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Deng, Chunyu, and Kehe Wu. "Residential Demand Response Strategy Based on Deep Deterministic Policy Gradient." Processes 9, no. 4 (April 9, 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 residential demand response strategy under different incentive policies can be trained iteratively through real-time interactions with the environmental conditions. In this paper, a novel optimization model of residential demand response strategy, based on a deep deterministic policy gradient (DDPG) algorithm, was proposed. The proposed work was validated with the actual electricity consumption data of a certain area in China. The results showed that the DDPG model could optimize residential demand response strategy under certain incentive policies. In addition, the overall goal of peak load-cutting and valley filling can be achieved, which reflects promising prospects of the electricity market.
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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 (February 11, 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 substitution and a renewable energy quota. This methodology is focused on a strategic demand shift to minimize the cost of supply; increase the dispatch of renewable energy; control CO2 emissions; and satisfy the generation, demand, and transmission operating constraints. These conditions encourage the development of a simulation tool that allows a sensitivity analysis to aid decision-making by operators and agents. The proposed methodology optimizes the operational cost of generation supply and specific performance indicators to determine the percentages of demand shift, the amount of CO2 emissions, the ratio of unserved power, the demand benefits obtained from an incentive scheme, and the natural market behavior.
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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, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.
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Leobner, Ines, Peter Smolek, Bernhard Heinzl, Philipp Raich, Alexander Schirrer, Martin Kozek, Matthias Rössler, and Benjamin Mörzinger. "Simulation-based Strategies for Smart Demand Response." Journal of Sustainable Development of Energy, Water and Environment Systems 6, no. 1 (March 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 (May 19, 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-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.
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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 (November 3, 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 lines taken into account. Subsequently, a reliability calculation method of distribution networks considering the shortage of power supply capacity and outages is presented. Finally, the Monte Carlo method is employed to calculate the reliability index of distribution networks with different load levels, and the impacts of different DR strategies on the reliability of distribution networks are analyzed. The results show that both DR strategies can improve the distribution system reliability.
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Liu, Shuxin, Jing Xu, Chaojian Xing, Yang Liu, Ersheng Tian, Jia Cui, and Junzhu Wei. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game." Sustainability 15, no. 16 (August 11, 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 pricing strategy for demand-side response in industrial parks based on master–slave game by establishing an exogenous model of demand-side response and incentives. Compared with the existing strategies, the strategy is more efficient and flexible, and effectively improves the economic efficiency of power trading and load regulation. Firstly, an exogenous model of demand-side response and incentive is built to characterize the demand-side response cost. The method focuses more on describing the exogenous characteristics of user incentives and response quantities. It only needs to analyze the exogenous indicators and random errors in various typical scenarios. The description of user demand-side response is more efficient. Secondly, a master–slave-game-based dynamic pricing strategy for industrial parks with demand-side response is proposed. The strategy is composed of a two-stage optimization. The primary regulation of customers is achieved by day-ahead time-of-use tariffs. The secondary regulation of customers is achieved by means of the same-day regulation of demand and purchase regarding clean electricity. The proposed two-stage price formation mechanism is more economical, more effective in load regulation, and improves the flexibility of industrial pricing. Finally, a case study is conducted on an industrial power user in a park in Liaoning Province. The results show that the proposed method is significantly better than existing methods in terms of improving the economic efficiency and load control effectiveness of the pricing strategy.
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Muraña-Silvera, Jonathan, Sergio Enrique Nesmachnow-Cánovas, Santiago Damián Iturriaga-Fabra, Sebastián Montes De Oca, Gonzalo Belcredi, Pablo Ariel Monzón-Rangeloff, Vladimir Dmitrievitch Shepelev, and Andrei Nikolaevitch Tchernykh. "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 is effective to provide appropriate demand response actions according to monetary incentives.
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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 (April 13, 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 model with allyl chloride storage was created based on real data and literature values. Results from this model, spot market and balancing power prices, and future electricity market scenarios were used in a mixed-integer linear optimisation. We find that benefits from demand response can be generated as soon as additional power and storage volume is provided. The composition of provided types of balancing power bids follows the price trend on the market. Additionally, the computation time could be lowered significantly by running the scenarios in parallel. The results encourage a practical validation of the flexibility of epichlorohydrin production.
