scholarly journals Bonus-Based Demand Response Using Stackelberg Game Approach for Residential End-Users Equipped With HVAC System

2021 ◽  
Vol 12 (1) ◽  
pp. 234-249 ◽  
Author(s):  
Mehdi Tavakkoli ◽  
Sajjad Fattaheian-Dehkordi ◽  
Mahdi Pourakbari-Kasmaei ◽  
Matti Liski ◽  
Matti Lehtonen
Keyword(s):  
Demand Response ◽  
Stackelberg Game ◽  
End Users ◽  
Hvac System

scholarly journals A User-Oriented Pricing Design for Demand Response in Smart Grid

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yanglin Zhou ◽  
Lin Cheng ◽  
Song Ci ◽  
Yang Yang ◽  
Shiqian Ma
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Stackelberg Game ◽  
Equilibrium Points ◽  
End Users ◽  
Pricing Policy ◽  
Utility Company ◽  
Pricing Scheme ◽  
High Satisfaction ◽  
Cooperative Relationship

Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

scholarly journals An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids

2018 ◽  
Vol 30 (1) ◽  
pp. 63-80 ◽  
Author(s):  
Paraskevas Panagiotidis ◽  
Andrew Effraimis ◽  
George A Xydis
Keyword(s):  
Demand Response ◽  
Energy Demand ◽  
Moving Average ◽  
Electricity Consumption ◽  
End Users ◽  
Market Model ◽  
Residential Sector ◽  
Response Strategies ◽  
System Operator ◽  
Modelling Methodology

The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

Model Predictive Control of Micro-CSP Integrated Into a Building HVAC System for Load Following Demand Response Programs

2019 ◽  
Author(s):  
Mohamed Toub ◽  
Mahdi Shahbakhti ◽  
Rush D. Robinett ◽  
Ghassane Aniba
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Demand Response ◽  
Dynamic Pricing ◽  
Power Flow ◽  
Voltage Regulation ◽  
Ancillary Services ◽  
Hvac System ◽  
Time Model ◽  
Load Following

Abstract Building heat, ventilation and air conditioning (HVAC) systems are good candidates for demand response (DR) programs as they can flexibly alter their consumption to provide ancillary services to the grid and contribute to frequency and voltage regulation. One of the major ancillary services is the load following demand response (DR) program where the demand side tries to track a DR load profile required by the grid. This paper presents a real-time Model Predictive Control (MPC) framework for optimal operations of a micro-scale concentrated solar power (MicroCSP) system integrated into an office building HVAC system providing ancillary services to the grid. To decrease the energy cost of the building, the designed MPC exploits, along with the flexibility of the building’s HVAC system, the dispatching capabilities of the MicroCSP with thermal energy storage (TES) in order to control the power flow in the building and respond to the DR incentives sent by the grid. The results show the effect of incentives in the building participation to the load following DR program in the presence of a MicroCSP system and to what extent this participation is affected by seasonal weather variations and dynamic pricing.

scholarly journals Blockchain-Enabled Demand Response Scheme with Individualized Incentive Pricing Mode

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5213
Author(s):  
Zishan Guo ◽  
Zhenya Ji ◽  
Qi Wang
Keyword(s):  
Demand Response ◽  
Stackelberg Game ◽  
Supply And Demand ◽  
Response Frequency ◽  
Electrical Systems ◽  
Wide Range ◽  
The Cost ◽  
Retail Company ◽  
Secure Implementation ◽  
Cost Characteristics

Demand response (DR) can offer a wide range of advantages for electrical systems by facilitating the interaction and balance between supply and demand. However, DR always requires a central agent, giving rise to issues of security and trust. Besides this, differences in user response cost characteristics are not taken into consideration during incentive pricing, which would affect the equal participation of users in DR and increase the costs borne by the electricity retail company. In this paper, a blockchain-enabled DR scheme with an individualized incentive pricing mode is proposed. First, a blockchain-enabled DR framework is proposed to promote the secure implementation of DR. Next, a dual-incentive mechanism is designed to successfully implement the blockchain to DR, which consists of a profit-based and a contribution-based model. An individualized incentive pricing mode is adopted in the profit-based model to decrease the imbalance in response frequency of users and reduce the costs borne by the electricity retail company. Then, the Stackelberg game model is constructed and Differential Evolution (DE) is used to produce equilibrium optimal individualized incentive prices. Finally, case studies are conducted. The results demonstrate that the proposed scheme can reduce the cost borne by the electricity retail company and decrease the imbalance among users in response frequency.

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