scholarly journals A Multivariate Load Trading Optimization Method for Energy Internet Based on LSTM and Gaming Theory

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5246
Author(s):  
Mingming Pan ◽  
Shiming Tian ◽  
Jindou Yuan ◽  
Songsong Chen ◽  
Sheng He
Keyword(s):  
Power System ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Optimal Solution ◽  
Optimization Method ◽  
Energy Internet ◽  
Market Operation ◽  
The Game Theory ◽  
The Impact

Energy Internet is a complex nonlinear system. There are many stakeholders in the load trading market, which is usually regarded as a multi-player gaming. Although gaming theory has been introduced to solve Multivariate Load trading problems, different conditions should be considered to accurately optimize the multivariate load trading problem. For example, the selling side needs to reduce the reserve capacity and improve profits, but the consumer side needs to reduce costs and minimize the impact on its own electricity consumption. These contradictory conditions require multiple Nash equilibrium to achieve obviously. To address this issue, a unified architecture of the power system cloud trading is constructed in this paper, which is combined with the multiple load classification of the power system. In addition, according to the power market operation mechanism, a price-guided multivariate load trading game strategy is designed. More importantly, a multivariate load trading optimization method based on LSTM (Long Short-Term Memory) and gaming theory is proposed in this work. LSTM is introduced for real time prediction, which can be combined with the game theory for strategy searching. The global stability and optimal solution theory prove the feasibility of the proposed neural network, and finally the effectiveness of the proposed method is verified by using numerical simulation.

scholarly journals Research on Short-Term Load Prediction Based on Seq2seq Model

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3199 ◽  
Author(s):  
Gangjun Gong ◽  
Xiaonan An ◽  
Nawaraj Kumar Mahato ◽  
Shuyan Sun ◽  
Si Chen ◽  
...  
Keyword(s):  
Power System ◽  
Prediction Model ◽  
Short Term Memory ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Attention Mechanism ◽  
Load Prediction ◽  
Short Term ◽  
System Load ◽  
The Impact

Electricity load prediction is the primary basis on which power-related departments to make logical and effective generation plans and scientific scheduling plans for the most effective power utilization. The perpetual evolution of deep learning has recommended advanced and innovative concepts for short-term load prediction. Taking into consideration the time and nonlinear characteristics of power system load data and further considering the impact of historical and future information on the current state, this paper proposes a Seq2seq short-term load prediction model based on a long short-term memory network (LSTM). Firstly, the periodic fluctuation characteristics of users’ load data are analyzed, establishing a correlation of the load data so as to determine the model’s order in the time series. Secondly, the specifications of the Seq2seq model are given preference and a coalescence of the Residual mechanism (Residual) and the two Attention mechanisms (Attention) is developed. Then, comparing the predictive performance of the model under different types of Attention mechanism, this paper finally adopts the Seq2seq short-term load prediction model of Residual LSTM and the Bahdanau Attention mechanism. Eventually, the prediction model obtains better results when merging the actual power system load data of a certain place. In order to validate the developed model, the Seq2seq was compared with recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU) algorithms. Last but not least, the performance indices were calculated. when training and testing the model with power system load data, it was noted that the root mean square error (RMSE) of Seq2seq was decreased by 6.61%, 16.95%, and 7.80% compared with RNN, LSTM, and GRU, respectively. In addition, a supplementary case study was carried out using data for a small power system considering different weather conditions and user behaviors in order to confirm the applicability and stability of the proposed model. The Seq2seq model for short-term load prediction can be reported to demonstrate superiority in all areas, exhibiting better prediction and stable performance.

scholarly journals A Methodological Approach to the Assessment of the Impact of Digital Technologies Development in Energy Industry on Electricity Price and Demand in a Region

2020 ◽  
Vol 209 ◽  
pp. 06006
Author(s):  
Elena Galperova ◽  
Vasiliy Galperov
Keyword(s):  
Power System ◽  
Electric Power ◽  
Intelligent Systems ◽  
Methodological Approach ◽  
Electricity Consumption ◽  
Economic Security ◽  
Electricity Price ◽  
Regional Power ◽  
Electric Power Supply ◽  
The Impact

