Consumption-Aware Data Analytical Demand Response Scheme for Peak Load Reduction in Smart Grid

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

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A Model of Continuous Optimized Power Flow with DR

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.805-806.452 ◽

2013 ◽

Vol 805-806 ◽

pp. 452-457

Author(s):

Wen Bo Mao ◽

Ke Wang ◽

Jian Tao Liu

Keyword(s):

Power System ◽

Demand Response ◽

Cost Reduction ◽

Power Flow ◽

Peak Load ◽

Load Reduction ◽

Interruptible Load ◽

Time Price ◽

The Cost ◽

Reduction Cost

A model of continuous optimized power flow (COPF) is proposed, concluding demand response (DR). According to different implementation mechanisms, a series of DR models are built, such as: time of use (TOU), real time price (RTP), critical peak price (CPP), and interruptible load (IL). The influences of these kinds of DR on power system are analyzed, including peak load reduction, cost reduction, and reservation optimization. The results show that: DR can cut the cost, reduce the peak load, and promote the reservation optimization.

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Developing an Algorithm to Consider Mutliple Demand Response Objectives

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.1819 ◽

2018 ◽

Vol 8 (1) ◽

pp. 2621-2626 ◽

Cited By ~ 3

Author(s):

D. Behrens ◽

T. Schoormann ◽

R. Knackstedt

Keyword(s):

Electric Vehicles ◽

Demand Response ◽

Peak Load ◽

Cost Savings ◽

Research Field ◽

Multiple Objectives ◽

Load Reduction ◽

Solution Strategy ◽

Research And Practice ◽

Technological Improvement

Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.

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A Framework for Demand-Side Management With Demand Response Input

ASME 2020 Power Conference ◽

10.1115/power2020-16635 ◽

2020 ◽

Author(s):

Miguel A. Peinado-Guerrero ◽

Nicolas A. Campbell ◽

Jesus R. Villalobos ◽

Patrick E. Phelan

Keyword(s):

Demand Response ◽

Production Scheduling ◽

Energy Use ◽

Peak Load ◽

Load Reduction ◽

Demand Side ◽

End User ◽

Physical Constraints ◽

Side Load ◽

To Receive

Abstract A framework is proposed for demand-side load management (DSLM) of manufacturers participating in demand response (DR) programs. Utilities are increasingly focused on enticing their portfolios of energy end-users to adjust their energy use patterns in a mutually beneficial manner such as with DR programs. DR programs allow the utility to receive bulk peak load reduction and the participating end-user to receive credit towards their electricity bills. Once an end-user is enrolled in a DR program, they receive periodic requests for some amount of load reduction, typically the day before. Failing to respond to a DR signal will usually cost the end-user handsomely. The end-user is often left to their own discretion on how to attain the level of load reduction requested by the utility. For a manufacturer, this means if the request in load reduction is high enough, they will need to figure out how to curtail production. On the other hand, if the load reduction requested is small enough to need no disruption to production, the utility may be missing out on untapped DR capabilities that could be offered from the ability of the manufacturer to reschedule their production. In either case, the availability of an optimal plan for the manufacturer to best schedule its production in response to a DR event can maximize the benefits for both parties. Most of the research found in literature addresses production scheduling with minimal energy use or cost with respect to a time-of-use price tariff. A system that communicates the desires of the utility to the end-user for a DR event and provides the end-user with support in the decision-making process remains to be developed. The framework proposed addresses these shortcomings, considering the introduction of IoT capabilities and the physical constraints of the manufacturer.

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