Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation

Aalami, Habib Allah; Nojavan, Sayyad

doi:10.1049/iet-gtd.2015.0473

Cited by 120 publications

(41 citation statements)

References 37 publications

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“…These insights reveal fundamental limitations of the approaches adopted in [6]- [14], in dealing effectively with the high-dimensional stochastic problem faced by the DER aggregator. When such approaches consider multivariate uncertainty in a multistage context, a combinatorial explosion of the possible realizations is expected as the planning horizon expands, and the problem soon becomes intractable.…”

Section: B Motivationmentioning

confidence: 99%

“…Existing literature modeling the optimal operation of DER aggregators has mainly focused on stochastic programming (SP) approaches, employing two-stage uncertainty models [6]- [14]. A simplified representation of the uncertainty space is usually adopted, utilizing various scenario generation and reduction techniques.…”

Section: Introductionmentioning

confidence: 99%

“…These challenges have triggered the introduction of DER aggregators, which group a substantial number of DER (not necessarily located in the same local area or connected through the same distribution network) and represent them in the market, by coordinating their operation according to market opportunities and their individual preferences and requirements [1], [5]. Nevertheless, the operation of a DER aggregator constitutes a challenging high-dimensional stochastic problem, since it entails the optimal real-time coordination of a large number of diverse DER, subject to numerous sources of uncertainty, such as the output of renewable micro-generators and consumers' demand and flexibility patterns.Existing literature modeling the optimal operation of DER aggregators has mainly focused on stochastic programming (SP) approaches, employing two-stage uncertainty models [6]- [14]. A simplified representation of the uncertainty space is usually adopted, utilizing various scenario generation and reduction techniques.…”

mentioning

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.…”

mentioning

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: B Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

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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“…In [3], the profit maximizing policy of a microgrid aggregator is derived, where scenarios about uncertain system load, wind power output and electricity prices are derived by MonteCarlo sampling. Finally, in [4] the problem of optimizing the energy procurement schedule of a DER aggregator under uncertain energy prices, load and RES output, is addressed. A small number of scenarios is obtained, assuming a normal probability distribution function, and a scenario reduction technique is applied to reduce the combined scenario set.…”

Section: A Backgroundmentioning

confidence: 99%

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

Fatouros

Konstantelos

Papadaskalopoulos

et al. 2017

2017 IEEE Manchester PowerTech

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Abstract-The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.

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Hybrid Heating and Power Energy Procurement

Khodaei

2018

Robust Energy Procurement of Large Electricity Consumers

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Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation

Cited by 120 publications

References 37 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

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

Hybrid Heating and Power Energy Procurement

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