With the increasing integration of various renewable energy resources into electric power systems, the stochastic power flow calculation, which falls into the uncertainty propagation (UP) problems, has attracted renewed interest recently. The adaptive sparse pseudospectral approximation method (A-SPAM) is highly efficient to solve UP problems. However, there still exist two major drawbacks: 1) its global error estimate is not accurate enough to evaluate the actual approximation error; and 2) it is intended for single-output systems and there is no efficient method for its application in multi-output systems. In order to overcome these two defects, in this study a new adaptive sparse pseudospectral approximation method (NA-SPAM) is proposed based on two improvements on A-SPAM. First, a new global error estimate is presented which features much higher estimation accuracy. Second, a revised version of error estimates is given based on unification of the index set, which apparently improves the calculation efficiency for multi-output problems. At last, the effectiveness of the two improvements and NA-SPAM is validated by mathematical and practical cases.

The industrial internet of things (IIoT) is emerging as a global trend to dramatically enhance the intelligence and efficiency of the industries in recent years. With the emphasis on data communication by IIoT, cyber vulnerabilities are introduced at the same time. As a key subsystem of the industrial automation systems, the supervisory control and data acquisition (SCADA) system is becoming one of the primary targets for cyberattacks in the IIoT paradigm. In this paper, the semi-Markov process (SMP) is employed to model and evaluate the cyberattacks against the SCADA systems considering the insider assistance. Based on the SMP model, the probability distribution of the time-to-compromise the system of the attacks is derived with the Monte Carlo simulation (MCS). Then, a FlipIt game model is developed to investigate the defense and attack strategies of the defender and attacker, and analyze the impacts of the insider assistance. Case studies were carried out to verify the proposed model. The results of the case studies show that the insider assistance will improve the payoff of the attacker and increase the defense action frequency of the system defender. With a high enough defense action frequency, the defender can force the attacker to drop out and eliminate the attack actions.

As one of the national critical infrastructures, the water distribution system supports our daily life and economic growth, the failure of which may lead to catastrophic results. Besides the uncertainty from the system component failures, cyberattacks are vital to the secure system operation and have great impacts on the reliability of the water supply service. Malicious attackers may intrude into the supervisory control and data acquisition (SCADA) system of pump stations in the water distribution networks and interrupt the water supply to the customers. Cyber insurance is emerging as a promising financial tool in system risk management. In this paper, cyber insurance is proposed for the cyber risk management of the water distribution system. A semi-Markov process (SMP) model is devised to model the cyberattacks against pump stations in the water distribution system. Both the impacts of the independent cyber risks in the individual distribution network and the correlated cyber risks shared across different water distribution networks are evaluated and modeled. A sequential Monte Carlo Simulation (MCS) based algorithm is developed to evaluate the system loss. Cyber insurance premiums for the water distribution networks are designed based on the actuarial principles and potential system losses. Case studies are also performed on multiple representative water distribution networks, and the results demonstrate the validity of the proposed cyber insurance model.

Photovoltaic-assisted charging station (PVCS) is expected to be one of the important charging facilities for serving electric vehicles (EVs). In this paper, a type of solid-state transformer (SST) is introduced to the PVCS design and an autonomous energy management strategy (EMS) for SST is proposed. This study aims to develop an effective real-time EMS for PVCS participating in ancillary service of smart grid, and the rule-based decision-making method is utilized. Considering the dynamic classification of EVs, an energy-bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible resources. Moreover, considering the EBC results and power command from the aggregator, a charging power allocation algorithm is designed for power distribution of flexible EVs. By case study and experiment analysis, the proposed EMS is effective in real-time energy management and suitable for practical applications.

