scholarly journals Optimal residential load scheduling under dynamic pricing and demand-side management

2021 ◽  
Vol 1 (2) ◽  
pp. 001-018
Author(s):  
Muhammad Hussain ◽  
Yan Gao ◽  
Falak Shair ◽  
Sherehe Semba
Keyword(s):  
Dynamic Pricing ◽  
Control Function ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Power Grids ◽  
Scheduling Scheme ◽  
Power Scheduling ◽  
Time Price ◽  
Residential Market ◽  
Energy Provider

Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.

An Economical Scheduling Strategy of Battery-Equipped Data Server with Dynamic-Pricing Power Supply in Smart Grid

2013 ◽  
Vol 869-870 ◽  
pp. 426-431
Author(s):  
Shan Jin Yu ◽  
Hui Yu Jin ◽  
Xiao Bin Tan ◽  
Kang Qi Wang
Keyword(s):  
Power Supply ◽  
Data Center ◽  
Dynamic Pricing ◽  
Real Life ◽  
Electricity Consumption ◽  
Power Grids ◽  
Storage Devices ◽  
Scheduling Strategy ◽  
Network Load ◽  
Consumption Cost

The electricity consumption by modern data center and data servers has significantly increased in recent year and continues to have this dramatic increase trend. Meanwhile, more and more modern power grids have adopted dynamic pricing electricity supply model. When a data center or data server is equipped with temporary power storage devices such as a battery, it is feasible and important to study how to schedule power supply to reduce the overall power consumption cost. In this paper, we present a dynamic programming based scheduling strategy by considering the stochastic arrival nature of network load and characteristic of battery storage. We demonstrate the effectiveness of our approach using simulation based on real power price data and real-life network load data.

scholarly journals A Demand-Response Scheme Using Multi-Agent System for Smart DC Microgrid

2019 ◽  
Vol 10 (1) ◽  
pp. 48-68 ◽  
Author(s):  
Diana Severine Rwegasira ◽  
Imed Saad Ben Dhaou ◽  
Aron Kondoro ◽  
Anastasia Anagnostou ◽  
Amleset Kelati ◽  
...  
Keyword(s):  
Demand Response ◽  
Dynamic Pricing ◽  
Supply And Demand ◽  
Load Shedding ◽  
Multi Agent System ◽  
Solar Panels ◽  
Remote Communities ◽  
Agent System ◽  
Dc Microgrid ◽  
Multi Agent

This article describes a framework for load shedding techniques using dynamic pricing and multi-agent system. The islanded microgrid uses solar panels and battery energy management system as a source of energy to serve remote communities who have no access to the grid with a randomized type of power in terms of individual load. The generated framework includes modeling of solar panels, battery storage and loads to optimize the energy usage and reduce the electricity bills. In this work, the loads are classified as critical and non-critical. The agents are designed in a decentralized manner, which includes solar agent, storage agent and load agent. The load shedding experiment of the framework is mapped with the manual operation done at Kisiju village, Pwani, Tanzania. Experiment results show that the use of pricing factor as a demand response makes the microgrid sustainable as it manages to control and monitor its supply and demand, hence, the load being capable of shedding its own appliances when the power supplied is not enough.

Dynamic Pricing Strategy and Optimal Resource Selection Strategy Based on Credibility Model

2014 ◽  
Vol 668-669 ◽  
pp. 1615-1620
Author(s):  
Yu Yang ◽  
Hua Zhou ◽  
Jun Hui Liu ◽  
Yun Feng
Keyword(s):  
Cloud Computing ◽  
Dynamic Pricing ◽  
Resource Selection ◽  
Supply And Demand ◽  
Market Price ◽  
Pricing Strategy ◽  
Selection Strategy ◽  
Fixed Rate ◽  
Gradual Development ◽  
Optimal Resource

With the gradual development of Cloud Computing, the model of work and business will fundamentally change in the future. Current market trading mechanism under the cloud computing environment is lacking in flexibility and most of companies adopt a fixed-rate pricing model, which is difficult to meet the different needs of users. Based on cloud bank model, this paper introduces economic theory to provide a theoretical basis for the development of resource prices and propose a dynamic pricing strategy and maximize utility resource selection strategy based on market supply and demand and credit for cloud bank. In the last part of this paper, we use simulation platform to do a simple experiment to test this dynamic pricing strategy. Experiment result shows the pricing strategy could adjust computing resource prices automatically under the general market price rule conditions and maximize utility resource selection strategy could get the max utility for resource consumers.

