Journal articles: 'Smart energy demand'

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Lai, Jingang, Hong Zhou, Wenshan Hu, Dongguo Zhou, and Liang Zhong. "Smart Demand Response Based on Smart Homes." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/912535.

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Smart homes (SHs) are crucial parts for demand response management (DRM) of smart grid (SG). The aim of SHs based demand response (DR) is to provide a flexible two-way energy feedback whilst (or shortly after) the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH) and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper.

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Borges, Yulle G. F., Rafael C. S. Schouery, Flávio K. Miyazawa, Fabrizio Granelli, Nelson L. S. da Fonseca, and Lucas P. Melo. "Smart energy pricing for demand‐side management in renewable energy smart grids." International Transactions in Operational Research 27, no. 6 (November 5, 2019): 2760–84. http://dx.doi.org/10.1111/itor.12747.

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Nambi, S. N. Akshay Uttama, R. Venkatesha Prasad, and Antonio R. Lua. "Decentralized Energy Demand Regulation in Smart Homes." IEEE Transactions on Green Communications and Networking 1, no. 3 (September 2017): 372–80. http://dx.doi.org/10.1109/tgcn.2017.2721818.

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Islam, M. A., M. Hasanuzzaman, N. A. Rahim, A. Nahar, and M. Hosenuzzaman. "Global Renewable Energy-Based Electricity Generation and Smart Grid System for Energy Security." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/197136.

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Energy is an indispensable factor for the economic growth and development of a country. Energy consumption is rapidly increasing worldwide. To fulfill this energy demand, alternative energy sources and efficient utilization are being explored. Various sources of renewable energy and their efficient utilization are comprehensively reviewed and presented in this paper. Also the trend in research and development for the technological advancement of energy utilization and smart grid system for future energy security is presented. Results show that renewable energy resources are becoming more prevalent as more electricity generation becomes necessary and could provide half of the total energy demands by 2050. To satisfy the future energy demand, the smart grid system can be used as an efficient system for energy security. The smart grid also delivers significant environmental benefits by conservation and renewable generation integration.

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Dwijendra, Ngakan Ketut Acwin, Oriza Candra, Ihsan Ali Mubarak, Hassan Taher Braiber, Muneam Hussein Ali, Iskandar Muda, R. Sivaraman, and A. Heri Iswanto. "Optimal Energy-Saving in Smart Energy Hub Considering Demand Management." Environmental and Climate Technologies 26, no. 1 (January 1, 2022): 1244–56. http://dx.doi.org/10.2478/rtuect-2022-0094.

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Abstract This study focused on energy saving in energy hub system using smart grid technologies and management of the energy demand. The two-layer energy management is proposed for implementing energy saving. In first layer, energy demand such as electrical, thermal and natural gas are optimized subject to optimal level of the demand at day-ahead. Then, optimized energy demand is applied in second layer to reduction energy generation costs. The optimization of the proposed approach is done by shuffled frog leaping algorithm (SFLA), and results at several case studies to confirmation of the proposed approach are investigated.

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Palensky, Peter, and Dietmar Dietrich. "Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads." IEEE Transactions on Industrial Informatics 7, no. 3 (August 2011): 381–88. http://dx.doi.org/10.1109/tii.2011.2158841.

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Ahmed, Ajaz. "Energy Smart Buildings: Potential for Conservation and Efficiency of Energy." Pakistan Development Review 53, no. 4II (December 1, 2014): 371–81. http://dx.doi.org/10.30541/v53i4iipp.371-381.

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Energy is the basic ingredient for economic growth and development [Lorde, et al. (2010)]. Presently demand for energy has significantly increased due to the overall expansion of economic and industrial activity in all important economic sectors e.g. industry, agriculture, and services. In addition to the expansion of economic activity and subsequent increase in energy demand at industrial level, population growth and increased consumption are also adding to the demand for energy [OECD (2011)]. In other words, modern economy has become highly dependent on energy resources. In order to meet the increased energy demand and ensure its sustainable supply, there is a need to have strong and robust plans with all options to consider at various levels.

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Wang, Xiaonan, Wentao Yang, Sana Noor, Chang Chen, Miao Guo, and Koen H. van Dam. "Blockchain-based smart contract for energy demand management." Energy Procedia 158 (February 2019): 2719–24. http://dx.doi.org/10.1016/j.egypro.2019.02.028.

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Pan, Feng, Guoying Lin, Yuyao Yang, Sijian Zhang, Jucheng Xiao, and Shuai Fan. "Data-driven demand-side energy management approaches based on the smart energy network." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261989161. http://dx.doi.org/10.1177/1748302619891611.

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The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management.

