Assessment of energy demand response options in smart grid utilizing the stochastic programming approach

Author(s):  
Seog-Chan Oh ◽  
James B. D'Arcy ◽  
Jorge F. Arinez ◽  
Stephan R. Biller ◽  
Alfred J. Hildreth
Systems ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 33 ◽  
Author(s):  
Stylianos Karatzas ◽  
Athanasios Chassiakos

Inelasticity of demand along with the distributed energy sources and energy market democratization pose significant challenges which have considerable negative impacts on overall grid balance. The need for increased capacity and flexibility in the era of energy market digitalization has introduced new requirements in the energy supply network which could not be satisfied without continuous and costly local power network upgrades. Additionally, with the emergence of Smart Homes (SHs) and Home Energy Management (HEM) systems for monitoring and operating household appliances, opportunities have arisen for automated Demand Response (DR). DR is exploited for the modification of the consumer energy demand, in response to the specific conditions within the electricity system (e.g., peak period network congestion). In order to optimally integrate DR in the broader Smart Grid (SG) system, modelling of the system parameters and safety analysis is required. In this paper, the implementation of STPA (System-Theoretic Process Analysis) structured method, as a relatively new hazard analysis technique for complex systems is presented and the feasibility of STPA implementation for loss prevention on a Demand Response system for home energy management, and within the complex SG context, is examined. The applied method delivers a mechanism useful in understanding where gaps in current operational risk structures may exist. The STPA findings in terms of loss scenarios can be used to generate a variety of safeguards to ensure secure operational control and in implementing targeted strategies through standard approaches of risk assessment.


2021 ◽  
Vol 267 ◽  
pp. 01002
Author(s):  
Ying Zhu ◽  
Zhao Wei ◽  
Yexin Li ◽  
Jingqi Luo ◽  
Bizhou Ge

In this study, a Copula-based stochastic industry-energy system management (CSIE) model was developed based on Copula-based stochastic programming and interval linear programming. CSIE model can not only deal with extreme random events in industry-energy system (IES) of resource-dependent cities, but also quantify the risks of industrial energy demand-supply. To prove the practicability, a case study of IES planning in Yulin city was represented. Reasonable solutions of energy production and industrial energy consumption strategy were obtained, which can guarantee that pollutant emission meets the environmental requirements, and the system cost gets the lowest during 2021-2035. Furthermore, CSIE model could be spread to IES management in similar resource-dependent cities.


Author(s):  
Marimuthu Krishna Paramathma ◽  
Durairaj Devaraj ◽  
Velusamy Agnes Idhaya Selvi ◽  
Murugesan Karuppasamypandiyan

2016 ◽  
Vol 128 ◽  
pp. 56-67 ◽  
Author(s):  
Fabiano Pallonetto ◽  
Simeon Oxizidis ◽  
Federico Milano ◽  
Donal Finn

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