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Journal articles on the topic 'Incentive-based demand response strategies'

Author: Grafiati

Published: 6 September 2023

Last updated: 7 September 2023

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1

Deng, Chunyu, and Kehe Wu. "Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient." Processes 9, no. 4 (2021): 660. http://dx.doi.org/10.3390/pr9040660.

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With the continuous improvement of the power system and the deepening of electricity market reform, the trend of users’ active participation in power distribution is more and more significant. Demand response has become the promising focus of smart grid research. Providing reasonable incentive strategies for power grid companies and demand response strategies for customers plays a crucial role in maximizing the benefits of different participants. To meet different expectations of multiple agents in the same environment, deep reinforcement learning was adopted. The generative model of residenti

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Alasseri, Rajeev, Ashish Tripathi, T. Joji Rao, and K. J. Sreekanth. "A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs." Renewable and Sustainable Energy Reviews 77 (September 2017): 617–35. http://dx.doi.org/10.1016/j.rser.2017.04.023.

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Domínguez-Garabitos, Máximo A., Víctor S. Ocaña-Guevara, Félix Santos-García, Adriana Arango-Manrique, and Miguel Aybar-Mejía. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market." Energies 15, no. 4 (2022): 1307. http://dx.doi.org/10.3390/en15041307.

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The energy transition has shown that fossil generation can be complemented with renewable energy and other resources capable of providing flexibility to the energy system’s operation, in compliance with the wholesale electricity market’s rules. This paper proposes a market-based methodology for introducing flexible demand in the energy dispatch, optimizing the scheduling of electricity system operation in the short-term, and considers the challenge of implementing an incentive scheme for participants in demand-response programs. The scheme includes the criteria of the elasticity of substitutio

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Li, Yuling, Xiaoying Wang, and Peicong Luo. "Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions." Mathematical Problems in Engineering 2020 (July 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/5206082.

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Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, w

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Leobner, Ines, Peter Smolek, Bernhard Heinzl, et al. "Simulation-based Strategies for Smart Demand Response." Journal of Sustainable Development of Energy, Water and Environment Systems 6, no. 1 (2017): 33–46. http://dx.doi.org/10.13044/j.sdewes.d5.0168.

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Deltetto, Davide, Davide Coraci, Giuseppe Pinto, Marco Savino Piscitelli, and Alfonso Capozzoli. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings." Energies 14, no. 10 (2021): 2933. http://dx.doi.org/10.3390/en14102933.

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Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-bu

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Chen, Tang, Sun, Zhou, Wang, and Mao. "Reliability Evaluation Method Considering Demand Response (DR) of Household Electrical Equipment in Distribution Networks." Processes 7, no. 11 (2019): 799. http://dx.doi.org/10.3390/pr7110799.

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The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment are proposed to obtain the load curve considering two different kinds of DR mechanisms. Afterwards, a load shedding strategy is introduced to improve the traditional reliability evaluation method for distribution networks, with the capacity constraints of tie

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Liu, Shuxin, Jing Xu, Chaojian Xing, et al. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game." Sustainability 15, no. 16 (2023): 12265. http://dx.doi.org/10.3390/su151612265.

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With the deepening of power market reform, further study on power trading mechanisms has become the core issue of power market study. The development stage of the industrial electricity market requires efficient and flexible pricing mechanisms. Currently available pricing strategies are inadequate for demand response management. Therefore, this paper provides an in-depth study of the pricing mechanism in the industrial electricity market in the context of electricity market reform. It proposes a demand–response-based dynamic pricing strategy for industrial parks. The method proposes a dynamic

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Muraña-Silvera, Jonathan, Sergio Enrique Nesmachnow-Cánovas, Santiago Damián Iturriaga-Fabra, et al. "Smart grid demand response strategies for datacenters." Proceedings of the Institute for System Programming of the RAS 33, no. 2 (2021): 125–36. http://dx.doi.org/10.15514/ispras-2021-33(2)-7.

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This article presents demand response techniques for the participation of datacenters in smart electricity markets under the smart grid paradigm. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacenter and clients and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling different types of clients. Results indicate that the proposed approach

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Lahrsen, Inga-Marie, Mathias Hofmann, and Robert Müller. "Flexibility of Epichlorohydrin Production—Increasing Profitability by Demand Response for Electricity and Balancing Market." Processes 10, no. 4 (2022): 761. http://dx.doi.org/10.3390/pr10040761.

