Modeling the effects of demand response on generation expansion planning in restructured power systems

Demand response is becoming a promising field of study in operation and planning of restructured power systems. More attention has recently been paid to demand response programs. Customers can contribute to the operation of power sys- tems by deployment demand response. The growth of customers＇ participation in such programs may affect the planning of power systems. Therefore, it seems necessary to consider the effects of demand response in planning approaches. In this paper, the impact of demand responsiveness on decision making in generation expansion planning is modeled. Avoidance or deferment in installation of new generating units is comprehensively investigated and evaluated by introducing a new simple index. The effects of demand responsiveness are studied from the points of view of both customers and generation companies. The pro- posed model has been applied to a modified IEEE 30-bus system and the results of the study are discussed. Simulation results show that reducing just 3% of the customers＇ demand （due to price elasticity） may result in a benefit of about 10% for customers in the long term.     

A study on robust design optimization of layered plate bonding process considering uncertainties

Design optimization of layered plate bonding process is conducted by considering uncertainties in a manufacturing process, to reduce the crack failure arising due to the difference of thermal expansion coefficients of the adherents. Robust optimization is performed to minimize the mean and variance of the residual stress, which is the major cause of the failure, while constraining the distortion and the instantaneous maximum stress to the allowable limits. In this optimization, the dimension reduction (DR) method is employed to quantify the uncertainty of the responses in the bonding process. It is expected that the DR method benefits the optimization from the perspectives of efficiency, accuracy, and simplicity. Response surface method (RSM) combined with sequential approximate optimization (SAO) technique is employed as an optimization tool. The obtained robust optimal solution is verified by the Monte Carlo simulation.     

A robust optimisation model and cutting planes for the planning of energy-efficient wireless networks

In this paper, we present an optimisation model for the energy-efficient planning of future wireless networks. By applying robust optimisation, we extend this model to a robust formulation which considers demand uncertainties. The computability of the resulting model is moderate. Hence, we apply three different cutting plane approaches for an improvement. Furthermore, an extensive case study is performed to examine the price of robustness, to compare the robust solution to conventional planning, and to explore the performance of the cutting planes.     

Harmony search-based multi-objective optimization model for multi-site order planning with multiple uncertainties and learning effects

This paper addresses a multi-objective multi-site order planning problem in make-to-order manufacturing with the consideration of various real-world features such as production uncertainties and learning effects. A novel harmony search-based multi-objective optimization model, mainly integrating a harmony search based Pareto optimization (HSPO) process and a Monte Carlo simulation process, is developed to tackle this problem. A series of experiments are conducted to evaluate the effectiveness of the proposed model based on real industrial data. Results demonstrate that (1) the proposed model can effectively solve the problem investigated; and (2) the HSPO process can generate the optimization performance superior to those generated by a multi-objective genetic algorithm (NSGA-II)-based process and an industrial method.     

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.     

A novel two-stage evolutionary optimization method for multiyear expansion planning of distribution systems in presence of distributed generation

In this paper, a new approach for multiyear expansion planning of distribution systems (MEPDS) is presented. The proposed MEPDS model optimally specifies the expansion schedule of distribution systems including reinforcement scheme of distribution feeders as well as sizing and location of distributed generations (DGs) during a certain planning horizon. Moreover, it can determine the optimal timing (i.e. year) of each investment/reinforcement. The objective function of the proposed MEPDS model minimizes the total investment, operation and emission costs while satisfying various technical and operational constraints. In order to solve the presented MEPDS model as a complicated multi-dimensional optimization problem, a new two-stage solution approach composed of binary modified imperialist competitive algorithm (BMICA) and Improved Shark Smell Optimization (ISSO), i.e. BMICA + ISSO, is presented. The performance of the suggested MEPDS model and also two-stage solution approach of BMICA + ISSO is verified by applying them on two distribution systems including a classic 34-bus and a real-world 94-bus distribution system as well as a well-known benchmark function. Additionally, the achieved results of BMICA + ISSO are compared with the obtained results of other two-stage solution methods.