Hierarchical and Distributed Optimal Control Strategy for Power and Power Quality of Microgrid Based on Finite-Time Consistency

Droop control is one of the main control strategies of islanded microgrid(MG),but the droop control cannot achieve reasonable power distribution of microgrid,resulting in frequency and voltage deviation from the rating value,which needs the upper control link to eliminate the deviation.However,at present,most layered control requires a centralized control center,which excessively relies on microgrid central controller(MGCC)and real-time communication among distributed generation(DG),which has certain limitations.To solve the above problems,this paper proposes a hierarchical distributed power and power quality optimization strategy based on multi-agent finite time consistency algorithm(MA-FTCA).Firstly,based on the first layer droop control,MA-FTCA is applied to introduce frequency and voltage compensation to stabilize the system frequency and voltage at the rated value.Secondly,in the third layer,the MA-FTCA is adopted to estimate the total active power and total reactive power spare capacity of the system,to realize the reasonable distribution of active power and reactive power output of each DG according to its proportion of spare capacity when the system load side changes.The control strategy proposed in this paper adopts a completely distributed control method and does not need a centralized control center in each layer of control.Finally,MATLAB/Simulink simulation platform is used to verify the correctness and effectiveness of the proposed optimization strategy.

ENTROPY CONSISTENCY OF LARGE TIME STEP SCHEMES FOR ISENTROPIC EQUATIONS OF GAS DYNAMICS

In this paper, We show for isentropic equations of gas dynamics with adiabatic exponent gamma=3 that approximations of weak solutions generated by large time step Godunov's scheme or Glimm's scheme give entropy solution in the limit if Courant number is less than or equal to 1.

A survey of time consistency of dynamic risk measures and dynamic performance measures in discrete time: LM-measure perspective

In this work we give a comprehensive overview of the time consistency property of dynamic risk and performance measures,focusing on a the discrete time setup.The two key operational concepts used throughout are the notion of the LMmeasure and the notion of the update rule that,we believe,are the key tools for studying time consistency in a unified framework.

Overlapping Generations and Environmental Policy： An Introduction

A small but growing body of literature uses overlapping generations （OLG） models to study environmental policy for long-lived problems such as climate change. An OLG model, unlike the infinitely lived representative agent model, dis- tinguishes between impatience with respect to one＇s own future utility, and attitudes toward successors＇ utility. I discuss the problem of time inconsistency, the role of Markov perfection, and show that a class of OLG models can be studied using me- thods developed to analyze models of non-constant discounting. An example illu- strates the techniques and determines the conditions under which, in equilibrium, there is under-investment or over-investment in natural capital.

Optimal liquidation with dynamic parameter updating: A forward approach

We propose a forward approach to study the performance of liquidation strategies under sequential model parameter updates.The forward liquidation program consists of pasting forward in time and in a time-consistent fashion a series of optimal liquidation problems.They are triggered at the parameter shift instances,thus entirely eliminating model error,and last at most till the next parameter update.However,due to the nature of the model dynamics,solutions may cease to exist in finite time,even before the subsequent parameter update.Furthermore,forward liquidation strategies may never lead to full liquidation,even though they maximize the average utility of revenue and always preserve time-consistency.In juxtaposition,the traditional approach delivers full liquidation at the sought horizon but encounters considerable model error,generates value erosion,and is time-inconsistent.

Rank-dependent ppredictableforward performance processes

Predictable forward performance processes(PFPPs)are stochastic optimal control frameworks for an agent who controls a randomly evolving system but can only prescribe the system dynamics for a short period ahead.This is a common scenario in which a controlling agent frequently re-calibrates her model.We introduce a new class of PFPPs based on rank-dependent utility,generalizing existing models that are based on expected utility theory(EUT).We establish existence of rank-dependent PFPPs under a conditionally complete market and exogenous probability distortion functions which are updated periodically.We show that their construction reduces to solving an integral equation that generalizes the integral equation obtained under EUT in previous studies.We then propose a new approach for solving the integral equation via theory of Volterra equations.We illustrate our result in the special case of conditionally complete Black-Scholes model.