A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

Abstract

The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

“…In MGs control, the steady state error cannot be completely eliminated when droop designs are used in the primary stage. Consequently, an auxiliary control system is implemented to restore voltage profiles and manage system frequency fluctuations, thereby enhancing the system's adaptability at the secondary stage [54–56]. The objectives of the auxiliary control level are to ensure robustness and minimize frequency and voltage deviations.…”

Optimal power flow solution using HFSS Algorithm

“…Some algorithms are based on an evolutionary-inspired algorithm like enhanced selfadaptive differential evolution (Pulluri et al, 2017), enhanced genetic algorithm (Kumari and Maheswarapu, 2010), backtracking search optimization (Chaib et al, 2016), and genetic algorithm (Osman et al, 2004]; some are human-inspired algorithms like biogeography-based optimization (Bhattacharya and Chattopadhyay, 2010), tabu search algorithm (Abido, 2002), gray wolf optimizer (El-Fergany and Hasanien, 2015) and some are nature inspired algorithms like particle swarm optimization (Abido, 2002), artificial bee colony algorithm (Jadon et al, 2014), the modified flower pollination (Barocio et al, 2016), hybrid cuckoo search algorithm (Balasubba, 2017), Nature-inspired algorithms have some features in common, firstly they emulate any natural occurring phenomenon, and secondly there is no need of gradient information and thirdly it implicit on random variables (Li et al, 2016). The performance of various metaheuristic techniques for single and multi-objective optimization with different facts devices are described (Mallala et al, 2022;Ahmed et al, 2014;Balasubbareddy et al, 2022:1-9;Ahmed et al, 2022a;Balasubbareddy et al, 2015:1-17;Ijaz et al, 2014;Balasubbareddy et al, 2017:44-53;Balasubbareddy, 2022;Ahmed et al, 2022b;Balasubbareddy et al, 2012;Ahmed et al, 2022c;Dhiraj et al, 2020;Ahmed et al, 2022d;Balasubba, 2016).…”

Research on the overvoltage suppression strategy of single-phase photovoltaic grid-connected systems based on FCS-MPDPC

“…Solving the optimal EDP (Energy Dispatch Problem) can not only achieve the sustainable development of the power system but also maximize the benefits. At present, the proportion of PV power plants connected to the distribution network is getting higher and higher, and the derived overvoltage problem of grid connection has seriously limited the grid connection capacity of PV power plants [1][2][3]. In recent years, the use of inverters for reactive voltage control has been studied by many scholars as the most cost-effective voltage regulation method.…”