scholarly journals Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming

2016 ◽  
Vol 8 ◽  
pp. 169-171 ◽  
Author(s):  
S. Sofana Reka ◽  
V. Ramesh
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Smart Grid ◽  
Stochastic Dynamic Programming ◽  
Demand Side Management ◽  
Stochastic Dynamic ◽  
Demand Side ◽  
Management Scheme ◽  
Computing Approach

GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid

2014 ◽  
Vol 8 (2) ◽  
pp. 588-597 ◽  
Author(s):  
Zubair Md Fadlullah ◽  
Duong Minh Quan ◽  
Nei Kato ◽  
Ivan Stojmenovic
Keyword(s):  
Smart Grid ◽  
Demand Side Management ◽  
Demand Side ◽  
Management Scheme ◽  
Game Theoretic

A NOVEL APPROACH OF DEMAND SIDE MANAGEMENT FRAMEWORK FOR RESIDENTIAL CONSUMERS IN SMART GRID

2016 ◽  
Vol 78 (6) ◽  
Author(s):  
Sofana Reka S. ◽  
Ramesh V.
Keyword(s):  
Smart Grid ◽  
Demand Side Management ◽  
Climatic Conditions ◽  
Demand Side ◽  
Peak Demand ◽  
Management Framework ◽  
Management Scheme ◽  
Novel Approach ◽  
Actual Load ◽  
New Strategy

In this paper, Demand Side Management scheme in a smart grid environment is proposed as a feature of reducing the peak demand for residential users and maximizing the profit for the utility. A smart grid scenario is considered for detailed analysis. Non-cooperative game theory approaches are generalized in this methodology for solving two dynamic conditions such as reducing peak demand and maximize the utility profit with respect to the users comfort. In this work a new strategy is developed among the various users for scheduling of appliances autonomously. Extensive simulations are carried out using actual load profiles and obtained results demonstrates reduction in Peak to average ratio. In addition the proposed system is analyzed by considering the changes in climatic conditions of summer and winter for a particular region.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

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