With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the sch... [more]

Off-site reservoirs use water from other watersheds to supplement their water quantity. Water quality is usually better in off-site reservoirs than in onsite reservoirs, because in comparison to onsite reservoirs, watershed areas are smaller and fewer pollutants are collected; moreover, cleaner water is introduced. However, in Taiwan, the water quality of some off-site reservoirs can gradually worsen, and this factor needs to be addressed. In this study, the Liyutan reservoir in central Taiwan was used as an example to demonstrate the process of evaluating pollution in an off-site reservoir. Pollution loads from point sources (PSs) and nonpoint sources (NPSs) were carefully estimated. Domestic sewage and tourist wastewater were considered the major PS loads in this study. The NPS load evaluation was dependent on the results of a verified watershed model, the stormwater management model (SWMM). The observed data in 2015 and 2016 and supplementary total phosphorous (TP) samplings in upst... [more]

Studying the influence of the demand response and dynamic characteristics of the battery energy storage on the configuration and optimal operation of battery energy storage system (BESS) in the Wind-Photovoltaic (PV)-Energy Storage (ES) hybrid microgrid. A demand response model that is based on electricity price elasticity is established based on the time-of-use price. Take the capital-operating cost and direct economic benefit of the BESS and the loss of abandoned photovoltaic and wind power as the optimization objective, an optimal configuration method that considers the dynamic characteristics of the BESS and the maximum absorption of photovoltaic and wind power is proposed while using particle swarm optimization to solve. The results show that the configuration results considering the demand side response of the microgrid BESS can obtain better economy and reduce the storage capacity requirement, and the result shows that the efficiency of BESS relates to the load of the system, th... [more]

For long-term storage systems such as rockets and missiles, most of the relevant models and algorithms for inspection and maintenance currently focus on analysis based on periodic inspection. However, considering factors such as the complexity of the degradation mechanisms of these systems, the constraints imposed by failure risk, and the uncertainty caused by environmental factors, it is preferable to dynamically determine the inspection intervals based on real-time status information. This paper investigates the issue of maintenance optimization modelling for long-term storage systems based on real-time reliability evaluation. First, the Wiener process is used to establish a performance degradation model for one critical unit of such a system, and a closed-form expression for the real-time reliability distribution is obtained by using the first-hitting-time theory. Second, sequential inspection intervals are dynamically determined by combining the real-time reliability function with... [more]

Scheduling is a major component for the efficient operation of the process industries. Especially in the current competitive globalized market, scheduling is of vital importance to most industries, since profit margins are miniscule. Prof. Sargent was one of the first to acknowledge this. His breakthrough contributions paved the way to other researchers to develop optimization-based methods that can address a plethora of process scheduling problems. Despite the plethora of works published by the scientific community, the practical implementation of optimization-based scheduling in industrial real-life applications is limited. In most industries, the optimization of production scheduling is seen as an extremely complex task and most schedulers prefer the use of a simulation-based software or manual decision, which result to suboptimal solutions. This work presents a comprehensive review of the theoretical concepts that emerged in the last 30 years. Moreover, an overview of the contribut... [more]

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) mode... [more]

Effective use of energy storage systems (ESS) is important to reduce unnecessary power consumption. In this paper, a day-ahead two-stage ESS-scheduling model based on the use of a machine learning technique for load prediction has been proposed for minimizing the operating cost of the energy system. The proposed algorithm consists of two stages of ESS. In the first stage, ESS is used to minimize demand charges by reducing the peak load. Then, the remaining capacity is used to reduce energy charges through arbitrage trading, thereby minimizing the total operating cost. To achieve this purpose, accurate load prediction is required. Machine learning techniques are promising methods owing to the ability to improve forecasting performance. Among them, ensemble learning is a well-known machine learning method which helps to reduce variance and prevent overfitting of a model. To predict loads, we employed bootstrap aggregating (bagging) or random forest technique-based decision trees after Ho... [more]

The mathematical models of productivity calculation for complex structural wells mainly focus on the single well or the regular well pattern. Previous research on the seepage theory of complex structural wells and vertical wells in mixed well pattern is greatly insufficient. Accordingly, this article presents a methodology of evaluating the productivity of infill complex structural wells in mixed well patterns. On the basis of the mirror-image method and source−sink theory, two semi-analytical models are established. These models are applied to the productivity prediction of an infill horizontal well inhorizontal-vertical well pattern and an infill multilateral well inmultilateral-vertical well pattern, respectively, in which the interference of other wells, the randomicity of well patterns, and the pressure drawdown along the horizontal laterals are taken into account. The semi-analytical models’ results are consistent with those calculated by the Eclipse reservoir simulator with the... [more]

