Energy Management Algorithm for Peak Demand Reduction

Ayan, Onur; Türkay, Belgin Emre

doi:10.1109/siela.2018.8447088

Cited by 8 publications

(6 citation statements)

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“…Trends created by the energy monitoring system for torque load of all machines showed that some of the machines required soft starters to lower the 70% starting torque load [21]. Most of the machines were already equipped with soft starters, and the rest were installed after checking the difference between the power consumption of both types of cases.…”

Section: Methodsmentioning

confidence: 99%

Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study

et al. 2020

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Energy-saving strategies cannot be implemented without having detailed and regular power consumption data of a facility. The installation of an energy monitoring and data logging system can help in planning energy efficiency improvement policies by providing daily, monthly, and yearly energy consumption reports and graphs. The purpose of this study was to demonstrate the impact of an energy monitoring and management system on the improvement of energy efficiency in the industrial sector of developing countries. This study introduced an energy monitoring and data logging system installed in an automobile factory in Pakistan. Energy consumption data, which also included power quality data, were collected with the help of energy analyzers and transmitted to a centralized supervisory control and data acquisition (SCADA) software for data logging and monitoring purposes. This system was developed by combining Modbus with industrial Ethernet to communicate real-time energy consumption data of the factory to multiple local and remote locations. Monitoring and logging the real-time energy consumption data helped the user to find the significant energy losses inside the factory and to implement various energy conservation policies inside the facility, resulting in energy efficiency improvement. The energy consumption results indicate that the proposed system can help achieve an approximately 8% improvement in energy efficiency.

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Section: Methodsmentioning

confidence: 99%

Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study

et al. 2020

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“…Figure 16a shows the supply and consumption profiles for the simulated HEMS. The consumption of 20 services (appliances) is adapted from [54], constrasted with the data in [55], and summarized in Table 4.…”

Section: B Description Of Generation and Consumption Profiles For Simulated Scenarios 1) Generation And Consumption Profilesmentioning

confidence: 99%

Heuristic Strategies for NFV-Enabled Renewable and Non-Renewable Energy Management in the Future IoT World

Tipantuña¹,

Hesselbach

Unger

2021

IEEE Access

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The ever-growing energy demand and the CO 2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands.

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“…The first steps performed by the heuristic (i.e., variations per service and combinations) define its complexity and as a function of N the growth rate of FASTTS is given by Equation (32). Moreover, if all of the partitions have the same size, the growth rate can be expressed, as shown in Equation (33).…”

Section: Complexity Analysis Of Fasttsmentioning

confidence: 99%

“…Finally, although case F is a small-scale scenario, only the evaluation of FASTTS is carried out due to the number of services and T k s that is not feasible for OPTTS. In this regard, the consumption information for the HEMS scenario of case F is adapted from [33] and detailed in Table 6. 6 according to Table 6 according to Table 6 For the case F, the priorities of the energy demands are detailed in Table 6, for the rest of cases there is a single priority level (i.e., l = 1).…”

Section: Case Studiesmentioning

confidence: 99%

NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms

Tipantuña

Hesselbach

2020

Future Internet

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The increasing worldwide energy demand, the CO2 emissions generated due to the production and use of energy, climate change, and the depletion of natural resources are important concerns that require new solutions for energy generation and management. In order to ensure energy sustainability, measures, including the use of renewable energy sources, the deployment of adaptive energy consumption schemes, and consumer participation, are currently envisioned as feasible alternatives. Accordingly, this paper presents the requirements and algorithmic solutions for efficient management of energy consumption, which aims to optimize the use of available energy, whether or not it is 100% renewable, by minimizing the waste of energy. The proposal works within a Demand-Response environment, uses Network Functions Virtualization as an enabling technology, and leverages the massive connectivity of the Internet of Things provided by modern communications technologies. The energy consumption optimization problem is formulated as an Integer Linear Program. It is optimally solved while using a brute-force search strategy, defined as OPTmathsizesmallTSmathsizesmall, to detect all concerns that are related to the problem. Given the NP-hard nature of the problem and the non-polynomial complexity of OPTmathsizesmallTSmathsizesmall, some heuristic solutions are analyzed. Subsequently, a heuristic strategy, described as FASTmathsizesmallTSmathsizesmall based on a pre-partitioning method, is implemented. The simulation results validate our proposed energy management solution. Exact and heuristic strategies, when deployed in the Network Functions Virtualization domain, demonstrate improvements in the way that energy is consumed, thereby offering an increase in service processing. The evaluation results also show that FASTmathsizesmallTSmathsizesmall produces high-quality solutions that are close to those of OPTmathsizesmallTSmathsizesmall while executing 230×–5000× faster.

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Energy Management Algorithm for Peak Demand Reduction

Cited by 8 publications

References 1 publication

Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study

Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study

Heuristic Strategies for NFV-Enabled Renewable and Non-Renewable Energy Management in the Future IoT World

NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms

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