Fast Evolutionary Programming Research Articles

Optimal sizing and placement of capacitors in the isolated microgrid throughout the day considering the demand response program

Reactive power compensation (RPC) is a big problem during power system operation. Parenthetically, capacitor allocation and sizing may be the only convenient solution for RPC of power systems. The loss sensitivity factor (LSF) is applied here for finding the optimum capacitor position. This paper presents quasi-oppositional fast convergence evolutionary programming (QOFCEP), fast convergence evolutionary programming (FCEP), and evolutionary programming (EP) for the optimum location and sizing of shunt capacitors in the isolated microgrid (MG) for minimizing total real power loss throughout the day with and without the demand response program (DRP). The 33-node, 69-node, and 118-node isolated MGs have been studied to authenticate the efficacy of the suggested approach. Each MG includes small hydro power plants (SHPPs), solar PV plants (SPVPs), wind turbine generators (WTGs), diesel generators (DGs), and plug-in electric vehicles (PEVs).

Optimum dynamic dispatch of clean water, methane, electricity and heat considering carbon capture

This paper suggests a novel clean water, natural gas, heat and power generation system using wind turbine generators, PVT panels, bioenergy power plants, micro-hydro power plants, micro-cogeneration units (MCUs), battery energy storage system, thermal energy storage system and plug-in electric vehicles (PEVs). In this system, a power to gas technology consisting of a carbon capture unit, an electrolyzer, a hydrogen storage unit, and a methanation process, is used to supply the natural gas demand using CO2 captured from the MCUs and the hydrogen generated by the clean water electrolyzer. Furthermore, the electricity is consumed by a desalination unit to procure the clean water demand, carbon captured unit (CCU), electrolyzer water inflow, and fill up the water storage tank. At peak clean water demand periods, the excess water is pumped from the storage tank to the consumption side. Demand response program (DRP) is used for levelling the demand. Here, total cost for water-gas-heat-power nexus model throughout the day with and without DRP for three different scenarios is minimized by using quasi-oppositional fast convergence evolutionary programming, fast convergence evolutionary programming, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and differential evolution. It is seen from numerical results that cost attained with DRP is less than the cost attained without DRP.

Multicounty fuel constraint‐based dynamic economic dispatch problem using horse herd optimization

AbstractDue to the diminution of fossil fuels, the profitable usage of fuel for the generation of power is reducing. It is an extremely vital concern for power generation companies. The fossil fuel‐based power plant must operate within its fuel limits and contractual restrictions. Therefore, economic optimization is required to optimize the fuel cost for generation under fuel constraints. To address this issue, in this work, a horse herd optimization algorithm (HOA) is developed to solve multicounty fuel‐constrained dynamic economic dispatch with demand‐side management incorporating wind turbine generators, solar photovoltaic plants, and pumped hydro storage plants. The efficiency of the suggested HOA algorithm has been revealed on a selected test system. The HOA technique shows better convergence as compared with the other methods. Moreover, the computation time requirement of the HOA technique is also lower than that of other compared methods. From numerical results, it is observed that the consumption of fuel is sufficiently high if constraints are incorporated into the problem. Simulation results are matched up to those obtained from hierarchical particle swarm optimization with time‐varying acceleration coefficients, fast convergence evolutionary programming, and differential evolution. After comparison, it is observed that the recommended HOA offers a better‐quality solution.

Fixed head hydrothermal scheduling considering uncertainty of load demand and renewable energy sources

AbstractThis manuscript suggests quasi‐oppositional fast convergence evolutionary programming (QOFCEP) technique to determine short‐term generation scheduling of fixed head hydrothermal system comprising pumped storage hydro plants (PSHPs), solar PV plants (SPVPs) and wind turbine generators (WTGs) with demand response program (DRP). PSHPs are used with fixed head hydro power plants (FHPPs), thermal generating units (TGUs), SPVPs and WTGs to operate system conveniently. Uncertainties related to renewable generations and load demands have been considered. Self‐adaptive tent mapped scenarios of uncertain parameters are generated within higher level of confidence space to deal with the system uncertainties. DRP is applied to level the load demand curve and to enhance system flexibility and reduce operational cost. Numerical results of typical test system are matched with those attained by fast convergence evolutionary programming (FCEP) and evolutionary programming (EP).

Fuel constrained combined heat and power dynamic dispatch using horse herd optimization algorithm

Due to gradually reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very significant concern of electric power utilities. This work recommends horse herd optimization algorithm (HOA) to solve fuel constrained combined heat and power dynamic economic dispatch with demand side management integrating wind turbine generators, solar PV plants and pumped storage hydro plant for three different scenarios. The effectiveness of the recommended method is divulged on an archetypal system. Numerical results of archetypal system for three different scenarios are compared with those obtained from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming, differential evolution and real-coded genetic algorithm. It has been seen from numerical results, that the total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the recommended HOA has the capability to confer with superior-quality solution.