Discrepancies between water demand and supply are intensifying and creating a need for sustainable water resource process management associated with rapid economic development, population growth, and urban expansion. In this study, a scenario-based interval fuzzy-credibility constrained programming (SIFCP) method is developed for planning a water resource management system (WRMS) that can handle uncertain information by using interval values, fuzzy sets, and scenario analysis. The SIFCP-WRMS model is then applied to plan the middle route of the South-to-North Water Diversion Project (SNWDP) in Henan Province, China. Solutions of different water distribution proportion scenarios and varied credibility levels are considered. Results reveal that different water-distribution proportion scenarios and uncertainties used in the SIFCP-WRMS model can lead to changed water allocations, sewage discharges, chemical oxygen demand (COD) emissions, and system benefits. Results also indicate that the... [more]

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can i... [more]

Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) ar... [more]

To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast err... [more]

The uncertain and variable nature of renewable energy sources in modern power systems raises significant challenges in achieving the dual objective of reliable and economically efficient system operation. To address these challenges, advanced scheduling strategies have evolved during the past years, including the co-optimization of energy and reserves under deterministic or stochastic Unit Commitment (UC) modeling frameworks. This paper presents different deterministic and stochastic day-ahead UC formulations, with focus on the determination, allocation and deployment of reserves. An explicit distinction is proposed between the uncertainty and the variability reserve, capturing the twofold nature of renewable generation. The concept of multi-timing scheduling is proposed and applied in all UC policies, which allows for the optimal procurement of such reserves based on intra-hourly (real-time) intervals, when concurrently optimizing energy and commitments over hourly intervals. The day-... [more]

In a carbon-constrained world, natural gas with low emission intensity plays an important role in the energy consumption area. Energy consumers and distribution networks are linked via energy hubs. Meanwhile, reconfiguration that optimizes operational performance while maintaining a radial network topology is a worldwide technique in the electricity distribution system. To improve the overall efficiency of energy infrastructure, the expansion of electricity distribution lines and elements within energy hubs should be co-planned. In this paper, the co-planning process is modeled as a mixed integer quadratic programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of total cost. Operational factors including energy storages and reconfiguration are considered within the systems to serve electricity, cooling and heating loads. Reconfiguration and elements in energy hubs can avoid or defer new elements’... [more]

Different agents in the smart grid infrastructure (e.g., households, buildings, communities) consume energy with their own appliances, which may have adjustable usage schedules over a day, a month, a season or even a year. One of the major objectives of the smart grid is to flatten the demand load of numerous agents (viz. consumers), such that the peak load can be avoided and power supply can feed the demand load at anytime on the grid. To this end, we propose two Energy Consumption Scheduling (ECS) problems for the appliances held by different agents at the demand side to effectively facilitate load leveling. Specifically, we mathematically model the ECS problems as Mixed-Integer Programming (MIP) problems using the data collected from different agents (e.g., their appliances’ energy consumption in every time slot and the total number of required in-use time slots, specific preferences of the in-use time slots for their appliances). Furthermore, we propose a novel algorithm to efficie... [more]

Recently, many researchers have proved that the electrification of the transport sector is a key for reducing both the emissions of green-house pollutants and the dependence on oil for transportation. As a result, Plug-in Hybrid Electric Vehicles (or PHEVs) are receiving never before seen increased attention. Consequently, large-scale penetration of PHEVs into the market is expected to take place in the near future, however, an unattended increase in the PHEVs needs may cause several technical problems which could potentially compromise the stability of power systems. As a result of the growing necessity for addressing such issues, topics related to the optimization of PHEVs’ charging infrastructures have captured the attention of many researchers. Related to this, several state-of-the-art swarm optimization methods (such as the well-known Particle Swarm Optimization (PSO) or the recently proposed Gravitational Search Algorithm (GSA) approach) have been successfully applied in the opti... [more]

The development of advanced metering technologies makes demand response (DR) able to provide fast response services, e.g., primary frequency control. It is recognized that DR can contribute to the primary frequency control like thermal generators. This paper proposes a day-ahead scheduling method that considers DR as a frequency control resource, so that the DR resources can be dispatched properly with other resources. In the proposed method, the objective of frequency control is realized by defining a frequency limit equation under a supposed contingency. The frequency response model is used to model the dynamics of system frequency. The nonlinear frequency limit equation is transformed to a linear arithmetic equation by piecewise linearization, so that the problem can be solved by mixed integer linear programming (MILP). Finally, the proposed method is verified on numerical examples.