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Faria, Pedro, and Zita Vale. "Demand Response in Smart Grids." Energies 16, no. 2 (January 12, 2023): 863. http://dx.doi.org/10.3390/en16020863.

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Guo, Hao Chi, Jin Peng Liu, Huan Huan Qiao, and Guan Qing Wang. "Study on DSM Energy-Saving Effect Evaluation under the Smart Grid Environment." Applied Mechanics and Materials 209-211 (October 2012): 1867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.1867.

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China's power conflicts between supply and demand have become increasingly prominent. The smart grid has become an inevitable trend of network development at the same time, and DSM implementation effect in smart grid research increasingly attracts people’s attention. Through the analysis of different subjects in demand side management, the paper established demand side management effectiveness evaluation system in smart grid from the aspects of economic , environmental and social. Finally, empirical research verified the validity of the index system ,which provides a good reference for improving the level of demand side management.

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Zunnurain, Izaz, Md Maruf, Md Rahman, and GM Shafiullah. "Implementation of Advanced Demand Side Management for Microgrid Incorporating Demand Response and Home Energy Management System." Infrastructures 3, no. 4 (November 13, 2018): 50. http://dx.doi.org/10.3390/infrastructures3040050.

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To facilitate the possible technology and demand changes in a renewable-energy dominated future energy system, an integrated approach that involves Renewable Energy Sources (RES)-based generation, cutting-edge communication strategies, and advanced Demand Side Management (DSM) is essential. A Home Energy Management System (HEMS) with integrated Demand Response (DR) programs is able to perform optimal coordination and scheduling of various smart appliances. This paper develops an advanced DSM framework for microgrids, which encompasses modeling of a microgrid, inclusion of a smart HEMS comprising of smart load monitoring and an intelligent load controller, and finally, incorporation of a DR strategy to reduce peak demand and energy costs. Effectiveness of the proposed framework is assessed through a case study analysis, by investigation of DR opportunities and identification of energy savings for the developed model on a typical summer day in Western Australia. From the case study analysis, it is evident that a maximum amount of 2.95 kWh energy can be shifted to low demand periods, which provides a total daily energy savings of 3%. The total energy cost per day is AU$2.50 and AU$3.49 for a house with and without HEMS, respectively. Finally, maximum possible peak shaving, maximum shiftable energy, and maximum standby power losses and energy cost savings with or without HEMS have been calculated to identify the energy saving opportunities of the proposed strategy for a microgrid of 100 houses with solar, wind, and a back-up diesel generator in the generation side.

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Tailor, Rikin, Zsolt Čonka, Michal Kolcun, and Ľubomír Beňa. "Electrical Energy Flow Algorithm for Household, Street and Battery Charging in Smart Street Development." Energies 14, no. 13 (June 23, 2021): 3771. http://dx.doi.org/10.3390/en14133771.

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The world demands a smart and green future in every sector, which directly corresponds to increases in electrical energy demand one way or another. It is unfeasible to attain future energy demand with the present electrical infrastructure. That means more research and development is required. Future energy sources should be intermittent, and, in addition, the energy sector should be more inwards for distributed energy generation with demand side control. In such cases, the smartest and most autonomous system would be essential to deliver an adequate power supply with all electrical properties. A real-time monitoring and control system with a self-healing infrastructure is a forthcoming desideratum. By accepting these challenges, we have designed a smart street. The basic idea of the smart street is presented in this paper as a landing page; the paper is more focused on emphasizing information regarding the electrical energy flow algorithm for the household, street, and street battery storages. This algorithm is helpful for two-way energy flow and the automatic detection of islanding and the grid connection mode. It will be not only helpful for the users but to the utility as well.

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Sheikhi, Aras, Mohammad Rayati, Shahab Bahrami, and Ali Mohammad Ranjbar. "Integrated Demand Side Management Game in Smart Energy Hubs." IEEE Transactions on Smart Grid 6, no. 2 (March 2015): 675–83. http://dx.doi.org/10.1109/tsg.2014.2377020.

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Wang, Chengshan, Dan Wang, Licheng Li, Hongjie Jia, and Weiliang Wang. "Key Technology Analysis of Demand-Side Smart Energy System." Chinese Journal of Engineering Science 20, no. 3 (2018): 132. http://dx.doi.org/10.15302/j-sscae-2018.03.019.

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Tang, Jun, and PengLing Li. "Research on Urban Smart Energy Evaluation Index System." E3S Web of Conferences 118 (2019): 01042. http://dx.doi.org/10.1051/e3sconf/201911801042.

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On the basis of the development demand of smart city, combing the connotation of urban smart energy, combined with the goal of smart city development planning, the evaluation index system of scientific urban smart energy construction is constructed, and the basis of quantitative and qualitative analysis of smart energy construction is initially formed, which is helpful for urban planners to take control of the strength and direction of smart energy construction and provide reference.

