Private and social benefits of a pumped hydro energy storage with increasing amount of wind power

Karhinen, Santtu; Huuki, Hannu

doi:10.1016/j.eneco.2019.05.024

Cited by 34 publications

(18 citation statements)

References 42 publications

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“…In order to interact with different markets, these approaches are formulated as sequential multistage optimization problems. Another family of approaches is based on dynamic programming and Markov processes [28]- [30]. While these approaches often provide global optimal solutions, they do not scale for large systems [31].…”

Section: B Control Of Nonseasonal Storage Systemsmentioning

confidence: 99%

“…The most common of them are trading in the day-ahead market together with the balancing market [19]- [21], [27], [33] or with the real-time market [24]- [26]. Other proposed strategies include frequency regulation coupled with energy arbitrage markets [29]; day-ahead market [30]; primary frequency response market [32]; real-time markets [22]; or day-ahead, intraday, and balancing markets [23], [28]. To the best of our knowledge, approaches that exploit the imbalance markets have not been proposed.…”

Section: B Control Of Nonseasonal Storage Systemsmentioning

confidence: 99%

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Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Lago

Suryanarayana²,

Sogancioglu

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control strategies for STESSs have been proposed before, none of them addressed electricity market interaction and trading. In particular, due to the seasonal nature of STESSs, accounting for the long-term uncertainty in electricity prices has been very challenging. In this article, we develop the first control algorithms to control STESSs when interacting with different wholesale electricity markets. As different control solutions have different merits, we propose solutions based on model predictive control and solutions based on reinforcement learning. We show that this is critical since different markets require different control strategies: MPC strategies are better for day-ahead markets due to the flexibility of MPC, whereas reinforcement learning (RL) strategies are better for real-time markets because of fast computation times and better risk modeling. To study the proposed algorithms in a real-life setup, we consider a real STESS interacting with the day-ahead and imbalance markets in The Netherlands and Belgium. Based on the obtained results, we show that: 1) the developed controllers successfully maximize the profits of STESSs due to market trading and 2) the developed control strategies make STESSs important players in the energy transition: by optimally controlling STESSs and reacting to imbalances, STESSs help to reduce grid imbalances.

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Section: B Control Of Nonseasonal Storage Systemsmentioning

confidence: 99%

Section: B Control Of Nonseasonal Storage Systemsmentioning

confidence: 99%

Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Lago

Suryanarayana²,

Sogancioglu

et al. 2021

IEEE Trans. Contr. Syst. Technol.

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show abstract

“…The most famous and installed storage system is the battery energy storage system (BESS); however, pumped hydro storage (PSH) is becoming a more attractive option due to effective load-leveling attributes in many places. PSHs also have very good and efficient response time for ramp rate and frequency control of wind turbine [102]. Due to the uncertainty of renewable generations and load demand, utility needs to smooth generated power by using ESSs and proper energy management.…”

Section: Ess With Uc Programmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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“…Zhang et al 23 proposed a multiobjective model to solve coordination scheduling of hydro‐thermal‐wind power which can minimize the total costs and emissions. Furthermore, the market revenue is also a crucial decision objective and predicting the electricity prices for day‐ahead should be one of the preconditions 24 …”

Section: Introductionmentioning

confidence: 99%

Optimal day‐ahead coordination on wind‐pumped‐hydro system by using multiobjective multistage model

Yao

Chen

et al. 2020

Int J Energy Res

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Summary This research is motivated by the practice of a well‐known basin—the Yalong River Basin located in the eastern part of the Qinghai‐Tibet Plateau in China, which requires a more reasonable day‐ahead scheduling and operation of a wind‐pumped‐hydro system. Optimal electricity flow remains a widely‐cultivated topic within power system research community since its inception about half‐a‐century ago. In contrast to previous studies, this article proposes a multiobjective multistage nonlinear model to not only for the technical pump‐operating constraints of the coordinated system, but also for lessening the variations on the active power output coming from the intermittence variability of the wind energy resource. Specifically, minimizing the fluctuations of the power output and minimizing the “abandoned rate” of the wind and hydro power are considered two objectives. A state transition equation to compute the energy balance in the reservoir is introduced in the constraint for scheduling the pump‐hydro system. Moreover, the expected operator and chance constraint are used to deal with the stochastic wind energy output. Finally, the scheduling of a wind‐pumped‐hydro system of the Yalong River Basin is proposed as a practical example to illustrate the effectiveness, feasibility, and validity of the proposed model.

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Private and social benefits of a pumped hydro energy storage with increasing amount of wind power

Cited by 34 publications

References 42 publications

Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Optimal Control Strategies for Seasonal Thermal Energy Storage Systems With Market Interaction

Energy Storage System Analysis Review for Optimal Unit Commitment

Optimal day‐ahead coordination on wind‐pumped‐hydro system by using multiobjective multistage model

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