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.

Real-time pricing scheme based on Stackelberg game in smart grid with multiple power retailers

2017 ◽  
Vol 260 ◽  
pp. 149-156 ◽  
Author(s):  
Yeming Dai ◽  
Yan Gao ◽  
Hongwei Gao ◽  
Hongbo Zhu
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Stackelberg Game ◽  
Pricing Scheme ◽  
Multiple Power ◽  
Real Time Pricing

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 Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2338
Author(s):  
Emad M. Ahmed ◽  
Rajarajeswari Rathinam ◽  
Suchitra Dayalan ◽  
George S. Fernandez ◽  
Ziad M. Ali ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Smart Grid ◽  
Demand Response ◽  
Optimization Algorithm ◽  
Dynamic Pricing ◽  
Particle Swarm ◽  
Modern World ◽  
Swarm Optimization ◽  
Pricing Scheme ◽  
Load Pattern

In the modern world, the systems getting smarter leads to a rapid increase in the usage of electricity, thereby increasing the load on the grids. The utilities are forced to meet the demand and are under stress during the peak hours due to the shortfall in power generation. The abovesaid deficit signifies the explicit need for a strategy that reduces the peak demand by rescheduling the load pattern, as well as reduces the stress on grids. Demand-side management (DSM) uses several algorithms for proper reallocation of loads, collectively known as demand response (DR). DR strategies effectively culminate in monetary benefits for customers and the utilities using dynamic pricing (DP) and incentive-based procedures. This study attempts to analyze the DP schemes of DR such as time-of-use (TOU) and real-time pricing (RTP) for different load scenarios in a smart grid (SG). Centralized and distributed algorithms are used to analyze the price-based DR problem using RTP. A techno-economic analysis was performed by using particle swarm optimization (PSO) and the strawberry (SBY) optimization algorithms used in handling the DP strategies with 109, 1992, and 7807 controllable industrial, commercial, and residential loads. A better optimization algorithm to go along with the pricing scheme to reduce the peak-to-average ratio (PAR) was identified. The results demonstrate that centralized RTP using the SBY optimization algorithm helped to achieve 14.80%, 21.7%, and 21.84% in cost reduction and outperformed the PSO.

scholarly journals Fostering Residential Demand Response through Dynamic Pricing Schemes: A Behavioural Review of Smart Grid Pilots in Europe

2016 ◽  
Vol 8 (9) ◽  
pp. 929 ◽  
Author(s):  
Kris Kessels ◽  
Carolien Kraan ◽  
Ludwig Karg ◽  
Simone Maggiore ◽  
Pieter Valkering ◽  
...  
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Dynamic Pricing ◽  
Energy Use ◽  
Considerable Effect ◽  
End Users ◽  
Automated Control ◽  
Survey Paper ◽  
Residential Demand ◽  
Pricing Schemes

Many smart grid projects make use of dynamic pricing schemes aimed to motivate consumers to shift and/or decrease energy use. Based upon existing literature and analyses of current smart grid projects, this survey paper presents key lessons on how to encourage households to adjust energy end use by means of dynamic tariffs. The paper identifies four key hypotheses related to fostering demand response through dynamic tariff schemes and examines whether these hypotheses can be accepted or rejected based on a review of published findings from a range of European pilot projects. We conclude that dynamic pricing schemes have the power to adjust energy consumption behavior within households. In order to work effectively, the dynamic tariff should be simple to understand for the end users, with timely notifications of price changes, a considerable effect on their energy bill and, if the tariff is more complex, the burden for the consumer could be eased by introducing automated control. Although sometimes the mere introduction of a dynamic tariff has proven to be effective, often the success of the pricing scheme depends also on other factors influencing the behavior of end users. An important condition to make dynamic tariffs work is that the end users should be engaged with them.

scholarly journals A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 499 ◽  
Author(s):  
Ghulam Hafeez ◽  
Noor Islam ◽  
Ammar Ali ◽  
Salman Ahmad ◽  
Muhammad Usman and Khurram Saleem Alimgeer
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Demand Response ◽  
Load Scheduling ◽  
Electricity Cost ◽  
Power Company ◽  
Pricing Scheme ◽  
Optimal Load ◽  
Home Energy Management ◽  
Modular Framework

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

scholarly journals An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective

2021 ◽  
Vol 13 (11) ◽  
pp. 6066
Author(s):  
Thamer Alquthami ◽  
Ahmad H. Milyani ◽  
Muhammad Awais ◽  
Muhammad B. Rasheed
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Optimization Problem ◽  
End Users ◽  
Electricity Price ◽  
Monetary Incentives ◽  
Electricity Pricing ◽  
Pricing Policy ◽  
Load Shifting ◽  
Demand And Supply

Price based demand response is an important strategy to facilitate energy retailers and end-users to maintain a balance between demand and supply while providing the opportunity to end users to get monetary incentives. In this work, we consider real-time electricity pricing policy to further calculate the incentives in terms of reduced electricity price and cost. Initially, a mathematical model based on the backtracking technique is developed to calculate the load shifted and consumed in any time slot. Then, based on this, the electricity price is calculated for all types of users to estimate the incentives through load shifting profiles. To keep the load under the upper limit, the load is shifted in other time slots in such a way to facilitate end-users regarding social welfare. The user who is not interested in participating load shifting program will not get any benefit. Then the well behaved functional form optimization problem is solved by using a heuristic-based genetic algorithm (GA), wwhich converged within an insignificant amount of time with the best optimal results. Simulation results reflect that the users can obtain some real incentives by participating in the load scheduling process.

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