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Suryadevara, Nagender Kumar, and Gyan Ranjan Biswal. "Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid." Energies 12, no. 10 (May 22, 2019): 1957. http://dx.doi.org/10.3390/en12101957.

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In the current energy ecosystem, the need for a Hybrid Appliance Load Monitoring System (HALMS) to establish a smarter grid and energy infrastructure is undeniable. The increasing popularity of the Internet of Things (IoT) has suddenly pushed the demand for smart and connected devices. This review introduces the term smart plug as a device that uses IoT for establishing HALMS. These smart plugs are a handy solution to make the so-called ‘dumb’ devices smart. The strategy of smart plugs to enhance the energy management experience in connected spaces is presented. This study extensively highlights the current smart plug technologies and the relevant activities and limitations that need to overcome the requirements of HALMS.

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Liaqat, Rehan, and Intisar Ali Sajjad. "An Event Matching Energy Disaggregation Algorithm Using Smart Meter Data." Electronics 11, no. 21 (November 3, 2022): 3596. http://dx.doi.org/10.3390/electronics11213596.

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Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recent years. Various machine learning and optimization-based NILM approaches are available in the literature, but bulk training data and high computational time are their respective drawbacks. Considering these drawbacks, we devised an event matching energy disaggregation algorithm (EMEDA) for NILM of multistate household appliances using smart meter data. Having limited training data, K-means clustering was employed to estimate appliance power states. These power states were accumulated to generate an event database (EVD) containing all combinations of appliance operations in their various states. Prior to matching, the test samples of aggregate demand events were decreased by event-driven data compression for computational effectiveness. The compressed test events were matched in the sorted EVD to assess the contribution of each appliance in the aggregate demand. To counter the effects of transient spikes and/or dips that occurred during the state transition of appliances, a post-processing algorithm was also developed. The proposed approach was validated using the low-rate data of the Reference Energy Disaggregation Dataset (REDD). With better energy disaggregation performance, the proposed EMEDA exhibited reductions of 97.5 and 61.7% in computational time compared with the recent smart event-based optimization and optimization-based load disaggregation approaches, respectively.

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Alhasnawi, Bilal Naji, Basil H. Jasim, Zain-Aldeen S. A. Rahman, and Pierluigi Siano. "A Novel Robust Smart Energy Management and Demand Reduction for Smart Homes Based on Internet of Energy." Sensors 21, no. 14 (July 12, 2021): 4756. http://dx.doi.org/10.3390/s21144756.

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In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.

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Asgher, Urooj, Muhammad Rasheed, Ameena Al-Sumaiti, Atiq Rahman, Ihsan Ali, Amer Alzaidi, and Abdullah Alamri. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources." Energies 11, no. 12 (December 14, 2018): 3494. http://dx.doi.org/10.3390/en11123494.

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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort..

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Chen, Guo Qing. "Research on the Demand of Dynamic Housing and the Application of Distributed Generation Management in Micro-Smart Grid." Applied Mechanics and Materials 380-384 (August 2013): 3217–21. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3217.

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Smart grid is a very important trend of power system. With the sharing of smart grid and micro-grid from the traditional power grid, the energy cost and the environmental degradation of power grid have reduced. This paper introduces a dynamic demand response and distributed generation management approach according to micro-smart grid of a residential area. This method has dynamic update mechanism, demand response automation operation and human intervention. Distributed management coordinates with demand response. For example, random load and wind power generation are used to reduce the cost of energy consumption of a residential area. This paper achieves the study of the demand of dynamic housing and the application of distributed generation management in micro-smart grid by the form of distributed generation management modeling. It introduces the specific distribution form of the distributed micro-smart grid taking the building of new rural micro smart grid for example. It also introduces the circuit schematic diagram of micro smart grid.

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Schäfer, Stefan, Ante Ljubas, and Scholeh Abedini. "Construction of Smart Building Skins with Flow and Bend Split Products." Advanced Materials Research 660 (February 2013): 222–29. http://dx.doi.org/10.4028/www.scientific.net/amr.660.222.

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Global energy price development causes a growing demand for energy efficient architectural building concepts. As a part of a systemic approach, smart building skins provide an option to reduce overall energy demands. In this regard, intelligent adaptivity of the building skin promotes environmental responsivity ensuring optimized energy use. Smart skins as eco-technological environments are mostly produced with serialized and prefabricated elements. Advances in serialized pre-fabrication reduce costs in relation to structural complexity of smart skin designs. Integral potentials of flow and bend split components for adaptable façade structures are projected with an exemplified foldable structure defining a cost-effective production solution for smart skins. Integrated, experimental scripting methods, jointing and mechanizing techniques are applied for this first prototype.

