Risk Constrained Self-scheduling of AA-CAES Facilities in Electricity and Heat Markets:A Distributionally Robust Optimization Approach

Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage technology in a low-carbon society.An appropriate self-scheduling model can guarantee AA-CAES’s profit and attract investments.However,very few studies refer to the cogeneration ability of AA-CAES,which enables the possibility to trade in the electricity and heat markets at the same time.In this paper,we propose a multimarket self-scheduling model to make full use of heat produced in compressors.The volatile market price is modeled by a set of inexact distributions based on historical data through-divergence.Then,the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory,and equivalently reformulated as a mixed-integer linear program(MILP).The numerical simulation results validate the proposed method and demonstrate that participating in multienergy markets increases overall profits.The impact of uncertainty parameters is also discussed in the sensibility analysis.

Topology Driven Cooperative Self Scheduling for Improved Lifetime Maximization in WSN

In Wireless Sensor Network(WSN),scheduling is one of the important issues that impacts the lifetime of entire WSN.Various scheduling schemes have been proposed earlier to increase the lifetime of the network.Still,the results from such methods are compromised in terms of achieving high lifetime.With this objective to increase the lifetime of network,an Efficient Topology driven Cooperative Self-Scheduling(TDCSS)model is recommended in this study.Instead of scheduling the network nodes in a centralized manner,a combined approach is proposed.Based on the situation,the proposed TDCSS approach performs scheduling in both the ways.By sharing the node statistics in a periodic manner,the overhead during the transmission of control packets gets reduced.This in turn impacts the lifetime of all the nodes.Further,this also reduces the number of idle conditions of each sensor node which is required for every cycle.The proposed method enables every sensor to schedule its own conditions according to duty cycle and topology constraints.Central scheduler monitors the network conditions whereas total transmissions occurs at every cycle.According to this,the source can infer the possible routes in a cycle and approximate the available routes.Further,based on the statistics of previous transmissions,the routes towards the sink are identified.Among the routes found,a single optimal route with energy efficiency is selected to perform data transmission.This cooperative approach improves the lifetime of entire network with high throughput performance.

Gain self-scheduled H_∞ control for morphing aircraft in the wing transition process based on an LPV model

This article investigates gain self-scheduled H 1 robust control system design for a tailless fold- ing-wing morphing aircraft in the wing shape varying process. During the wing morphing phase, the aircraft＇s dynamic response will be governed by time-varying aerodynamic forces and moments. Nonlinear dynamic equations of the morphing aircraft are linearized by using Jacobian linearization approach, and a linear parameter varying （LPV） model of the morphing aircraft in wing folding is obtained. A multi-loop controller for the morphing aircraft is formulated to guarantee stability for the wing shape transition process. The proposed controller uses a set of inner-loop gains to provide stability using classical techniques, whereas a gain self-scheduled H 1 outer-loop controller is devised to guarantee a specific level of robust stability and performance for the time-varying dynamics. The closed-loop simulations show that speed and altitude vary slightly during the whole wing folding process, and they converge rapidly after the process ends. This proves that the gain self-scheduled H 1 robust controller can guarantee a satisfactory dynamic performance for the morphing aircraft during the whole wing shape transition process. Finally, the flight control system＇s robustness for the wing folding process is verified according to uncertainties of the aerodynamic parameters in the nonlinear model.

From Viewpoint of Reserve Provider:A Day-ahead Multi-stage Robust Optimization Reserve Provision Method for Microgrid with Energy Storage

Energy storage(ES),as a fast response technology,creates an opportunity for microgrid(MG)to participate in the reserve market such that MG with ES can act as an independent reserve provider.However,the potential value of MG with ES in the reserve market has not been well realized.From the viewpoint of reserve provider,a novel day-ahead model is proposed comprehensively considering the effect of the real-time scheduling process,which differs from the model that MG with ES acts as a reserve consumer in most existing studies.Based on the proposed model,MG with ES can schedule its internal resources to give reserve service to other external systems as well as to realize optimal self-scheduling.Considering that the proposed model is just in concept and cannot be directly solved,a multi-stage robust optimization reserve provision method is proposed,which leverages the structure of model constraints.Next,the original model can be converted into a mixed-integer linear programming problem and the model is tractable with guaranteed solution feasibility.Numerical tests in a real-world context are provided to demonstrate efficient operation and economic performance.