Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management

Akbari, Ehsan; Shabestari, Seyed Farzin Mousavi; Pirouzi, Sasan; Jadidoleslam, Morteza

doi:10.1016/j.renene.2023.02.050

Cited by 76 publications

(23 citation statements)

References 33 publications

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“…Recently, as a novel solution, renewable energy sources (RESs) like wind turbines and photovoltaics have gained much attention to provide environmental-friendly alternatives to obsolete fossil fuels [1]. However, renewables are associated with uncertain, intermittent power output [2].…”

Section: The Necessity For the Proposed Researchmentioning

confidence: 99%

“…Information FIGURE 1 The suggested collaborative VPP energy management at the transmission system level. BURO, bounded uncertainty-based robust optimization; DA, day-ahead; DRP, demand responce program; ESS, energy storage system; HSRS, hybrid stochastic robust scheduling; SBSP, scenario-based stochastic programming; VPP, virtual power plant; WF, wind farm.…”

Section: Network Operatormentioning

confidence: 99%

“…The power constraint on the DRP can be found in Equation ( 19) and the daily energy of the DRP needs to be zero based on Equation (20). To elaborate, the suggested DRP model can displace the demand during the off-peak period as per the price of electricity in the day-ahead market, as given by Equations ( 19) and ( 20) and the objective function (1). The limitations applied to the ESS are presented in Equations ( 21)- (25), showing the charge and discharge limits ( 21) and ( 22) [55][56], energy storage in the ESS (23), the initial energy of the ESS (24) and the energy limit, (25) [57].…”

Section: Vpp Constraintsmentioning

confidence: 99%

“…Equation ( 31) refers to the expected value of the objective function in (1), that is, the difference between generation oper- ating cost and VPP revenue in the energy and reserve day-ahead markets, where indices of w are added to energy and reserve prices, λ, to express the SBSP model for the mentioned uncertainties. Also, Equations ( 32)-( 37) present the proposed HSRS constraints, where constraints ( 32)-( 36) state the power balance equations in the system and VPP, WF power limit and up-limit of VPP in the presence of a robust model of load and WF maximum power.…”

Section: Hsrs Model Of Uncertaintiesmentioning

confidence: 99%

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Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Pirouzi

2023

IET Generation Trans & Dist

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Energy management of a virtual power plant (VPP) that consists of wind farm (WF), energy storage systems and a demand response program is discussed in the present study. The introduced strategy is realized at the electrical power transmission level and takes into account the collaboration between VPPs in day‐ahead energy and reserve markets. One notable feature of the proposed strategy is attempting to make the revenue of VPPs close to the operating cost of generating units as much as possible. The objective function is subjected to the network‐constrained unit commitment model, up and down reserve requirements and the proposed VPP constraints. This method is taking into account the uncertainty in system and VPP loads, day‐ahead market energy and reserve price and WF power generation. This strategy is applied as hybrid stochastic‐robust scheduling to the VPP at the electrical power transmission level, where the scenario‐based stochastic programming models the uncertainty of day‐ahead market prices, and the bounded uncertainty‐based robust optimization has been adopted to model the uncertainties related to the load and WF power. Scheme has been tested on IEEE systems. According to the obtained results, the proposed coordination of VPPs in the mentioned markets demonstrates capability of suggested strategy.

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Section: The Necessity For the Proposed Researchmentioning

confidence: 99%

Section: Network Operatormentioning

confidence: 99%

Section: Vpp Constraintsmentioning

confidence: 99%

Section: Hsrs Model Of Uncertaintiesmentioning

confidence: 99%

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Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Pirouzi

2023

IET Generation Trans & Dist

Self Cite

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“…The allowed voltage magnitude is in the range [0.95-1.05] p.u. [72][73][74][75][76][77][78][79][80]. The characteristics of its transmission lines are presented in [67], where the resistance of each transmission line is 10% of its reactance.…”

Section: Datamentioning

confidence: 99%

Participation of energy hubs in the market clearing price‐based energy market considering generation and consumption uncertainty modelling

Farsani

Joorabian

Mortazavi

2023

IET Renewable Power Gen

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The market clearing pricing (MCP) model is used to operate electricity, gas, and heating networks (EGHNs) with flexible energy hubs (EHs) in the day‐ahead energy market. It's two‐level optimization. Its higher level refers to EHs' participation in the market and their profit maximization bound by the operational model of power sources, storage devices, and responsive loads in the form of EHs and their flexibility limit. In the lower‐level problem, the MCP model calculates energy price and evaluates EH performance's effects on the networks' technical and economic indices. It optimizes power flow in the networks to reduce centralized generator operating costs. This approach is linear approximation. Unscented transformation (UT) model load and renewable power uncertainties. This technique contains the fewest situations, reducing issue volume and computing time. Benders decomposition (BD) technique calculates energy prices, EHs, and networks. Finally, the numerical results show that the proposed scheme can extract the optimal economic and flexibility states of EHs. EHs' optimal performance enhanced energy networks' economic and operating status compared to power flow studies and promoted societal welfare by lowering energy prices.

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A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

Huang,

Yüksel,

Dinçer

2024

J Knowl Econ

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This study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

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Network flexibility regulation by renewable energy hubs using flexibility pricing-based energy management

Cited by 76 publications

References 33 publications

Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Network‐constrained unit commitment‐based virtual power plant model in the day‐ahead market according to energy management strategy

Participation of energy hubs in the market clearing price‐based energy market considering generation and consumption uncertainty modelling

A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

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