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YU, Miao, and Chuanchen BI. "A STUDY OF THE MARKET DEMAND OF MOBILE SMART HOUSE UNDER THE SMART CITY DEVELOPMENT BACKGROUND." International Journal of Engineering Technologies and Management Research 9, no. 11 (November 16, 2022): 18–26. http://dx.doi.org/10.29121/ijetmr.v9.i11.2022.1248.

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The smart city development is the tendency of China’s urban development. The implementation of smart city provides a guiding direction for the development and integration of the whole industry. The double carbon policy of carbon emissions peak and carbon neutrality is the primary goals in China’s energy transition and environmental protection in the coming decades. Mobile smart house can meet the needs of users in the mobile lifestyle, energy conservation, energy management, smart home and smart house, and support and promote the smart house policy and double carbon policy. Through the secondary data collection and literature analysis methods, the paper concludes that how mobile smart houses meet people’s needs in energy and information and analyzes the kind of free life that mobile smart houses can help people achieve. The SWOT method is used to analyze the advantages and disadvantages of mobile smart house, to provide solutions for the development direction of the mobile smart housing products and for the aim of better meeting the market demand.

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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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Cai, Sheng Xia. "Analysis of Energy Demand and Smart Grid Policy-Making Issues of China." Applied Mechanics and Materials 260-261 (December 2012): 576–80. http://dx.doi.org/10.4028/www.scientific.net/amm.260-261.576.

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Energy demand is steadily increasing in the world. People face challenges to meet this demand. Smart grid technology is helpful to renewable energy utilization and thus is drawn more and more attention. This paper makes a comparison of several indicators among five countries or districts from 2002 to 2010. These indicators include energy use indicator, GDP per unit of energy use indicator, net energy imports indicator, CO2 emissions indicator, fossil fuel energy consumption indicator, and Alternative and nuclear energy indicator. From the trend analysis of these indicators, it can be seen that developing countries especially China has a rapid increase in energy consumption and have a high environment protection pressure. Hence smart grid development is an urgent task for China. Some issues in the development of smart grid are discussed and some policy suggestions are presented.

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Wang, Zhi Qi, and Yue Leng. "Review of Smart Grid Demand Response with Clean Energy Access." Applied Mechanics and Materials 446-447 (November 2013): 847–52. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.847.

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The smart grid has expanded the capacity of clean energy, has made the power grid load consume clean energy reasonably, it can realize the maximization of social benefit, it is a scientific problem to be resolved in the field of energy. This article will apply the interruptible load to the power system with wind power, study the significance of the interruptible load, summarize the research and practice of clean energy carried out at home and abroad, consider the impact of the wind machine on power grid after it is connected to the grid, finally put forward the power package by analyzing the method of the demand response.

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Loganathan, Arun S., Vijayapriya Ramachandran, Angalaeswari Sendraya Perumal, Seshathiri Dhanasekaran, Natrayan Lakshmaiya, and Prabhu Paramasivam. "Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions." Energies 16, no. 1 (December 20, 2022): 6. http://dx.doi.org/10.3390/en16010006.

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Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

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Nassourou, Mohamadou, Joaquim Blesa, and Vicenç Puig. "Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty." Energies 13, no. 3 (February 5, 2020): 696. http://dx.doi.org/10.3390/en13030696.

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Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied.

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HASANOVA, Nazlı, and Seçil VARBAK NEŞE. "Demand-Side Energy Management in Smart Buildings: A Case Study." European Journal of Technic 11, no. 2 (December 30, 2021): 239–47. http://dx.doi.org/10.36222/ejt.969881.

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He, Min-fan, Fu-xing Zhang, Yong Huang, Jian Chen, Jue Wang, and Rui Wang. "A Distributed Demand Side Energy Management Algorithm for Smart Grid." Energies 12, no. 3 (January 29, 2019): 426. http://dx.doi.org/10.3390/en12030426.

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This paper proposes a model predictive control (MPC) framework-based distributed demand side energy management method (denoted as DMPC) for users and utilities in a smart grid. The users are equipped with renewable energy resources (RESs), energy storage system (ESSs) and different types of smart loads. With the proposed method, each user finds an optimal operation routine in response to the varying electricity prices according to his/her own preference individually, for example, the power reduction of flexible loads, the start time of shift-able loads, the operation power of schedulable loads, and the charge/discharge routine of the ESSs. Moreover, in the method a penalty term is used to avoid large fluctuation of the user’s operation routines in two consecutive iteration steps. In addition, unlike traditional energy management methods which neglect the forecast errors, the proposed DMPC method can adapt the operation routine to newly updated data. The DMPC is compared with a frequently used method, namely, a day-ahead programming-based method (denoted as DDA). Simulation results demonstrate the efficiency and flexibility of the DMPC over the DDA method.

