Dynamic properties of chasers in a moving queue based on a delayed chasing model

A delayed chasing model is proposed to simulate the chase behavior in the queue, where each member regards the closest one ahead as the target, and the leader is attracted to a target point with slight fluctuation. When the initial distances between neighbors possess an identical low value, the fluctuating target of the leader can cause an amplified disturbance in the queue. After a long period of time, the queue recovers the stable state from the disturbance, forming a straightline-like pattern again, but distances between neighbors grow. Whether the queue can keep stable or not depends on initial distance, desired velocity, and relaxation time. Furthermore, we carry out convergence analysis to explain the divergence transformation behavior and confirm the convergence conditions, which is in approximate agreement with simulations.

POLYNOMIAL DYNAMIC PROGRAMMING ALGORITHMS FOR LOT SIZING MODELS WITH BOUNDED INVENTORY AND STOCKOUT AND/OR BACKLOGGING

This paper addresses a dynamic lot sizing problem with bounded inventory and stockout where both no backlogging and backlogging allowed cases are considered. The stockout option means that there is outsourcing in a period only when the inventory level at that period is non-positive. The production capacity is unlimited and production cost functions are linear but with fixed charges. The problem is that of satisfying all demands in the planning horizon at minimal total cost. We show that the no backlogging case can be solved in O（T^2） time with general concave inventory holding and outsourcing cost functions where T is the length of the planning horizon. The complexity can be reduced to O（T） when the inventory holding cost functions are also linear and have some realistic properties, even if the outsourcing cost functions remain general concave functions. When the inventory holding and outsourcing cost functions are linear, the backlogging case can be solved in O（ T^3 logT） time whether the outsourcing level at each period is bounded by the sum of the demand of that period and backlogging level from previous periods, or only by the demand of that period.

Energy storage resources management:Planning,operation,and business model

With the acceleration of supply-side renewable energy penetration rate and the increasingly diversified and complex demand-side loads,how to maintain the stable,reliable,and efficient operation of the power system has become a challenging issue requiring investigation.One of the feasible solutions is deploying the energy storage system(ESS)to integrate with the energy system to stabilize it.However,considering the costs and the input/output characteristics of ESS,both theinitial configuration process and the actual operation process require efficient management.This study presents a comprehensive reviewof managing ESs from the perspectives of planning,operation,and business model.First of all,in terms of planning and configuration,it is investigated from capacity planning,location planning,as well as capacity and location combined planning.This process is generally the first step in deploying ESS.Then,it explores operation management of ESS from the perspectives of state assessment and operation optimization.The so-called state assessment refers to the assessment of three aspects:The state of charge(SOC),the state of health(SOH),and the remaining useful life(RUL).The operation optimization includes ESS operation strategy optimization and joint operation optimization.Finally,it discusses the business models of ESS.Traditional business models involve ancillary services and load transfer,while emerging business models include electric vehicle(EV)as energy storage and shared energy storage.