Multi-objective optimization for the multi-mode finance-based project scheduling problem

The finance-based scheduling problem(FBSP)is about scheduling project activities without exceeding a credit line financing limit.The FBSP is extended to consider different execution modes that result in the multi-mode FBSP(MMFBSP).Unfortunately,researchers have abandoned the development of exact models to solve the FBSP and its extensions.Instead,researchers have heavily relied on the use of heuristics and meta-heuristics,which do not guarantee solution optimality.No exact models are available for contractors who look for optimal solutions to the multi-objective MMFBSP.CPLEX,which is an exact solver,has witnessed a significant decrease in its computation time.Moreover,its current version,CPLEX 12.9,solves multi-objective optimization problems.This study presents a mixed-integer linear programming model for the multi-objective MMFBSP.Using CPLEX 12.9,we discuss several techniques that researchers can use to optimize a multi-objective MMFBSP.We test our model by solving several problems from the literature.We also show how to solve multi-objective optimization problems by using CPLEX 12.9 and how computation time increases as problem size increases.The small increase in computation time compared with possible cost savings make exact models a must for practitioners.Moreover,the linear programming-relaxation of the model,which takes seconds,can provide an excellent lower bound.

Sequential dynamic resource allocation in multi-beam satellite systems:A learning-based optimization method

Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources.One of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)strategy.This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in DRA.By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly.To pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load simultaneously.Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance.

Whole-lifetime Coordinated Service Strategy for Battery Energy Storage System Considering Multi-stage Battery Aging Characteristics

One battery energy storage system(BESS)can be used to provide different services,such as energy arbitrage(EA)and frequency regulation(FR)support,etc.,which have different revenues and lead to different battery degradation profiles.This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS.A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime.Considering the uncertainty of electricity price in EA service and frequency deviation in FR service,the whole problem is formulated as a twostage stochastic programming problem.At the first stage,the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit.At the second stage,the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale,whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale.The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming(MINLP)model with bilinear items.Mc Cormick envelopes and a bound tightening algorithm are used to solve it.Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.