光储联合发电系统的优化调度策略是实现光储联合发电系统经济及安全运行的重要保障,然而传统的经济优化调度模型并未考虑电池储能电站内部电池的有效管理.本文提出了一种经济优化调度策略,依据储能系统各电池组性能参数和运行状态,以储能系统运行一天总成本最低为优化目标,以系统平衡、荷电状态、功率限值和调度循环为约束条件,建立了经济优化调度数学模型,并应用改进粒子群算法进行求解.最后,算例仿真结果验证了改进粒子群算法的优越性和优化调度策略在光储联合发电系统中应用的可行性.

This paper deals with the problem of identifying linear multi-variable systems from data which is corrupted by an unknown,non-centered and sparse vector error sequence.This problem is sometimes considered as error correcting prob-lem in coding theory or robust estimation problem in statistics field.By taking advantage of some recent developments in sparse optimization theory,this paper presents a recursive approach to solve the problem.

This paper addresses the problem of driving the state of a linear discrete-time system to zero in minimum time. The inputs are constrained to lie in a bounded and convex set. The solution presented in the paper is based on the observation that the state sequence induced by the minimum-time control sequence is the sparsest possible state sequence over a certain finite horizon. That is, the desired state sequence must contain as many zero vectors as possible, all those zeros corresponding to the highest values of the time index. Hence, by taking advantage of some recent developments in sparse optimization theory, we propose a numerical solution. We show in simulation that the proposed method can effectively solve the minimum-time problem even for multi-inputs linear discrete-time systems.

The minimum-time control problem consists in finding a control policy that will drive a given dynamic system from a given initial state to a given target state (or a set of states) as quickly as possible. This is a well-known challenging problem in optimal control theory for which closed-form solutions exist only for a few systems of small dimensions. This paper presents a very generic solution to the minimum-time problem for arbitrary discrete-time linear systems. It is a numerical solution based on sparse optimization, that is the minimization of the number of nonzero elements in the state sequence over a fixed control horizon. We consider both single input and multiple inputs systems. An important observation is that, contrary to the continuous-time case, the minimum-time control for discrete-time systems is not necessarily entirely bang-bang.

This paper addresses the problem of identifying linear multi-variable models from the input-output data which is corrupted by an unknown, non-centered, and sparse vector error sequence. This problem is sometimes referred to as error correcting problem in coding theory and robust estimation problem in statistics. By taking advantage of some recent developments in sparse optimization theory, we present here a recursive approach. We then show that the proposed identification method can be adapted to estimate parameter matrices of Jump Markov Linear Systems (JMLS), that is, switched linear systems in which the discrete state sequence is a stationary Markov chain. Some numerical simulation results illustrate the potential of the new method.