In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

The ideal places and size of the distribution generators were determined by reducing the loss of power in the distribution networks. The ideal positioning of various kinds of DGs has been suggested in the current job. In this job, the ideal power factor for DG supply has been acquired, both the active power as well as the reactive power. In the proposed approach, different types of distribution generation (DG) supply both reactive and real power. For the optimal placement of DG sources, particle swarm optimization techniques have been used in this job. Each of these innovations has its own strengths and drawbacks. Most of the methods that have been proposed so far to formulate DG's optimum placement problem only consider Type-I DGs, Type-II and Type-III DGs that are considered for optimal position in the existing research. In the reference, artificial bee colony algorithm was used to determine sites of DGs and condenser combinations and optimal size. The author used PSO method in the reference to determine the appropriate positioning of the DG's and to maximize the savings of power loss and voltage profile in the distribution network.

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Being more than a decade old idea, the Demand-side management (DSM) is among the most vital part of the modern smart grid. DSM enables the utilities to minimize the gap between the supply and the demand by optimizing their pattern of user loads. At the same time, it helps them in achieving economic and energy efficient systems by reducing the peak to average (PAR). The implementation of DSM programs by the utilities could help them in improving their reliability, power quality, energy, and system efficiency. On the other hand, customers could be use it to improve their load profile, reduce the peak demands, save energy, and motivate them use more and more renewable energy. Thus, both the utilities and the consumers get benefitted by the implementation of DSM program in the smart grid. This study tries to understand the application of energy efficient policies and the demand response techniques with various DSM strategies. The study mainly focuses on the various characteristics that would lead to effective implementation of DSM programs with particular attention of the residential energy demand. Also, there will be a focus on enhancement of energy efficiency leading to more effective policy responses. The researchers could find this study very helpful as it could be employed to maximize the utility profits, the total load factor, peak demand and also minimize the consumer usage bills.

In conventional power grids, the supply and demand sides of electricity are largely separate, and the operations side is the only one who has access to grid monitoring data. Power networks need to be able to meet electrical demand on a regular basis in order to remain stable, which necessitates planning and communication from both parties. Electric power generation and supply must advance if grid stability, security, and effectiveness are to be improved. The smart grid concept makes the next-generation electricity network smarter and more intelligent by enabling information flow in both ways and active engagement from all connected parties. Modern technologies must be created in order to address issues with economic growth, energy security, and energy sustainability. Demand-side management (DSM) gives consumers and utilities the power to make informed choices about how they use energy, changing the load profiles and lowering peak demands in the smarter distribution network. DSM has been employed recently as a method to maintain a balance between increasing energy needs and consumption.

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Electric smart grid reliability and stability could be increased by the application of demand response initiatives and renewable energy resources. This study provides a brand new demand side management paradigm for smart grids with renewable energy integration that is based on intelligent optimisation. The suggested system combines real-time demand response programmes from electric utility companies and makes use of fuzzy logic to forecast consumer energy consumption patterns. Using demand response programmes, a smart energy management controller adjusts consumer energy usage forecasts to produce an operation schedule. Using simulations employing real-world data, we assess the efficacy of the suggested intelligent demand side management framework. According to the findings, compared to the load management-free method, total electricity costs and carbon emissions have significantly decreased. A potential strategy for demand side management with the integration of renewable energy, the proposed intelligent hybrid optimisation method of load management achieves superior performance in regulating energy consumption, peak loads, and carbon emissions. By presenting a useful and effective paradigm for demand-side management with renewable energy integration, this research makes a contribution to the field of energy management.