Demand response scheme with electricity market prices for residential sector using stochastic dynamic optimization

2016 ◽  
Author(s):  
S. Sofana Reka ◽  
V. Ramesh
Keyword(s):  
Dynamic Optimization ◽  
Demand Response ◽  
Electricity Market ◽  
Stochastic Dynamic ◽  
Market Prices ◽  
Residential Sector ◽  
Stochastic Dynamic Optimization ◽  
Response Scheme

scholarly journals Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1413 ◽  
Author(s):  
Arsalan Najafi ◽  
Mousa Marzband ◽  
Behnam Mohamadi-Ivatloo ◽  
Javier Contreras ◽  
Mahdi Pourakbari-Kasmaei ◽  
...  
Keyword(s):  
Decision Making ◽  
Power Systems ◽  
Wind Turbine ◽  
Demand Response ◽  
Electricity Market ◽  
Programming Model ◽  
Decision Making Process ◽  
Market Prices ◽  
Gas Network ◽  
Energy Hub

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

Stochastic Dynamic Optimization of Batch and Semicontinuous Bioprocesses

1997 ◽  
Vol 13 (3) ◽  
pp. 326-335 ◽  
Author(s):  
J.R. Banga ◽  
A.A. Alonso ◽  
R.P. Singh
Keyword(s):  
Dynamic Optimization ◽  
Stochastic Dynamic ◽  
Stochastic Dynamic Optimization
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