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The increasing share of variable renewable energies in the power grid is an incentive to explore demand response strategies. Chlor-alkali processes are high potential candidates, according to previous publications. Within Germany’s chemical industry, chlorine production accounts for approximately 20% of electricity use and could play a significant role in power grid stabilisation on the consumer end. This study focuses on the feasibility of load flexibilisation in epichlorohydrin plants, with the second biggest estimated demand response potential for chlorine-based products in Germany. A plant

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Zhang, Pan, Xun Dou, Wenhao Zhao, Mingtao Hu, and Xin Zhang. "Analysis of Power Sales Strategies Considering Price-Based Demand Response." Energy Procedia 158 (February 2019): 6701–6. http://dx.doi.org/10.1016/j.egypro.2019.01.019.

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Pansota, Muhammad Shahzad, Haseeb Javed, H. A. Muqeet, et al. "An Optimal Scheduling and Planning of Campus Microgrid Based on Demand Response and Battery Lifetime." Pakistan Journal of Engineering and Technology 4, no. 3 (2021): 8–17. http://dx.doi.org/10.51846/vol4iss3pp8-17.

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Existing electricity supply systems face several challenges, including increasing energy prices with greenhouse gas (GHG) emissions and fossil fuel depletion. These issues have a significant impact on all power system stakeholders, including customers/prosumers, utilities, and microgrid operators. Renewable energy incorporation and different energy managing strategies such as demand-side management (DSM), demand response (DR), and others may help to overcome these limitations. Campus microgrids are among the largest energy consumers in the United States, with high energy expenditures. This art

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Ahmed, Emad M., Rajarajeswari Rathinam, Suchitra Dayalan, et al. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm." Mathematics 9, no. 18 (2021): 2338. http://dx.doi.org/10.3390/math9182338.

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In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively

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Mugnini, Alice, Fabio Polonara, and Alessia Arteconi. "Demand response strategies in residential buildings clusters to limit HVAC peak demand." E3S Web of Conferences 312 (2021): 09001. http://dx.doi.org/10.1051/e3sconf/202131209001.

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Due to the increasing spread of residential heating systems electrically powered, buildings show a great potential in producing demand side management strategies addressing their thermal loads. Indeed, exploiting the intrinsic characteristics of the heating/cooling systems (i.e. the thermal inertia level), buildings could represent an interesting solution to reduce the electricity peak demand and to optimize the balance between demand and supply. The objective of this paper is to analyse the potential benefits that can be obtained if the electricity demand derived from the heating systems of a

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Kumar, Sampath, and M. Sushama. "Strategic demand response framework for energy management in distribution system based on network loss sensitivity." Energy & Environment 31, no. 8 (2020): 1385–402. http://dx.doi.org/10.1177/0958305x19893041.

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This paper discusses an energy management system–based demand response scheduling strategy in distribution system. The proposed strategy includes customer payment minimization and network loss minimization as responsive load scheduling objectives through centralized approach. Two types of optimization strategies each based on payment minimization and network loss sensitivity are discussed in this paper. Thus, the proposed scheduling strategy can effectively resolve the optimality issue between different objectives of the distribution system scheduling under demand response penetration. The dem

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Agrawal, Anjali, Seema N. Pandey, Laxmi Srivastava, Pratima Walde, R. K. Saket, and Baseem Khan. "Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management." International Transactions on Electrical Energy Systems 2022 (December 7, 2022): 1–17. http://dx.doi.org/10.1155/2022/8256908.

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In the newly emerged electric supply industry, the profit maximizing tendency of market participants has developed the problem of transmission congestion as the most crucial issue. This paper proposes a multiobjective salp swarm algorithm (MOSSA) approach for transmission congestion management (CM), implementing demand side management activities. For this, demand response (DR) and distributed generation (DG) have been employed. For willingly reducing the demand, demand response has been called by providing appropriate financial incentives that supports in releasing the congestion over critical

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Kou, Xiao, Yan Du, Fangxing Li, et al. "Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response." IEEE Open Access Journal of Power and Energy 8 (2021): 186–97. http://dx.doi.org/10.1109/oajpe.2021.3075426.

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Panagiotidis, Paraskevas, Andrew Effraimis, and George A. Xydis. "An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids." Energy & Environment 30, no. 1 (2018): 63–80. http://dx.doi.org/10.1177/0958305x18787259.

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The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response st

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Closs, David J., Anthony S. Roath, Thomas J. Goldsby, James A. Eckert, and Stephen M. Swartz. "An Empirical Comparison of Anticipatory and Response‐Based Supply Chain Strategies." International Journal of Logistics Management 9, no. 2 (1998): 21–34. http://dx.doi.org/10.1108/09574099810805816.