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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, Hamza Ali Khan, Naveed Ahmed, Muhammad Usama Nadeem, Syed Usman Faheem Ahmed, and Ali Sarfraz. "An Optimal Scheduling and Planning of Campus Microgrid Based on Demand Response and Battery Lifetime." Pakistan Journal of Engineering and Technology 4, no. 3 (September 30, 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 article presents a new energy management (EMS) system for a university campus microgrid with onsite solar PV and ESS that operates in a grid exchange scenario. The suggested EMS not only lowers power consumption costs by prolonging storage life; however, it also guarantees grid stability through limiting and shifting loads using price-based and incentive-based demand response methods. ESS is utilized as a stand-by energy reserve to maintain the microgrid system stability and to assist the utility network in the event of a power outage. In MATLAB, a quadratic approach is used to solve the proposed framework. According to the findings, the suggested EMS decreases the prosumer's operating cost and increasing self-consumption, minimizes peak load from the national grid, and encourages campus stakeholders and energy controllers to engage in large-scale ESS installations and distributed generation (DG).
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Ahmed, Emad M., Rajarajeswari Rathinam, Suchitra Dayalan, George S. Fernandez, Ziad M. Ali, Shady H. E. Abdel Aleem, and Ahmed I. Omar. "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 (September 21, 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 culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.
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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 building cluster is managed with demand response strategies. A simulation-based analysis is presented in which a cluster of residential archetypal buildings are investigated. The buildings differ from each other for construction features and type of heating system (e.g. underfloor heating or with fan coil units). By supposing to be able to activate the energy flexibility of the single building with thermostatic load control, an optimized logic is implemented to produce programmatically an hourly electricity peak reduction. Results show how the involvement of buildings with different characteristics depends on the compromise that wants to be achieved in terms of minimization of both the rebound effects and the variation of the internal temperature setpoint.
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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 (January 21, 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 demand response scheduling strategies are simulated using standard IEEE 37 bus distribution test system through different cases of scheduling and optimization scenarios. The simulation results are presented, discussed, and compared with the base test cases without demand response penetration and without optimization strategies under demand response penetration to demonstrate the effectiveness of network loss, sensitivity consideration and optimization strategies in carrying out distribution system scheduling. In addition, sensitivity analysis is performed. The variation of distribution network performance is analyzed for various test cases and scenarios at different penetration levels.
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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 lines. Distributed generation implementing wind plant as renewable independent power producer (RIPP) has also been included in order to reduce the load curtailment of responsive customers to manage transmission congestion. The proposed incentive-based demand response and distributed generation approach of CM, has been framed with various strategies employing different thermal limits over transmission lines and has resulted into significant reduction in congestion and in-turn improvement of transmission reliability margin. Diversity has been obtained in multiobjective optimization by taking two and three objective functions, respectively (minimization of overall operational cost, CO2 emission, and line loading). The by-products of the proposed algorithm for multiobjective optimization are minimized demand reduction, optimum size, and location of DG. To examine the proposed approach, it has been implemented on IEEE 30-bus system and a bigger power system IEEE 118-bus system; as well as the proposed technique of MOSSA has been compared and found better than reported methods and two other meta heuristic algorithms (multiobjective modified sperm swarm optimization and multiobjective adoptive rat swarm optimization).
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Kou, Xiao, Yan Du, Fangxing Li, Hector Pulgar-Painemal, Helia Zandi, Jin Dong, and Mohammed M. Olama. "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 (July 10, 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 strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.
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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 (July 1, 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 chain models demonstrate that the retailers' inventory burden is significantly lower in the response‐based scenario. The inventory savings enjoyed by retailers in the response‐based model are substantial enough to lower system‐wide inventories. In sum, the study supports the feasibility of achieving both improved service and lower inventories as a result of information sharing.
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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 (August 7, 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 functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform.