The relevance of this study is due to the importance of assessing the prospective dynamics and structure of demand for energy carriers when developing and making strategic decisions in the field of energy and economic security of the country and its regions. The advance of digital technology redefines the properties of electric power supply systems, erases the boundary between electric power producers and consumers, and impacts the formation of electricity price and demand in the region. This study presents a method of electricity costing in the regional power system, which serves as an integral part of the approach to assessing the impact of intelligent systems development on the demand for electricity in the region. The approach is unique in that it simulates the behavior of electricity consumers and producers of various types as they pursue their own interests and assesses the impact of this behavior on the demand and price of electricity in the regional power system. Determining the cost of electricity in the system is based on the consistent alignment of the required amount of electricity consumption with the capabilities of producers seeking to achieve their best economic performance. Each producer is described as an optimization model, which is a standalone agent in a multi-agent power system model.

scholarly journals Business classification of Power Grid Enterprises in the development environment of Energy Internet

2020 ◽  
Vol 185 ◽  
pp. 02002
Author(s):  
Xin Li ◽  
Yuan Zhang
Keyword(s):  
Power Grid ◽  
Energy System ◽  
Utilization Efficiency ◽  
Low Carbon ◽  
Development Environment ◽  
Energy Internet ◽  
Information And Communication ◽  
Technology Control ◽  
The Impact

Energy Internet is centered on electricity, based on a strong smart grid, and deeply integrates advanced information and communication technology, control technology and advanced energy technology to support the clean and low-carbon transformation of energy and electric power, comprehensive utilization efficiency optimization of energy, and the access of flexible and convenient multiple entities, which is a smart energy system with the characteristics of green safety, ubiquitous interconnection, efficient interaction, and intelligent openness. This article focuses on the impact of the development of the Energy Internet on power grid companies and the main business of power grid companies in the future development environment of the Energy Internet, providing references for the transformation and development of power grid companies.

The Ancillary Service Purchase Model in the Background of High Proportion of Renewable Energy Connecting with the Grid

2018 ◽  
Vol 19 (6) ◽  
Author(s):  
Fugui Dong ◽  
Chunxu Jin ◽  
Lei Shi ◽  
Meimei Shang
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Thermal Power ◽  
Optimal Solution ◽  
System Stability ◽  
Ancillary Service ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Objective Programming ◽  
The Impact

Abstract With the trend of high proportion of renewable energy connecting with grid, peaking services has played a crucial role in the integration of wind power and other renewable energy sources. The current peaking service takes into account only the cost of thermal power units that providing peaking service without considering the impact of its own reliability on the peaking service and the safe operation of the power system. In this paper, the traditional uniform clearing mechanism has been improved, and the reliability factor of ancillary service provided by thermal power units has been included in the previous quotation-based sorting rule, and a multi-objective programming optimal purchasing model considering synergistic capacity cost and power system stability is established. According to the optimal solution of each objective, Pareto optimal solution of multi-objective programming problem is obtained by the ideal point method. Then the ancillary service price and purchase cost are obtained based on the solution.

scholarly journals Risk analysis and optimization for communication transmission link interruption in Smart Grid cyber-physical system

2018 ◽  
Vol 14 (2) ◽  
pp. 155014771875603 ◽  
Author(s):  
Xingyu Chen ◽  
Puyuan Zhao ◽  
Peng Yu ◽  
Baoju Liu ◽  
Wenjing Li ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Power System ◽  
Network Structure ◽  
Optimization Method ◽  
Path Optimization ◽  
Analysis Method ◽  
Service Distribution ◽  
Transmission Link ◽  
The Stability ◽  
The Impact