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This paper reports simulation research that empirically investigates and compares supply chain performance under varying conditions of information exchange and demand uncertainty. Specifically, the research objective is to quantitatively document the characteristics and performance impact of information exchange among supply chain entities. The findings suggest that the response‐based supply chain model consistently outperforms the anticipatory model in terms of customer service delivered under conditions of both low and high demand variation. Comparisons of inventory holdings across supply ch

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Wang, Qi, Hongru Wang, Lei Zhu, Xingquan Wu, and Yi Tang. "A Multi-Communication-Based Demand Response Implementation Structure and Control Strategy." Applied Sciences 9, no. 16 (2019): 3218. http://dx.doi.org/10.3390/app9163218.

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Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with func

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Li, Qifen, Yihan Zhao, Yongwen Yang, Liting Zhang, and Chen Ju. "Demand-Response-Oriented Load Aggregation Scheduling Optimization Strategy for Inverter Air Conditioner." Energies 16, no. 1 (2022): 337. http://dx.doi.org/10.3390/en16010337.

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In recent years, the peak–valley differences in urban power loads have been increasing. It is difficult to maintain the real-time balance of a power system by relying solely on the generation-side resources. As a typical flexible load, an air conditioning load can balance the supply and demand of a power grid by adjusting power using the thermal inertia of buildings. From the perspective of a load aggregator, this study models and aggregates the dispatch of a single inverter air conditioner distributed in a region to determine the adjustment potential of an air conditioning cluster. Then, acco

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Tang, Qiang, Ming-zhong Xie, Kun Yang, Yuan-sheng Luo, and Ping Li. "Price Learning Based Load Distribution Strategies for Demand Response Management in Smart Grid." International Journal of Smart Home 10, no. 11 (2016): 79–94. http://dx.doi.org/10.14257/ijsh.2016.10.11.08.

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Li, Ming, and Jin Ye. "Design and Implementation of Demand Side Response Based on Binomial Distribution." Energies 15, no. 22 (2022): 8431. http://dx.doi.org/10.3390/en15228431.

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The application of microgrids (MG) is more and more extensive, therefore it is important to improve the system management method of microgrids. The intended costs can be further minimized when the energy management system is unified with demand side response (DSR) strategies. In this work, we propose a generic method of modeling the equipment in a microgrid including multiple stochastic loads. The microgrid model can be generated on a computer by converting the energy circuit diagram into a signal flow diagram. Then, a demand side response method based on binomial distribution is introduced, a

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Chen, Yongbao, Zhe Chen, Xiaolei Yuan, Lin Su, and Kang Li. "Optimal Control Strategies for Demand Response in Buildings under Penetration of Renewable Energy." Buildings 12, no. 3 (2022): 371. http://dx.doi.org/10.3390/buildings12030371.

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The penetration rates of intermittent renewable energies such as wind and solar energy have been increasing in power grids, often leading to a massive peak-to-valley difference in the net load demand, known as a “duck curve”. The power demand and supply should remain balanced in real-time, however, traditional power plants generally cannot output a large range of variable loads to balance the demand and supply, resulting in the overgeneration of solar and wind energy in the grid. Meanwhile, the power generation hours of the plant are forced to be curtailed, leading to a decrease in energy effi

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Cruz, Carlos, Esther Palomar, Ignacio Bravo, and Alfredo Gardel. "Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm." Sensors 19, no. 18 (2019): 3973. http://dx.doi.org/10.3390/s19183973.

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The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy

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Yao, Yin, Yedong Zhu, Dongdong Li, Bo Zhou, and Shunfu Lin. "Priority Analysis of Influence Factors for Electric Vehicle Demand Response Strategies." International Transactions on Electrical Energy Systems 2023 (March 21, 2023): 1–15. http://dx.doi.org/10.1155/2023/7242304.

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As the penetration of renewable energy continues to increase, the demand-side resources in the grid will become more and more important. Electric vehicles (EVs) account for a relatively large proportion of demand-side resources, but individual and social factors have been less considered in multifactorial studies affecting EV participation in demand response (DR), and the multiscenario DR process has not been adequately studied. Therefore, an EV demand response strategy considering the influence of multiple factors is proposed in this paper. Firstly, a multisource charging load characteristic

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Zhang, Chunyu, Qi Wang, Jianhui Wang, Pierre Pinson, Juan M. Morales, and Jacob Ostergaard. "Real-Time Procurement Strategies of a Proactive Distribution Company With Aggregator-Based Demand Response." IEEE Transactions on Smart Grid 9, no. 2 (2018): 766–76. http://dx.doi.org/10.1109/tsg.2016.2565383.

