Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(...
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Taking the consumption rate of renewable energy and the operation cost of hybrid AC/DC microgrid as the optimization objectives,the adjustment of load demand curves is carried out considering the demand side response(DSR)on the load *** complementary utilization of renewable energy between AC area and DC area is achieved to meet the load demand on the source *** the network side,the hybrid AC/DC microgrids purchase electricity from the power grid at the time-of-use(TOU)price and sell the surplus power of renewable energy to the power grid for *** improved memetic algorithm(IMA)is introduced and applied to solve the established mathematical *** promotion effect of the proposed source-network-load coordination strategies on the optimal operation of hybrid AC/DC microgrid is verified.
Community integrated energy system (CIES) couples multiple energy forms and links, it is important to cope with the inadequacy of flexibility for realizing efficient utilization and reliable supply. Based on the detai...
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Community integrated energy system (CIES) couples multiple energy forms and links, it is important to cope with the inadequacy of flexibility for realizing efficient utilization and reliable supply. Based on the detailed modeling of central energy station, district heating network (DHN) and building loads, a unified scheduling model is established by fully considering the coupling characteristics of the sources, the networks, and the loads. Meanwhile, the flexibilities from adjustable capacity of thermal storage device and the indoor temperature on the demand side, as well as the energy storage of DHN, are utilized to improve the economy. Furthermore, a model predictive control (MPC) based robust scheduling strategy is proposed to maintain and utilize CIES flexibility for enhancing the uncertainty adaptability. The scheduling framework consists of rolling optimization and robust constraint generation. The rolling optimization schedules the system with a better adaptation to flexibility demands, and the robust constraints generate adjustable margin for building demands by modifying the upper/lower and ramping limits. After the linearization of nonlinear terms in the model, case studies are conducted based on the data of a typical day in summer. The results show that the unified source-network-load scheduling strategy can give full play to the flexibilities of different energy links with a better operation economy. Additionally, the MPC-based optimization can well adapt to the rolling forecasting uncertainties, and the schedule generated by advance reserving means has stronger robustness for real-time errors.
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