Heuristic Optimization for Microload Shedding in Generation Constrained Power Systems

While the causes of power system outages are often complex and multi-faceted, an apparent deficit in generation compared to a known demand for electricity could be more alarming. A sudden hike in demand at any given time may ultimately result in the total failure of an electricity network. In this paper, algorithms to efficiently allocate the available generation is investigated. Dynamic programming based algorithms are developed to achieve this constraint by uniquely controlling home appliances to reduce the overall demands for electricity by the consumers on the grid in context. To achieve this, heuristic optimization method (HOM) based on the consumers' comfort and the benefits to the electricity utility is proposed. This is then validated by simulating microload management in generation constrained power systems. Three techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS) techniques were studied for effecting efficient microload shedding. The research is aimed at reducing the burden imposed on the consumers in a generation constrained power system by the traditional load shedding approach. Additionally, the reduction of the excess curtailment is a prime objective in this paper as it helps the utility companies to reduce wastage and ultimately reduce losses resulting from over shedding. Reducing the peak-to-average ratios (PAR) on the entire network in context as a critical factor in the determination of the efficiency of an electricity network is also investigated. In the long run, the PAR affects the price charged to the final consumer. Simulation results show the associated benefits that include effectiveness, deployability, and scalability of the proposed HOM to reduce these burdens.