In this paper, an intelligent control system for indoor air quality in energy-efficient buildings is proposed. The goal of intelligent air quality control for energy-efficient buildings is to maintain the indoor CO2concentration in the comfort zone with a minimum amount of energy consumption. In this study, the CO2concentration is used as the indicator of indoor air quality and a CO2predictive model is utilized to forecast the indoor CO2concentration. Particle swarm optimization (PSO) is applied to derive the optimal ventilation rate. As compared with the traditional ON/OFF ventilation control system, the performance of the proposed intelligent control system has demonstrated its advantage in terms of energy savings. A case study and corresponding simulation results are detailed in the paper.

Central Force Optimization (CFO) is a powerful new metaheuristic algorithm that has been demonstrated to be competitive with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Group Search Optimization (GSO). While CFO often shows superiority in terms of functional evaluations and solution quality, the algorithm is complex and often requires increased computational time. In order to decrease CFO's computational time, we have implemented the concept of local neighborhoods and implemented CFO on a Graphics Processing Unit (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) extensions for C/C++. Pseudo Random CFO (PR-CFO) is examined using four test problems ranging from 30 to 100 dimensions. Results are compared and analyzed across four unique implementations of the PR-CFO algorithm: Standard, Ring, CUDA, and CUDA-Ring. Decreases in computational time along with superiority in terms of solution quality are demonstrated.

In this paper a fuzzy adaptive comfort temperature (FACT) model has been proposed for the intelligent control of smart buildings. A multi-agent control system is applied for the energy management and building operation. Particle Swarm Optimization (PSO) is applied to optimize the set points based on the comfort zone. Integrating a grey predictor to predict outdoor temperature with the FACT model shows great promise in systematically determining the customer temperature comfort zone for smart buildings. With the application of the FACT model and other intelligent technologies, the multi-agent control system has successfully provided a high-level of temperature comfort with low power consumption to customers in smart building environments. Case studies and corresponding simulation results are presented and discussed in this paper.

Monte Carlo Simulation (MCS) is a very powerful and flexible tool when used for sampling states during the probabilistic reliability assessment of power systems. Despite the advantages of MCS, the method begins to falter when applied to large and more complex systems of higher dimensions. In these cases it is often the process of classifying states that consumes the majority of computational time and resources. This is especially true in power systems reliability evaluation where the main method of classification is typically an Optimal Power Flow (OPF) formulation in the form of a linear program (LP). Previous works have improved the computational time required for classification by using Neural Networks (NN) of varying types in place of the OPF. A method of classification that is lighter weight and often more computationally efficient than NNs is the Support Vector Machine (SVM). This work couples SVM with the MCS algorithm in order to improve the computational time of classification and overall reliability evaluation. The method is further extended through the use of a multi-core architecture in order to further decrease computational time. These formulations are tested using the IEEE Reliability Test Systems (IEEE-RTS79 and IEEE-RTS96). Significant improvements in computational time are demonstrated while a high level of accuracy is maintained.

Intelligent buildings are a trend of next-generation's buildings, which facilitate intelligent control of the building in order to fulfill occupants' comfort demands. The primary objective in building control is to achieve a comfortable building environment with high energy efficiency. By dividing the whole building into several zones, a multi-zone building model is built for developing an effective energy and comfort management scheme. This study proposes a multi-agent control system coupled with an intelligent optimizer for intelligent building control. Particle swarm optimization (PSO) is utilized to optimize the building energy management by enhancing the intelligence of the multi-zone building during its operations. A case study of multi-zone building control is carried out and the corresponding simulation results are presented in this paper.

The speed gap between processor and memory is the major bottleneck for modern computing systems. Many modern processors, such as the CELL processor, employ multi-core, multimodule architecture to hide memory access latency. However, making effective use of multiple memory modules remains difficult, considering the combined effect of performance and energy requirements. This paper studies the scheduling and assignment problem that optimize both energy and performance. An efficient algorithm, EALSPP (Energy Aware Loop Scheduling with Prefetching and Partition), is proposed. The algorithm attempts to maximize energy saving while hiding memory latency with the combination of loop scheduling, data prefetching, memory partition, and heterogeneous memory module type assignment. Experimental results demonstrate the effectiveness of our approach.