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Nguyen, Hung Khanh, Ju Bin Song, and Zhu Han. "Distributed Demand Side Management with Energy Storage in Smart Grid." IEEE Transactions on Parallel and Distributed Systems 26, no. 12 (December 1, 2015): 3346–57. http://dx.doi.org/10.1109/tpds.2014.2372781.

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Turner, William J. N., Oliver Kinnane, and Biswajit Basu. "Demand-side Characterization of the Smart City for Energy Modelling." Energy Procedia 62 (2014): 160–69. http://dx.doi.org/10.1016/j.egypro.2014.12.377.

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Giordano, Ghiani, Pilo, and Rosetti. "Planning of Energy Production and Management of Energy Resources in Local Energy Communities: The Case of Berchidda Municipality (Italy)." Proceedings 20, no. 1 (July 24, 2019): 16. http://dx.doi.org/10.3390/proceedings2019020016.

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This paper aims to present the ideas and the strategies behind the project called “Berchidda Energy 4.0” which proposes the development of a smart Local Energy Community in the Municipality of Berchidda (Italy). The project is focused on increasing energy efficiency by fostering renewable generation production and maximizing the self-consumption of the energy produced, as well as increasing the active involvement of the consumers that will be equipped with smart home automation system for demand response applications.

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Chen, Xiangping, Kui Weng, Fanlin Meng, and Monjur Mourshed. "Smart Energy Management for Unlocking Demand Response in the Residential Sector." Proceedings 2, no. 15 (August 24, 2018): 1136. http://dx.doi.org/10.3390/proceedings2151136.

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This paper presents a smart energy management system for unlocking demand response in the UK residential sector. The approach comprises the estimation of one-hour energy demand and PV generation (supply) for scheduling the 24-h ahead demand profiles by shifting potential flexible loads. Real-time electrical demand is met by combining power supplies from PV, grid and batteries while minimizing consumer’s cost of energy. The results show that the peak-to-average ratio is reduced by 22.9% with the cost saving of 34.6% for the selected day.

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Markakis, Evangelos K., Yannis Nikoloudakis, Kalliopi Lapidaki, Konstantinos Fiorentzis, and Emmanuel Karapidakis. "Unification of Edge Energy Grids for Empowering Small Energy Producers." Sustainability 13, no. 15 (July 29, 2021): 8487. http://dx.doi.org/10.3390/su13158487.

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The current energy landscape is largely comprised of big stakeholders, who are often the monopolistic drivers of their local market. This fact does not leave any room for smaller players to participate in this procedure by contributing their part in the energy pool. Moreover, the dynamic demand for power along with the current power production rate are not corelated, rendering the power distribution grid, a best effort network, prone to power failures, due to the inevitable irregularities in demand. This paper introduces a novel concept that allows small energy producers, such as solar panel grids, to offer their production excess through an intelligent energy brokerage blockchain-based framework. The proposed framework ingests the vast amounts of bigdata stemming from the distributed smart energy grids smart metering and allows for automatic commercial transactions of power between the participants of a dedicated marketplace. Values dynamically fluctuate depending on the real-time offer and demand and the grid’s state. Thus, all partaking stakeholders are able to take the most out of their product by leveraging the intelligence provided by the energy marketplace, and contribute to the overall stabilization of the energy grid.

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Khan, Taimoor Ahmad, Amjad Ullah, Ghulam Hafeez, Imran Khan, Sadia Murawwat, Faheem Ali, Sajjad Ali, Sheraz Khan, and Khalid Rehman. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach." Energies 15, no. 23 (November 30, 2022): 9074. http://dx.doi.org/10.3390/en15239074.

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A real-time energy management strategy using dynamic pricing mechanism by deploying a fractional order super twisting sliding mode controller (FOSTSMC) is proposed for correspondence between energy users and providers. This framework, which controls the energy demand of the smart grid’s users is managed by the pricing signal provided by the FOSTSMC, issued to the smart meters, and adjusts the users’ demand to remove the difference between energy demand and generation. For the implementation purpose, a scenario based in MATLAB/Simulink is constructed where a sample renewable energy–integrated smart microgrid is considered. For the validation of the framework, the results of FOSTSMC are compared with the benchmark PI controller’s response. The results of the benchmark PI controller are firstly compared in step response analysis, which is followed by the comparison in deploying in renewable energy–integrated smart grid scenario with multiple users. The results indicate that the FOSTSMC-based controller strategy outperformed the existing PI controller-based strategy in terms of overshoot, energy balance, and energy price regulation.