Since the communication network of the cyber-physical power system is responsible for communicating information, which guarantees the operation of the cyber-physical power system, researchers focus on the stability of the communication system. This article analyzes the risk of the communication transmission link interruption based on the network structure and characteristics of service transmission and proposes a path optimization method. First, we analyze the impact of link disruption on network structure and service, respectively, and quantify the impact as link interruption risk. According to the risk analysis, we propose an optimization method utilizing the Dijkstra’s algorithm and the genetic algorithm to reconfigure service paths, which aims to minimize time delay and realize the equilibrium of service distribution. Through a particular situation, we calculate the link interruption risk and use the optimization method to configure service path for affected service. The results show that the time delays of the optimized service paths are in the acceptable level as well as the balance index of the service distribution is decreased obviously. The simulation experiment reveals the operability of the risk analysis method and the effectiveness of the path optimization method, which provides a technical reference for risk analysis and service path configuration.

scholarly journals Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1881 ◽  
Author(s):  
Xiaorui Shao ◽  
Chang-Soo Kim ◽  
Palash Sontakke
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Sensor Data ◽  
Short Term ◽  
Multi Scale ◽  
Deep Model ◽  
Cost Of Time ◽  
The Impact

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting.

Optimized Design of the Horn of Ultrasonic Roll Welding

2012 ◽  
Vol 482-484 ◽  
pp. 2223-2226 ◽  
Author(s):  
Kuen Ming Shu ◽  
Yu Guang Li ◽  
Chun Chi Chan ◽  
Jonq Bor Kuan
Keyword(s):  
Random Search ◽  
Optimal Solution ◽  
Optimization Method ◽  
Resonant Mode ◽  
Mode Frequency ◽  
Optimized Design ◽  
Solar Panels ◽  
Design Variables ◽  
Local Optimal Solution ◽  
The Impact

Previous studies on the amplitude horn only calculated sizes in consistent with the axial resonant mode frequency and disc bending resonant mode frequency without considering the overall stress and the amplitude of the disc’s outer ring. The resonant frequency of the amplitude horn cannot occur around 35 kHz. Such a design results in the inability to weld and may damage solar panels or lead to poor welding quality. Using the optimization method to address these problems, the proposed design process in this study is to conduct sensitivity analysis by the gradient method to understand the impact of design variables on the objective function for the selection of design variables. Then, this study applied the random search method to find out the feasible design of arrays to optimize the structure of two arrays closest to the design objective by the full factorial experiment method to ensure to get the global optimal solution rather than the local optimal solution. Finally, by design examples, this study used the sub-problem approximation method to search the optimized solution and compared the differences of the two methods, in order to confirm whether the objective of optimized design of amplitude horn had been achieved.

scholarly journals Cyber Attacks in Transactive Energy Market-Based Microgrid Systems

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1137
Author(s):  
Rumpa Dasgupta ◽  
Amin Sakzad ◽  
Carsten Rudolph
Keyword(s):  
Power System ◽  
Cyber Attacks ◽  
Smart Devices ◽  
Energy Market ◽  
Distributed Energy ◽  
Demand And Supply ◽  
Market Operation ◽  
Transactive Energy ◽  
Distributed Energy Generation ◽  
The Impact

Due to the increasing integration of distributed energy generation in the electric grid, transactive energy markets (TEMs) have recently emerged to balance the demand and supply dynamically across the grid. TEM enables peer to peer (P2P) energy trading and brings flexibility by reducing users’ demand in the grid. It also enhances the system’s efficiency and reduces the pressure on electricity networks. However, it is vulnerable to major cyber attacks as users equipped with smart devices are participating autonomously in the energy market, and an extensive amount of information is exchanged through the communication channel. The potential attacks and impacts of those attacks need to be investigated to develop an attack resilient TEM-based power system. Hence, in this paper, our goal is to systematically identify possible cyber attacks associated with a TEM-based power system. In order to achieve this goal, we classify the attacks during the P2P and flexibility schemes of TEM into three main categories. Then, we explore the attacks under each category in detail. We further distinguish the adversary roles of each particular attack and see what benefits will be received by an adversary through each specific attack. Finally, we present the impact of the attacks on the market operation, consumers, and prosumers of the TEM in this paper.

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