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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 (December 28, 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, according to the demand response capacity requirements, an optimal strategy for the aggregate dispatch of an inverter air conditioner considering incentive compensation measures is proposed with the objective of maximizing the load quotient economic benefit. The sensitivity analysis of the compensation factor for temperature rise is also performed. The results show that 3000 inverter air conditioners in the load quotient dispatch area participate in the demand response for 4 h, with a load reduction of 1.267 MW and a net income of RMB 14,435.97. Secondly, an increase in the temperature rise compensation factor will reduce the cost of temperature rise compensation by the loader to the user, but it will also reduce the load reduction and the net income of the loader. This study has practical significance for load aggregators to formulate compensation strategies and improve the economic benefits of participating in demand response.
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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 (November 30, 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 (November 11, 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, and loads are set to different probabilities according to importance. By applying the probability of loads and changing the return coefficient of loads, the problem of individual differences in demand side responses is solved, so as to improve consumer satisfaction. The proposed model is constructed as a mixed-integer linear program (MILP). Cases studies demonstrate feasibility of the proposed modeling method. The demand side response achieves the expected goal. The system management method reduces the operation cost of the energy system of microgrids.
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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 (March 17, 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 efficiency. Building demand response (DR) is considered as a promising technology for the collaborative control of energy supply and demand. Conventionally, building control approaches usually consider the minimization of total energy consumption as the optimization objective function; relatively few control methods have considered the balance of energy supply and demand under high renewable energy penetration. Thus, this paper proposes an innovative DR control approach that considers the energy flexibility of buildings. First, based on an energy flexibility quantification framework, the energy flexibility capacity of a typical office building is quantified; second, according to energy flexibility and a predictive net load demand curve of the grid, two DR control strategies are designed: rule-based and prediction-based DR control strategies. These two proposed control strategies are validated based on scenarios of heating, ventilation, and air conditioning (HVAC) systems with and without an energy storage tank. The results show that 24–55% of the building’s total load can be shifted from the peak load time to the valley load time, and that the duration is over 2 h, owing to the utilization of energy flexibility and the implementation of the proposed DR controls. The findings of this work are beneficial for smoothing the net load demand curve of a grid and improving the ability of a grid to adopt renewable energies.
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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 (September 14, 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 resource information into the community’s energy management. Initially conceived in a centralised way, a data collector called the “aggregator” will handle the operation scheduling requirements given the consumers’ time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.
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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 model is constructed by analyzing the characteristics of EV charging behavior under multiple scenarios. Secondly, the DEMATEL-AISM method is used to analyze the degree of influence of personal and social factors on users’ charging behavior under complex social environments, and the dominant factors in each scenario are identified. Finally, based on the analysis of the dominant factors in multiple scenarios, an EV regulation strategy under the influence of multiple factors is developed to achieve peak shaving. The feasibility of the proposed method is verified through simulation cases. The simulation results reveal that the revenues of aggregators and users are improved, and the stability of the power system is enhanced.
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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 (March 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 (February 23, 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 applications are causing an increasing demand for 5G-enabled products in the market. However, PC systems based on PCI Express, NVMe, USB, and TPM have been introduced into the market with more secure solutions, and common designs and predictable market demand can provide more reliable strategies for navigating these issues. The research reported here is based on the serial bus system, which should simplify protocol transition between PCI Express, CXL, USB 4, and NVMe. Serial bus behavior should also influence performance and power consumption. Product strategies could be based on securing demand with power and performance in AI, the IoT, cloud storage, and high-performance computing. Based on performance and power requirements, application layer devices can use PCIe-based systems to provide secure solutions to extend 5G system reliability.
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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 (June 22, 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 customer response. It also details the main aspects of the mathematical model, which was implemented in the Modelica modeling language. This paper includes simulations of intra-day and inter-day load shifting strategies using real data from the electricity sector in Colombia and different tariff factors. Finally, the results obtained show changes in daily consumption profiles, energy cost, system power peak, and load duration curve. Three conclusions are drawn: (i) Energy culture and pedagogy are essential to accelerate customer response time. (ii) The amount of the bill paid by customers decreases more quickly in the intra-day strategy than in its inter-day counterpart; in both cases, the cost reduction percentage is similar. (iii) Tariff increases accelerate customer response, and this relationship varies according to the Demand-Side Management strategies that are available
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Ju, Yuchen, Joakim Lindholm, Moritz Verbeck, Juha Jokisalo, Risto Kosonen, Philipp Janßenc, Yantong Li, Hans Schäfers, and Natasa Nord. "Demand response in the German district heating system." IOP Conference Series: Earth and Environmental Science 1185, no. 1 (May 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 and gas boilers and the other one included a heat pump, an electric heater, and a solar thermal storage. After that, the production of the two scenarios with and without DR was optimized by the HGSO tool and it calculated the total production costs and CO2 emissions. It indicates that building owners and DH producers all earn benefits from the application of demand response. The maximum heating cost saving by DR is 4.9% for building owners. In the optimized two production scenarios, DH producers gain higher financial benefits and there are less CO2 emissions. The maximum total generation cost and CO2 emission savings are 12.6% and 8.6%, respectively.
