Energy Management of Hybrid AC-DC Microgrid Under Demand Response Programs: Real-Time Pricing Versus Time-of-Use Pricing

2019 ◽  
pp. 75-93
Author(s):  
Ramin Nourollahi ◽  
Kazem Zare ◽  
Sayyad Nojavan
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Dc Microgrid ◽  
Real Time Pricing ◽  
Time Of Use Pricing ◽  
Demand Response Programs

Optimum residential load management strategy for real time pricing (RTP) demand response programs

Energy Policy ◽  
2012 ◽  
Vol 45 ◽  
pp. 671-679 ◽  
Author(s):  
Juan M. Lujano-Rojas ◽  
Cláudio Monteiro ◽  
Rodolfo Dufo-López ◽  
José L. Bernal-Agustín
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Management Strategy ◽  
Load Management ◽  
Real Time Pricing ◽  
Demand Response Programs

scholarly journals Demand response program for smart grid through real time pricing and home energy management system

2021 ◽  
Vol 11 (5) ◽  
pp. 4558
Author(s):  
Shibily Joseph ◽  
E. A. Jasmin
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Management System ◽  
Peak Load ◽  
Energy Management System ◽  
Home Energy Management ◽  
Real Time Pricing ◽  
Home Energy Management System

Aim of demand response (DR) programs are to change the usage pattern of electricity in such a way that, beneficial to the consumers as well as to the distributors by applying some methods or technology. This way additional cost to erect new energy sources can be postponed in power grid. Best method to implement demand response (DR) program is by influencing consumer through the implementation of real time pricing scheme. To harness the benefit of DR, automated home energy management system is essential. This paper presents a comprehensive demand response system with real time pricing. The real time price is determined after considering price elasticity of various classes of consumers and their load profiles. A real time clustering algorithm suitable for big data of smart grid is devised for the segmentation of consumers. This paper is novel in its design for real time pricing and modelling and automatic scheduling of appliances for home energy management. Simulation results showed that this new real time pricing method is suitable for DR programs to reduce the peak load of the system as well as reducing the energy expenditure of houses, while ensuring profit for the retailer.

scholarly journals Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4597
Author(s):  
Zi-Xuan Yu ◽  
Meng-Shi Li ◽  
Yi-Peng Xu ◽  
Sheraz Aslam ◽  
Yuan-Kang Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Positive Impact ◽  
Environmental Aspects ◽  
Component Size ◽  
Cost Of Energy ◽  
Four Seasons ◽  
Demand Response Program ◽  
Real Time Pricing

The optimal planning of grid-connected microgrids (MGs) has been extensively studied in recent years. While most of the previous studies have used fixed or time-of-use (TOU) prices for the optimal sizing of MGs, this work introduces real-time pricing (RTP) for implementing a demand response (DR) program according to the national grid prices of Iran. In addition to the long-term planning of MG, the day-ahead operation of MG is also analyzed to get a better understanding of the DR program for daily electricity dispatch. For this purpose, four different days corresponding to the four seasons are selected for further analysis. In addition, various impacts of the proposed DR program on the MG planning results, including sizing and best configuration, net present cost (NPC) and cost of energy (COE), and emission generation by the utility grid, are investigated. The optimization results show that the implementation of the DR program has a positive impact on the technical, economic, and environmental aspects of MG. The NPC and COE are reduced by about USD 3700 and USD 0.0025/kWh, respectively. The component size is also reduced, resulting in a reduction in the initial cost. Carbon emissions are also reduced by 185 kg/year.

Energy management of a microgrid using demand response strategy including renewable uncertainties

2021 ◽  
Vol 22 (1) ◽  
pp. 85-100
Author(s):  
Suchitra Dayalan ◽  
Rajarajeswari Rathinam
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Effective Means ◽  
Operating Cost ◽  
Energy Resources ◽  
Response Strategy ◽  
Distributed Energy ◽  
Real Time Control ◽  
Minimum Operating

Abstract Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.

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