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Li, Tao, Kun Peng Xu, and Xue Qing Qi. "Design of Intelligent Home Energy Management for Demand Response Applications." Advanced Materials Research 827 (October 2013): 78–83. http://dx.doi.org/10.4028/www.scientific.net/amr.827.78.

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A Home Energy Management (HEM) system is an integral part of a smart grid that can potentially enable demand response applications for residential customers. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customers preference and load priority. This paper presents an intelligent HEM for managing high power consumption household appliances for demand response (DR) analysis. The proposed HEM manages household loads according to their preset priority and guarantees the total household power consumption below certain levels.Given the lack of understanding about DR potentials in this market, this work serves as an essential stepping-stone toward providing an insight into how much DR can be performed for residential customers.

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Siano, Pierluigi. "Demand response and smart grids—A survey." Renewable and Sustainable Energy Reviews 30 (February 2014): 461–78. http://dx.doi.org/10.1016/j.rser.2013.10.022.

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Gupta, Rajat, and Johanna Morey. "Empirical evaluation of energy profiles of thermally-efficient homes with smart energy systems and controls." Journal of Physics: Conference Series 2042, no. 1 (November 1, 2021): 012023. http://dx.doi.org/10.1088/1742-6596/2042/1/012023.

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Abstract Smart control technologies are beginning to be deployed in homes to optimise heating and alter the timing of domestic energy demand to enable residential demand side response (DSR). This paper presents before (baseline phase) and after (control phase) evaluation of the monitored indoor temperature and energy demand during the heating season in 10 new-build dwellings, each of which received a 5kWh electro-chemical battery and smart control to enable shifting of heating energy demand. The dwellings had air source heat pumps (ASHP) and 2kWp solar photovoltaic (PV) panels, and were located in a social housing estate in Barnsley, England. For eight dwellings, heat pump electricity use per heating degree day was found to decrease by 10% and narrow baseline peaks were suppressed during the control phase. Daily mean grid electricity import and heat pump electricity use in the peak period (4pm – 7pm) were measured as 4.0 kWh and 1.4 kWh during the control phase as compared to 3.8kWh and 1.3 kWh for the baseline phase. However the use of a flat tariff (single-rate) meant that battery charging-discharging capability was not fully utilised. Time-of-use tariff would further enhance cost savings associated with the change in the timing of energy demand.

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Zhang, You You, Rui Ma, and Zi Heng Xu. "The Review of Demand Response Programs in Smart Grid." Advanced Materials Research 614-615 (December 2012): 1800–1803. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1800.

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Demand response (DR) has become an important part of smart grid, mainly for modulating load curve, emergency treatment, balancing the demand and supply of power system. The features of smart grid-self healing and interactive-give the new meaning to DR. In smart grid, the promotion strategies of ISO/RTO in energy and auxiliary service markets implement DR programs in the project of auxiliary services and can reduce energy consumption. This paper summarizes the global related researches and practices, including the classification, progress of each subclass, the future development potential and DR program outlook in China, and puts forward the following two ideas: 1) The DR for single market cannot satisfy the needs of smart grid, advanced measurement and remote sensing technologies shall be made to implement the DR programs combining with multiple markets; 2) The service agency of DR on transmission and distribution shall be conducted and supervised by the government departments.

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Albogamy, Fahad R., Ghulam Hafeez, Imran Khan, Sheraz Khan, Hend I. Alkhammash, Faheem Ali, and Gul Rukh. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids." Sustainability 13, no. 20 (October 16, 2021): 11429. http://dx.doi.org/10.3390/su132011429.

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In smart grid, energy management is an indispensable for reducing energy cost of consumers while maximizing user comfort and alleviating the peak to average ratio and carbon emission under real time pricing approach. In contrast, the emergence of bidirectional communication and power transfer technology enables electric vehicles (EVs) charging/discharging scheduling, load shifting/scheduling, and optimal energy sharing, making the power grid smart. With this motivation, efficient energy management model for a microgrid with ant colony optimization algorithm to systematically schedule load and EVs charging/discharging of is introduced. The smart microgrid is equipped with controllable appliances, photovoltaic panels, wind turbines, electrolyzer, hydrogen tank, and energy storage system. Peak load, peak to average ratio, cost, energy cost, and carbon emission operation of appliances are reduced by the charging/discharging of electric vehicles, and energy storage systems are scheduled using real time pricing tariffs. This work also predicts wind speed and solar irradiation to ensure efficient energy optimization. Simulations are carried out to validate our developed ant colony optimization algorithm-based energy management scheme. The obtained results demonstrate that the developed efficient energy management model can reduce energy cost, alleviate peak to average ratio, and carbon emission.

