Evaluation of scenario reduction methods for stochastic inflow in hydro scheduling models

Larsen, Camilla Thorrud; Doorman, Gerard; Mo, Birger

doi:10.1109/ptc.2015.7232819

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…The uncertainties are considered by two sequential stages: 1) generating a large set of scenarios using MCS [33], and then 2) reducing the generated scenarios using FFS [34].…”

Section: Modeling Of Uncertaintiesmentioning

confidence: 99%

“…The equation includes the energy stored in the pervious time interval 𝒕 − 𝟏, and the charging or discharging energy at each time interval 𝒕. Equation (34) ensures that the power flow from/to BES is in one direction only at each time interval 𝒕 (i.e., either charging or discharging). Equations ( 35), (36), and (37) ensure that the energy stored, the discharging power, and the charging power are within their upper and lower limits.…”

Section: Constraintsmentioning

confidence: 99%

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Energy Management of Multi-Area Islanded Hybrid Microgrids: A Stochastic Approach

Ibrahim,

Hasanien,

Farag

et al. 2023

IEEE Access

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This paper provides a stochastic framework for the energy management of multi-area hybrid microgrids to identify the optimal daily power dispatch in the islanded mode of operation. The primary goal of this framework is to minimize operational costs and power outages for customers by effectively managing the power resources of the hybrid microgrid. These resources include renewable energy sources, battery energy storage systems, and electric vehicles. To obtain accurate results, the costs related to active and reactive powers of diesel generator units are considered in this study. In addition, the uncertainties in the, power generated from renewable energy sources, electric vehicles charging and microgrid demand are modeled using the Monte Carlo Simulation as a scenario generation technique. The number of generated scenarios is reduced using the Fast Forward Scenario reduction technique. The proposed problem is formulated as a constrained mixed-integer nonlinear programming optimization problem and is solved using the Transient Search Optimization algorithm. The obtained solution is compared against other meta-heuristic optimization techniques to ensure the reliability of the results. To investigate the effectiveness of the proposed framework, several case studies are simulated on an islanded hybrid microgrid. The simulation results show the improvement in the economic performance and the reliability of the hybrid MG. The results also indicate the impact of increasing penetration levels of renewable energy resources on the operation cost and power exchange between AC and DC areas.INDEX TERMS AC/DC power flow, energy management, islanded hybrid microgrid, renewable energy sources ABBREVIATION

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“…The uncertainties are considered by two sequential stages: 1) generating a large set of scenarios using MCS [33], and then 2) reducing the generated scenarios using FFS [34].…”

Section: Modeling Of Uncertaintiesmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

Energy Management of Multi-Area Islanded Hybrid Microgrids: A Stochastic Approach

Ibrahim,

Hasanien,

Farag

et al. 2023

IEEE Access

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show abstract

“…scenario tree), where a trade-off between representation complexity and accuracy exists [29]. When multidimensional uncertainty is considered, in order to avoid a combinatorial explosion, this approach ends up with very simple tree structures, unable to accurately capture .…”

Section: Sddp Extension For Capturing Multivariate Dependent Uncermentioning

confidence: 99%

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

Fatouros

Konstantelos

Papadaskalopoulos

et al. 2019

IEEE Trans. Sustain. Energy

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Abstract--The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise, can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming (SDDP) approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a porder vector autoregressive (VAR) model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.Index Terms--Aggregator, distributed energy resources, multidimensional uncertainty, stochastic dual dynamic programming, vector autoregressive modeling. I. NOMENCLATURE A. IndexesIndex of time periods, running from 1 to . Index of energy storage (ES) units, running from 1 to . Index of flexible loads (FL), running from 1 to . Index of wind turbines (WT), running from 1 to . Index of micro-generators, running from 1 to . B. ParametersEnergy market price at period . Operating cost of micro-generator . Cost of demand shedding. C. VariablesPower sold to (positive)/bought from (negative) the market at period . ,Power output of micro-generator at period . Demand shed at period ., Energy level of ES unit at period . ,Power input (positive) / output (negative) of ES unit at period . , ℎChange of demand of FL at period due to load shifting. ,Demand of FL at period after load shifting., Dispatched wind power output of WT at period . II. INTRODUCTIONA. Background FUNDAMENTAL feature of the emerging Smart Grid paradigm involves the integration of a large number of distributed energy resources (DER) storage units, in order to support the economic operation of the future low-carbon power system [1]- [2]. However, the large number, small individual size and inherent stochasticity characterizing these DER have complicated system scheduling and market coordination. Furthermore, driven by the wide integration of renewable generation in power systems, there is a general consensus to move energy trading as close as possible to real-time [3]- [4], which intensifies the complexity of DER coordination. These challenges have...

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Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

Zhou

Xie

et al. 2017

Water Resour Manage

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Evaluation of scenario reduction methods for stochastic inflow in hydro scheduling models

Cited by 4 publications

References 18 publications

Energy Management of Multi-Area Islanded Hybrid Microgrids: A Stochastic Approach

Energy Management of Multi-Area Islanded Hybrid Microgrids: A Stochastic Approach

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

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