A deregulated electricity market provides open access to all market players. In an open-access power market, the system operator is responsible for ensuring that all contracted power is dispatched. However, if this results in line flows that exceed their acceptable range, then it could threaten the system’s security. Therefore, the system operator checks for congestion as the line flow exceeds its limit. For congestion management, the system operator applies different curtailment strategies to limit the requested transaction. Therefore, in this work, an optimal power dispatch model has been presented in order to reduce the curtailment of requested power. A modified moth flame optimization technique has been implemented to frame this OPD model. The impact of congestion management on power dispatch has been analyzed, considering bilateral and multilateral dispatch in an electricity market. In addition, the effect of FACTS devices on reducing congestion and curtailing power is studied. Verification studies showed that the proposed solution reduces congestion costs by 27.14% and 29.4% in 14- and 30-bus systems, respectively. It has been verified that the MMFO approach with the FACTS device improves transaction deviations and ensures that the deregulated system provides secure energy with less cost reflected on the customers.

In the present market scenario, energy cost has been increased and also restrictions have been faced in fossil fuels energy sources. This leads to evolvement of combined heat and power generating units having higher efficiency as compared to the existing systems. The optimal dispatch of CGUs involves a number of intricate constraints, making it a practical optimization problem. So, an improved heap‐based optimizer algorithm has been implemented in this work for solving optimal dispatch problem by accomplishing a proper balance between initial and final stages of global search and convergence, respectively. This problem provides optimal scheduling of heat and power generating units and seeks to minimize the overall fuel cost supply of combined units considering their operational limits. Here, three test systems, that is, 7‐units, 24‐units and 48 units system have been taken into consideration to verify the performance of proposed IHBO algorithm. The impact of prohibited zones on power dispatch has been analysed for both the system in an electricity market. In addition, the valve‐point loading effect, mutual dependency of produced heat and power of combined units and also transmission power losses in transmission lines have been studied. Comparisons among different methods have been made based on their respective solutions obtained and from that it manifests the robustness and superior performance of the proposed technique. Verification studies showed that impact of prohibited zones on generating units, increases the production cost with respect to without prohibited zones. However the proposed technique yields least production cost among other techniques.

In this study, for optimal power dispatch of a grid-connected micro-grid, a new stochastic model has been built up to minimize the operating cost of micro-grid that equipped with plug-in hybrid electric vehicles, renewable energy sources, and storage devices. Impact of electric vehicles on power dispatch is studied by considering its uncertainty charging characteristics. Monte–Carlo simulation is employed for uncertainty modeling. For this work, three different charging strategies are followed up, namely, uncontrolled, controlled, and smart charging strategy to observe the impact of electric vehicles on micro-grid. So, here an endeavor has been made using a potent and robust technique i.e. improved whale optimization algorithm for obtaining optimal power dispatch. The suggested method’s appropriateness and effectiveness are evaluated by modeling a grid-connected micro-grid. The outcomes of this technique improve the MG’s performances in terms of best solution and economic operation. On different case studies, the outcomes are compared with other methods without and with charging strategy of electric vehicles. It is seen that operating cost obtained without electric vehicles is nearly 300$ and with charging strategy, i.e. uncontrolled, controlled, and smart of electric vehicles are 664$, 390$, and 327$, respectively. So, inclusion of vehicles on MG increases operating cost but its impact on micro-grid significantly reduces the operating cost for smart charging strategy in comparison with other charging strategy.