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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 (May 17, 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 proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.
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Salo, Sonja, Aira Hast, Juha Jokisalo, Risto Kosonen, Sanna Syri, Janne Hirvonen, and Kristian Martin. "The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System." Energies 12, no. 9 (May 3, 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 in the system, demand response can become more profitable, resulting in 1.4% cost savings. It is concluded that the hot water storage tank can balance district heating peak loads for longer periods of time, which enhances the ability to use demand response strategies on a larger share of the building stock.
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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 (December 7, 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 implications for research and practice. Findings – The findings suggest that there are diverse outcomes associated with each demand. The outcomes were not inherently negative as evidence suggests that positive performance outcomes can arise from response expectations and constant availability, although they may be allied by health and well-being costs. Practical implications – A number of practical strategies are described to help organizations address computer-mediated communication demands, including tailored training, organizational policies and role modeling. The paper also outlines suggestions for future research on the dark side of IT use. Originality/value – This paper integrates four interrelated demands that employees can face when communicating through technology. The authors extend knowledge by analyzing interventions which enables a synthesis of implications for practice.
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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 (June 16, 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 develop an integrated scheduling model that combines power and gas demand response by converting multiple energy sources with different types of residential end users. Then, we formulate the pricing strategy as a Markov Decision Process with an unknown transition. The novel soft actor-critic algorithm is utilized to efficiently train neural networks with the entropy function and to learn the pricing strategies to maximize demand response providers’ profits under various sources of uncertainties. Case studies are conducted to demonstrate the effectiveness of the proposed approach in both deterministic and stochastic environment settings. Our proposed approach is also shown to be effective in handling different levels of uncertainties and achieving the near-optimal pricing strategy.
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Dhaliwal, Amandeep, and Shilpa Arora. "Rapid Response Logistics." International Journal of Service Science, Management, Engineering, and Technology 12, no. 6 (November 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 business environment. It discusses the information sharing technologies based on different level of sophistication which are used by the different echeloned supply chains. The paper reviews the important literature in these aspects and brings forth the challenges of the rapid response systems and the new directions of research which needs to be undertaken to gauge the real potential of rapid response systems.
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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 (June 28, 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 demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.
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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 (December 14, 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. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.
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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 (August 31, 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 for fire suppression [MFS] and mobilization for ambulance service [MAS]), and fire budget. In the results, SFI, FSE, and MFS had a significant negative influence on the fire budget. Meanwhile, MAS had a significant positive effect on the fire budget (p < 0.01). These results reflect public policy in Korea; there has been a paradigm shift in fire administration: from disaster acceptance (focusing on recovery) to disaster response (focusing on field response) to disaster preparedness (focusing on preparedness).
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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 strategies, and vehicle routing optimization procedure. The significant feature of the proposed solution framework is that the response to dynamic customers is proactive rather than passive. The solution is applied to the distribution practice of a large logistics company in Chongqing. The results show that the proposed method has better dynamic scene adaptability and customer response capabilities in traffic limit.
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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 (December 6, 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 to control hospital congestion. The results indicated that diverting a small number of ambulances seems to be more effective and efficient in congestion reduction compared to other approaches. Furthermore, instead of implementing an isolated approach continuously, combining one approach with other strategies is recommended as a method for dealing with hospital overcrowding.
