Quantum-behaved bat algorithm for many-objective combined economic emission dispatch problem using cubic criterion function

In this research, a quantum computing idea based bat algorithm (QBA) is proposed to solve many-objective combined economic emission dispatch (CEED) problem. Here, CEED is represented using cubic criterion function to reduce the nonlinearities of the system. Along with economic load dispatch, emissions of SO₂, NO_x, and CO₂ are considered as separate three objectives, thus making it a four-objective (many-objective) optimization problem. A unit-wise price penalty factor is considered here to convert all the objectives into a single objective in order to compare the final results with other previously used methods like Lagrangian relaxation (LR), particle swarm optimization, and simulated annealing. QBA is applied in six-unit power generation system for four different loads. The obtained results show QBA successfully solve many-objective CEED problem with greater superiority than other methods found in the literature in terms of quality results, robustness, and computational performance. In the end of this paper, a detailed future research direction is provided based on the simulation results and its analysis. The outcome of this research demonstrates that the inclusion of quantum computing idea in metaheuristic technique provides a useful and reliable tool for solving such many-objective optimization problem.

     

A Solution to the Combined Economic and Emission Dispatch Using Hybrid PSOGSA Algorithm

This article presents a new hybrid algorithm based on particle swarm optimization (PSO) and the gravitational search algorithm (GSA) for solving the combined economic and emission dispatch (CEED) problem in power systems. Performance of this approach for the CEED problem is studied and evaluated on three test systems with 3, 6, and 40 generating units, with various cost curve nature and different constraints. The results obtained are compared to those reported in the recent literature. Those results show that the proposed algorithm provides an effective and robust high-quality solution of the CEED problem.     

基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度和经济排放联合调度问题

电力生产装置运行中各种燃料的成本逐步增加,需要最小化成本函数以求解此类复杂经济负荷调度问题.鉴于此,提出一种基于动态惩罚因子的改进蚱蜢算法求解经济负荷调度(economic load dispatch, ELD)问题和经济排放联合调度(combined economic emission dispatch, CEED)问题.为了提高蚱蜢算法(grasshopper optimization algorithm, GOA)性能,提出一种改进的混合蚱蜢算法(hybrid grasshopper optimization algorithm, HGOA),将重力搜索算子和鸽群搜索算子-地标算子加入GOA中,增强算法的搜索能力,平衡算法的勘探和开发.同时,为了更好地解决ELD和CEED问题中的约束问题,提出6个惩罚函数,包括2个V型函数、反正切函数、反正弦函数、线性函数和二次函数,并使用动态惩罚策略代替传统的固定值惩罚策略.选取3个ELD问题案例和4个CEED问题案例验证所提出方法的有效性,实验结果表明, HGOA相较于其他元启发式算法在求解质量上表现更好,且动态惩罚策略比固定值惩罚策略效果更...     

Adaptive Grey Wolf Optimization for Weightage-based Combined Economic Emission Dispatch in Hybrid Renewable Energy Systems

Nowadays, the electric power networks comprise diverse renewable energy resources, with the rapid development of technologies. In this scenario, the optimal Economic Dispatch is required by the power system due to the increment of power generation cost and ever growing demand of electrical energy. Thus, the reduction of power generation cost in terms of fuel cost and emission cost has become one of the main challenges in the power system. Accordingly, this article proposes the Grey Wolf Optimization-Extended Searching (GWO-ES) algorithm to provide the excellent solution for the problems regarding Combined Economic and Emission Dispatch (CEED). It validates the robustness of the proposed algorithm in seven Hybrid Renewable Energy Systems (HRES) test bus systems, which combines the wind turbine along with the thermal power plant. Furthermore, it compares the performance of the proposed GWO-ES algorithm with conventional algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and GWO. Next, the article emulates a valuable convergence analysis and justification for the quality of CEED through the GWO-ES algorithm. Finally, the result was compared to four other conventional algorithms to assure the efficiency of the proposed algorithm in terms of fuel cost and emission cost reduction.     