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Lin, Yen, Jeng-Ywan Jeng, Yi-Yu Liu, and Jheng-Jia Huang. "A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand." Electronics 11, no. 5 (2022): 678. http://dx.doi.org/10.3390/electronics11050678.

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In the Personal Computer (PC) industry, systems with updated configurations, components, and new technologies are introduced to the market each year. Resource arrangement and the prediction of market requirements for products are common challenges in each development phase of these products. Technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and cloud services influence the PC industry, and product strategies must be examined to fit the requirements of the market. Common designs and market predictions can influence product line resource arrangements, and 5G applic

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Téllez-Gutiérrez, Sandra, and Oscar Duarte-Velasco. "A Model for Quantifying Expected Effects of Demand-Side Management Strategies." TecnoLógicas 25, no. 54 (2022): e2357. http://dx.doi.org/10.22430/22565337.2357.

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This paper presents a quantitative dynamic model that can assess the response of a set of users to different Demand-Side Management strategies that are available. The main objective is to conceptualize, implement, and validate said model. As a result of a literature review, the model includes classical demand response techniques and proposes new customer actions and other novel aspects, such as energy culture and energy education. Based on the conceptualization of the model, this paper presents the structure that interrelates customer actions, demand proposals, cost-benefit analysis, and custo

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Ju, Yuchen, Joakim Lindholm, Moritz Verbeck, et al. "Demand response in the German district heating system." IOP Conference Series: Earth and Environmental Science 1185, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1755-1315/1185/1/012016.

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Abstract The renewable energy share in energy supply systems is increasing for carbon neutrality. The realization of carbon neutrality can be supported by demand response (DR) strategies. This paper analyzed the DR control benefits of a German district heating (DH) system. For the first step, in German conditions, three building types were simulated by IDA-ICE software with and without a rule-based DR control. Secondly, a community was established based on the heat demand of the simulated buildings. This paper selected two different production scenarios. One scenario consisted by a biofuel CHP

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Kiptoo, Mark Kipngetich, Oludamilare Bode Adewuyi, Harun Or Rashid Howlader, Akito Nakadomari, and Tomonobu Senjyu. "Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies." Energies 16, no. 10 (2023): 4147. http://dx.doi.org/10.3390/en16104147.

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A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also propose

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Salo, Sonja, Aira Hast, Juha Jokisalo, et al. "The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System." Energies 12, no. 9 (2019): 1678. http://dx.doi.org/10.3390/en12091678.

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Demand response has been studied in district heating connected buildings since the rollout of smart, communicating devices has made it cost-effective to control buildings’ energy consumption externally. This research investigates optimal demand response control strategies from the district heating operator perspective. Based on earlier simulations on the building level, different case algorithms were simulated on a typical district heating system. The results show that even in the best case, heat production costs can be decreased by only 0.7%. However, by implementing hot water thermal storage

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Stich, Jean-François, Samuel Farley, Cary Cooper, and Monideepa Tarafdar. "Information and communication technology demands: outcomes and interventions." Journal of Organizational Effectiveness: People and Performance 2, no. 4 (2015): 327–45. http://dx.doi.org/10.1108/joepp-09-2015-0031.

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Purpose – The purpose of this paper is to review four demands employees face when communicating through information and communication technologies (ICTs). The authors review the outcomes associated with each demand and discuss relevant interventions to provide a set of evidence-based recommendations. Design/methodology/approach – This paper reviews the following demands associated with ICTs: response expectations, constant availability, increased workload and poor communication. The authors draw upon empirical research to highlight outcomes and intervention strategies, before discussing implic

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Xu, Chenhui, and Yunkai Huang. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach." Energies 16, no. 12 (2023): 4769. http://dx.doi.org/10.3390/en16124769.

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The increasing complexity of multi-energy coordinated microgrids presents a challenge for traditional demand response providers to adapt to end users’ multi-energy interactions. The primary aim of demand response providers is to maximize their total profits via designing a pricing strategy for end users. The main challenge lies in the fact that DRPs have no access to the end users’ private preferences. To address this challenge, we propose a deep reinforcement learning-based approach to devise a coordinated scheduling and pricing strategy without requiring any private information. First, we de

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Dhaliwal, Amandeep, and Shilpa Arora. "Rapid Response Logistics." International Journal of Service Science, Management, Engineering, and Technology 12, no. 6 (2021): 73–88. http://dx.doi.org/10.4018/ijssmet.2021110105.