Coordinated Design of FACTS Controller with PSS for Stability Enhancement Using a Novel Hybrid Whale Optimization Algorithm – Nelder Mead Approach

This article investigates a novel cascaded fractional order PI-PD structure for power system stabilizers and flexible AC transmission system-based damping controllers to enhance the stability of the power system. The proposed controller parameters are optimized by a hybrid Modified Whale Optimization Algorithm (MWOA) with the Nelder-Mead algorithm. The novel hybrid algorithm accomplishes a proper balance between the exploration and exploitation phases of the modified whale optimization algorithm. This capability of the hybrid technique is certified by using the benchmark test functions compared with that of an MWOA, WOA, gravitational search algorithm, fast evolutionary programming, particle swarm optimization, genetic algorithm, and differential evolution. The proposed controller is optimized and verified under various loading conditions using a hybrid technique. Furthermore, to demonstrate its superiority, the results of the proposed hybrid algorithm are compared with recently published MWOA, WOA, and well-established genetic algorithms.

Fuel constrained short-term hydrothermal generation scheduling

Due to gradually diminution of fossil fuel, the cost-effective utilization of available fuel for power generation has turn out to be a vital concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work suggests social group entropy optimization (SGEO) technique to solve short-term generation scheduling of a power system consisting of fuel constrained thermal generating units, cascaded hydro power plants, solar PV plants, wind turbine generators and pumped storage hydro (PSH) plants with demand side management (DSM). Simulation results of the test system have been compared with those acquired by self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Numerical results show that fuel consumption can be adequately controlled for fulfilling constraints imposed by suppliers and total cost with fuel constraints is more than the cost without fuel constraints. It has been also observed from the comparison that the suggested SGEO has the ability to bestow with superior-quality solution.

Horse Herd Optimization Algorithm for Fuel Constrained Day-ahead Scheduling of Isolated Nanogrid

ABSTRACT Day-ahead scheduling of isolated nanogrid (NG) is an important task in power system. Owing to slowly lessening of fossil fuel, the profitable use of available fuel for electric power generation has turn out to be an extremely important concern of electric power utilities. This paper suggests horse herd optimization algorithm (HOA) to solve fuel constrained day-ahead scheduling of isolated nanogrid (NG) for five neighboring homes. NG comprises diesel generators, solar PV plants, battery energy storage system (BESS) and plug-in electric vehicles (PEVs). Simulation results of the test system have been compared with those obtained from social group entropy optimization, self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution. It has been observed from the comparison that the suggested HOA has the capacity to give better solution.

Social group entropy optimization for day-ahead heat and power scheduling of an isolated microgrid

This article presents day-in-advance microgrid heat and electric power scheduling with and without demand-side management. The objective is to minimize the cost of heat and electric power generation, considering solar and wind power uncertainty. This scheduling problem is solved using a social group entropy optimization (SGEO) technique. The proposed microgrid comprises diesel generators, biomass-fuel-fired combined heat and power (BCHP) units, mini-hydro power plants, solar photovoltaic plants, wind turbine generators (WTGs), a hydrogen storage system (HSS) and plug-in electric vehicles (PEVs). A system with three diesel generators, three BCHP units, one mini-hydro power plant, two WTGs, two solar photovoltaic plants, one HSS and PEVs is studied to evaluate the suggested SGEO technique. Test results of the proposed SGEO technique are compared with those from self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution, and the recommended SGEO is shown to produce superior results.

An improved flower pollination solution for economic dispatch with valve point effect

<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions. However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems. The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>

Fuel constrained dynamic economic dispatch with demand side management

Owing to slowly reduction of fossil fuel, the cost-effective use of available fuel for electric power generation has turn out to be a very important concern of electric power utilities. Thermal power plants have to operate within their fuel confines and contractual constraints. This work recommends social group entropy optimization (SGEO) technique to solve fuel constrained dynamic economic dispatch (FCDED) with demand side management (DSM) integrating renewable energy sources and pumped hydro storage plant. Here the dynamic economic dispatch (DED) problem is solved with and without fuel constrained. The effectiveness of the recommended technique is revealed on two test systems. Simulation results of two test systems have been compared with those acquired from self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE). Test results show that fuel consumption can be sufficiently controlled for fulfilling constraints imposed by suppliers. It has been observed from the comparison that the suggested SGEO has the capability to confer with superior-quality solution.

