Risk-Constrained Offering Strategy for Aggregated Hybrid Power Plant Including Wind Power Producer and Demand Response Provider

Aghaei, Jamshid; Barani, Mostafa; Shafie‐khah, Miadreza; Nieta, Agustín A. Sánchez de la; Catalào, João P. S.

doi:10.1109/tste.2015.2500539

Cited by 102 publications

(72 citation statements)

References 24 publications

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“…Ref. [15] suggests a procedure to create a VPP including a wind generator and a DR provider. A VPP with wind generation units and pumped storage unit station working in a remote and isolated island is studied in [16].…”

Section: Tao Yumentioning

confidence: 99%

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A review on the virtual power plant: Components and operation systems

Ghavidel

Aghaei

et al. 2016

2016 IEEE International Conference on Power System Technology (POWERCON)

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Abstract--Due to the high penetration of Distributed Generations (DGs) in the network and the presently involving competition in all electrical energy markets, Virtual Power Plant (VPP) as a new concept has come into view, with the intention of dealing with the increasing number of DGs in the system and handling effectively the competition in the electricity markets. This paper reviews the VPP in terms of components and operation systems. VPP fundamentally is composed of a number of DGs including conventional dispatchable power plants and intermittent generating units along with possible flexible loads and storage units. In this paper, these components are described in an all-inclusive manner, and some of the most important ones are pointed out. In addition, the most important anticipated outcomes of the two types of VPP, Commercial VPP (CVPP) and Technical VPP (TVPP), are presented in detail. Furthermore, the important literature associated with Combined Heat and Power (CHP) based VPP, VPP components and modeling, VPP with Demand Response (DR), VPP bidding strategy, and participation of VPP in electricity markets are briefly classified and discussed in this paper.

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Section: Tao Yumentioning

confidence: 99%

“…In [36], a functioning process with novel DR programs for a VPP to take part in an energy market is suggested. Also, a method to gain the best offering strategy for a VPP consisting of a wind power producer and a DR in electricity markets is proposed in [15].…”

Section: Flexible Loadsmentioning

confidence: 99%

A review on the virtual power plant: Components and operation systems

Ghavidel

Aghaei

et al. 2016

2016 IEEE International Conference on Power System Technology (POWERCON)

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“…It is essential for the deployment of DR, because customers can respond to grids in almost real time. The DR has been noted in studies for supply-demand scheduling [7]- [9]. The EV which is considered as a mobile load is also an important part of SG, because it has the potential to reducing carbon emissions in the transport sector.…”

Section: Introductionmentioning

confidence: 99%

Unit commitment in achieving low carbon smart grid environment with virtual power plant

Hua

Sun

et al. 2017

2017 International Smart Cities Conference (ISC2)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-This paper proposes a novel unit commitment (UC) model under smart grid (SG) environment, which intends to strike a balance pursuing minimum carbon emissions for policy maker, minimum costs for generators and minimum payment bills for consumers. This leads to a multiobjective optimization problem (MOP) which can be solved through the multiobjective immune algorithm (MOIA). Therefore, the energy market scheduling problem considering low carbon smart grid environment can be analysed. The case studies are conducted to demonstrate the proposed model and present the allocation of power generations as well as the daily energy market scheduling results. It has been proved that the penetration of SG contributes to the mitigation of carbon emissions during the peak demand time by around 500 ton/h. It is also suggested that if the policy maker can provide appropriate monetary compensation for the deployment of SG technologies, generators will be encouraged to participate in the SG deployment.Index Terms-Smart grid (SG), unit commitment (UC), multiobjective optimization (MOP), virtual power plant (VPP), electric vehicle (EV), demand response (DR).

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“…Among the various residential load types, thermostatic loads such as air conditioner (AC) and water heater (WH) loads, and deferrable loads such as electric vehicle (EV) loads are placed at the top of the merit list of DR appliances due to their greater load shifting potential and larger daily energy demand share [14][15][16]. Recently, the load management benefits of controlled appliances for the increased deployment of intermittent renewable generation have been investigated [17][18][19][20][21]. A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated.…”

Section: Introductionmentioning

confidence: 99%

“…A stochastic unit commitment model is presented for assessing the reserve requirements resulting from the integration of renewable sources in [17], where alternative DR paradigms for assessing the benefits of demand-side flexibility on absorbing the variability and uncertainty of renewable supply are investigated. Aghaei et al [18] considered flexible loads as a storage device in a hybrid power plant of a wind farm to compensate for wind power imbalances and proposed a procedure to derive the strategic offer for a hybrid power plant selling energy in the pool-based market. The joint operation of wind farms and DR producer is demonstrated, and DR is effective in coping with the wind power uncertainties in the intraday market.…”

Section: Introductionmentioning

confidence: 99%

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

2017

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Abstract:The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system's imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers' comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

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Risk-Constrained Offering Strategy for Aggregated Hybrid Power Plant Including Wind Power Producer and Demand Response Provider

Cited by 102 publications

References 24 publications

A review on the virtual power plant: Components and operation systems

A review on the virtual power plant: Components and operation systems

Unit commitment in achieving low carbon smart grid environment with virtual power plant

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

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