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Greater internet penetration and ecommerce has led to demand for personalized product development and faster fulfilment. This has increased global competition wherein the manufacturer and retailers not only have to deal with an unprecedented number and variety of products but also makes forecasting and scheduling difficult. To address these problems, rapid response logistics has become a necessity. The current study discusses the role of such rapid response systems and various implementation strategies in both the demand and supply side of supply chains that can be the solutions to the dynamic

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Abbasi, Ayesha, Kiran Sultan, Sufyan Afsar, Muhammad Adnan Aziz, and Hassan Abdullah Khalid. "Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids." Energies 16, no. 13 (2023): 5024. http://dx.doi.org/10.3390/en16135024.

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Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power

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Li, Yuchun, Yinghua Han, Jinkuan Wang, and Qiang Zhao. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort." Energies 11, no. 12 (2018): 3495. http://dx.doi.org/10.3390/en11123495.

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Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings

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Lee, Wonjoo, Jae Hoon Lim, and Kwang Min Moon. "Impact of Fire Demand on Fire Service Budget." Fire Science and Engineering 34, no. 4 (2020): 125–34. http://dx.doi.org/10.7731/kifse.5d63e027.

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This paper aimed to statistically analyze the impact of fire needs not considered in previous reports based on preventive and preparedness strategies of fire administration and fire budget.. The panel data came from 16 metropolitan councils from 2008 to 2018 and was statistically analyzed based on the preventive measures of the fire administration (agreement for building permission, specific target for fire-fighting, public use facilities, and special fire inspection [SFI]), preparedness of the fire administration (fire safety education [FSE]), response of the fire administration (mobilization

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Ge, Xianlong, Guiqin Xue, and Pengzhe Wen. "Proactive Two-Level Dynamic Distribution Routing Optimization Based on Historical Data." Mathematical Problems in Engineering 2018 (November 21, 2018): 1–15. http://dx.doi.org/10.1155/2018/5191637.

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In view of the dynamic dispersion of e-commerce logistics demand, this paper uses the historical distribution data of logistics companies to study data-driven proactive vehicle routing optimization. First, based on the classic 2E-VRP problem, a single-node/multistage 2E-VRP mathematical model is constructed. Then, a framework for solving the proactive vehicle routing optimization problem is proposed in combination with the characteristics of the proposed model, including four modules: data-driven demand forecasting methods, customer clustering methods, proactive demand quotas and replenishment

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Hou, Wanxin, Shaowen Qin, and Campbell Henry Thompson. "Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions." International Journal of Environmental Research and Public Health 19, no. 23 (2022): 16348. http://dx.doi.org/10.3390/ijerph192316348.

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Hospital overcrowding is becoming a major concern in the modern era due to the increasing demand for hospital services. This study seeks to identify effective and efficient ways to resolve the serious problem of congestion in hospitals by testing a range of decongestion strategies with simulated scenarios. In order to determine more efficient solutions, interventions with smaller changes were consistently tested at the beginning through a simulation platform. In addition, the implementation patterns were investigated, which are important to hospital managers with respect to the decisions made

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Capone, Martina, and Elisa Guelpa. "Implementing Optimal Operation of Multi-Energy Districts with Thermal Demand Response." Designs 7, no. 1 (2023): 11. http://dx.doi.org/10.3390/designs7010011.

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The combination of different energy vectors in the context of multi-energy systems is a crucial opportunity to reach CO2 reduction goals. In the case of urban areas, multi-energy districts can be connected with district heating networks to efficiently supply heat to the buildings. In this framework, the inclusion of the thermal demand response allows for significantly improve the performance of multi-energy districts by smartly modifying the heat loads. Operation optimization of such systems provides excellent results but requires significant computational efforts. In this work, a novel approa

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Chantzis, Georgios, Effrosyni Giama, and Agis M. Papadopoulos. "Building Energy Flexibility Assessment in Mediterranean Climatic Conditions: The Case of a Greek Office Building." Applied Sciences 13, no. 12 (2023): 7246. http://dx.doi.org/10.3390/app13127246.

