Demand Response Strategies Based On Dynamic Price In Smart Grid

Smart Grid is another breakthrough in the power industry revolution, which is a comprehensive and effective solutions to deal with the global and humani-ty energy shortages、environmental pollution、global warming、economic and social sustainable development. It represent the future development of the grid research direction. Demand-side management and demand response as one of the important research direction in the future smart grid, causing more and more attention to research scholars. Allows users to participate in the grid, and timely response to price in order to changes the strategy of using workload is a top prior-ity of our study. Meanwhile, the combination of renewable energy and the grid is an important feature of modern smart grid. With the increasingly serious energy shortage problem, the trend of clean energy added to grid can not be reversed.At present, the global research about the demand response strategy based on the dynamic price in smart grid environment have grow significantly. However, those research either did not take the uncertainly price into account, or failed to consider the use of clean energy in the power grid. Thus, we have summarized the related research of the demand response after practice, and focusing on analyzes the demand response strategy based on the price under the smart grid system, then we establishes a real-time price model and study the power users’sensitivity analysis to the dynamic price and the response curve in this paper. At the last, we proposed algorithms with aimed at the single workload and multiple workloads and established the relevant optimization model.Consider the dynamic electricity price and the intermittency nature of renew-able energy, we are looking for an intelligent schedule policy to control the work-loads to determine their start and end time in order to maximize the utilization of renewable energy and minimize the total electricity cost. For the characteristics of the price-sensitive workloads, the workloads that can be shifted/scheduled is classified into interruptible load and non-interruptible load. Based on the above two types of workloads, we established a dynamic programming model, and pro-posed two novel recursive scheduling strategies, calculated the optimal electricity price threshold, thus we can find the best time to start the workload. The sim-ulation experiment demonstrates that our strategies can effectively save energy expenditure and increase the proportion of renewable energy in overall energy consumption.After the proposed scheduling optimization model for a single load, we will continue to deepen the study of multiple load energy optimization strategy. Next, for more workloads in a joint scheduling, we describe the power consumption of household appliances not only the workloads’electricity costs but also the costs on wait for workloads to fulfill the task, then established residential energy control optimization model based on integer programming. In the joint control strategies in multiple loads, we both take the user’s electricity cost and loads’waiting time costs into account, not only from the economic interests of users but also the user’s comfort and satisfaction on the basis of considering the home energy optimization problem. Finally, simulation experiments verified the effectiveness of the model, and analyzed the relevant parameter’s related effects for the overall effectiveness of the model.