Energy saving is critical to real-time embedded systems. In many embedded systems, some tasks contain conditional instructions or operations that could have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable. We propose a practical algorithm to minimize the expected value of total energy consumption while satisfying the timing constraint with a guaranteed confidence probability for uniprocessor embedded systems with continuous probability distributions. The experimental results show that our approach achieves significant energy saving than previous work.

Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted.

Reliability evaluation of power generation systems using probabilistic methods has drawn much attention due to their capacity to account for system uncertainties. However, because of the large number of possible failure states involved in the power system, it is normally not viable to exhaustively enumerate and evaluate all the states which may contribute to system failure. Meanwhile, time-dependent sources such as wind turbine generators are being more significantly integrated into the traditional power grid for cleaner power generation. The intermittency of wind power sources further complicates the reliability evaluation process. In this paper, a binary particle swarm optimization (BPSO) is adopted to derive a set of meaningful system states, which significantly affects the adequacy indices of generation system including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). A numerical example is used to verify the applicability and validity of the proposed population-based intelligent search (PIS) based evaluation procedure. Especially, a comparative study in relation to the exact method and Monte Carlo simulation (MCS) is carried out.

With the stricter environmental regulation and diminishing fossil-fuel reserve, various renewable sources of energy are being exploited. These alternative sources of energy are usually environmentally friendly and emit no pollutants. However, the capital investments for those renewable sources of energy are normally high and there are also maintenance cost differences to be considered. Furthermore, due to the variability of these power sources, reliability issues should be addressed when integrating different power sources. In this paper, a grid-connected hybrid generating system comprising wind turbine generators, photovoltaic panels, and storage batteries is designed. In this multi-source generation system design, three design objectives are considered, that is, costs, reliability, and pollutant emissions. Considering the complexity of this problem, we have developed a multi-objective particle swarm optimization (MOPSO) algorithm to derive a set of non-dominated solutions, each of which represents a candidate system design. A numerical example is discussed to illustrate the design procedure and the simulation results are analyzed

The widespread applications of rotating machines, such as turbine machinery, in both industry and commercial life requires advanced technologies to efficiently and effectively test their operational status before they begin their practical productions in the plant. This paper discusses the development of a general flexible automatic test system (ATS) for turbine machinery. In order to meet the demanding test requirements for a large and diverse community of turbine machinery, the proposed automatic test system has a contemporary Windows interface, graphical interaction, and can be easily configured to include functions required by current and emerging test demands. The design and implementation of such a test system is approached from an object-oriented (OO) software engineering point of view for ease of operation, expansion, and maintenance. Practical implementation upon a real industrial plant shows the validity and effectiveness of the implemented ATS for improving the performance and quality of turbine machinery. The obtained test system delivers the performance to meet all rigorous test throughput requirements. Software design in the industrial automation arena becomes more challenging nowadays than ever, due to the increasingly complicated industrial processes and more demanding measurement tasks. This paper presents a flexible automatic test system (ATS) for rotating-turbine machinery based on the systematic object-oriented (OO) software engineering. In this OO method, the process of software development is divided into five major phases: requirement capture, analysis, design, programming, and testing. Requirement capture collects both functional and nonfunctional user requirements for developing the intended system. In the analysis phase, the objects in the problem domain are modeled, and the desired system operations are studied. In the design phase, the results obtained from the analysis phase are converted into a form that can be implemented using programming languages. In the design phase, the issues on how the structures are formed and how they collaborate with one another via interfaces are figured out. In the programming phase, the code for realizing the target system is developed. Finally, in the test phase, the system is tested against the specified requirements to ensure its correctness in both functionality and performance aspects. By adopting the OO software-development method, the flexible ATS for turbine machinery software is developed in an efficient manner. Meanwhile, other novel technologies such as configuration, database management, graphical user interface, Internet, multithreaded programming, and ActiveX Automation are all incorporated into the system development. The method discussed in the paper can be easily extended to the development of other software-intensive industrial automation systems.