Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources

Hemmati, Reza; Saboori, Hedayat; Jirdehi, Mehdi Ahmadi

doi:10.1016/j.energy.2017.05.167

Cited by 145 publications

(59 citation statements)

References 32 publications

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“…The authors chose to classify them, as it seems difficult to reduce the model structures in personnel scheduling. According to the latest classification structure in the literature, there are four main topics [3]: It has been determined that the working trend related to PSPs is to develop methods with heuristic solutions and use hybrid algorithms [10][11][12]17,45,49,60,62]. In addition, specific personnel scheduling models are available in the literature; for example, models based on flexible demand, models in which the workplace is changed, and models with restricted task changes.…”

Section: Personnel Scheduling Problemmentioning

confidence: 99%

Sustainable Personnel Scheduling Problem Optimization in a Natural Gas Combined-Cycle Power Plant

2019

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This paper deals with a sustainable personnel scheduling problem of personnel working in a large-scale natural gas combined-cycle power plant in Turkey. The proposed model focuses on employee complaints due to unfair work schedules and the results of balanced assignments based on power plant interruptions. Eighty personnel work in three shifts at this natural gas combined-cycle power plant. The model is solved with respect to some of the workers’ skills, and there are 20 criteria regarding skills. The analytic network process method is used to get the weights of workers’ skills, which are calculated and included in the model. Goal programming is used in this paper. Our proposed model gives cost minimization and fair work schedules for the power plant. Compared with the literature, the number and set of criteria are unique in terms of personnel competency in the energy sector. Minimizing cost and imbalanced assignments was achieved by the proposed model for the first time without considering the sector.

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Section: Personnel Scheduling Problemmentioning

confidence: 99%

Sustainable Personnel Scheduling Problem Optimization in a Natural Gas Combined-Cycle Power Plant

2019

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“…Reference [12] proposes a scenario tree-based stochastic programming model for CAES planning considering nonanticipative operating behaviors. In [13,14], the impacts of uncertain wind power on the voltage profiles, transmission congestion costs are respectively considered in ESS planning problems by using the AC power flow model. All these models [12][13][14] are formulated to cater for the full utilization of available wind power.…”

Section: Introductionmentioning

confidence: 99%

“…In [13,14], the impacts of uncertain wind power on the voltage profiles, transmission congestion costs are respectively considered in ESS planning problems by using the AC power flow model. All these models [12][13][14] are formulated to cater for the full utilization of available wind power. In addition, robust optimization has also been well adopted in ESS planning studies [15,16] to obtain robust solutions that can ensure reliable system operation without any wind curtailment under uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Investment optimization of grid-scale energy storage for supporting different wind power utilization levels

Wang

et al. 2019

J. Mod. Power Syst. Clean Energy

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With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.

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“…Previously, numerous inexact optimization approaches such as Monte Carlo simulation (MCS), chance-constrained programming (CCP), two-stage stochastic programming (TSP) and multistage stochastic programming (MSP) were proposed for dealing with stochastic problems with known probability distributions in the energy system [4][5][6][7][8][9]. For example, Hemmati et al [4] used a MCS-based stochastic planning method for congestion management in electric power systems, in which uncertainties of wind and solar resources were handled. Odetayo et al [6] proposed a CCP approach to integrated planning of distributed power generation and natural gas network in the presence of uncertain real and reactive power demand.…”

Section: Introductionmentioning

confidence: 99%

A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

Huang

et al. 2018

Applied Energy

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In this study, a copula-based flexible-stochastic programming (CFSP) method is developed for planning regional energy system (RES). CFSP can deal with multiple uncertainties expressed as interval values, random variables and fuzzy sets as well as their combinations employed to objective function and soft constraints. It can also reflect uncertain interactions among random variables through using copula functions even having different probability distributions and previously unknown correlations. Then, based on the developed CFSP approach, a CFSP-RES model is formulated for planning RES of the urban agglomeration of Beijing and Tianjin (China). Results disclose that uncertainties existed in the system components have significant effects on the outputs of decision variables and system cost, and the variation of system cost is reached 16.3%. Results also reveal that air pollutant emissions can be mitigated if the urban agglomeration can co-implement renewable energy development plans (REDP) over the planning horizon, with the reductive rates of [3.3, 7.6] % of sulfur dioxide (SO2), [2.7, 4.1] % of nitrogen oxides (NOx) and [7.0, 11.5] % of particulate matter (PM10). Compared to joint-probabilistic chance-constrained programming (JCP), the CFSP method is more effective for handling multiple random parameters associated with different probability distributions in which their correlations are unknown. Thus, it is not limited to some unjustified assumptions and can be applied to a wider range of problems than previous studies. The findings are helpful to explore the influence of interaction among random variables on modeling outputs and provide in-depth analysis for identifying desired decision schemes for planning RES.

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Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources

Cited by 145 publications

References 32 publications

Sustainable Personnel Scheduling Problem Optimization in a Natural Gas Combined-Cycle Power Plant

Sustainable Personnel Scheduling Problem Optimization in a Natural Gas Combined-Cycle Power Plant

Investment optimization of grid-scale energy storage for supporting different wind power utilization levels

A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin

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