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This paper presents a method for real-time transmission congestion management. Congestion can be alleviated by incorporating line capacity constraints in the dispatch and scheduling process. The objective of this paper is to alleviate the overload and minimize the cost of operation. Here, two objectives congestion and cost are simultaneously minimized. Generation rescheduling of participating generators is done to overcome the congestion. Particle Swarm Optimization (PSO) technique, aims in finding the global optimum of the real-valued function (fitness function) defined in the given space (search space). The technique has been tested on Western System Coordinating Council (WSCC) 3-Machine 9-Bus system and the results are discussed. 

The awareness of self-consumption of grid-connected roof-top solar photovoltaic (PV) owners in a community and the advancement in information and communication technologies (ICT) led to the development of a novel peer-to-peer energy trading mechanism for next-generation power systems. In the peer-to-peer (P2P) energy trading landscape, the prosumers and consumers self-organize and trade energy among themselves. In recent years, the large penetration of distributed energy resources, as well as the advancement of technologies in the fields of protection, power electronics, and storage devices, led to the use of direct current (DC) home appliances by the end-users, i.e., consumers in a community. In this paper, the operational costs of an individual household and community when operated with alternating current (AC) and DC home appliances are calculated using bill sharing and the mid-market rate method for various degrees of PV penetration. The bill sharing method shares the operational cost and income among all the peers in proportion to the amount of energy they consume/generate. The mid-market rate method calculates the P2P internal price at the median of the import and export price based on the relationship between total generation and demand. In terms of operational cost, both producers and consumers benefit fairly when the mid-market rate method is used when the households in a community are operated with DC home appliances.

Congestion of the power system is the most common challenge an Independent System Operator (ISO) faces in restructured electricity markets. It affects the efficiency of the market when transmission lines are congested causing transmission costs to rise. To prevent transmission line congestion, ISO needs to take the necessary steps. To solve these issues, this paper introduces a new method namely the Adaptive Red Fox Optimization algorithm (ARFOA) to compute the congestion cost considering the power losses in the transmission line system. Initially, all the generators in the system are selected to reschedule real power outputs. Second, by establishing a proposed optimization issue, ARFOA is employed to control transmission line congestion. The implementation of the proposed method is evaluated on the IEEE 30 bus system. The algorithm’s adaptability is tested using several case studies involving the base case and line outages, also compared with the other existing techniques such as PSO, ASO, and GSO approaches. The simulation outcomes indicate that the proposed strategy outperforms existing techniques in terms of congestion cost, power loss, generation rescheduled power, and computational time.

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