Main Article Content
The sustainable developments and energy strategies currently being developed in the world imply policies of innovation and technological optimization. In this way, we will advance towards green intelligence and a smart city . Furthermore, the electric power grid faces significant changes in the supply of resources and the type, scale, and demand patterns of residential users . Usually, the energy industry is based on a centralized grid that is supported by its natural resources and generation plants. Power generation in existing utilities generally exploits non-renewable sources, which negatively impacts the environment . Demand response (DR) is a solution to this challenge that has excellent benefits and efficiency. This means that consumers can intelligently manage their use to pay the low cost in peak hours when energy prices are high . DR schedule optimization is the potential to schedule a portion of the electricity demand in smart energy systems. It is a significant opportunity to improve the grid efficiency .
Power systems currently face different challenges, such as low efficiency, high energy losses, high emissions, and a high possibility of exercising market power . Integrated Demand Response (IDR) optimization methods mainly address the dynamic switching of energy forms (by disconnection, renewable connection, conventional electrical connection, among others) and uncertain variables in the systems caused by the domestic load and unconventional energy . Furthermore, it is estimated that the electricity demand will increase significantly due to population growth, the increasing penetration of green technologies, electric vehicles, and cogeneration systems .
This article presents simulation scenarios of demand response for incentives based on the "day before." Its main objective is to help power systems during peak demand hours and also during contingencies. This study is essential for the following reasons: (1) A short DR Integration overview is performed on consumers' time-series responses to prices based on the "day before" transaction scheme, (2) provides preferred options for a solution that reflects the uncertainty caused by volatile electricity market prices and demand in the decision-making problem.
Through computational experiments (Gamside software), we demonstrate the validity of the data obtained. We achieved active participation based on the “day before” work scheme in DR. 100% coverage of the Demand was achieved for each hour in percentages from 25% to 100% in optimization case studies. The maximum capacities of contribution to the grid were from 2500 to 6000 kW/h. This allowed having inputs for decision-making by introducing the rational response of the consumer and the agents of the energy integrator.
The optimization problem is developed in Gamside supported by its solution algorithms that are an optimal development tool. Two case studies were modeled, and the main results are shown in Figures 1 and 2.
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