Coordination on Industrial Load Control and Climate Control in Manufacturing Industry Under TOU Prices

Demand side management (DSM) can help promote the efficiency and reliability of a power system. However, the benefits of DSM in the manufacturing industry will be weakened if the interactions between industrial load control and climate control are not considered. For example, industrial load control may impact the indoor temperature and humidity, affecting climate control. This interaction may ultimately result in an unexpected deviation from the ideal DSM plan. Hence in this paper, coordinated industrial load control and climate control are investigated in a cold machine shell manufacturer under time-of-use prices. The interaction of industrial loads and climate control is modeled, and a model predictive control-based method is proposed to address forecast uncertainties. The objective of DSM is to minimize electricity costs meanwhile meeting the respective objectives of the industrial load control and climate control. Case studies have verified the above benefits for a cold machine shell manufacturer, and the results show that additional reductions in demand during peak hours can be achieved using the proposed method.

[1]  Go Hasegawa,et al.  Reducing Power Consumption in Data Center by Predicting Temperature Distribution and Air Conditioner Efficiency with Machine Learning , 2016, 2016 IEEE International Conference on Cloud Engineering (IC2E).

[2]  Shahab Bahrami,et al.  Industrial load scheduling in smart power grids , 2013 .

[3]  Haozhong Cheng,et al.  Demand response based and wind farm integrated economic dispatch , 2015 .

[4]  Yonghua Song,et al.  Situation awareness of active distribution network: roadmap, technologies, and bottlenecks , 2016 .

[5]  Cristian Perfumo,et al.  Mapping the Effect of Ambient Temperature on the Power Demand of Populations of Air Conditioners , 2018, IEEE Transactions on Smart Grid.

[6]  Evangelos Vrettos,et al.  Robust Energy-Constrained Frequency Reserves From Aggregations of Commercial Buildings , 2015, IEEE Transactions on Power Systems.

[7]  Robert G. Pratt,et al.  Transactive Control of Commercial Buildings for Demand Response , 2017, IEEE Transactions on Power Systems.

[8]  Hamed Mohsenian Rad,et al.  Optimal Industrial Load Control in Smart Grid , 2016, IEEE Transactions on Smart Grid.

[9]  S. Ashok,et al.  Optimal operation of industrial cogeneration for load management , 2003 .

[10]  Yingying Chen,et al.  Optimal Dispatch of Air Conditioner Loads in Southern China Region by Direct Load Control , 2016, IEEE Transactions on Smart Grid.

[11]  Kankar Bhattacharya,et al.  Optimal Operation of Climate Control Systems of Produce Storage Facilities in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[12]  Kankar Bhattacharya,et al.  Optimal Energy Management of Greenhouses in Smart Grids , 2015, IEEE Transactions on Smart Grid.

[13]  Farrokh Rahimi,et al.  Demand Response as a Market Resource Under the Smart Grid Paradigm , 2010, IEEE Transactions on Smart Grid.

[14]  M. K. Sheikh-El-Eslami,et al.  A Stochastic-Based Decision-Making Framework for an Electricity Retailer: Time-of-Use Pricing and Electricity Portfolio Optimization , 2011, IEEE Transactions on Power Systems.

[15]  Xinxin Zhu,et al.  Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch , 2014, IEEE Transactions on Smart Grid.

[16]  Rachid Bennacer,et al.  Residential building energy demand and thermal comfort: Thermal dynamics of electrical appliances and their impact , 2016 .

[17]  Hongbin Sun,et al.  Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism , 2015 .

[18]  Shaohua Zhang,et al.  An Enhanced Network-Constrained UC Model for Leveraging System Operation Cost and Financial Profitability of Incentive-Based DR Loads , 2018, IEEE Transactions on Smart Grid.

[19]  T. G. Doeswijk,et al.  Reducing prediction uncertainty of weather controlled systems , 2007 .

[20]  Jhi-Young Joo,et al.  Efficient Coordination of Wind Power and Price-Responsive Demand—Part I: Theoretical Foundations , 2011, IEEE Transactions on Power Systems.

[21]  M. Hashem Nehrir,et al.  Real-time demand response through aggregate electric water heaters for load shifting and balancing wind generation , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[22]  Feng Gao,et al.  An integrated optimization model for generation and batch production load scheduling in energy intensive enterprise , 2012, PES 2012.

[23]  Hongbin Sun,et al.  A New Real-Time Smart-Charging Method Considering Expected Electric Vehicle Fleet Connections , 2014, IEEE Transactions on Power Systems.

[24]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[25]  T. Tominaga,et al.  Minimum data set for controlling data center equipment for energy saving management , 2012, 2012 IEEE Power and Energy Society General Meeting.

[26]  Xue Liu,et al.  Adaptive Power Management through Thermal Aware Workload Balancing in Internet Data Centers , 2015, IEEE Transactions on Parallel and Distributed Systems.

[27]  Sean P. Meyn,et al.  Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems , 2014, IEEE Transactions on Smart Grid.

[28]  Jiangfeng Zhang,et al.  An optimal control model for load shifting—with application in the energy management of a colliery , 2009 .

[29]  Luiz Augusto N. Barroso,et al.  Time-of-Use Tariff Design Under Uncertainty in Price-Elasticities of Electricity Demand: A Stochastic Optimization Approach , 2013, IEEE Transactions on Smart Grid.

[30]  S. Ashok,et al.  Peak Load Management in Electrolytic Process Industries , 2008, IEEE Transactions on Power Systems.

[31]  Kit Po Wong,et al.  Distributed control of thermostatically controlled loads in distribution network with high penetration of solar PV , 2017 .

[32]  Xinghuo Yu,et al.  Efficient Computation for Sparse Load Shifting in Demand Side Management , 2017, IEEE Transactions on Smart Grid.