Day-Ahead Short-Term Optimization of Renewable Energy of Microgrid in Multiple Timescales
Microgrid is an effective way to accept distributed renewable energy, and the development and application of renewable energy can effectively solve the current energy and environmental crisis. However, due to the uncontrollable and intermittent nature of renewable energy, coupled with the complexity of the operation modes of the microgrid, it is more difficult to optimize and control its operation, which has become a key issue in the energy management of microgrid. Considering the randomness of renewable energy, a multiple timescale optimization plan is proposed, which is a two-stage optimization scheme. The scheduling period of day-ahead optimization is 24 h. The targets of load supply and cost are selected as the objective function of an independent microgrid, and the power constraint of each distributed power source is set. The particle swarm optimization algorithm is used to optimize the system. Short-term optimization optimizes the results of day-ahead optimization for a second time, and takes 15 min as a scheduling period. The objective functions of revisions to the plan of day-ahead and the cost are selected and solved by the particle swarm optimization algorithm. The results are verified by the particle swarm optimization and show rationality and feasibility of the method proposed in the paper.
KeywordsMicrogrid Energy management Multiple time scales Particle swarm optimization
This research is supported by the Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (Project Number: X18031); National Key R&D Program of China, Research and Demonstration of Key Technology of Net-Zero Energy Building (Project Number: 2016YFE0102300); Research Fund Project of Beijing University of Civil Engineering and Architecture (Project Number: 00331616040); Research Program of Beijing City Board of Education (KM201610016002); and Science and Technology Project of Ministry of Housing and Urban-Rural Construction, China (Project Number: 2015-K1-012).
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