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Babar, Muhammad, Jakub Grela, Andrzej Ożadowicz, Phuong Nguyen, Zbigniew Hanzelka, and I. Kamphuis. "Energy Flexometer: Transactive Energy-Based Internet of Things Technology." Energies 11, no. 3 (March 6, 2018): 568. http://dx.doi.org/10.3390/en11030568.

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Effective Energy Management with an active Demand Response (DR) is crucial for future smart energy system. Increasing number of Distributed Energy Resources (DER), local microgrids and prosumers have an essential and real influence on present power distribution system and generate new challenges in power, energy and demand management. A relatively new paradigm in this field is transactive energy (TE), with its value and market-based economic and technical mechanisms to control energy flows. Due to a distributed structure of present and future power system, the Internet of Things (IoT) environment is needed to fully explore flexibility potential from the end-users and prosumers, to offer a bid to involved actors of the smart energy system. In this paper, new approach to connect the market-driven (bottom-up) DR program with current demand-driven (top-down) energy management system (EMS) is presented. Authors consider multi-agent system (MAS) to realize the approach and introduce a concept and standardize the design of new Energy Flexometer. It is proposed as a fundamental agent in the method. Three different functional blocks have been designed and presented as an IoT platform logical interface according to the LonWorks technology. An evaluation study has been performed as well. Results presented in the paper prove the proposed concept and design.

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Ma, Yonghong, and Baixuan Li. "Hybridized Intelligent Home Renewable Energy Management System for Smart Grids." Sustainability 12, no. 5 (March 9, 2020): 2117. http://dx.doi.org/10.3390/su12052117.

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The incorporation of renewable energies and power storage at distribution facilities are one of the important features in the smart grid. In this paper, a hybridized intelligent home renewable energy management system (HIHREM) that combines solar energy and energy storage services with the smart home is planned based on the demand response and time of consumption pricing is applied to programs that offer discounts to consumers that reduce their energy consumption during high demand periods. The system is designed and handled with minimal energy requirements at home through installation of renewable energy, preparation, and arrangement of power stream during peak and off-peak periods. The best energy utilization of residential buildings with various overlapping purposes is one of the most difficult issues correlated with the implementation of intelligent micro-network systems. A major component of the smart grid, the domestic energy control system (HIHREM) provides many benefits, such as power bill reductions, reduction in wind generation, and demand compliance. This showed that the proposed energy scheduling method minimizes the energy consumption by 48% and maximizes the renewable energy consumed at the rate 65% of the total energy generated. A new model for smart homes with renewable energies is introduced in this report. The proposed HIHREM method achieves high performance and reduces cost-utility.

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Kailas, Aravind, Valentina Cecchi, and Arindam Mukherjee. "A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation." Journal of Computer Networks and Communications 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/932181.

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With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

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Shigenobu, Ryuto, Oludamilare Bode Adewuyi, Atsushi Yona, and Tomonobu Senjyu. "Demand response strategy management with active and reactive power incentive in the smart grid: a two-level optimization approach." AIMS Energy 5, no. 3 (2017): 482–505. http://dx.doi.org/10.3934/energy.2017.3.482.

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Tuğba SARIKAYA and Sibel ZORLU PARTAL. "A Case Study of Load Scheduling For Home Energy Management with Integrated Renewable Energy." International Journal of Engineering and Management Research 12, no. 5 (October 7, 2022): 27–34. http://dx.doi.org/10.31033/ijemr.12.5.4.

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Smart grids are very comprehensive systems where each sub-unit from generation to consumption should be considered separately. Home energy management systems and demand-side load management applications are also among the most important issues of smart grid systems. In this study, a smart home energy management model was developed in this concept, and energy management solutions were presented through an exemplary model. For this purpose, a home energy management algorithm was developed and simulated in the MATLAB Simulink environment by taking an apartment in Esenler, Istanbul as a reference. The smart home model discussed in this simulation study can generate its own electricity with renewable energy sources, store excess electrical energy in battery groups and also sell the surplus to the grid. This home model also enables end-users to control peak loads and schedule home appliances, especially during peak hours, following a demand-response program to consume energy more efficiently. Then, in order to see the electricity consumption results, electricity bill calculations were made according to both single and triple tariff pricing. The benefits of this model to the consumer and the grid were investigated, and also its effects on efficiency were examined. The results are given comparatively and the consumer’s saving is depicted in figures.

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Al-Salaymeh, Ahmed, Sara AlTwassi, Rasha AlBeek, Kholoud Hassouneh, Diana Athamneh, Noor Eldin Alkiswani, Rashed Manna, and Natalie Asfour. "Smart Meters Rollout in Jordan: Opportunities, Business Models, Challenges, and Recommendations." International Journal of Energy Economics and Policy 12, no. 4 (July 19, 2022): 394–408. http://dx.doi.org/10.32479/ijeep.13008.