Recurrent neural network for combined economic and emission dispatch

Recently, the combined economic and emission dispatch (CEED) problem, which aims to simultaneously decrease fuel cost and reduce environmental emissions of power systems, has been a widespread concern. To improve the utilization efficiency of primary energy, combined heat and power (CHP) units are likely to play an important role in the future. The goal of this study is to propose an approach to solve the CEED problems in a CHP system which consists of eight power generators (PGs), two CHP units and one heat only unit. Owing to the existence of power loss in power transmission line and the non-convex feasible region of CHP units, the proposed problem is a nonlinear, multi-constraints, non-convex multi-objectives (MO) optimization problem. To deal with it, a recurrent neural network (RNN) combined with a novel technique is developed. It means that the feasible region is separated into two convex regions by using two binary variables to search for different regions. In the frame of the neurodynamic optimization, existence and convergence of the dynamic model are analyzed. It shows that the convergence solution obtained by RNN is the optimal solution of CEED problem. Numerical simulation results show that the proposed algorithm can generate solutions efficiently.     

An economic load dispatch and multiple environmental dispatch problem solution with microgrids using interior search algorithm

Microgrid is a novel small-scale system of the centralized electricity for a small-scale community such as villages and commercial area. Microgrid consists of micro-sources like distribution generator, solar and wind units. A microgrid is consummate specific purposes like reliability, cost reduction, emission reduction, efficiency improvement, use of renewable sources and continuous energy source. In the microgrid, the Energy Management System is having a problem of Economic Load Dispatch (ELD) and Combined Economic Emission Dispatch (CEED) and it is optimized by meta-heuristic techniques. The key objective of this paper is to solve the Combined Economic Emission Dispatch (CEED) problem to obtain optimal system cost. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The newly introduced Interior Search Algorithm (ISA) is applied for the solution of ELD and CEED problem. The minimization of total cost and total emission is obtained for four different scenarios like all sources included all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of ISA with the Reduced Gradient Method (RGM), Ant Colony Optimization (ACO) technique and Cuckoo Search Algorithm (CSA) for the two different cases which are ELD without emission and CEED with emission. The results are calculated for different Power Demand of 24 h. The results obtained to ISA give comparatively better cost reduction as compared with RGM, ACO and CSA which shows the effectiveness of the given algorithm.

     

Advanced chance-constrained predictive control for the efficient energy management of renewable power systems

This study presents a complete advanced control structure aimed at the optimal and most efficient energy management for a Grid-Connected Hybrid Power plant. This control scheme is composed of process supervision and process control layers, and it is a possible technology to enable improvements in the energy consumption of industrial systems subject to constraints and process demands. The proposed structure consists of the combination of a Model-Based Predictive Controller, formulated within the Chance Constraints framework to deal with stochastic disturbances (renewable sources, as solar irradiance), an optimal finite-state machine decision system and the use of disturbance estimation techniques for the prediction of renewable sources. The predictive controller uses feedforward compensation of estimated future disturbances, obtained by the use of Nonlinear Auto-Regressive Neural Networks with time delays. The proposed controller aims to perform the management of which energy system to use and to decide where to store energy between multiple storage options. This has to be done while always maximizing the use of renewable energy and optimizing energy generation due to contract rules (maintain maximal economic profit). The proposed method is applied to a case study of energy generation in a sugar cane power plant, with non-dispatchable renewable sources (such as photovoltaic and wind power generation), as well as dispatchable sources (as biomass and biogas). This hybrid power system is subject to operational constraints, as to produce steam in different pressures, sustain internal demands and, imperiously, produce and maintain an amount of electric power throughout each month, defined by strict contract rules with a local Distribution Network Operator (DNO). This paper aims to justify the use of this novel approach to optimal energy generation in hybrid microgrids through simulation, illustrating the performance improvement for different cases.     

具有自适应搜索策略的混合人工蜂群算法

基本人工蜂群算法及其搜索策略侧重探索,为增强算法的开发能力,提出一种具有自适应搜索策略的混合人工蜂群算法。将目标函数值信息和最优解引导信息引入搜索策略,提出具有自适应机制、开发能力强的搜索策略;为防止“早熟”现象,利用三个不同随机食物源和高斯分布,设计出全局探索能力较强的搜索策略。将两个搜索策略在雇佣蜂阶段混合以平衡算法的探索与开发能力,在观察蜂阶段使用具有自适应机制、开发能力强的搜索策略以加快收敛。与基本及具有代表性的改进人工蜂群算法在20个标准测试函数中进行对比实验,结果表明所提算法具有更好的搜索能力和更快的收敛速度。     

Hybrid shortest path algorithm for vehicle navigation

Vehicle navigation is one of the important applications of the single-source single-target shortest path algorithm. This application frequently involves large scale networks with limited computing power and memory space. In this study, several heuristic concepts, including hierarchical, bidirectional, and A^*, are combined and used to develop hybrid algorithms that reduce searching space, improve searching speed, and provide the shortest path that closely resembles the behavior of most road users. The proposed algorithms are demonstrated on a real network consisting 374,520 nodes and 502,485 links. The network is preprocessed and separated into two connected subnetworks. The upper layer of network is constructed with high mobility links, while the lower layer comprises high accessibility links. The proposed hybrid algorithms are implemented on both PC and hand-held platforms. Experiments show a significant acceleration compared to the Dijkstra and A^* algorithm. Memory consumption of the hybrid algorithm is also considerably less than traditional algorithms. Results of this study showed the hybrid algorithms have an advantage over the traditional algorithm for vehicle navigation systems.     

