Operation And Planning Of The Integrated Rural Energy System With Smart Agricultural Loads

With the implementation of the rural revitalization strategy and the rapid development of smart agriculture,the energy demand in rural areas is growing with each passing day.The traditional energy supply in rural areas mainly comes from the rural grid and fossil energy.The rural power grid is relatively weak,and fossil energy causes great environmental pollution and low utilization efficiency.The integrated energy system(IES)combines multiple energy sources for joint planning and coordinated operation,which can improve the reliability of power supply,energy utilization efficiency,and reduce emissions.However,the existing research on IES operation and planning mainly focuses on urban areas,and there are obvious differences in sources and loads between rural areas and urban areas.Based on this background,this paper proposes the integrated rural energy system(IRES)optimization framework and conducts research from three aspects: typical smart agricultural load modeling,IRES optimal operation,and IRES planning.These works can enrich the theoretical basis of IES research and provide a reference for the transformation and development of the rural energy system.Firstly,energy consumption modeling is carried out for two typical smart agricultural loads of greenhouses and broiler farms,which provide model bases for the operation and planning of IRES.For the greenhouse,its heat load model is established through the analysis of the heat energy flow of the greenhouse and the calculation of solar radiation,and its electric load model is established by using the light saturation point curve and the characteristic parameters of the agricultural sodium lamps.The energy consumption characteristics of smart agricultural loads are analyzed by numerical case simulation.The results show that the daily heat load of the greenhouse is in the form of double peaks.The variation of the greenhouse heat load caused by the change in the indoor temperature setpoint is not affected by the ambient temperature.The daily heat load of the broiler house shows an opposite trend to that of the ambient temperature.The variation of the broiler house heat load caused by the change in the ventilation rate is linearly related to ambient temperature.Secondly,based on the establishment of IRES component models such as rural loads,biogas digester,and energy network,a distributionally robust chance-constrained dispatch model for the IRES is proposed.The objective function of the model is the sum of biogas production cost,net electricity purchase cost,and equipment operation and maintenance cost.The constraints include distributionally robust chance constraints considering the uncertainty of wind power and residential electricity load forecast errors and deterministic operation constraints of energy networks,energy conversion,and energy storage devices.The Kullback-Leibler divergence is used to establish the uncertainty sets of random variable distribution.Based on the Bernstein approximation theory,the distributionally robust chance-constraint model is transformed into a deterministic optimization model that can be directly solved.The proposed model is applied to an IRES case,and the results show that the distributionally robust optimization better balances the robustness and economy of the dispatch results than the stochastic optimization and robust optimization methods.Compared with the scheme of independent energy supply for rural life and agricultural production,the scheme of cooperative scheduling the two has a better economy,and its operating cost is reduced by 9.03%.Thirdly,an IRES dispatch model considering the flexibility of multiple rural loads is established to further improve the economics of IRES operation.The greenhouse heat load flexibility model is established according to the temperature integration theory of crops and that of the broiler farm is established according to the elastic requirements of animal welfare on the ventilation of the broiler house.The worst-case conditional value at risk(WCVa R)is used to describe the operation cost of real-time dispatch stages.A dispatch model is constructed that takes the sum of energy production cost,demand response incentive cost,and WCVa R-based real-time dispatch cost as the objective function,and considers rural load flexibility constraints,risk cost-related constraints,and other component operating constraints.The model is applied to an IRES case,and the results show that the utilization of the flexibility of the rural loads can improve the operation economy.When the flexibility of various rural loads is considered,the operation cost of the system is reduced by 10.70%.The disturbance vector of the random variable reflects the uncertainty of the distribution of the random variable.As the disturbance increases,the real-time dispatch cost also increases.Finally,a dynamic energy hub(DEH)model is proposed according to the efficiency of devices under changing operating conditions,and an IRES bi-level planning model is established based on DEH that takes into account changes in device operation conditions.The upper layer is the planning model,which takes the annual investment cost and operation and maintenance cost as the objective function,and determines the optimal type and capacity of the devices under the constraints of the installed capacities.The lower layer is the operation model,which takes the annual operation cost as the optimization goal,and determines the optimal operation plan of the selected devices under the condition that the power balance constraints and device operation constraints are met,and the flexibility of the rural loads is taken into account.The model is piecewise linearized,and based on the Benders decomposition,the bi-layer model is decomposed into a planning main problem and operation sub-problems of multiple scenarios and iteratively solved.The model is applied to an IRES case,and the results show that in the planning results based on the traditional energy hub(EH),the average operation load rates of devices are very different from the preset values,and the planning results based on DEH are more accurate.When the flexibility of rural loads is taken into account,the planning cost is reduced by 5.56%.