Impacts of Energy Storage on Short Term Operation Planning Under Centralized Spot Markets

Suazo-Martínez, Carlos; Pereira-Bonvallet, Eduardo; Palma‐Behnke, Rodrigo; Zhang, Xiaoping

doi:10.1109/tsg.2013.2281828

Cited by 53 publications

(23 citation statements)

References 13 publications

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“…Thus, the reserve supplied by the BES can be twice its rated power, theoretically [31]. Constraint (19) defines the reserve range of BES, which is restricted by both the rated power and the SOC limit.…”

Section: Bes Constraintsmentioning

confidence: 99%

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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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Section: Bes Constraintsmentioning

confidence: 99%

“…The power balance constraint and the reserve constraint are described as (31) and (32), respectively. In (31), the interruptible power provided by DRA contains two parts.…”

Section: System Constraintsmentioning

confidence: 99%

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Fan

Piao

2018

Energies

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“…The arbitrage application is widely discussed in the literature, both from a system (e.g., [10][11][12][13][14][15]) and from an individual storage plant's PBUC perspective, the latter being the focus of this article. Generally, there are two important assumptions in PBUC arbitrage models: the first is related to the storage operator's assumed knowledge of future prices, i.e., the (im)perfect price foresight assumption, while the second is related to whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or price-making assumption [16,17].…”

Section: Scope and Approachmentioning

confidence: 99%

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

Brijs

Geth

Siddiqui

et al. 2016

Journal of Energy Storage

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Electricity storage plants can be used for many applications, with one of the most studied applications being arbitrage in the day-ahead market. Terms of use: Documents inAlthough the arbitrage value is related to the presence of price spreads, it also depends on the effect of (dis)charge actions on prices, as arbitrage generally reduces price spreads by increasing off-peak prices when charging and decreasing peak prices when discharging. As such, there are two important assumptions in price-based unit commitment arbitrage models: first, whether the storage operator is assumed to have perfect knowledge of future prices, and second, whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or pricemaking assumption. This article proposes a comprehensive formulation of the arbitrage problem including detailed operating constraints, and focuses on relaxing the price-taking assumption by considering real-world price-effect data, published in the form of hourly piecewise linear relationships between quantity and price based on submitted bids, which are referred to as "market resilience functions". These can be used to (1) evaluate the price-taking and price-making assumptions based on simplified price-effects, and to (2) provide an upper limit to the arbitrage value under the assumption that prices and price-effects are known at the decision stage. In addition, a stepwise approximation to the piecewise linear functions is developed to reduce computation time, i.e., from mixed-integer nonconvex quadratic programming to mixed-integer linear programming, while providing lower-and upper bound approximations to the arbitrage value. The developed models are applied to the Belgian day-ahead market for 2014, and show that the price-effect has a strong impact on the operation and arbitrage value of large-scale storage.

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“…Main technical constraints of storage are related to energy balance in the ESS (14), charging and discharging capacity limits (15)- (16), and also maximum and minimum levels of storage (17)- (18). The boundary condition is considered in (19):…”

Section: Mathematical Formulation Of the Unit Commitment Modelmentioning

confidence: 99%

“…The contribution of the present work comprises a simulation approach to optimal ESS sizing, based on an adapted Unit Commitment formulation that includes ESS operational constraints [18] and the use of high performance computing (HPC) in order to perform detailed short-term system operation simulations over a multiple year horizon. The proposed methodology permits analysis of the key technical challenges and impacts of including ESS in short-term operation as well as its contribution in the long-term, striking a balance between detailed modeling and trackable solution times.…”

mentioning

confidence: 99%

A Simulation Framework for Optimal Energy Storage Sizing

Suazo-Martínez¹,

Pereira-Bonvallet

Palma‐Behnke

2014

Energies

Self Cite

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Despite the increasing interest in Energy Storage Systems (ESS), quantification of their technical and economical benefits remains a challenge. To assess the use of ESS, a simulation approach for ESS optimal sizing is presented. The algorithm is based on an adapted Unit Commitment, including ESS operational constraints, and the use of high performance computing (HPC). Multiple short-term simulations are carried out within a multiple year horizon. Evaluation is performed for Chile's Northern Interconnected Power System (SING). The authors show that a single year evaluation could lead to sub-optimal results when evaluating optimal ESS size. Hence, it is advisable to perform long-term evaluations of ESS. Additionally, the importance of detailed simulation for adequate assessment of ESS contributions and to fully capture storage value is also discussed. Furthermore, the robustness of the optimal sizing approach is evaluated by means of a sensitivity analyses. The results suggest that regulatory frameworks should recognize multiple value streams from storage in order to encourage greater ESS integration.

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Impacts of Energy Storage on Short Term Operation Planning Under Centralized Spot Markets

Cited by 53 publications

References 13 publications

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Hierarchical Energy Management of Microgrids including Storage and Demand Response

Price-based unit commitment electricity storage arbitrage with piecewise linear price-effects

A Simulation Framework for Optimal Energy Storage Sizing

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