A probabilistic determination of required reserve levels in an energy and reserve co-optimized electricity market with variable generation

Goudarzi, Arman; Viray, Z.N.C.; Siano, Pierluigi; Swanson, Andrew; Coller, John van; Kazemi, M. F.

doi:10.1016/j.energy.2017.04.145

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Gholizadeh (2015) used the firefly algorithm in the framework of SUMT to optimize steel structures. Goudarzi et al (2017) used SUMT method to assess the needed reserve levels in a power grid with variable generation. Guo et al (2018) used SUMT to solve a nonlinear model of a learning problem to maximize the margin for Bayesian pattern classification.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

Atabaki

Pasandideh

Mohammadi

2020

JM2

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Purpose Lot-sizing is among the most important problems in the production and inventory management field. The purpose of this paper is to move one step forward in the direction of the real environment of the dynamic, multi-period, lot-sizing problem. For this purpose, a two-warehouse inventory system, imperfect quality and supplier capacity are simultaneously taken into consideration, where the aim is minimization of the system costs. Design/methodology/approach The problem is formulated in a novel continuous nonlinear programming model. Because of the high complexity of the lot-sizing model, invasive weed optimization (IWO), as a population-based metaheuristic algorithm, is proposed to solve the problem. The designed IWO benefits from an innovative encoding–decoding procedure and a heuristic operator for dispersing seeds. Moreover, sequential unconstrained minimization technique (SUMT) is used to improve the efficiency of the IWO. Findings Taking into consideration a two-warehouse system along with the imperfect quality items leads to model nonlinearity. Using the proposed hybrid IWO and SUMT (SUIWO) for solving small-sized instances shows that SUIWO can provide satisfactory solutions within a reasonable computational time. In comparison between SUIWO and a parameter-tuned genetic algorithm (GA), it is found that when the size of the problem increases, the superiority of SUIWO to GA to find desirable solutions becomes more tangible. Originality/value Developing a continuous nonlinear model for the concerned lot-sizing problem and designing a hybrid IWO and SUMT based on a heuristic encoding–decoding procedure are two main originalities of the present study.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

Atabaki

Pasandideh

Mohammadi

2020

JM2

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“…Determining the required SR is one of the main concerns of the operators in power systems with random components. In many regional power systems, deterministic criteria are implemented by the operators to decide the required amount of SR. For example, the SR capacity is assumed to be a specific percentage of the hourly system loads or the capacity of the largest online generator [2]. Although deterministic criteria are easy to implement, they do not reflect the random behavior of system components, which leads to inconsistent decisions and variable operating risk levels.…”

Section: Introductionmentioning

confidence: 99%

“…Probabilistic methods to address reserve assessment problems are well established in the literature [1][2][3][4][5][6][7][8][9][10][11]. These methods provide realistic evaluations of SR requirements by incorporating the probabilities of the occurrence of each contingency, unforeseen fluctuations of load or wind power in the decision-making issues.…”

Section: Introductionmentioning

confidence: 99%

Risk Analysis and Utility Function-Based Decision-Making Model for Spinning Reserve Allocations

Yao

Ouyang

et al. 2021

IEEE Access

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Spinning reserve (SR) is an essential resource for system operators to compensate for unpredictable imbalances between load and generation caused by sudden unit outages and unforeseen fluctuations of load demand and wind power. System operators could face many alternative SR allocations determined by different methods and decide the optimal one based on the experience and consideration of risks in the power system. Considering the stochastic nature of power system components, this paper proposes a utility function-based decision-making model for SR allocations, which incorporates the whole distribution of social benefits (SBs) of the SR allocation and the risk preferences of decision-makers together. Three different utility functions are established that relate the SBs of SR allocations to riskseeking, risk-neutral, and risk-averse preferences, respectively. Besides, the risk preference degree is represented by a shape parameter. The risk of different SR allocations is analyzed and the expected utilities, which reflect the relative satisfaction, are used to determine the optimal SR allocation for the operators with various risk preferences. Incorporating both the distribution of SBs and decision-makers' risk preferences makes the final decision for optimal SR allocation more flexible than the decision strategies based only on the expected social benefit or a probabilistic reliability index (such as loss of load probability and expected load not served). The effectiveness is examined using the IEEE-RTS.

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A review of co-optimization approaches for operational and planning problems in the energy sector

Dranka

Ferreira

2021

Applied Energy

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A probabilistic determination of required reserve levels in an energy and reserve co-optimized electricity market with variable generation

Cited by 8 publications

References 26 publications

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

A hybrid invasive weed optimization for an imperfect, two-warehouse, lot-sizing problem

Risk Analysis and Utility Function-Based Decision-Making Model for Spinning Reserve Allocations

A review of co-optimization approaches for operational and planning problems in the energy sector

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