scholarly journals A Hierarchical Game Theory Based Demand Optimization Method for Grid-Interaction of Energy Flexible Buildings

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinyi Xu ◽  
Chengchu Yan ◽  
Yizhe Xu ◽  
Jingfeng Shi ◽  
Kai Sheng ◽  
...  
Keyword(s):  
Game Theory ◽  
Smart Grids ◽  
Energy Demand ◽  
Power Grid ◽  
Management Strategies ◽  
Interaction Model ◽  
Optimization Method ◽  
Equilibrium Solutions ◽  
Electrical Grids ◽  
Nash Equilibrium Solutions

Building demand-side management is an effective solution for relieving the peak and imbalance problems of electrical grids. How to explore the energy flexibility of buildings and to coordinate a variety of buildings with different energy flexibilities for effective interactions with smart grids are a great challenge. This paper proposes a game theory–based hierarchical demand optimization method for energy flexible buildings for achieving better grid interactions. This method consists of two optimization strategies at the grid and building levels. At the grid level, a demand-price interaction model for buildings and the grid is established to identify the Nash equilibrium solutions based on game theory; these solutions are used to determine the optimized energy demand of buildings and the associated electricity prices by accommodating the interests of all participants involved. At the building level, three types of buildings with different energy flexibilities are investigated to analyze the influence of building management strategies on grid interactions. The effectiveness of the proposed method is verified in a simulated case study. The results show that the optimization method can reduce building operational cost by 3–18%, reduce the fluctuation of the power grid by 30–50%, and ensure that the power grid increases income by 8–20%.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

UNCERTAINTY BASED ROBUST OPTIMIZATION IN AERODYNAMICS

2009 ◽  
Vol 23 (03) ◽  
pp. 477-480 ◽  
Author(s):  
ZHILI TANG
Keyword(s):  
Nash Equilibrium ◽  
Robust Design ◽  
Single Point ◽  
Optimization Method ◽  
Equilibrium Solutions ◽  
Design Problems ◽  
Nash Equilibrium Solutions ◽  
Definition Of ◽  
Taguchi Robust Design ◽  
Statistical Definition

The Taguchi robust design concept is combined with the multi-objective deterministic optimization method to overcome single point design problems in Aerodynamics. Starting from a statistical definition of stability, the method finds, Nash equilibrium solutions for performance and its stability simultaneously.

scholarly journals Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2642 ◽  
Author(s):  
Eric Bernardes C. Barros ◽  
Dionísio Machado L. Filho ◽  
Bruno Guazzelli Batista ◽  
Bruno Tardiole Kuehne ◽  
Maycon Leone M. Peixoto
Keyword(s):  
Response Time ◽  
Energy Demand ◽  
Power Grid ◽  
Fog Computing ◽  
High Volume ◽  
Local Source ◽  
Power Demand ◽  
Small Power ◽  
Control Power ◽  
Electrical Grids

Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralized power grid or singly, as a power island mode. Microgrids address these challenges using sensing technologies and Fog-Cloudcomputing infrastructures for building smart electrical grids. A smart Microgrid can be used to minimize the power demand problem, but this solution needs to be implemented correctly so as not to increase the amount of data being generated. Thus, this paper proposes the use of Fog computing to help control power demand and manage power production by eliminating the high volume of data being passed to the Cloud and decreasing the requests’ response time. The GridLab-d simulator was used to create a Microgrid, where it is possible to exchange information between consumers and generators. Thus, to understand the potential of the Fog in this scenario, a performance evaluation is performed to verify how factors such as residence number, optimization algorithms, appliance shifting, and energy sources may influence the response time and resource usage.

