In deregulated electricity markets, generation companies (GENCO) try to maximize their economic benefits considering the electricity demand, transmission network condition, and other participants’ behaviors. The increasing penetration of renewable sources such as wind power generation with intermittent nature poses several challenges to the participation of GENCOs in the electricity market. Thus, this paper presents a stochastic bilevel optimization model to determine the coordinated bidding strategy of a wind-thermal GENCO with the aim of maximizing its profit in the day-ahead and real-time balancing market. Herein, the model aims to maximize the profit of GENCO in the day-ahead and the balancing market in the upper-level problem while minimizing the operation cost of the system in the lower-level problem. The uncertainties of wind power generation and electricity demand are modeled by defining a set of scenarios considering their mutual correlation using the copula technique. Additionally, incorporating AC power flow constraints in the proposed optimization model offers a better solution to the coordinated bidding strategy of the wind-thermal GENCO. Further, the nonlinear AC power flow equations are linearized using the piecewise approximation technique to reduce the computational complexity and enhance the accuracy of the optimal solution. In the end, the developed algorithm is implemented on the IEEE 24-bus RTS, and the simulation results are provided to validate the efficiency and applicability of the proposed coordinated bidding strategy model. The results advocate that the participation of the thermal unit along with the wind farm might mitigate the risk of uncertainties, but it causes an intense increase in the locational marginal price of the system. Importantly, the simulation results indicate the computational efficiency of the model by developing an exact AC power flow model without compromising the results. Notably, it has been found that the profit of the wind-thermal GENCO would be increased by 35.2% employing the copula technique to model the mutual correlation of uncertain parameters.

Background: Most non-developing and under developing countries strive hard to tackle the situation of power crisis and to combat the imbalance between the power generation and load demand, especially in the case of increasing of the population. In this situation, the load shedding scheme has been extremely implemented as a fast solution for unbalance conditions. Thus, load shedding is crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible; however its implementation is mostly undesirable. Objective: Prioritize the loads according to their importance and apply reduction strategy in the demands while the supplied power to the important loads such as health care and security installation are kept intact without any interruption as possible. Methods: The conventional methods of load shedding lead to over or under shedding and this may lead to many problems with the network. Under the scheme, these methods disconnect the load or the entire feeder without considering their priorities and may not perform as anticipated. In this work, we propose a logarithmic reduction method to reduce the load according to the priority and day life criticality. The method for shedding the load base on Reduction Matrix and which in turn depend on the priority demands. Results: The higher priority demands are fed with a reliable power source by the real time monitoring of the network accompanied with power reducing for the lower priority demands. Conclusion: We test a real data sample provided by the Iraqi national grid control center in Baghdad. Our simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

No abstract

The load shedding  scheme has been extensively implemented as a fast solution for unbalance conditions. Therefore, it's crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible, however its implementation is mostly undesirable. One of the solutions to minimize the amount of load shedding is the integration renewable energy  resources, such as wind power, in the electric power generation could contribute significantly to minimizing power cuts as it is ability to positively improving the stability of the electric grid. In this paper propose a method for shedding the load base on the priority demands with incorporating the wind power generated. The higher priority demands are fed with a reliable wind energy resource in order to protect them from shedding under contingency condition such as high overloading by the real time monitoring of the network accompanied with power reducing for the lower priority demands. The simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

Most developing countries are now working to combat imbalances between power generation and load demand. In such situations, load shedding schemes have been implemented frequently as rapid solutions for unbalanced conditions to protect networks from collapsing and to sustain stability. The conventional methods of load shedding disconnect loads without considering their priorities. In this work, a logarithmic reduction method is thus proposed to reduce loads according to the priority and criticality. A Reduction Matrix is introduced as a tuning factor scaled to the size of the network, and the paper thus presents an optimisation tool based on Genetic Algorithms, developed in MATLAB, that can be applied to minimise the error between the amount of load to be reduced and the actual load reduced in electric power systems. The proposed algorithm was tested on a practical data system sample as provided by the Iraqi Ministry of Electricity from the control center of the Iraqi national grid in Baghdad, and was shown to offer optimal load reduction with reduce error, which is necessary to eliminate the impact of load reduction in electrical networks on critical loads when total demand cannot be supplied. The simulation results thus support the effectiveness and practicality of the applied method, paving the way for its possible application in power systems.