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With the growth in demand for energy and the boom in energy internet (EI) technologies, comes the multi-energy complementary system. In this paper, we first model the components of the micro-energy-grid for a greenhouse, and then analyzed two types of protected agriculture load: time-shifting load and non-time-shifting load. Next, multi-scenario technology is directed against the uncertainty of photovoltaic (PV). Latin Hypercube Sampling (LHS) and the backward reduction algorithm are the two main methods we use to generate the representative scenarios and their probabilities, which are the basis for PV prediction in day-ahead scheduling. Third, besides the time of day (TOD) tariff, we present a model using real-time pricing of consumers’ electricity load, which is proposed to compare consumers’ demand response (DR). Finally, we establish a new optimization model of micro-energy-grid for greenhouses. By calculating the dispatch of electricity, heat, energy storage and time-shifting load under different conditions, the local consumption of PV and the comprehensive operational cost of micro-energy-grid can be analyzed. The results show that a storage device, time-shifting load and real-time pricing can bring more possibilities to the micro-energy-grid. By optimizing the time schedule of time-shifting load, the cost of the greenhouse is reduced.
Yuntao Ju; Mingxin Jin; Jiankai Wang; Jianhua Yang; Mingyu Dong; Dezhi Li; Kun Shi; Haibo Zhang. Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture. Applied Sciences 2019, 9, 3929 .
AMA StyleYuntao Ju, Mingxin Jin, Jiankai Wang, Jianhua Yang, Mingyu Dong, Dezhi Li, Kun Shi, Haibo Zhang. Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture. Applied Sciences. 2019; 9 (18):3929.
Chicago/Turabian StyleYuntao Ju; Mingxin Jin; Jiankai Wang; Jianhua Yang; Mingyu Dong; Dezhi Li; Kun Shi; Haibo Zhang. 2019. "Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture." Applied Sciences 9, no. 18: 3929.
As more clean energy sources contribute to the electrical grid, the stress on generation scheduling for peak-shaving increases. This is a concern in several provinces of China that have many nuclear power plants, such as Guangdong and Fujian. Studies on the unit commitment (UC) problem involving the characteristics of both wind and nuclear generation are urgently needed. This paper first describes a model of nuclear power and wind power for the UC problem, and then establishes an objective function for the total cost of nuclear and thermal power units, including the cost of fuel, start-stop and peak-shaving. The operating constraints of multiple generation unit types, the security constraints of the transmission line, and the influence of non-gauss wind power uncertainty on the spinning reserve capacity of the system are considered. Meanwhile, a model of an energy storage system (ESS) is introduced to smooth the wind power uncertainty. Due to the prediction error of wind power, the spinning reserve capacity of the system will be affected by the uncertainty. Over-provisioning of spinning reserve capacity is avoided by introducing chance constraints. This is followed by the design of a UC model applied to different power sources, such as nuclear power, thermal power, uncertain wind power, and ESS. Finally, the feasibility of the UC model in the scheduling of a multi-type generation unit is verified by the modified IEEE RTS 24-bus system accommodating large scale green generation units.
Yuntao Ju; Jiankai Wang; Fuchao Ge; Yi Lin; Mingyu Dong; Dezhi Li; Kun Shi; Haibo Zhang. Unit Commitment Accommodating Large Scale Green Power. Applied Sciences 2019, 9, 1611 .
AMA StyleYuntao Ju, Jiankai Wang, Fuchao Ge, Yi Lin, Mingyu Dong, Dezhi Li, Kun Shi, Haibo Zhang. Unit Commitment Accommodating Large Scale Green Power. Applied Sciences. 2019; 9 (8):1611.
Chicago/Turabian StyleYuntao Ju; Jiankai Wang; Fuchao Ge; Yi Lin; Mingyu Dong; Dezhi Li; Kun Shi; Haibo Zhang. 2019. "Unit Commitment Accommodating Large Scale Green Power." Applied Sciences 9, no. 8: 1611.