150 years ago, Stanley Jevons introduced the concept of energy rebound: that anticipated energy efficiency savings may be “taken back” by behavioural responses. This is an important issue today because, if energy rebound is significant, this would hamper the effectiveness of energy efficiency policies aimed at reducing energy use and associated carbon emissions. However, empirical studies which estimate national energy rebound are rare and, perhaps as a result, rebound is largely ignored in energy-economy models and associated policy. A significant difficulty lies in the components of energy rebound assessed in empirical studies: most examine direct and indirect rebound in the static economy, excluding potentially significant rebound of the longer term structural response of the national economy. In response, we develop a novel exergy-based approach to estimate national energy rebound for the UK and US (1980⁻2010) and China (1981⁻2010). Exergy—as “available energy”—allows a consistent,... [more]

Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW) and Victorian... [more]

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by... [more]

This study investigates the process of how the original equipment manufacturers (OEMs) in automobile and consumer electronics industries design their supply networks. In contrast to the sociological viewpoint, which regards the emergence of networks as a social and psychological phenomenon occurring among non-predetermined individuals, this paper attempts to provide a strategic supply network perspective that views the supply network as a strategic choice made by an OEM. Anchored in the multiplex investigation of supply network architectures, this study looks into the following specific questions: (1) Are an OEM’s strategic intent choices associated with supply network architecture and (2) If so, what differential effects do those strategic intents have on the architectural properties of the supply network? Further field investigations were conducted to provide deeper insights into the quantitative and qualitative findings from statistical analyses.

The best pricing method for any company in a perfectly competitive market is the pricing scheme with regards to the marginal cost. In contrast to this environment, there is a market with imperfect competition. In this market, the price can be affected by some players in the generation/demand side (i.e., suppliers and/or buyers). In the economic literature, “market power” refers to a company that has the power to affect prices. In fact, market power is often defined as the ability to divert prices from competitive levels. In the electricity market, especially because of the integration of intermittent renewable energy resources (RESs) along with the inflexibility of demand, there are levels of market power on the supply side. Hence, implementation of demand response (DR) programs is necessary to increase the flexibility of the demand side to deal with the intermittency of renewable generations and at the same time tackle the market power of the supply side. This paper uses economic theo... [more]

The integration of scheduling and process planning can eliminate resource conflicts and hence improve the performance of a manufacturing system. However, the focus of most existing works is mainly on the optimization techniques to improve the makespan criterion instead of more efficient uses of energy. In fact, with a deteriorating global climate caused by massive coal-fired power consumption, carbon emission reduction in the manufacturing sector is becoming increasingly imperative. To ease the environmental burden caused by energy consumption, e.g., coal-fired power consumption in use of machine tools, this research considers both makespan as well as environmental performance criteria, e.g., total power consumption, in integrated process planning and scheduling using a novel multi-objective memetic algorithm to facilitate a potential amount of energy savings; this can be realized through a better use of resources with more efficient scheduling schemes. A mixed-integer linear programmi... [more]

In this paper, a novel pattern-based method is developed for the generation of optimal schedules for energy-integrated batch process systems. The proposed methodology is based on the analysis of available schedules for the identification of repetitive patterns. It is shown that optimal schedules of energy-integrated batch processes are composed of several repeating sections (or building blocks), and their sizes and relative positions are dependent on the scheduling horizon and constraints. Based on such a decomposition, the proposed pattern-based algorithm generates an optimal schedule by computing the number and sequence of these blocks. The framework is then integrated with rigorous optimization-based approach wherein it is shown that the learning from the pattern-based solution significantly improves the performance of rigorous optimization. The main advantage of the pattern-based method is the significant reduction in computational time required to solve large scheduling problems,... [more]

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an object... [more]