Dynamic Manufacturing Scheduling Under Real-Time Electricity Pricing Based on MILP and ARIMA

2017 ◽  
Author(s):  
Yuxin Zhai ◽  
Haiyan Wang ◽  
Fu Zhao ◽  
John W. Sutherland
Keyword(s):  
Real Time ◽  
Production Scheduling ◽  
Flow Line ◽  
The United States ◽  
Electricity Price ◽  
Electricity Pricing ◽  
Manufacturing Scheduling ◽  
Price Forecast ◽  
Electricity Cost ◽  
The Cost

The scheduling of manufacturing equipment is critical in production facilities. Research on production scheduling has traditionally focused on component throughput and cycle time. However, the increase of electricity price in the United States following the market deregulation in 1990s has led to efforts to reduce energy cost via manufacturing scheduling. This paper explores the possibility of reducing electricity cost of a manufacturing facility subject to real time electricity pricing by dynamically changing operation schedules, while maintaining a pre-determined production throughput. A time series model is developed to forecast the hourly electricity price and time-indexed integer programming is used to determine the manufacturing schedule. The electricity price forecast is updated every hour based on the price history, and manufacturing schedule is updated according to the updated price forecast. A hypothetical flow line with 3 processes operating 16 hours per day is used as a case study. The line has a limited public buffer between processes and all machines in the shop have three operational states. With a throughput of 60 parts per day, the results suggest that it is possible to reduce the cost by 3.6% using an hourly forecast compared with a schedule based on a day-ahead price forecast.

scholarly journals Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

2017 ◽  
Vol 168 ◽  
pp. 239-253 ◽  
Author(s):  
Xu Gong ◽  
Marlies Van der Wee ◽  
Toon De Pessemier ◽  
Sofie Verbrugge ◽  
Didier Colle ◽  
...  
Keyword(s):  
Empirical Study ◽  
Real Time ◽  
Production Scheduling ◽  
Energy Efficient ◽  
Efficient Production ◽  
Electricity Pricing

A Research into Probabilistic Electricity Load Prediction Based on Demand Response Feature under Smart Grid Environment

2013 ◽  
Vol 380-384 ◽  
pp. 3098-3102
Author(s):  
Ning Lu ◽  
Ying Liu
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Electricity Market ◽  
Electricity Price ◽  
Electricity Pricing ◽  
Load Prediction ◽  
Characteristics Analysis ◽  
System Risk ◽  
Probabilistic Load ◽  
Demand Changes

The construction of grid plays an important role in national economic development, social stability and peoples life. In case that electricity market adopts real time electricity price, users active participation and real time response to electricity price will change the traditional load prediction from rigid forecasting to flexible forecasting which takes electricity demand response into consideration. By using wavelet analysis and error characteristics analysis, the researches into the probabilistic predicting method for demand changes under the real time electricity pricing is carried out. The probabilistic load prediction result shall enable decision makers to better understand the load change range in the future and make more reasonable decision. Meanwhile, it shall provide support to electricity system risk analysis.

A Study on Factors and Causal Loop of Chinese Residential Electricity Price Policy

2014 ◽  
Vol 1070-1072 ◽  
pp. 1446-1449
Author(s):  
Zhuo Chen ◽  
Bin Luo ◽  
Chen Yu Huang
Keyword(s):  
Alternative Energy ◽  
Market Failure ◽  
Price Policy ◽  
Natural Monopoly ◽  
Electricity Price ◽  
Conduction Path ◽  
Electricity Cost ◽  
Factors Influencing ◽  
Residential Electricity ◽  
The Cost

High electricity cost generated from the residents dispersion and the market failure resulted from the natural monopoly of the electricity supply industry make the traditional pricing theory which is based on the cost, market and competition suffer challenge in the decision of residential electricity price (REP). The paper analyzes the main factors influencing residential electricity price policy (REPP) to assist the relevant government decisions from perspectives of the residential affordability, the average electricity sales price, alternative energy and other policies. Furthermore, the conduction path of the factors influencing and deciding REPP is analyzed and a cause-effect diagram is produced with system dynamics software.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating an cost function, it is suitably analysed and then a dynamic penalty function to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for the real-time energy management.

Research on Formulating an Electricity Sales Price Basic Theory Based on Fair Burden Coefficient

2014 ◽  
Vol 543-547 ◽  
pp. 4258-4263
Author(s):  
Xue Liang ◽  
Bing Qing Li ◽  
Dong Sheng Yang ◽  
Bo Hu ◽  
Dan Li
Keyword(s):  
Power Quality ◽  
Electricity Generation ◽  
Cost Model ◽  
Electricity Supply ◽  
Electricity Pricing ◽  
Pricing Model ◽  
Electricity Cost ◽  
State Taxes ◽  
The Cost ◽  
Cost Factors

With the improving of the electricity users requirements for power quality, traditional electricity pricing model cant meet the needs of users. This thesis first carries on the analysis of electricity cost factors, and then establishes the cost model of electricity generation companies and electricity supply companies. The formulation of electricity price should consider electricity users, electricity generation companies, electricity supply companies and state taxes. And each factor should bear responsibility of electricity price's formulation based on social fair burden coefficient. So the thesis establishes an electricity price's model based on above theory.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2019 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

scholarly journals Modified PSO Algorithm for Real-Time Energy Management in Grid-Connected Microgrids

2018 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Hemanshu Roy Pota ◽  
Stefano Squartini ◽  
Ahmed Fathi Abdou
Keyword(s):  
Cost Function ◽  
Real Time ◽  
Energy Management ◽  
Storage System ◽  
Pso Algorithm ◽  
Operational Cost ◽  
Electricity Pricing ◽  
Optimal Operating ◽  
The Cost ◽  
Operating Points

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management.

scholarly journals Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1311
Author(s):  
Shuai Chen ◽  
Chengpeng Jiang ◽  
Jinglin Li ◽  
Jinwei Xiang ◽  
Wendong Xiao
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Energy Storage ◽  
The United States ◽  
Electricity Price ◽  
Utilization Efficiency ◽  
Battery Energy Storage ◽  
Industrial Parks ◽  
Battery Energy ◽  
The Cost

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

The Intelligent Traffic Monitoring System Based on Beidou Research and Design

2014 ◽  
Vol 599-601 ◽  
pp. 710-713
Author(s):  
Hong Hua Wang
Keyword(s):  
Communication Network ◽  
Real Time ◽  
Satellite Communications ◽  
The United States ◽  
Traffic Monitoring ◽  
Radio Communication ◽  
Real Time Monitoring ◽  
Vehicle Monitoring ◽  
Normal Transport ◽  
The Cost

Most transportation monitoring systems are based on the U.S. GPS + GSM / GPRS / CDMA or shortwave radio communication network to achieve positioning and monitoring. This type of system positioning part, because the United States belong GPS applications in key areas may be controlled by others; monitoring transport vehicles through the mobile communication network, increasing the cost of the system, and does not have the characteristics of real-time monitoring. In this paper, the characteristics of the above proposed transport vehicle monitoring and dispatching system for China's own Beidou navigation system developed is based. The system uses positioning and communication system Beidou satellite communications, real-time monitoring of transport vehicles and scheduling, to ensure normal transport.

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