto-regressive integrated moving average model, accelerated particle swarm optimization, firefly algorithm, short term hydrothermal scheduling, independent t-test.

Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

“…Reference [135], has presented implementation of fully informed PSO (FIPSO), a variant of canonical PSO, to solve NCSTHTS problems.The paper discussed the local and global neighbourhood variants of FIPSO to solve the problem. References [136] and [137] discussed the implementation of metaheuristic optimization algorithms, i.e. two variants of Accelerated Particle Swarm Optimization algorithm and a variant of firefly algorithm on NCSTHTS problems.…”

“…Reference in solving NCSTHTS problem. Reference [137], solve the solarhydro-thermal scheduling problem using firefly and APSO algorithms. The comparison was statistically made between the performances of the two algorithms using independent sample t-test.…”

“…Table 8 summarizes implementation of PSO and its variants for STHTS. [92], [94], [97], [105], [110], [112], [124], [128], [134] Near With different neighborhood topologies [92], [123], [130] Constriction factor PSO [113], [115], [117], [129] Hybrid of PSO and Evolutionary programming [95] Updating Inertia weights PSO [106] Quantum behaved PSO [98], [99], [127] Modified adaptive PSO [100] Self-organizing hierarchical PSO [101], [102] Time varying acceleration coefficients PSO [103], [122] Improved PSO [96], [104], [109] Efficient PSO [107] Mixed-binary evolutionary PSO [111] Dynamically controlled PSO [114], [125] Hybrid of PSO and DE [116], [121] Hybrid of PSO and direct search method [118] Enhanced PSO [120] Auxiliary search based PSO [131] Hybrid of PSO and GSA [132] Fully informed PSO [135] Accelerated PSO [136], [137] FIGURE 9. Year wise distribution of arti...…”

“…However, due to the stochastic nature of the metaheuristic algorithms that are utilized in the solution of STHTS need a proper statistical test to establish the superiority of one type of algorithm over the other algorithm. References [136] and [137] discussed the implementation of metaheuristic optimization algorithms, i.e. two variants of Accelerated Particle Swarm Optimization algorithm and a variant of firefly algorithm on NCSTHTS problems.…”

Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System

“…The integration of renewable energy sources (RESs) into electrical systems has become a necessary and essential matter to face the large increase in electrical energy demand and reduce pollution problems caused by using fossil fuels. Different types of RESs are used in generating electric power, including wind, photovoltaic (PV), Fuel cell, and biomass [1][2][3][4]. The two most RESs integrated into the electrical systems are the wind and the PV in favor of their multiple advantages, which is evident from the annual growth rate of RESs, Fig.1, [5].…”