Abstract
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.
Highlights
Energy is the basis of human survival and development, as well as the key element of industrial production and residents’ life
combined heating and power (CHP), air source heat pumps (ASHPs) and transformer are the main components of the agricultural greenhouse micromicro-energy-grid
demand response (DR) Model Considering Real-Time Tariff In China, time of day (TOD) tariff is the main measure of Demand Side Management (DSM)
Summary
Energy is the basis of human survival and development, as well as the key element of industrial production and residents’ life. Gan et al [7] established a multi-energy flow scheduling model including combined cooling, heating and power (CCHP) as well as distributed wind power and PV. They analyzed its impact on renewable energy consumption and overall cost. Cui et al [10] established a scheduling model, which was based on solar-thermal power station and price-based DR, to improve the capacity of wind power absorption through coordinated scheduling on both sides of source and load. DR can be adjusted by controlling load demand, which can make consumers’ electricity habits more reasonable This process can maximize the system efficiency by reducing unnecessary spinning reserve, installed capacity and peak–trough load difference, optimizing load curve through peak-cut and trough-fill. Reasonable dispatching and planning of time-shifting load in agricultural production is a measure of DR with rural characteristics
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