Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy

Nosratabadi, Seyyed Mostafa; Hooshmand, Rahmat‐Allah; Gholipour, Eskandar

doi:10.1016/j.apenergy.2015.12.024

Cited by 108 publications

(41 citation statements)

References 36 publications

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“…As an agent in the retail market, VPP aggregates the capacity of DERs (ie, wind power plants [WPPs], photovoltaic generators [PV], and conventional diesel generators [DG]) and makes a single operational profile to participate in electricity market. 2 The main difference between a VPP and a microgrid is that the VPP is not necessarily limited to a specific geographical location. 3 Moreover, in addition to considering technical aspects of DERs, VPPs pay special attention to the economical operation of their components in the electricity market environment.…”

Section: Motivation and Problem Descriptionmentioning

confidence: 99%

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

Emarati

Keynia

Rashidinejad

2020

Int Trans Electr Energ Syst

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Summary With the increasing use of distributed energy resources (DERs), new technical and economic issues have been raised in power systems. Integration of DERs and energy storage systems (ESSs) in the form of virtual power plant (VPP) resolves an important part of these issues. This paper proposes a risk‐based two‐stage stochastic optimization framework to address the energy management problem for a VPP. The objective of the proposed framework is to optimize the operation of a VPP in day‐ahead (DA) and real‐time (RT) markets. In order to include the risk parameter in the proposed decision‐making problem, conditional value at risk (CVaR) index is applied in the objective function. The considered uncertain parameters in the model are price in DA market, as well as wind and solar generation for the next day. Markov chain Monte Carlo (MCMC) method is applied to model these uncertain parameters through generation of different scenarios. Also, the effects of using ESS on daily operation of considered VPP is investigated. The performance of the proposed method is illustrated through a case study using real data. The obtained results guarantee the appropriate operation of a VPP considering different values for level of the risk.

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Section: Motivation and Problem Descriptionmentioning

confidence: 99%

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

Emarati

Keynia

Rashidinejad

2020

Int Trans Electr Energ Syst

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“…With deep concern about virtual power plants, scheduling optimization considering DR is presented in [21][22][23]. In [21,22], the DR uncertainty is addressed by using stochastic approaches for the maximization of virtual power plant profit. Different DG resources are included.…”

Section: Related Work and Contributionmentioning

confidence: 99%

A Demand Response Approach to Scheduling Constrained Load Shifting

Faria

Vale

2019

Energies

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Demand response (DR) and its advantages are nowadays unquestionable due to the success of several recent implementations of DR programs. Improved methodologies and approaches are needed for the adequate consumers’ schedule in DR events, taking the consumers’ behaviour and preferences into account. In this paper, a virtual power player manages DR programs, minimizing operation costs, respecting the consumption shifting constraints. The impact of the consumption shifting in the target periods is taken into consideration. The advantages of the DR use in comparison with distributed generation (DG) are evaluated. Two scenarios based on 218 consumers in a frame of 96 periods have been implemented. It is demonstrated the advantages of DR in the operation of distributed energy resources, namely when considering the lack of supply.

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“…A comprehensive survey of DR programs and their benefits in SDNs has been presented in the study of Siano . In the study of Nostratabadi, two types of DR programs have been incorporated in the energy scheduling of the industrial virtual power plants (VPPs) aiming at maximizing profit. The utilization of DERs aiming at providing reserve capacity would be an appropriate solution for compensating the unpredicted power variations of intermittent renewable generations.…”

Section: Introductionmentioning

confidence: 99%

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

Samimi

Rezaei

2019

Int Trans Electr Energ Syst

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Summary Distributed energy resources (DERs) are main components for smart distribution networks (SDNs). Generally, energy and reactive power dispatch scheduling problems are managed separately in SDNs. In this paper, a robust simultaneous active/reactive scheduling framework is presented for SDNs. In order to handle the uncertainties of wind power generation, upstream grid prices, and load demand forecasting in a robust framework, information gap decision theory (IGDT) technique is proposed. The proposed robust model considers the minimization of the energy and reactive power cost, carbon taxes of CO2 emission of all distributed resources along with the cost of implementation of demand response program. Numerical results based on a 22‐bus distribution system validate the effectiveness of the proposed method from determining boundaries of uncertainties viewpoint. The obtained results verify that through the proposed IGDT‐based model, the distribution system operator (DSO) can effectively schedule the all DERs while considering the possibility of problem with a specific uncertainty budget.

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Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy

Cited by 108 publications

References 36 publications

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant

A Demand Response Approach to Scheduling Constrained Load Shifting

Robust optimal energy and reactive power management in smart distribution networks: An info‐gap multi‐objective approach

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