Hybrid probabilistic-harmony search algorithm methodology in generation scheduling problem

This paper attempts to propose a fair solution in generation scheduling problem in the presence of inherent uncertainties in short-term power system operation. The proposed methodology incorporates probabilistic methodology in the uncertainties representation section, while harmony search algorithm is adopted as a fast and reliable soft computing algorithm to solve the proposed nonlinear, non-convex, large-scaled and combinatorial problem. As an indispensable step towards a more economical power system operation, the optimal generation scheduling strategy in the presence of mixed hydro-thermal generation mix, deemed to be the most techno-economically efficient scheme, comes to the play and is profoundly taken under concentration in this study. This paper devises a comprehensive hybrid optimisation approach by which all the crucial aspects of great influence in the generation scheduling process can be accounted for. Two-point estimation method is also adopted probabilistically approaching the involved uncertain criteria. In the light of the proposed methodology being implemented on an adopted test system, the anticipated efficiency of the proposed method is well verified.     

A Novel Cascaded PID Controller for Automatic Generation Control Analysis With Renewable Sources

Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, automatic generation control (AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper. Also, a new hybrid scheme of the improved teaching learning based optimization-differential evolution (hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a proportional integral derivative (PID), integral double derivative (IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as dead-band of, generation rate constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by ± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.      

混合自适应粒子群算法在电力经济调度中的应用

以电力系统中发电成本最低为目标,结合实际发电运行中系统平衡约束和机组操作约束条件,建立电力经济调度(ED)模型。由于标准粒子群算法存在易陷入局部最优的问题,用这种方法求解ED模型得到的最终结果会不太理想。为此,本文提出一种非线性自适应权重调整策略来增强算法全局搜索和局部搜索能力,首先引入小生境优化种群策略使算法跳出局部最优,然后将这种改进后的混合自适应粒子群算法（HAPSO）应用于求解ED模型。最后,算例分析结果表明本文所改进算法的有效性,提高了求解精度。     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

Two hybrid differential evolution algorithms for engineering design optimization

This paper presents two hybrid differential evolution algorithms for optimizing engineering design problems. One hybrid algorithm enhances a basic differential evolution algorithm with a local search operator, i.e., random walk with direction exploitation, to strengthen the exploitation ability, while the other adding a second metaheuristic, i.e., harmony search, to cooperate with the differential evolution algorithm so as to produce the desirable synergetic effect. For comparison, the differential evolution algorithm that the two hybrids are based on is also implemented. All algorithms incorporate a generalized method to handle discrete variables and Deb's parameterless penalty method for handling constraints. Fourteen engineering design problems selected from different engineering fields are used for testing. The test results show that: (i) both hybrid algorithms overall outperform the differential evolution algorithms; (ii) among the two hybrid algorithms, the cooperative hybrid overall outperforms the other hybrid with local search; and (iii) the performance of proposed hybrid algorithms can be further improved with some effort of tuning the relevant parameters.     

基于混合双种群差分进化的电力系统经济负荷分配

针对电力系统经济负荷分配本质上的非线性约束优化问题,提出一种双种群混合差分进化算法.采用两个种群且以较小的计算量实现目标函数的寻优并保持解的可行性,同时引入单纯型法来提高算法的局部搜索能力.基于典型算例对该算法的进化行为进行测试,并通过仿真和比较验证了所提出算法的有效性.     

