Distributionally robust optimization of home energy management system based on receding horizon optimization

This paper investigates the scheduling strategy of schedulable load in home energy management system (HEMS) under uncertain environment by proposing a distributionally robust optimization (DRO) method based on receding horizon optimization (RHO-DRO). First, the optimization model of HEMS, which contains uncertain variable outdoor temperature and hot water demand, is established and the scheduling problem is developed into a mixed integer linear programming (MILP) by using the DRO method based on the ambiguity sets of the probability distribution of uncertain variables. Combined with RHO, the MILP is solved in a rolling fashion using the latest update data related to uncertain variables. The simulation results demonstrate that the scheduling results are robust under uncertain environment while satisfying all operating constraints with little violation of user thermal comfort. Furthermore, compared with the robust optimization (RO) method, the RHO-DRO method proposed in this paper has a lower conservation and can save more electricity for users.     

A new ship energy management algorithm to the smart electricity grid system

In this paper, a new energy management algorithm has been suggested for the ships connected with alternative energies considering the smart electricity grid features. The algorithm focuses on the use of optimum energy source on the shipboard based on the priorities of authorities such as the most economic, environmental, or both criteria at the ports. The algorithm is performed in MATLAB, and several case studies are simulated to validate the results. The 5 maritime nations, which are at different regions: United States, Belgium, Turkey, China, and Australia, are taken into account in the case studies. The actual data of a bulk carrier ship have been used to achieve tangible results in the simulations. The results of the case studies are compared to determine the changes of energy cost and released emission to meet demand of electricity on the ships. Capital cost of the proposed concept is also given to make an economic evaluation. The results show that the ship energy management algorithm provides the significant economic and environmental advantages. This paper aims to contribute to the importance of the ships in the smart electricity grid concept for the maritime and electricity sector representatives.     

Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art,challenges and opportunities

Blockchain possesses the potential of transforming global supply chain management. Gartner predicts that blockchain could be able to track $2?T of goods and services in their movement across the globe by 2023, and blockchain will be a more than $3 trillion business by 2030. Nowadays, a growing number of blockchain initiatives are disrupting traditional business models in each sector. In this paper, we provide a timely and holistic overview of the state-of-the-art, challenges, gaps and opportunities in global supply chain and trade operations for both the private sector and governmental agencies, by synthesising a wide range of resources from business leaders, global international organisations, leading supply chain consulting firms, research articles, trade magazines and conferences. We then identify collaborative schema and future research directions for industry, government, and academia to jointly work together in ensuring that the full potential of blockchain is unleashed amidst the socioeconomic, geopolitical and technological disruptions that global supply chains and trade are facing.     

Thermal-Disrupting Interface Mitigates Intercellular Cohesion Loss for Accurate Topical Antibacterial Therapy

Bacterial infections remain a leading threat to global health because of the misuse of antibiotics and the rise in drug-resistant pathogens. Although several strategies such as photothermal therapy and magneto-thermal therapy can suppress bacterial infections, excessive heat often damages host cells and lengthens the healing time. Here, a localized thermal managing strategy, thermal-disrupting interface induced mitigation (TRIM), is reported, to minimize intercellular cohesion loss for accurate antibacterial therapy. The TRIM dressing film is composed of alternative microscale arrangement of heat-responsive hydrogel regions and mechanical support regions, which enables the surface microtopography to have a significant effect on disrupting bacterial colonization upon infrared irradiation. The regulation of the interfacial contact to the attached skin confines the produced heat and minimizes the risk of skin damage during thermoablation. Quantitative mechanobiology studies demonstrate the TRIM dressing film with a critical dimension for surface features plays a critical role in maintaining intercellular cohesion of the epidermis during photothermal therapy. Finally, endowing wound dressing with the TRIM effect via in vivo studies in S. aureus infected mice demonstrates a promising strategy for mitigating the side effects of photothermal therapy against a wide spectrum of bacterial infections, promoting future biointerface design for antibacterial therapy.     

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

A semiconductor distributor that plays a third-party role in the supply chain will buy diverse components from different suppliers, warehouse and resell them to a number of electronics manufacturers with vendor-managed inventories, while suffering both risks of oversupply and shortage due to demand uncertainty. However, demand fluctuation and supply chain complexity are increasing due to shortening product life cycle in the consumer electronics era and long lead time for capacity expansion for high-tech manufacturing. Focusing realistic needs of a leading distributor for semiconductor components and modules, this study aims to construct a UNISON framework based on deep reinforcement learning (RL) for dynamically selecting the optimal demand forecast model for each of the products with the corresponding demand patterns to empower smart production for Industry 3.5. Deep RL that integrates deep learning architecture and RL algorithm can learn successful policies from the dynamic and complex real world. The reward function mechanism of deep RL can reduce negative impact of demand uncertainty. An empirical study was conducted for validation showing practical viability of the proposed approach. Indeed, the developed solution has been in real settings.     

基于二元水循环的水量-水质-水效联合调控模型开发与应用

为将我国最严格水资源管理制度的实践与二元水循环理论相结合，提高“三条红线”控制指标的科学性与合理性，本文以SWAT模型为基础，通过改进子流域划分方法、添加经济社会模块和人工用水模块，对其人工侧支循环模拟进行了系统的完善，开发了基于SWAT的水量-水质-水效联合调控模型SWAT_WAQER。以广西南流江流域为例，从国民经济用水量、河道径流与水质等方面对模型进行校验，并在此基础上划分了2030年“三条红线”控制指标。结果表明：该模型性能良好，能够用于不同节水情景下的国民经济用水量、污染物排放量、水功能区水质达标结果分析，可以作为科学制定“三条红线”控制指标的有力支撑工具。     

Management of excess energy in a photovoltaic/grid system by production of clean hydrogen

In this article, the authors design a new clean storage device for a photovoltaic system (PV) reinforced by the electrical grid. The photovoltaic system supplies power to a DC load. When the power of the photovoltaic source is insufficient, the electrical grid compensates the energy deficit. On the other hand, if the load is satisfied and the PV source is still able of supplying energy, the energy excess is diverted to an own storage unit materialized by an electrolysis which produces gaseous hydrogen by the process of electrolysis of water. The authors show that the quantity of hydrogen produced is proportional to the photovoltaic energy excess and also to the flow of water injected into the electrolysis. In this case, it is a question of designing an electrolysis with specific characteristics, which takes into account the quantity of energy excess and the flow of water injected into it. The authors abandon the idea of controlling the water flow by means of a pumping-electrovalve system, and initiate the idea of replacing the function of the pump by the action of gravity. The work focuses on the development of an electrolysis optimization approach using the water flow control in its alliance with the PV power excess which is also maximized. For an optimized use of the global system (load and electrolysis), the authors present an architecture based on energy-converting structures (DC/DC and AC/DC). In addition, to increase the reliability and safety of the system, the authors finish by developing a power management strategy (PMS) in the designed system. This power management strategy organizes the energy flow and selects the appropriate path of this flow between the two energy sources (PV and electrical grid) and the two possible energy receivers (load and electrolysis). A complete modeling of the system is developed in the Matlab/Simulink environment. The simulation results show that the hybrid system (PV and electrical grid) is able to permanently supplying the load and potentially storing the excess of the PV energy in the form of hydrogen gas.     

EBHP分解飞温模型建立及控制分析

对乙苯过氧化氢(EBHP)分解构建了飞温模型,绘制有冷却和无冷却体系两种工况的飞温曲线,分析了乙苯注入、紧急冷却水通入措施对飞温的抑制作用。工艺模拟试验结果表明:该EBHP飞温模型能较好预测温度随时间的变化情况,认定120℃为系统报警温度值,140℃为系统联锁值。以某63/30万t/a的苯乙烯/环氧丙烷(SMPO)装置为例,假定EBHP进料量为1000 t/h,用EBHP飞温模型核算,乙苯注入量(w)为30%的进料量时、或者紧急冷却水通入量(w)为进料量的6.5%时,系统均能快速终止飞温,且效果明显。这一研究结果为装置的安全设计和稳定运行提供了依据。     

从几个典型案例谈燃油系统管理

基于典型故障案例的阐述和故障原因分析，归纳总结了船舶柴油机燃油系统在日常使用和管理中应注意的问题，可为船舶操作人员和管理人员提供参考。     

A cash-constrained stochastic inventory model with consumer loans and supplier credits: the case of nanostores in emerging markets

We consider a small traditional retailer that is managing its inventory under strict cash constraints, mainly because typically informal loans are offered to customers. These stores are widely present in emerging markets, and we refer to them as nanostores (also called ‘mom-and-pop stores’). As the suppliers require immediate payments for goods delivered, a nanostore can only replenish products to the level for which it has on-hand cash available. To improve delivery efficiency, a supplier might offer a nanostore credit for its replenishments. However, currently, suppliers are often reluctant to do so as these nanostores quickly go bankrupt or disappear, hence defaulting on all outstanding credits. The objective of this paper is to determine when it is beneficial to offer supplier credits. We propose a multi-period, stochastic inventory model, and numerically compare scenarios with and without supplier credits. Our study shows that even in the presence of this risk, suppliers often have good incentives to provide these credits, even if interest is not incurred. For this to hold, the operations of the retailer should be (a little) profitable in the first place, for which we provide analytical conditions.