Research On Energy Saving Control Strategy Of Heating Station With Peak-shaving Boiler

Energy problem is an important issue in the twenty-first century. Consideringthe large Energy consumption in Chin, the reduction of energy consumption and lowcarbon economy is one of basic national policy of China. Building heating is a majordomain of energy-saving, and the heating energy consumption of China’s northernregions had reached27.2%of total social energy consumption. The current problemof heating is high energy consumption but low efficiency, the heating energyconsumption in China per unit area is2and3times greater than that of thedeveloped countries, there is brilliant future in heating energy-saving. Therefore, itis significant to study the approach of heating energy-saving and control strategies.Heating station with peak-shaving boiler is a heating pattern in heating system,this pattern can effectively solve the problem of insufficient in the heating load inthe peak period, and play an important role in stability and good operation ofheating system. Energy-saving of heating station with peak-shaving can be donefrom three aspects. First, the heating load forecasting provides accurate heat load ofthe system requirements. Second, optimal dispatch makes heating station in theoptimal heat load distribution. Third, advanced control strategy achieves the aims ofenergy-saving.Heat load forecast is the premise of optimal dispatch. For the trend andperiodicity of heating load data, this thesis uses a multiple seasonal autoregressiveintegrated moving average (ARIMA) method in heating load forecasting. As thismethod is difficult to aquire accurate prediction results for where big mutations exist,so the Kalman recursive online is used to predict multiple seasonal ARIMA modelparameters in order to improve the prediction accurace of the load mutation. Tosolve nonlinear problem in heat load data, this paper introduces the minimaxprobability machine(MPM) theory and phase space reconstruction with acombination of MPM in order to forecast heating load, and compare this methodwith neural network and support vector machines(SVM) forecasting methods.Finally the thesis analyzes the performance of different forecasting methods withsimulation.Optimal dispatch of heating station with peak-shaving boiler is the key toachieve heat energy saving. This paper utilizes heat load forecasting results to meetthe national heating standards, and provides accurate output heat load of heatingstation. Optimal dispatch of heating station with peak-shaving considers the heatenergy consumption and economy to achieve comprehensive best results. Nonlinearprogramming method is used to solve optimal dispatching problems, but this method depends on initial value. Immune particle swarm (IPSO) has better search ability.The simulation results show that the immune particle swarm optimization method ismore convenient and faster than traditional optimal computing.Optimal dispatching results achieved through the control, this paper uses acombination of rising curves and least-squares method to establish quality-adjustmodel, quantity-adjust model, boiler temperature disturbance model and flowdisturbance model. In order to reduce the impact of boiler temperature disturbanceand flow disturbance, a three parameters predictive control based on feedforwardcompensation is proposed, and a neural network predictive control is used in thequality-adjust channel, and an improved differential evolution method is used tosolve the control law. Simulation results show the effectiveness of the threeparameters predictive control method.Finally, based on the engineering requirements of heating station withpeak-shaving boiler, the thesis develops a heating station monitoring device whichincludes hardware and software, PLC and GPRS wireless communication. Theintelligent algorithm was programmed by MATLAB, data exchange was realiezedbetween the optimal sofeware and MATLAB through the OPC. Operating in Ranghudistrict of Daqing for more than a year, the monitoring device achieved the purposeof energy-saving, and pass the performance tests of national quality supervisiondepartment.