Grid Cyber-Physical System Risk Management Model Based on Cooperative Game Theory

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Cunbin Li ◽  
Ding Liu ◽  
Yi Wang ◽  
Chunyan Liang
Keyword(s):  
Game Theory ◽  
Risk Management ◽  
Cooperative Game ◽  
Physical System ◽  
Cooperative Game Theory ◽  
Power Grid ◽  
Management Model ◽  
Cyber Physical System ◽  
Model Based ◽  
Generation Power

AbstractAdvanced grid technology represented by smart grid and energy internet is the core feature of the next-generation power grid. The next-generation power grid will be a large-scale cyber-physical system (CPS), which will have a higher level of risk management due to its flexibility in sensing and control. This paper explains the methods and results of a study on grid CPS’s behavior after risk. Firstly, a behavior model based on hybrid automata is built to simulate grid CPS’s risk decisions. Then, a GCPS risk transfer model based on cooperative game theory is built. The model allows decisions to ignore complex network structures. On this basis, a modified applicant-proposing algorithm to achieve risk optimum is proposed. The risk management model proposed in this paper can provide references for power generation and transmission decision after risk as well as risk aversion, an empirical study in north China verifies its validity.

scholarly journals Multi-Robot Searching using Game-Theory Based Approach

10.5772/6232 ◽  
2008 ◽  
Vol 5 (4) ◽  
pp. 44 ◽  
Author(s):  
Yan Meng
Keyword(s):  
Game Theory ◽  
Dynamic Programming ◽  
Utility Function ◽  
Real Time ◽  
Dynamic Environment ◽  
Equilibrium Solutions ◽  
Strategy Equilibrium ◽  
The Game Theory ◽  
Target Searching ◽  
Multi Robot

This paper proposes a game-theory based approach in a multi–target searching using a multi-robot system in a dynamic environment. It is assumed that a rough priori probability map of the targets' distribution within the environment is given. To consider the interaction between the robots, a dynamic-programming equation is proposed to estimate the utility function for each robot. Based on this utility function, a cooperative nonzero-sum game is generated, where both pure Nash Equilibrium and mixed-strategy Equilibrium solutions are presented to achieve an optimal overall robot behaviors. A special consideration has been taken to improve the real-time performance of the game-theory based approach. Several mechanisms, such as event-driven discretization, one-step dynamic programming, and decision buffer, have been proposed to reduce the computational complexity. The main advantage of the algorithm lies in its real-time capabilities whilst being efficient and robust to dynamic environments.

scholarly journals Proposal and Implementation of a User-Centered Design Approach for Smart Grid Technologies: Example of the VERTPOM® Project

2021 ◽  
Vol 14 (4) ◽  
pp. 57
Author(s):  
Helios Raharison ◽  
Emilie Loup-Escande
Keyword(s):  
Distribution System ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Management ◽  
Real Life ◽  
Positive Energy ◽  
User Centered Design ◽  
Electrical Networks ◽  
Evaluation Approach ◽  
User Centered

Acting to preserve our planet as much as possible is no longer optional in today's world. To do so, Smart Grids within the framework of electrical networks - involving not only Distribution System Operators (DSOs), but also consumers in their Energy Demand Management (EDM) activity - represent an innovative and sustainable solution. However, the integration of Smart Grids into network management or into consumers' homes implies changes at several levels: organizational, social, psychological, etc. This is why it is essential to consider the human factor in the design of the technologies used in these Smart Grids. This paper proposes the integration of DSO operators and consumers within a user-centered evaluation approach in order to design Smart Grids that are sufficiently acceptable to users to enable Positive Energy Territories that produce more energy than they consume. This demonstration will be illustrated by the VERTPOM® project aiming at facilitating the use of renewable energies specific to each territory in order to contribute to the reduction of greenhouse gases and make the territories less dependent on traditional energies, and thus make Picardy (in France) a Positive Energy Territory. This paper presents the user-centered evaluation approach applied to three technologies (i.e., the VERTPOM-BANK® supervision tool intended for DSO operators, the private web portal and the IBox smart meter intended for households) from the upstream design phase to the implementation of the technologies in real-life situations.

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