Stochastic‐based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads

Talari, Saber; Yazdaninejad, Mohsen; Haghifam, Mahmoud‐Reza

doi:10.1049/iet-gtd.2014.0040

Cited by 211 publications

(91 citation statements)

References 41 publications

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“…Equation (13) clearly demonstrates the recursive nature of the problem, where decisions are made sequentially as the stochastic process unfolds and more information becomes available to the aggregator. This structure is perfectly aligned with the concept of SDDP that follows.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In both cases a very limited number of scenarios (100 and 185 respectively) are retained for a 24h scheduling horizon. A wide variety of uncertain variables, including solar irradiance, wind speed, inflexible demand and the availability of distributed generation (DG), energy storage units and the main grid are explored in [13]- [14]. In both cases, even though a large number of scenarios is initially sampled, only a small number is preserved after the scenario reduction procedure, resulting in a coarse coverage of the uncertainty space.…”

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confidence: 99%

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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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Section: Problem Definitionmentioning

confidence: 99%

mentioning

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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“…Constraint (15) indicates that the charging and the discharging status of BES are mutually exclusive. The variation characteristics of the state of charge (SOC) of BES are defined in (16).…”

Section: Bes Constraintsmentioning

confidence: 99%

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Fan

Piao

2018

Energies

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Battery energy storage (BES) and demand response (DR) are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES) and the load in the microgrid (MG). Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS), hour-ahead scheduling (HAS), and real-time scheduling (RTS). In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.

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“…In [19], the authors develop a cuckoo optimization algorithm to solve the stochastic unit commitment problem of microgrids. In [20], the availability of distributed generators, energy storage units and responsive loads are studied through analyzing their uncertainty natures, and then, a stochastic optimization method based on Monte Carlo simulation is used to cope with the uncertainties. For a typical microgrid with a diesel generator, renewable energies and energy storage, the authors in [21] present a probabilistic constrained approach to model the uncertainties of load and renewable power predictions and use stochastic dynamic programming to find the optimal day-ahead scheduling of the microgrid.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

et al. 2017

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Abstract:In this paper, an optimal day-ahead scheduling problem is studied for a microgrid with multiple distributed resources. For the sake of coping with the prediction uncertainties of renewable energies and loads and taking advantage of the time-of-use price for buying/selling electricity, an interval-based optimization model for maximum profits is developed. To reduce the computational complexity in solving the model, the possibility degree comparison between an interval and a real number is used to convert the interval constraints into the general ones; meanwhile, some slack variables and complementary conditions are introduced to eliminate the absolute-value operation. Unlike the stochastic optimization, the interval optimization only needs the upper-lower bounds of the uncertain variables instead of their probability distribution functions, which is beneficial to the practical application. Furthermore, the possible profit interval and the expected optimal profit can be determined by solving the optimization model. Numerical simulations are performed on a microgrid system modified from the benchmark low voltage network in the European Union project "Microgrid", and the results demonstrate the effectiveness of the proposed method.

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Stochastic‐based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads

Cited by 211 publications

References 41 publications

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

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

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization

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