Energy-consumption-related robust optimization scheduling strategy for elevator group control system

The maintenance and service support of carrier aircraft (MSSCA) is a complex process involving many types of resources and activities that require optimisation which can be considered as a multi-resource constrained multi-project scheduling problem (MRCMPSP). It is of great significance to optimize the makespan and obtain a proactive robust schedule to be adaptive to the changes in the dynamic flight deck environment. This paper develops a critical chain method (CCM) for carrier aircraft support robust scheduling in a time-critical and resource-constrained operation environment. The CCM consists of developing a desirable deterministic schedule under multi resource constraints and time-critical issues, and adding a project buffer (PB) to the end of the schedule is used to get a robust proactive scheduling and deal with uncertainty. The triangular fuzzy number (TFN) is applied to describe the durations of each activities and calculate the PB size and obtain an appropriate proportionality between the activity duration and the buffer size. In the numerical research, different size simulations are designed to calculated the robust optimization scheduling. The computational results show that the CMM for carrier aircraft support robust scheduling can make a better decision in the robust proactive optimization scheduling about the resource allocation.

With the increasing penetration of renewable power source into the distribution network, maintaining system reliability has been a challenging issue due to the uncertainty and volatility characteristics of renewable power source. Based on the robust optimization theory, this paper proposed a multi-stage robust optimal scheduling of active distribution to maintain the voltage within the allowed range as well as develop the operation of distribution network. This methodology applies the demand responds method into the model in order to make full advantage of elastic load adjustment. The proposed method is accomplished in three phases. In the first stage, in order to determine the uncertain parameters, renewable energy output uncertainty is described by uncertain set. Meanwhile, the extreme scenario method is adopted to cut down the field of sets. In the second stage, the elastic load is used to moderate the load fluctuation by applying demand respond theory. In the third stage, based on the bi-level planning, distributed generation's (DG's) reactive output adjustment cooperates with traditional voltage regulation method, which reduces the regulating times of switching device operations and system network loss. Furthermore, the economy and stability of system operation improve significantly. Finally, the efficiency of the built model is analyzed using the PG&E 69-bus system.

From the commercial point of view, in tall buildings with multiple lifts or elevators, owners face problems like waste of space, more managing time and money. People travelling by lifts face problem of high waiting time. Elevator Group Control System (EGCS) manages several elevators in tall buildings for efficient carrying of passengers. Waiting time of people and car are major factors affecting the performance of EGCS. EGCS organizes elevators to reduce the performance evaluation measures. But, it is hard to achieve all measures. Hence, here we deal with the improvement of performance by reducing waiting time of the passengers. The proposed system introduces dynamic scheduling algorithm to schedule elevators dynamically. The proposed system uses fuzzy logic to schedule and dispatch the elevators. The main motive of the research work is to diminish the passenger’s waiting time and car allocation time.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

Chapter 25 - Uncertain optimization (with a special focus on data envelopment analysis)

Genetic programming network (GNP), a graph-based evolutionary method, has been proposed as an extension of genetic algorithm (GA) and genetic programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with ant colony optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to elevator group supervisory control systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

Elevator group control system (EGCS) with multi-objective, stochastic and nonlinear characteristics is a complex optimization system. After analyzing characteristic of typical traffic mode of elevator. This paper proposed a new simulation platform of an elevator group control system implemented in C# using the fuzzy-neural network technology. The result of simulation shows that this method realizes reasonable elevator dispatching under various passenger traffic conditions and indicates the validity of this method.

An efficient nonlinear interval uncertain optimization method using Legendre polynomial chaos expansion

An elevator system consists of an elevator group control system (EGCS) and a micro-processed sub-system that implements a local control system (LCS) for each elevator. This paper describes the validation of five dispatching algorithms for an elevator system that was implemented on Spartan 3 FGPA based boards in an integrated approach. The focus was on area reduction and performance improvement. The overall system is composed of several LCS, which implement the dispatching algorithms, an RS485 based network and a virtual environment called virtual elevator interface system (VEI). The latter includes a simulator/monitoring system and an EGCS based on fuzzy logic (FEGCS). The FEGCS runs on a PC and, under different traffic situations, determines the best algorithm to be run in each LCS reducing the user waiting time and the power consumption. The novelty of this approach is that the LCSs are capable to run several dispatching algorithms that are suitable for different passenger traffic situations, while the FEGCS only must determine the best algorithm to be run in each LCS. The VEI allows the designer to test and validate in a flexible way the algorithm performance for different traffic situations.

Aiming at the problem of uncertainty for information collection in traditional elevator group control system (EGCS), this study proposes a method about dispatching approach optimization for a new type of EGCS with destination floor guidance. The characteristic of destination floor guidance is information admixture of hall call registration and destination selection. EGCS with destination floor guidance can attain nearly complete and reliable information aggregation in advance to guide the passenger flow effectively. In order to realize the accurate prediction of input parameters, all of the hall call registrations are classified to four groups. Based on it, the optimized prediction algorithms of evaluation items are established, including the average waiting time of passengers (AWT), the average riding time of passengers (ART), rate of waiting long time of passengers (LWR), and riding power consumption (RPC). Especially, Fuzzy Neural Network (FNN) is applied to optimize multiple objectives for realizing reasonable dispatching approach. The simulated results show that the algorithm applied in the paper improves the evaluation items by 10% at least as compared with the traditional algorithm and it indicates an excellent application to enhance the holistic capability of EGCS.

This paper presents a new group controller's adaptation mechanism with fuzzy logic for elevator group control system (EGCS) applications. Instead of depending heavily on the predicted passenger traffic pattern for adaptation, the fuzzy logic group controller (FLGC) adjusts itself to suit the system's environment through a self-tuning scheme. The average-waiting-time data that reflect the measured performance results of the EGCS are exploited to tune the membership functions of the input parameters and to modify the fuzzy rule sets employed by the FLGC, thus leading to better controller's performance and improved EGCS service quality. Computer simulation shows satisfactory improvements made in passenger waiting time and passenger riding time as compared to the performance of the conventional group controllers in three traffic peaks. The hardware implementation of the self-tuning FLGC on a TMS320C6416T digital signal processor further demonstrates its practicality.

In this paper we consider general form of a optimization problem in face of data uncertainty. We determine a necessary and sufficient condition ensuring the equality between the worst value and the robust value of this uncertain problem with attainment of the worst value. And we deduce robust strong duality and robust stable strong duality property. Keywords: Uncertain optimization problem; Robust counterpart; Robust value; Worst value; Robust strong duality. Mathematics Subject Classification: 90C17, 90C31, 90C46

Chapter 4 - Data-driven robust stochastic optimization for power systems operations

In engineering design, an optimized solution often turns out to be suboptimal when errors are encountered. Although the theory of robust convex optimization has taken significant strides over the past decade, all approaches fail if the underlying cost function is not explicitly given; it is even worse if the cost function is nonconvex. In this work, we present a robust optimization method that is suited for unconstrained problems with a nonconvex cost function as well as for problems based on simulations, such as large partial differential equations (PDE) solver, response surface, and Kriging metamodels. Moreover, this technique can be employed for most real-world problems because it operates directly on the response surface and does not assume any specific structure of the problem. We present this algorithm along with the application to an actual engineering problem in electromagnetic multiple scattering of aperiodically arranged dielectrics, relevant to nanophotonic design. The corresponding objective function is highly nonconvex and resides in a 100-dimensional design space. Starting from an “optimized” design, we report a robust solution with a significantly lower worst-case cost, while maintaining optimality. We further generalize this algorithm to address a nonconvex optimization problem under both implementation errors and parameter uncertainties.

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties