Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

Revenue management method and critical techniques of railway passenger transport

Purpose–Revenue management(RM)is a significant technique to improve revenue with limited resources.With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China,it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.Design/methodology/approach–This paper proposes the theory and framework of generalized RM of railway passenger transport(RMRPT),and the thoughts and methods of the main techniques in RMRPT,involving demand forecasting,line planning,inventory control,pricing strategies and information systems,are all studied and elaborated.The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT.The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.Findings–The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.Originality/value–The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.

An empirical study on travel demand management modeling based on discrete choice method

In view of the problem that the requirements of travel demand management and traffic policy-sensitivity are ignored during the establishment process of the travel demand forecasting model, a discrete-choice-based travel demand forecasting model is proposed to demonstrate its applicability to travel demand management. A car-bus discrete choice model is established, including three variables, i. e,, individual socioeconomic characteristics, time, and cost, and the traffic policy-sensitivity is evaluated through two kinds of traffic policies： parking charges and bus priorities. The empirical results show that travel choice is insensitive to the policy of parking charges as 88. 41% of the travelers are insensitive to parking charges; travel choice is, however, sensitive to the policy of bus priorities as 67.70% of the car travelers and 77.02% of the bus travelers are sensitive to bus priorities. The discrete-choice-based travel demand forecasting model is quite policy-sensitive and also has a good adaptability for travel demand management when meeting the basic functions of the demand forecasting model.

Delivery Service Management System Using Google Maps for SMEs in Emerging Countries

This paper proposes a Delivery Service Management(DSM)system for Small and Medium Enterprises(SMEs)that own a delivery fleet of pickup trucks to manage Business-to-Business(B2B)delivery services.The proposed DSM system integrates four systems:Delivery Location Positioning(DLP),Delivery Route Planning(DRP),Arrival Time Prediction(ATP),and Communication and Data Sharing(CDS)systems.These systems are used to pinpoint the delivery locations of customers,plan the delivery route of each truck,predict arrival time(with an interval)at each delivery location,and communicate and share information among stakeholders,respectively.The DSM system deploys Google applications,a GPS tracking system,Google Map APIs,ATP algorithms(embedded in Excel Macros),Line,and Telegram as supporting tools.To improve the accuracy of the ATP system,three tech-niques are applied considering driver behaviors.The proposed DSM system has been implemented in a Thai SME.From the process perspective,the DSM system is a systematic procedure for end-to-end delivery services.It allows the interactions between planner-driver decisions and supporting tools.The supporting tools are simple,can be easily used with little training,and require low capital expenditure.The statistical analysis shows that the ATP algorithm with the three techniques provides high accuracy.Thus,the proposed DSM system is beneficial for practitioners to manage delivery services,especially for SMEs in emerging countries.

Determination of Effectiveness of Energy Management System in Buildings

Building Energy Management Systems(BEMS)are computer-based systems that aid in managing,controlling,and monitoring the building technical services and energy consumption by equipment used in the building.The effectiveness of BEMS is dependent upon numerous factors,among which the operational characteristics of the building and the BEMS control parameters also play an essential role.This research develops a user-driven simulation tool where users can input the building parameters and BEMS controls to determine the effectiveness of their BEMS.The simulation tool gives the user the flexibility to understand the potential energy savings by employing specific BEMS control and help in making intelligent decisions.The simulation is developed using Visual Basic Application(VBA)in Microsoft Excel,based on discrete-event Monte Carlo Simulation(MCS).The simulation works by initially calculating the energy required for space cooling and heating based on current building parameters input by the user in the model.Further,during the second simulation,the user selects all the BEMS controls and improved building envelope to determine the energy required for space cooling and heating during that case.The model compares the energy consumption from the first simulation and the second simulation.Then the simulation model will provide the rating of the effectiveness of BEMS on a continuous scale of 1 to 5(1 being poor effectiveness and 5 being excellent effectiveness of BEMS).This work is intended to facilitate building owner/energy managers to analyze the building energy performance concerning the efficacy of their energy management system.

Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm

Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

Water Demand Management in Jordan

Jordan is located in the Middle East in the eastern Mediterranean. It has a surface area of approximately 90,000 km2 and its population reaches 6.3 million. Jordan is the fourth driest countries in the World and water demand exceeds Jordan’s available water resources. Annual per capita water availability has declined from 3600 m3/year in 1946 to 145 m3/year today. It is estimated that the population will continue to grow from about 5.87 million in 2008 to over 7.80 million by 2022. Total projected water demand will be 1673 million cubic meters by 2022. Fifteen-year complete records for water consumption were studied to see the supply and demand variation with time. It had been noticed that water demand management will address the actual needs for water. This management program will ensure further reduction in water use, reduce water loses through the distribution supply net, prevent pollution. In addition, it will help minimize water disposal in nature, make efficient use of available water resources, plan for future new water resources prudently and finally impose a real cost for water supply that would be acceptable. In addition to the above, public awareness program is to be put in action. Such a program should be used in schools as well as the media. The public is to be aware of the problem and how they can assist with overcoming the water shortage crisis.

Demand Side Management for Thermally Activated Building Systems Based on Multiple Linear Regression

The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.

Employing Water Demand Management Option for the Improvement of Water Supply and Sanitation in Nigeria

The aim of this paper is to assess the importance of Water Demand Management (WDM) strategy to the improvement of water supply and sanitation in Nigeria. Persistent water supply shortages and poor sanitation have since remained important features of the Nigerian urban and rural communities. Most often governmental solution to these problems has been to develop and exploit the available water resources and the level of sanitation for the people. This predominant approach which is also known as augmentation method is supply driven with the primary purpose being how best to meet the perceived water and sanitation demand. One of the major disadvantages of this approach is the huge financial involvement associated with it. Conversely, quite recently water resource managers have begun to direct attention on how consumers can be motivated to regulate the amount and manner in which they use and dispose water to alleviate pressure on freshwater supplies. This new approach is known as water demand management. It is demand driven in that consumers determine their own water need. Employment of WDM by consumers especially in water scarce areas as was discussed in the paper will decrease the amount of water use, thereby limiting unnecessary financial expenditure in exploiting new sources to meet the ever increasing demand.

The 3D simulation and optimized management model of groundwater systems based on eco-environmental water demand

Through the study of mutual process between groundwater systems and eco-environmental water demand, the eco-environmental water demand is brought into groundwater systems model as the important water consumption item and unification of groundwater抯 economic, environmental and ecological functions were taken into account. Based on eco-environmental water demand at Da抋n in Jilin province, a three-dimensional simulation and optimized management model of groundwater systems was established. All water balance components of groundwater systems in 1998 and 1999 were simulated with this model and the best optimal exploitation scheme of groundwater systems in 2000 was determined, so that groundwater resource was efficiently utilized and good economic, ecologic and social benefits were obtained.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

Optimal Demand-Side Management for Smart Micro Grid with Storage

In this paper, an autonomous and distributive demand-side management based on Bayesian game theory is developed and applied among users in a grid connected micro-grid with storage. To derive that strategy, an energy consumption of shiftable loads belonging to a given user is modelled as a noncooperative three-player game of incomplete information, in which each user plays against the storage unit and an opponent gathering all the other users in the micro-grid. Each player is assumed to be endowed with statistical information about its behavior and that of its opponents so that he can take actions maximizing his expected utility. Results of the proposed strategy evaluated by simulating, under MATLAB environment, a connected micro-grid with storage device evidence its efficacy when employed to manage the charging of electric vehicles.

Water Demand Management Is a Must in MENA Countries…But Is It Enough?

The majority of Middle East and North Africa(MENA)countries suffer from chronic imbalance between available water supply and rising water demand.This imbalance is expected to worsen even further in the future as a result of sharp population growth,rapid economic development and climate change,unless major positive measures are implemented to augment water supply and manage water demand.The supply management approach,on its own,practiced by many countries in the region for so many years has so far demonstrated its inability to bridge the“water gap”between available water resources and rising water demand,as most traditional water resourced in almost all MENA countries have been exploited(or over exploited),and the cost of non-traditional water resources has become increasingly prohibitively high,apart from its environmental impact.Demand management is regarded by many water experts in the region as the answer or“panacea”for the water imbalance problem.But,is demand management approach alone able to solve the problem of water scarcity in the MENA region?In other words,if all demand management measures have been fully implemented,would there still be gaps between supply and demand that need to be filled with supply augmentation,andwill supply management options still need to be part of the solution?This paper tries to answer this question by reviewing several works in this domain,particularly,recent studies by the World Bank[9-11].It was concluded that,although water demand management measures should be given the first priority,especially,in the agricultural sector where it has the maximum impact,demand management on its own will not be able to bridge the“water gap”,and supply management options,such as sea water desalination and the re-use of treated wastewater,will be part of the solution.

Urban Growth Management through Travel Demand Modeling in Washington and Oregon

Among the fast growing states in the USA,the States of Washington and Oregon have enacted legislative land use and transportation concurrency/balancing planning policies for orderly urban growth management since 1990 and 1991,respectively.Regional or urban travel demand forecasting models play an instrumental role in implementing the Washington GMA(Growth Management Act)and the Oregon TPR(Transportation Planning Rule).Both program-and project-level modeling approaches to urban land use/transportation system management are evaluated through the selected cities in Washington and Oregon.

Research on inventory management and demand forecasting

With the rapid economic development and the increasing speed and scale of grid construction, material procurement and management, cost control is facing new demands and challenges.This paper proposes on innovative management and forecasting methods, from inventory management and demand forecasting perspective supplies,through these two key nodes in-depth research and analysis, this paper provides a theoretical support for the realization of effective materials management.

Internet of Things for Demand Side Management

The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.