With the continuous development of social economy and the introduction of various kinds of distributed energy resources, regular grid is gradually replaced by the smart grid in order to realize the bidirectional service relationship between supply and demand. On the basis of vigorous development of the smart grid, people further put forward the concept of smart energy resource grid. Smart energy resource grid binds resources together, such as electricity, heat, gas, energy storage, and uniformly solves the scheduling problems. Taking an example of the smart energy resource grid in the low- district of Shanghai Tower, this paper presents a kind of hierarchical optimal strategy and applies mathematical programming and distributed model predictive control algorithm(DMPC) so as to optimize the steady state and dynamic characteristics of the cooling system and guarantee efficient operation of the whole smart energy resource grid.First of all, this paper describes structure features of smart energy resource grid, introduces basic operation characteristics and control method of primary distributed energy which mainly includes conventional electric chillers, ice storage system, small-sized wind turbines and combined cooling, heating and power system(CCHP).Then considering the actual situation of the smart energy resource grid under research, this paper simplifies the scheduling problem under steady state, focuses on joint cooling system of conventional electric chillers and ice storage system, establishes corresponding economic models and uses continuous and integer variables to describe the refrigeration power and on-off state of each cold source respectively. In this way a mixed integer programming problem is formed to solve out the minimum total electricity cost consumed by entire smart energy resource grid in a certain period by optimizing on-off state and cooling power of each cold source. Simulation examples of single period and continuous periods indicate that the scheduling method under steady state is able to quantitatively figure out power allocation of each cold source and reduce expenses.Next considering dynamic regulation features and physical constraints of each chiller, a coordinated method with coupling targets based on distributed model predictive control is proposed so as to achieve better dynamic performance. First order inertial elements and delay elements are used to characterize dynamic performance of chillers. The setting value of each chiller is re-optimized by rolling optimization algorithm under distributed framework to guarantee each track the best setting value and the total load is satisfied as well. In order to solve out this distributed model predictive control problem, this paper establishes a kind of iterative algorithm. Simulation results indicate that the optimization strategy proposed in this paper shows good dynamic performance. |