Experimental Study of Methods of Scenario Lattice Construction for Stochastic Dual Dynamic Programming

The stochastic dual dynamic programming (SDDP) algorithm is becoming increasingly used. In this paper we present analysis of different methods of lattice construction for SDDP exemplifying a realistic variant of the newsvendor problem, incorporating storage of production. We model several days of work and compare the profits realized using different methods of the lattice construction and the corresponding computer time spent in lattice construction. Our case differs from the known one because we consider not only a multidimensional but also a multistage case with stage dependence. We construct scenario lattice for different Markov processes which play a crucial role in stochastic modeling. The novelty of our work is comparing different methods of scenario lattice construction. We considered a realistic variant of the newsvendor problem. The results presented in this article show that the Voronoi method slightly outperforms others, but the k-means method is much faster overall.

Design of 1 kbit antifuse one time programmable memory IP using dual program voltage

A 1 kbit antifuse one time programmable(OTP) memory IP,which is one of the non-volatile memory IPs,was designed and used for power management integrated circuits(ICs).A conventional antifuse OTP cell using a single positive program voltage(VPP) has a problem when applying a higher voltage than the breakdown voltage of the thin gate oxides and at the same time,securing the reliability of medium voltage(VM) devices that are thick gate transistors.A new antifuse OTP cell using a dual program voltage was proposed to prevent the possibility for failures in a qualification test or the yield drop.For the newly proposed cell,a stable sensing is secured from the post-program resistances of several ten thousand ohms or below due to the voltage higher than the hard breakdown voltage applied to the terminals of the antifuse.The layout size of the designed 1 kbit antifuse OTP memory IP with Dongbu HiTek's 0.18 μm Bipolar-CMOS-DMOS(BCD) process is 567.9 μm×205.135 μm and the post-program resistance of an antifuse is predicted to be several ten thousand ohms.

TOPOLOGY OPTIMIZATION DESIGN OF CONTINUUM STRUCTURES UNDER STRESS AND DISPLACEMENT CONSTRAINTS

Topology optimization design of continuum structures that can take account of stress and displacement constraints simultaneously is difficult to solve at present. The main obstacle lies in that, the explicit function expressions between topological variables and stress or displacement constraints can not be obtained using homogenization method or variable density method. Furthermore, large quantities of design variables in the problem make it hard to deal with by the formal mathematical programming approach. In this paper, a smooth model of topology optimization for continuum structures is established which has weight objective considering stress and displacement constraints based on the independent_continuous topological variable concept and mapping transformation method proposed by Sui Yunkang and Yang Deqing. Moreover, the approximate explicit expressions are given between topological variables and stress or displacement constraints. The problem is well solved by using dual programming approach, and the proposed element deletion criterion implements the inversion of topology variables from the discrete to the continuous. Numerical examples verify the validity of proposed method.

Two-Stage Stochastic Dual Dynamic Programming for Transmission Expansion Planning with Significant Renewable Generation and N-k Criterion

The large-scale integration of renewable energy sources(RES)is the global trend to deal with the energy crisis and greenhouse emissions.Due to the intermittent nature of RES together with the uncertainty of load demand,the problem of transmission expansion planning(TEP)is facing more and more challenges from uncertainties.In this paper,the TEP problem is modeled as a two-stage formulation,so as to minimize the total of investment costs and generation costs.To ensure the utilization level of the RES generation,the expansion plan is required to provide sufficient transmission capacity for the integration of RES.Also,N-k security criterion is considered into the model,so the expansion plan can meet the required security criteria.The stochastic dual dynamic programming(SDDP)approach is applied to consider the uncertainties,and the whole model is solved by Benders’decomposition technique.Two case studies are carried out to compare the performance of the SDDP approach and the deterministic approach.Results show that the expansion plan obtained by the SDDP approach has a better performance than that of the deterministic approach.

Primal dual interior point dynamic programming for coordinated charging of electric vehicles

Coordinated charging of electric vehicles(EVs)is critical to provide safe and cost effective operation of distribution systems where household single phase charging of EV could contribute to imbalance of the distribution system.To date,reported researches on optimization methods for coordinated charging aiming at minimizing power losses have the disadvantages of low calculation efficiency when applied to large systems or have not taken the voltage constraints into account.The phase component and polar coordinates power flow equations of an unbalanced distribution system are derived.Primal dual interior point dynamic programming is introduced for coordinated charging of EVs to minimize distribution system losses where charging demand,voltage and current constraints have been taken into account.The proposed optimization is evaluated using an actual 423-bus case as the test system.Results are promisingwith the proposed method having good convergence under time-efficient calculations while providing optimization of power losses,lower load variance,and improvement of voltage profile versus uncoordinated scenarios.

A NEW DUAL PROBLEM FOR NONDIFFERENTIABLE CONVEX PROGRAMMING

This paper gives a new dual problem for nondifferentiable convex programming and provesthe properties of weak duality and strong duality and offers a necessary and sufficient condition ofstrong duality.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.

Identification of the Worst-case Static Voltage Stability Margin Interval of AC/DC Power System Considering Uncertainty of Renewables

Calculation of static voltage stability margin(SVSM)of AC/DC power systems with lots of renewable energy sources(RESs)integration requires consideration of uncertain load growth and renewable energy generation output.This paper presents a bi-level optimal power flow(BLOPF)model to identify the worst-case SVSM of an AC/DC power system with line commutation converter-based HVDC and multi-terminal voltage sourced converter-based HVDC transmission lines.Constraints of uncertain load growth’s hypercone model and control mode switching of DC converter stations are considered in the BLOPF model.Moreover,uncertain RES output fluctuations are described as intervals,and two three-level optimal power flow(TLOPF)models are established to identify interval bounds of the system worst-case SVSM.The two TLOPF models are both transformed into max–min bi-level optimization models according to independent characteristics of different uncertain variables.Then,transforming the inner level model into its dual form,max–min BLOPF models are simplified to single-level optimization models for direct solution.Calculation results on the modified IEEE-39 bus AC/DC case and an actual large-scale AC/DC case in China indicate correctness and efficiency of the proposed identification method.

Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy

With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation costs.In this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical data.Furthermore,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart formulation.Based on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart problem.Specifically,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative process.Finally,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.