# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 1 Introduction  
Energy is the basis of human survival and development, as well as the key element of industrial production and residents' life. How to reduce environmental pollution in the process of using energy while ensuring the sustainable supply of energy is the common concern of today's society [1; 2; 3]. Conventional energy systems are mutually independent and different energy sources cannot cooperate to produce a comprehensive optimal scheme [4], thus energy efficiency is reduced. An integrated energy system (IES) combines independent energy systems together. As an important carrier of energy internet (EI), IES monitors and manages the production, transmission, storage and utilization of various energy sources at the system level. IES can realize not only multi-energy complementary, but also stepped utilization of energy. It meets the abundant demands of users for diverse energy forms, such as electricity, heating, cooling and gas, as well as improves the utilization efficiency of renewable energy. At present, research on IES mainly focuses on a series of problems about simulation modeling and optimization scheduling. [PERSON] et al. [5] first conceived the basic architecture of the energyhub (EH). As an energy management center, EH is widely used in IES modeling. Based on the fundamental concepts and modeling methods of EH, [PERSON] et al. [6] summarized the optimization and operation of EH planning. [PERSON] 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. In addition, they analyzed its impact on renewable energy consumption and overall cost. In [8], a general method for CCHP optimal scheduling is proposed, Moreover, a multi-energy coordinated optimal scheduling model including CCHP, wind power and photovoltaic output, ice storage air conditioning and battery energy storage is established. The authors used the scenario reduction method to deal with the wind power and PV uncertainty problem. DR is able to timely feedback power supply and demand, which has great significance to improve the operating efficiency of the power system and maintain its stable operation [9]. Nowadays, the demand response of the electricity market is mainly divided into two types: price-based DR and incentive-based DR. [PERSON] 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. In [11], the authors established a scheduling model for incentive-based DR, and performed reliability assessment and economic analysis on the test system based on Monte Carlo simulation method. 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, also optimizing load curve through peak-cut and trough-fill. Compared with the traditional one, the interconnections among different energy systems in IES improve the agility of consumers' response. For example, in the process of cogeneration, combined heating and power (CHP) on the user's side, the demand can be satisfied by adjusting power or heat output separately [12; 13; 14]. It is meaningful and potential in the future to reconcile the DR and IES. Northwest China has sufficient solar and wind energy [15; 16], thus during 2013-2017, the PV cumulative installed capacity kept increasing and exceeded 30,000 MW. To achieve the goal of poverty alleviation in rural areas by 2020, China implements the strategy of supporting the poor in rural areas of western China. Since renewable energy is uncertain, robust optimal scheduling algorithms [17; 18] are usually applied to cope with this issue. To accommodate more renewable energy sources, flexibility from the power load is frequently adopted [19; 20].