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Capone, Martina, and Elisa Guelpa. "Implementing Optimal Operation of Multi-Energy Districts with Thermal Demand Response." Designs 7, no. 1 (January 10, 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 approach is proposed for the fast optimization of multi-energy district operations, enabling real-time demand response strategies. A 3-step optimization method based on mixed integer linear programming is proposed aimed at minimizing the cost operation of multi-energy districts. The approach is applied to a test case characterized by strongly unsteady heat/electricity and cooling demands. Results show that (a) the total operation cost of a multi-energy district can be reduced by order of 3% with respect to optimized operation without demand side management; (b) with respect to a full optimization approach, the computational cost decreases from 45 min to 1 s, while the accuracy reduces from 3.6% to 3.0%.
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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 (June 17, 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 was evaluated. The research focused mainly on the evaluation of energy flexibility offered by energy stored in the form of thermal energy by utilizing the building’s thermal mass. The demand response potential under hourly CO2eq intensity and energy prices was investigated. The flexibility potential was evaluated under different demand response strategies, and the effect of demand response on energy consumption, operational costs, CO2eq emissions and thermal comfort was analyzed and discussed. The results showed that both control strategies based on both the CO2eqintensity signal and spot price signal have, in some cases, the potential for cost and emission savings, and in other cases, the potential to depreciate in terms of emissions and cost the increase of energy consumption due to load shifting.
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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 (December 20, 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 behavior is constructed to facilitate storage of students’ learning record. Combined with the advantages of hierarchical selection, SH method and improved polynomial model, this paper puts forward a new type of item section model. This paper introduces the basic theory and related technology, and then makes an in-depth study on the demand analysis of English learning system. Finally, this paper realizes the design of English learning system based on item response theory and validates the good effect of English item selection from the perspective of application. The system provides teachers and students with convenient learning strategies, item selection strategies, test strategies and academic performance strategies. The introduction of item response theory enables the system to become truly student-centered and provides a more comprehensive and self-adaptive learning model, which is of great significance for improving the learning efficiency of English learning and the learning efficiency of college students in China.
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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 (October 12, 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 strategies were taken into consideration on distribution system reliability indices in this study. The load model of this study was created by using load data of the distribution feeder provided by Bosphorus Electric Distribution Corporation (BEDAS). Additionally, the reliability parameters of the feeder components were determined based on these provided data. The calculated load point and feeder side indicators were analyzed comprehensively from technical and economic perspectives. In order to validate the effectiveness of the proposed structure, four case studies were carried out in both DigSILENT PowerFactory and MATLAB environments.
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Lowson, Robert H. "Retail Operational Strategies in Complex Supply Chains." International Journal of Logistics Management 12, no. 1 (January 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 operational strategy in a retail logistics context? From empirical research, we demonstrate that the various elements forming an operational strategy are often part of a distinct implementation pattern that can be customized at the level of individual product and/or customer behavior and replicates the complexity of the setting. The research aims to help management better understand demand and tailor a number of operational strategies to the behavior of that demand.
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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 (October 5, 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 savings and potential impacts—to occupants and energy markets—are not well understood. We present a case-study analysis of the technical potential for residential natural gas demand response in the northeast United States that utilizes diverse whole-building energy simulations and high-performance computing. Our results show that NG-DR applied to residential heating systems during extreme cold-weather conditions could reduce natural gas demand by 1–29% based on conservative and aggressive strategies, respectively. This indicates a potential to improve the resilience of gas and electric systems during stressful events, which we examine by estimating the impact on energy costs and electricity generation from natural gas. We also explore relationships between hourly indoor temperatures, demand response, and building envelope efficiency.
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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 (July 25, 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 collectively optimize their own interests; meanwhile, the global optimal solution is achieved. The agent perceives its environment, predicts electricity consumption, and forecasts electricity price, based on which it takes intelligent actions to minimize electrical energy cost and time delay of using household appliances. The decision-making action is formulated into a convex program (CP) model. A distributed heuristic algorithm is developed to solve the proposed multi-agent optimization model. Case studies and numerical analysis show promising results with low variation of the aggregated load profile and reduction of electrical energy cost. The proposed approaches can be utilized to investigate various emerging technologies and DR strategies.
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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 (April 30, 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 performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile.
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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 (July 7, 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 (December 18, 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 proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.