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The EU energy and climate policy has set quantitative goals for decarbonization based on the energy efficiency and the evolution of energy systems. The utilization of demand side flexibility can help towards this direction and achieve the target of higher levels of penetration in regard to intermittent renewable energy production and carbon emission reduction. This paper presents a simulation-based assessment of thermal flexibility in a typical office building in Greece, which is a representative Mediterranean country. The use of variable speed heat pumps coupled with hydronic terminal units w

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Liu, Yuemei, and Xuetao Zhao. "Design Flow of English Learning System Based on Item Response Theory." International Journal of Emerging Technologies in Learning (iJET) 12, no. 12 (2017): 91. http://dx.doi.org/10.3991/ijet.v12i12.7958.

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The popularity of computer technology in English teaching has led to the establishment of many English learning platforms, but the enhancement of students’ English proficiency is limited due to the lack of relevance, self-adaptive test questions and analytical ability. The project management theory is introduced into English learning, which can provide students with teaching content and test questions that are more suitable for their own actual situation through a more intelligent, personalized way. At the same time, the static and dynamic database model based on students’ own learning behavio

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Güner, Sıtkı, Ayşe Kübra Erenoğlu, İbrahim Şengör, Ozan Erdinç, and João P. S. Catalão. "Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability." Applied Sciences 10, no. 20 (2020): 7062. http://dx.doi.org/10.3390/app10207062.

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In the last few decades, there has been a strong trend towards integrating renewable-based distributed generation systems into the power grid, and advanced management strategies have been developed in order to provide a reliable, resilient, economic, and sustainable operation. Moreover, demand response (DR) programs, by taking the advantage of flexible loads’ energy reduction capabilities, have presented as a promising solution considering reliability issues. Therefore, the impacts of combined system architecture with on-site photovoltaic (PV) generation units and residential demand reduction

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Lowson, Robert H. "Retail Operational Strategies in Complex Supply Chains." International Journal of Logistics Management 12, no. 1 (2001): 97–111. http://dx.doi.org/10.1108/09574090110806253.

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The correct choice, implementation and evolution of an operations strategy can provide considerable competitive advantage. However, how many organizations in the Fast Moving Consumer Goods (FMCG) industries really understand the components of such strategies and their power when properly deployed? Supply chain management, lean thinking, agile operations, quick response, virtual organization, time‐based competition to name but a few, currently receive extensive coverage in management literature. But, what core competencies, management activities, resources and technologies comprise an effective

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Speake, Andrew, Paul Donohoo-Vallett, Eric Wilson, Emily Chen, and Craig Christensen. "Residential Natural Gas Demand Response Potential during Extreme Cold Events in Electricity-Gas Coupled Energy Systems." Energies 13, no. 19 (2020): 5192. http://dx.doi.org/10.3390/en13195192.

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In regions where natural gas is used for both power generation and heating buildings, extreme cold weather events can place the electrical system under enormous stress and challenge the ability to meet residential heating and electric demands. Residential demand response has long been used in the power sector to curtail summer electric load, but these types of programs in general have not seen adoption in the natural gas sector during winter months. Natural gas demand response (NG-DR) has garnered interest given recent extreme cold weather events in the United States; however, the magnitude of

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Wang, Zhanle, Raman Paranjape, Zhikun Chen, and Kai Zeng. "Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing." Energies 12, no. 15 (2019): 2867. http://dx.doi.org/10.3390/en12152867.

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Demand response (DR) programs encourage consumers to adapt the time of using electricity based on certain factors, such as cost of electricity, renewable energy availability, and ancillary request. It is one of the most economical methods to improve power system stability and energy efficiency. Residential electricity consumption occupies approximately one-third of global electricity usage and has great potential in DR applications. In this study, we propose a multi-agent optimization approach to incorporate residential DR flexibility into the power system and electricity market. The agents co

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Morovat, Navid, Andreas K. Athienitis, José Agustín Candanedo, and Benoit Delcroix. "Model-Based Control Strategies to Enhance Energy Flexibility in Electrically Heated School Buildings." Buildings 12, no. 5 (2022): 581. http://dx.doi.org/10.3390/buildings12050581.

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This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performa

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Zhang, Yan, Yajie Liu, Bo Guo, Tao Zhang, and Rui Wang. "Model predictive control-based operation management for a residential microgrid with considering forecast uncertainties and demand response strategies." IET Generation, Transmission & Distribution 10, no. 10 (2016): 2367–78. http://dx.doi.org/10.1049/iet-gtd.2015.1127.

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Ruiz-Abellón, María Carmen, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Alberto Falces, Ana García-Garre, and Antonio Gabaldón. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation." Energies 13, no. 1 (2019): 11. http://dx.doi.org/10.3390/en13010011.

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The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been propo

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