Droop Control for Micro-grid Operations Including Generation Cost and Demand Side Management

A new cost-based droop control method based upon generation cost and demand side cost management of the microgrid is proposed in this paper.At present,many droop control methods have been developed based on either the power rating or the generation cost of the distributed generation(DG)unit,without consideration of the demand side participation in the operation and control.This exclusion might not be appropriate,if different types of consumers are connected in the micro-grid systems.This study proposes a droop control method considering both DG and load operating cost characteristics in order to minimize the generation cost of the micro-grid.

Distributed Periodic Event-Triggered Optimal Control of DC Microgrids Based on Virtual Incremental Cost

This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.

SMO Algorithm to Unravel CEED Problem using Wind and Solar

This research proposes a more advanced way to address Combined Economic Emission Dispatch(CEED)concerns.Economic Load Dispatch(ELD)and Economic Emission Dispatch(EED)have been implemented to reduce generating unit fuel costs and emissions.When both economics and emission tar-gets are taken into account,the dispatch of an aggregate cost-effective emission challenge emerges.This research affords a mathematical modeling-based analyti-cal technique for solving economic,emission,and collaborative economic and emission dispatch problems with only one goal.This study takes into account both the fuel cost target and the environmental impact of emissions.This bi-inten-tion CEED problem is converted to a solitary goal function using a price penalty factor technique.In this case,a metaheuristic and an environment-inspired,intel-ligent Spider Monkey Optimization technique(SMO)are used to address the CEED dilemma.By following the generator’s scheduling process,the SMO meth-od is used to regulate the output from the power generation system in terms of pollution and fuel cost.The Fission-Fusion social(FFS)structure of spider mon-keys promotes them to utilize a global optimization method known as SMO dur-ing foraging behaviour.The emphasis is mostly on lowering the cost of generation and pollution in order to improve the efficiency of the power system and han-dle dispatch problems with constraints.The economic dispatch has been reme-died,and the improved result demonstrates that the system’s performance is stable andflexible in real time.Finally,the system’s output demonstrates that the system has improved in resolving CEED difficulties.When compared to ear-lier investigations,the proposed model’sfindings have improved.As the gener-ating units,wind and solar are used to explore the CEED crisis in the IEEE 30 bus system.

Calculation of Available Transfer Capability Using Hybrid Chaotic Selfish Herd Optimizer and 24 Hours RES-thermal Scheduling

As fossil fuel stocks are being depleted,alternative sources of energy must be explored.Consequently,traditional thermal power plants must coexist with renewable resources,such as wind,solar,and hydro units,and all-day planning and operation techniques are necessary to safeguard nature while meeting the current demand.The fundamental components of contemporary power systems are the simultaneous decrease in generation costs and increase in the available transfer capacity(ATC)of current systems.Thermal units are linked to sources of renewable energy such as hydro,wind,and solar power,and are set up to run for 24 h.By contrast,new research reports that various chaotic maps are merged with various existing optimization methodologies to obtain better results than those without the inclusion of chaos.Chaos seems to increase the performance and convergence properties of existing optimization approaches.In this study,selfish animal tendencies,mathematically represented as selfish herd optimizers,were hybridized with chaotic phenomena and used to improve ATC and/or reduce generation costs,creating a multi-objective optimization problem.To evaluate the performance of the proposed hybridized optimization technique,an optimal power flow-based ATC was enforced under various hydro-thermal-solar-wind conditions,that is,the renewable energy source-thermal scheduling concept,on IEEE 9-bus,IEEE 39-bus,and Indian Northern Region Power Grid 246-bus test systems.The findings show that the proposed technique outperforms existing well-established optimization strategies.