基于综合储能设备的工业园区联络线功率实时MPC控制方法

工业园区微电网基础设施完善、储能设备众多,具有参与日前调峰、日内实时跟踪控制的潜力。工业园区内可能存在多个企业厂房,生产设备的种类有所差异,通过调整燃机出力实时控制联络线功率的传统控制方式,在不同的考核方案下难以兼顾各利益主体间的关系。因此从园区角度,利用园区内可支配的储能设备实行日内实时控制,能够有效协调各方经济利益,获得良好的控制效果。文章建立了储气、储冷、储电等多种储能设备的数学模型和房间热模型,为应对由于光伏、风电等清洁能源日前预测误差所产生的联络线功率波动,同时确保多种储能设备满足运行约束和冷、热、电、气负荷能量平衡约束,提出了一种基于模型预测控制(model predictive control,MPC)的日内滚动优化校正策略,在日前经济调度结果的基础上,通过调整储能设备的运行功率消除微电网中不确定因素的影响,以确保实现对日前调度结果的跟踪。以南方某蓄电池生产工业园区为例,通过算例分析验证了所提优化模型及算法的有效性。

Real-Time MPC Control Method for Tie-Line Power in Industrial Park Considering Integrated Energy Storage Devices

With complete infrastructure and numerous energy storage devices， the industrial parks have the potential to participate in day-ahead peak-load regulation and day-ahead real-time tracking control. There may be many factory buildings in the industrial park， and the types of production devices are different. By adjusting the traditional control method of tie-line power real-time controlled by gas turbine power output， it is difficult to take into account the relationship between various interest subjects under different assessment schemes. Therefore， from the perspective of the park， by using of disposable energy storage devices in the park to implement real-time control， can effectively coordinate the economic interests of all parties， and obtain good control effect. Mathematics model of gas storage， cooling storage， electricity storage and some other storage devices and the thermal model of the room are established in this paper. In response to the tie-line power fluctuations caused by forecast error of the photovoltaic， wind power and other clean energy， and at the same time to ensure a variety of energy storage devices to meet operating constraints and energy balance constraints of cooling， heat， electricity， gas load， this paper proposes an intra-day rolling optimization correction strategy on the basis of MPC. According to the results of day-ahead economic operation， operators can adjust the operating power of energy storage devices to eliminate the influence of uncertain factors in the microgrid， and to ensure the implementation of scheduling results of tracking. Taking an industrial park for battery production in southern China as an example， the effectiveness of the proposed optimization model and algorithm is verified by numerical analysis.