Reconfigurable hybrid vision enhancement system using tone mapping and adaptive gamma correction algorithm for night surveillance robot

Night vision system has become a critical component of modern warfare and the ability to see in nighttime conditions allows military maneuvers and a potential advantage to the forces equipped with this technology. These night vision systems rely on the very low light levels of night sky illumination to help image the targeted scene and its surroundings. Many research works have been undertaken to overcome issues in hardware implementation. In this paper, we contribute to enhance night vision sources by hybrid vision enhancement (HVE) system without affecting performance of hardware implementation. The proposed hybrid system consists of two algorithms such asoptimizedtone mapping (OTM) and adaptive gamma correction (AGC) algorithm. Normally, hybrid systems are not an area efficient, here we modify the tone mapping algorithm byoptimized filter design with the exponential basis. The differential evolution optimization algorithm is used to enhance the filter design. The proposed HVE system implementation is designed in Verilog language and synthesized with different FPGA device families in Xilinx tool. Simulation result shows that our proposed HVE system is able to enhance vision of wide dynamic range (WDR) images to good visual quality. The synthesis result shows that our proposed HVE system perform very efficient than existing system in terms of hardware utilization, maximum clock frequency, and power.

     

Optimization and control of a stand-alone hybrid solid oxide fuel cells/gas turbine system coupled with dry reforming of methane

This paper presents the hybrid solid oxide fuel cells (SOFC)/gas turbine (GT) system coupled with dry reforming of methane (DRM). The DRM is a syngas producer by consuming greenhouse gas. The stand-alone (off-the-grid) power system is developed by using a combination of a post-burner, recuperators and pressurized recycles in place of external energy supplies. To address the stand-alone operation and meet the complete combustion condition for the burner, the optimal operating conditions are initially determined by solving a constrained optimization algorithm for maximizing the hybrid power efficiency, and the dynamic control loops are implemented in a plantwide environment. In the proposed plantwide control strategy, the inventory control framework is added to regulate the plant component inventory, an air/fuel cross-limiting combustion control is added to ensure complete combustion and reduce heat loss, and the power and CO₂ emission control configuration is added to achieve the quality control performance. Finally, the simulation shows that the IMC-based multi-loop control scheme can efficiently regulate the total system power and control CO₂ emissions per kWh of electricity as well.     

FPGA based substantial power evolution controlling strategy for solar and wind forecasting grid connected system

The solar and wind are both the most promising renewable and clean energy sources, the solar stable energy progress and environmental protection have been increasingly noticeable. In this regard, an accurate solar and wind energy prediction is extremely important to avoid large voltage changes to the power grid and to provide a mechanism for the system to optimally manage the generated energy. Wind energy forecasting is widely practiced among modest power systems for high levels of windmills. This paper aims to develop a new hybrid system for wind and solar energy prediction. The proposed hybrid (wind & solar) energy prediction model is based on a Substantial Power Evolution Strategy (SPES) dedicated to short-term forecasting. The proposed forecasting system SPES is implemented using MATLAB. This paper implements the short-term and hybrid power forecasting using Substantial Power Evolution Strategy based on Prediction Intervals (PIs). This feature is one of the major innovations in the proposed hybrid renewable energy forecasting system. The accuracy of the proposed system will be revealed by comparing the results of the corresponding values of the independent forecasting models called persistence models. The designed device presents a real-time application of predicting daily total solar and wind power using any geographic location and environmental conditions using FPGA. Finally, fully developed system packages can be commercialized and/or utilized for further research projects, and researchers can analyze, validate and visualize their models for related fields.     

An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.     

A novel quasi-oppositional chaotic antlion optimizer for global optimization

This paper proposes a novel quasi-oppositional chaotic antlion optimizer (ALO) (QOCALO) for solving global optimization problems. ALO is a population based algorithm motivated by the unique hunting behavior of antlions in nature and exhibits strong influence in solving global and engineering optimization problems. In the proposed QOCALO algorithm of the present work, the initial population is generated using the quasi-opposition based learning (QOBL) and the concept of QOBL based generation jumping is utilized inside the main searching strategy of the proposed algorithm. Utilization of QOBL ensures better convergence speed of the proposed algorithm and it also provides better exploration of the search space. Alongside the QOBL, a chaotic local search (CLS) is also incorporated in the proposed QOCALO algorithm. The CLS guides local search around the global best solution that provides better exploitation of the search space. Thus, a better trade-off between exploration and exploitation holds for the proposed algorithm which makes it robust. It is observed that the proposed algorithm offers better results than the original ALO in terms of solution quality and convergence speed. The proposed QOCALO algorithm is implemented and tested, successfully, on nineteen mathematical benchmark test functions of varying complexities and the experimental results are compared to those offered by the basic ALO and some other recently developed nature inspired algorithms. The efficacy of the proposed algorithm is further utilized to solve three real world engineering optimization problems viz. (a) the placement and sizing problem of distributed generators in radial distribution networks, (b) the congestion management problem in power transmission system and (c) the optimal design of pressure vessel.