Heat and power generation augmentation planning of isolated microgrid

Abstract- Due to raising heat and power demand the microgrid has to be expanded to meet increased power and heat demand. A short-range heat and power generation augmentation planning (HPGAP) of an isolated microgrid incorporating plug-in electric vehicles (PEVs) is described and evaluated here. The objective of HPGAP is to find out the most cost-effective and reliable expansion plan for meeting the forecasted power demand and heat demand over a short-range horizon while fulfilling a large number of technical, reliability and social constraints. An archetypal test system with existing diesel generators, small hydro power plant (SHPP), solar PV plant, wind turbine generator (WTG), biomass-fuel-fired combined heat and power (BCHP) unit, battery energy storage system (BESS), plug-in electric vehicles (PEVs) and candidate solar PV plant, WTG, SHPP, BCHP units and PEVs is considered here. The problem is solved by using self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), fast convergence evolutionary programming (FCEP) and differential evolution (DE).

Optimal generation scheduling of fixed head hydrothermal system with demand‐side management considering uncertainty and outage of renewable energy sources

Due to the rising infiltration of renewable energy sources, it is indispensable to investigate its brunt on the optimal power generation scheduling. However, the highly intermittent nature of renewable energy sources and their higher rate of outages may harm the entire grid. This work recommends fast convergence evolutionary programming (EP) with a time-varying mutation scale (FCEP_TVMS) for solving fixed head hydrothermal scheduling incorporating pumped-storage-hydraulic unit with demand-side management considering the uncertainty and outage of renewable energy sources. Simulation outcomes of the test system have been matched up to those acquired by fast convergence EP, colonial competitive differential evolution and heterogeneous strategy particle swarm optimisation. It is seen from the comparison that the recommended FCEP_TVMS technique can give a better-quality solution.

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski's information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided.

Multi-region dynamic economic dispatch of solar–wind–hydro–thermal power system incorporating pumped hydro energy storage

This paper suggests chaotic fast convergence evolutionary programming (CFCEP) for solving multi-region dynamic economic dispatch (MRDED) problem, multi-region economic dispatch (MRED) problem and economic dispatch (ED) problem. MRDED problem is based on multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost function, wind and solar power units with uncertainty and pumped hydro energy storage. MRED problem deals with tie line constraints, transmission losses, valve point effect and proscribed workable area of thermal generators. ED problem deals with valve point effect, proscribed workable area and ramp rate limits of thermal generators. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficacy of the proposed technique has been verified on convoluted three-area system for MRDED problem, four-area system for MRED problem and 140-unit Korean system for ED problem. Test results acquired from the suggested CFCEP technique have been fit to that acquired from FCEP, differential evolution (DE) and particle swarm optimization (PSO). It has been observed from the comparison that the recommended CFCEP technique has the capability to bestow with better-quality solution.

The naked mole-rat algorithm

This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm .

Dynamic economic dispatch with demand-side management incorporating renewable energy sources and pumped hydroelectric energy storage

This paper recommends chaotic fast convergence evolutionary programming (CFCEP) for solving real-world dynamic economic dispatch (DED) with demand-side management (DSM) incorporating renewable energy sources and pumped-storage hydroelectric unit. Here, solar–wind–thermal energy system has been considered taking into account pumped-storage hydroelectric unit and uncertainty of solar and wind energy sources. DSM programs reduce cost and boost up power system security. To investigate the upshot of DSM, the DED problem is solved with and without DSM. In the recommended technique, chaotic sequences have been applied for acquiring the dynamic scaling factor setting in FCEP. The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched against those acquired by fast convergence evolutionary programming (FCEP), colonial competitive differential evolution and heterogeneous strategy particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the ability to give better-quality solution.

Combined heat and power dynamic economic dispatch with demand side management incorporating renewable energy sources and pumped hydro energy storage

This study suggests chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving intricate actual world combined heat and power dynamic economic dispatch (CHPDED) problem with demand side management (DSM) incorporating renewable energy sources and pumped-storage-hydraulic unit. The valve point effect and proscribed workable area of thermal generators and solar and wind power uncertainty have been pondered. DSM programmes decrease cost and boost up power system security. To investigate the upshot of DSM, the CHPDED problem is solved with and without DSM. In the recommended CFCEP technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). Introduction of chaotic sequences helps FCEP to avoid premature convergence. The efficiency of the recommended technique is revealed in a test system. Simulation outcomes of the recommended technique have been evaluated with those attained by FCEP and differential evolution. It has been examined from the assessment that the recommended CFCEP has the capability to bestow with a better-quality solution.

Dynamic economic dispatch incorporating renewable energy sources and pumped hydroenergy storage

Due to mounting infiltration of solar and wind energy sources, it becomes essential to investigate its brunt on the dynamic economic dispatch. Here, solar–wind–thermal system integrating pumped-storage hydraulic unit has been considered. This work recommends chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving dynamic economic dispatch problem incorporating renewable energy sources and pumped-storage hydraulic unit. Chaotic sequences increase the exploitation ability in the searching space and enhance the convergence property. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched up to those acquired by FCEP, differential evolution and particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the capability to confer with better quality solution.

Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.