scholarly journals A Novel Pricing Algorithm Based on Reward-Punishment Mechanism for Supply and Demand Balancing

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaofei Chen ◽  
Liguo Weng ◽  
Haiyan Zhu ◽  
Deqiang Lian
Keyword(s):  
Electricity Market ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Single Type ◽  
Pricing Scheme ◽  
Punishment Mechanism ◽  
The Stability ◽  
Different Characteristics ◽  
Reward And Punishment

Demand response (DR) is a powerful tool to maintain the stability of the power system and maximize the profit of the electricity market, where the customers engage in the pricing scheme and adjust their electricity demand proactively based on the price. In DR programs, most existing works are based on the assumption that the prediction of the electricity demand from customers is always accurate and trustworthy, which will lead to high cost and fluctuation of the electricity market once the prediction is obeyed. In this paper, we design a reward and punishment mechanism to constrain customers’ dishonest behaviors and propose a novel pricing algorithm based on the reward and punishment mechanism to relax the assumption, which guarantees the total electricity demands of all customers are within a secure range and obtain the maximum profit of the supplier. Meanwhile, we obtain the optimal demand and provide a upper and lower bound of the proposed price for the electricity market. In addition to a single type of customer, we also consider multiple types of customers, each of whom has different characteristics to prices. Extensive simulation results are constructed to demonstrate the effectiveness of the proposed algorithm compared with other pricing algorithms. It also shows that the average electricity consumption of a whole community is mostly affected by the residents’ electricity consumption and the balance of the supply and all types of customers is achieved under the proposed pricing algorithm.

scholarly journals A novel optimization model for integrating carbon constraint with demand response and real-time pricing

2020 ◽  
pp. 2050005
Author(s):  
Dinh Hoa Nguyen
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Optimization Model ◽  
Consumer Satisfaction ◽  
Carbon Emission ◽  
Renewable Energy Sources ◽  
Power Grids ◽  
Power Scheduling ◽  
Generation Cost ◽  
Real Time Pricing

Since the global warming has recently become more severe causing many serious changes on the weather, economy, and society worldwide, lots of efforts have been put forward to prevent it. As one of the most important energy sectors, improvements in electric power grids are required to address the challenge of suppressing the carbon emission during electric generation especially when utilizing fossil-based fuels, while increasing the use of renewable and clean sources. This paper hence presents a novel optimization model for tackling the problems of optimal power scheduling and real-time pricing in the presence of a carbon constraint while taking into account a demand response possibility, which may provide a helpful method to limit the carbon emission from conventional generation while promoting renewable generation. The critical aspects include explicitly integrating the cost of emission with the total generation cost of conventional generation and combining it with the consumer satisfaction function. As such, conventional generation units must carefully schedule their power generation for their profits, while consumers, with the help from renewable energy sources, are willing to adjust their consumption to change the peak demand. Overall, a set of compromised solution called the Pareto front is derived upon which the conventional generating units choose their optimal generation profile to satisfy a given carbon constraint.

Auction-Based Pricing in Cloud Environment

2021 ◽  
pp. 86-97
Author(s):  
Branka Mikavica ◽  
Aleksandra Kostic-Ljubisavljevic
Keyword(s):  
Cloud Computing ◽  
Dynamic Pricing ◽  
Rapid Development ◽  
Supply And Demand ◽  
Bidding Strategy ◽  
Cloud Environment ◽  
Allocation Mechanism ◽  
Bidding Strategies ◽  
Selection Of ◽  
Cloud Resources

The rapid development of cloud computing requires improvement of pricing and allocation mechanisms of cloud resources. Dynamic pricing and allocation mechanisms are considered convenient, due to characteristics of cloud resources and the fact that demand for cloud resources is not uniform. The aims of such a mechanism are to optimize the utilization of cloud resources, to maximize cloud providers' revenues, and to minimize prices for cloud customers. Auction-based pricing and allocation mechanisms are often used since resources are allocated to the customers that value them the most, and prices are determined depending on the supply and demand conditions. Selection of an appropriate bidding strategy is a very important issue and requires the comprehensive approach. This chapter analyses the benefits of auction-based pricing and allocation mechanism in the cloud environment. In addition, the effects of different bidding strategies application are addressed.

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