This paper presents a Quorum Sensing Driven Bacterial Swarm Optimization (QBSO) to solve Dynamic Power Economic Ecological Emission Dispatch (DPEEED) problem with valve point loading effects. The exploration capability of the bacterial swarm is improved in this paper using quorum sensing and the anti-predatory activity. The performance of the proposed QBSO method is verified for optimality, practicability, convergence and robustness on a 5, 10-unit system. Furthermore, a practical south Indian 20-thermal generating units with recorded load demand profiles of three different weather situations. The numerical dispatch result indicates that the presented technique finds the minimized generating cost and consistently progresses the computational time. The presented technique outperforms the Bacterial Swarm Optimization (BSO) and the previous presented methods.

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Purpose The distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS). Design/methodology/approach This paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied. Findings The voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations. Practical implications The optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system. Originality/value Moreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

Distributed devices in smart grid systems are decentralized and connected to the power grid. The connection made through different types of equipment transmits. This produces the numerous energy losses, when power flows from one bus to another bus. The most efficient approaches to reduce energy losses is to integrate the renewable energy resources in Distributed Generation's (DG's). The uncertainty of DG may cause instability issues. The major issue includes congestion in the power grid due to the sudden power consumption by the customers, which affects the efficient energy delivery. Energy management with DG regulation is one of the most efficient solutions to solve these instability issues. In the considered power system with DG's and consumers, the Locational Marginal Pricing (LMP) based unified Energy Management System (uEMS) model is considered. This model increases the profit benefits for DG's and increases the stability of Distributed Energy System (DES). In this paper, the Bacterial Foraging Optimization (BFO) is employed to reduce losses i.e. based on Loss Reduction Allocation (LRA) method. Using LRA method the energy loss reduction is calculated and this model accurately rewards DG contribution and offers a good competitive market. Moreover, the entire DG's profit is increase by the BFO technique. The IEEE 37 bus feeder system is to be considered to validate the proposed uEMS model to increase the DG system stability. Furthermore, this implementation gives the idea of formulating efficient energy management system of future Indian scenario.

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