Differential game-theoretic framework for a demand-side energy management system

2013 ◽  
Author(s):  
Ryohei Arai ◽  
Koji Yamamoto ◽  
Masahiro Morikura
Keyword(s):  
Energy Management ◽  
Differential Game ◽  
Management System ◽  
Energy Management System ◽  
Demand Side ◽  
Game Theoretic

scholarly journals Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2587 ◽  
Author(s):  
Zhou ◽  
FenghuaZou ◽  
Wu ◽  
Gu
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Energy Management System ◽  
Demand Side ◽  
Electricity Pricing ◽  
Operating Characteristics ◽  
Model Free ◽  
Artificial Neural Network Ann

This paper presents a detailed description of data predictive control (DPC) applied to a demand-side energy management system. Different from traditional model-based predictive control (MPC) algorithms, this approach introduces two model-free algorithms of artificial neural network (ANN) and random forest (RF) to make control strategy predictions on system operation, while avoiding the huge cost and effort associated with learning a grey/white box model of the physical system. The operating characteristics of electrical appliances, system energy consumption, and users’ comfort zones are also considered in the selected energy management system based on a real-time electricity pricing system. Case studies consisting of two scenarios (0% and 15% electricity price fluctuation) are delivered to demonstrate the effectiveness of the proposed approach. Simulation results demonstrate that the DPC controller based on ANN pays only 0.18% additional bill cost to maintain users’ comfort zones and system economy standardization while using only 0.096% optimization time cost compared with the MPC controller.

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