A Human Expert-Based Approach to Electrical Peak Demand Management

Bian, Desong; Pipattanasomporn, Manisa; Rahman, Saifur

doi:10.1109/tpwrd.2014.2348495

Cited by 43 publications

(19 citation statements)

References 18 publications

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“…It should be noted that DSM programs comprise two different activities, demand response (DR) and energy efficiency/conservation (EE/C). A DR action transfers customer load during periods of high demand to off-peak periods and can reduce critical peak demand while EE/C program encourages customers to give up some energy use in return for saving money and allows them to use less energy while receiving the same level of end service [7].…”

Section: • Flexible and Bidirectionalmentioning

confidence: 99%

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

Anvari‐Moghaddam

Mokhtari²,

Guerrero³

2016

Energy Management of Distributed Generation Systems

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Nowadays with the emerging of small-scale integrated energy systems IESs in form of residential smart microgrids SMGs , a large portion of energy can be saved through coordinated scheduling of smart household devices and management of distributed energy resources DERs . There are significant potentials to increase the functionality of a typical demand-side management DSM strategy, and typical implementation of building-level DERs by integrating them into a cohesive, networked package that fully utilizes smart energy-efficient end-use devices, advanced building control/automation systems, and an integrated communications architecture to efficiently manage energy and comfort at the end-use location. "y the aid of such technologies, residential consumers have also the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. Regarding these points, this chapter initially defines an efficient framework for coordinated DSM and DERs management in an integrated building and SMG system. Then a working energy management system EMS for applications in residential IESs is described and mathematically modeled. Finally, the effectiveness and applicability of the proposed model is tested and validated in different operating modes compared to the existing models. The findings of this chapter show that by the use of an expert EMS that coordinates supply and demand sides simultaneously, it is very possible not only to reduce energy costs of a residential IES, but also to provide comfortable lifestyle for occupants.

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Section: • Flexible and Bidirectionalmentioning

confidence: 99%

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

Anvari‐Moghaddam

Mokhtari²,

Guerrero³

2016

Energy Management of Distributed Generation Systems

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“…Ma and Chowdhury [10] also consider wind power generators for the frequency regulation by applying combined control strategy. A novel DCA method -which is an expert-based demand curtailment allocation approach using Analytic Hierarchy Process (AHP) method that has the ability to respond faster than DR programs and avoid UFLS operation -is introduced in [11]. This paper extends the application of DCA method previously proposed by authors to mitigate fluctuation of large-scale renewable output with the presence of communication and cyber security limitations.…”

Section: Introductionmentioning

confidence: 98%

“…To fill the gap of traditional DR and UFLS programs, researchers [8]- [11] have proposed methods to mitigate renewable penetration and keep the system operating within its normal frequency range. In [8], a strategy for supporting high penetration of renewable generation via implementation of real-time electricity pricing and demand response is introduced.…”

Section: Introductionmentioning

confidence: 99%

Mitigating the Impact of Renewable Variability With Demand-Side Resources Considering Communication and Cyber Security Limitations

et al. 2019

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With the rapid development of smart grid, the penetration of renewable energy resources is higher than ever and keeps growing. However, the output of renewable energy units, such as solar photovoltaics and wind turbines, is characterized by sudden and unpredictable changes. This paper proposes a novel electrical peak demand curtailment allocation (DCA) method to manage demand-side resources in response to fluctuations in renewable energy outputs. The proposed DCA method can curtail end-use loads faster than traditional demand response (DR) programs and prevent under frequency load shedding (UFLS) operation when facing sudden and unpredictable outputs of renewable energy. This DCA method considers DR potential and load curtailment priority. Case studies are conducted to demonstrate how the developed DCA method can be implemented to mitigate fluctuation in renewable outputs by curtailing electrical demand, considering communication network latency. This paper also evaluates the impact of applying different cybersecurity encryption methods on DCA operation. The simulation results prove that the developed DCA method can mitigate the impact of renewable energy fluctuation and respond fast enough to avoid traditional UFLS operation.

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“…Work efficiency is perceived to be the most important core selection criterion for various IB systems; user comfort, safety, and cost effectiveness are also considered significant. Brian et al [2] introduced an expert-based demand curtailment allocation approach using the AHP, which allowed an electric utility to prioritize the load curtailment for each of its distribution substations using loading levels, capacity, customer types, and load categories. The AHP was used to model a decision-making process according to opinions from experts and objective parameters.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an intelligent load control algorithm

Kim

Katipamula

2017

Energy and Buildings

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The renewable generation technologies form a significant (>20%) fraction of grid capacity, however their generation capabilities remain variable in nature. Therefore, utilities will be forced to maintain a significant standby capacity to mitigate the imbalance between supply and demand. Because more than 75% of electricity consumption occurs in buildings, building loads can be used to mitigate some of the imbalance. This paper describes the development and validation of an intelligent load control (ILC) algorithm that can be used to manage loads in a building or group of buildings using both quantitative and qualitative criteria. ILC uses an analytic hierarchy process to prioritize the loads for curtailment. The ILC process was developed and tested in a simulation environment to control a group of rooftop units (RTUs) to manage a building's peak demand while still keeping zone temperatures within acceptable deviations. The ILC algorithm can be implemented at a low cost on a supervisory controller without the need for additional sensing. By anticipating future demand, the process can be extended to add advanced control features such as precooling and preheating to alleviate comfort when operation of the RTUs is curtailed to manage the peak demand.

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A Human Expert-Based Approach to Electrical Peak Demand Management

Cited by 43 publications

References 18 publications

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids

Mitigating the Impact of Renewable Variability With Demand-Side Resources Considering Communication and Cyber Security Limitations

Development and validation of an intelligent load control algorithm

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