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Jordan's energy demand is growing steadily due to many factors ranging from the growing population, to the needed energy for heating, cooling, desalination, and industry (Azzuni et al., 2020). EMRC has stated a new electricity tariff starting from April 2022. The high cost of electricity bill in Jordan with fixed tariff has resulted the need to have a dynamic tariff to transform energy use that lead to demand reductions, a shift in peak demand, a better management of distribution networks, in addition to reductions in operation costs. A full transition towards smart meters in Jordan is one of the main pillars to achieve a compatible smart grid system that will be a great solution to sustain the energy security. Also, it will lead to flatness the demand profile which will have economic consequences by reducing the cost of electricity generation. The current status of smart meters’ rollout, the optimal business model, challenges, and awareness towards energy strategies and smart meters deployment in Jordan have been investigated. It has been found that the optimal business model for the Jordanian market is a hybrid model of DGC and EaaS models. Also, a set of opportunities and recommendations have been concluded.

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İzmitligil, Hasan, and Hanife Apaydn Özkan. "A home energy management system." Transactions of the Institute of Measurement and Control 40, no. 8 (February 1, 2018): 2498–508. http://dx.doi.org/10.1177/0142331217741537.

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In this study, an offline home energy management system that reduces electricity expense and peak demand without deteriorating residents’ contentment is considered. The main goal is to improve the system in the sense of reducing electricity expense, via interfering with appliances by means of interrupting as well as shifting their operation; and keeping up with the benefits of the newest technology, via plug-in hybrid electrical vehicle integration. The proposed offline home energy management system (OF-HEM) consists of smart electrical appliances, power resources (photovoltaic system, grid, backup battery), main controller, communication network and plug-in hybrid electrical vehicle. The main controller manages the power resources, appliances and plug-in hybrid electrical vehicle based on the solution of a mixed integer linear program with defined smart and energy-efficient operation constraints related to the smart appliances and power sources for data collected at the beginning of the day from the power resources and residents’ preferences. Conducted case studies demonstrate that OF-HEM significantly reduces electricity expenses and high peak demand.

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Karbasioun, Mohammad M., Gennady Shaikhet, Ioannis Lambadaris, and Evangelos Kranakis. "Asymptotically optimal scheduling of random malleable demands in smart grid." Discrete Mathematics, Algorithms and Applications 10, no. 02 (April 2018): 1850025. http://dx.doi.org/10.1142/s1793830918500258.

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We study the problem of scheduling random energy demands within a fixed normalized time horizon. Each demand has to be serviced without interruption at a constant intensity, while its duration is bounded by a pair of malleability constraints. Such constraints are assumed to be characterized by an i.i.d random vector that follows a general distribution. At each time instance, the total power consumption is computed as the sum of the intensities of all demands being serviced at that moment. Our objective is to minimize both the maximum and the total convex cost of the power consumption of the grid. The problem is considered in the asymptotic regime. In this regime, the number of demands is assumed to be large, and their (random) energy requirements are inversely proportional to the number of demands. Such setting allows us to introduce a linear-time scheduling policy and shows its asymptotic optimality with respect to both cost criteria. We first study the optimization problem in the case where all demands are available a priori, i.e., before scheduling starts. Then we extend our approach for the case of demand scheduling in an arbitrary length time horizon, where the demands arrive randomly during this time interval.

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Et.al, Vigneshwaran Perumal. "Smart Energy Home Management Using Hybrid system." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 2443–46. http://dx.doi.org/10.17762/turcomat.v12i3.1236.

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Energy balance is one of the main criteria to operate the grid in stable condition. If there is any energy imbalance occurs due to increase in demand then the grid will not operate in stable condition. Increase demand is one of the major issues facing by grid. To address this problems many countries promoting the renewable energy generation this will support the grid to avoid energy imbalance problem. Prosumerplay a key role in this situation, where they can utilize the generated energy and remaining surplus power can integrate it to the grid. While utilising the renewable power generate it should be controlled and monitored effectively so that we can reduce the tariff cost. Solar panels installed in house need to bemonitored continuously to improve the performance of battery. In this paper, the power line communication (PLC) compliant with Home Plug is implementing to check energy consumption. The system design is composed of three components: Driver circuit, ac supply, and smart device application. This system will switch over from solar energy consumption to AC supply according to the load requirement. This scheme will maintain the performance of a PV system and contribute to enhancing home energy management system. The system design was stimulated with Matlaband verified with the hardwareresults.

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Journal articles on the topic 'Smart energy demand'

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