Duck curve problem solving strategies with thermal unit commitment by introducing pumped storage hydroelectricity &amp; renewable energy

Howlader, Harun Or Rashid; Furukakoi, Masahiro; Matayoshi, Hidehito; Senjyu, Tomonobu

doi:10.1109/peds.2017.8289132

Cited by 20 publications

(23 citation statements)

References 8 publications

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“…For instance, if unit g is off at (k−1), that is, δ g (k − 1) = 0 and then turned on at k, that is, δ g (k) = 1, (9) will force all the binary optimization variables corresponding to the unit ON/OFF-state to be equal to 1 for next U t g − 1 or T . Constraints in (11) and (12) ensure that generator g is only in one state, that is, on or off at any given time step k. δ g (k), y g (k) and z g (k) are defined as binary variables by (13). The start-up and shut-down costs of the units are defined by (14 and (15).…”

Section: System Modelmentioning

confidence: 99%

“…Floch et al presented a distributed charging algorithm for plug-in electric vehicles to mitigate the duck curve challenges [12]. Recent works on handling duck-curve using ESS considered concentrated solar power (CSP) and pumped storage hydroelectricity (PSH) as ESS for handling the duck-curve peaks [13], [14]. PV curtailment is economically better solution to flexibility than ESS as shown by better results in dealing with flexibility issues, this is pointed out by Segundo et al [15].…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes an MPCbased optimal scheduling of generating units, BESS, load shedding and PV curtailment. Current researches on duckcurve handling generally model the economic dispatch in an open-loop system one day ahead [13], [14]. The models are formulated based on the forecast of the weather conditions, the predictions of the demand and electricity price, and the optimal unit dispatch is carried out in one cycle for each hour of the day ahead.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration

et al. 2020

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In this paper, an energy management system (EMS) has been developed based on model predictive control (MPC) to optimally dispatch the power units and particularly handle the duck curve fast ramping events. The methodology is specifically developed considering higher penetration of solar photovoltaic power subjected to realistic physical constraints. Battery energy storage, load shedding and solar curtailment have been utilized to effectively control the duck curve fast ramping events. The proposed system has been assessed with the help of a case study using a 24-bus RTS system. Consequently, detailed flexibility analyses were carried out and it has been proven that the given energy management and control system is capable of handling fast ramping events of duck curve. Furthermore, it has been observed that the overall operation cost of the system is also minimized. The performance of the developed model is compared with traditional non-MPC based mixed-integer linear programming approaches and it has been concluded that MPC-based optimization is more economical and effective in handling the duck curve challenges.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration

et al. 2020

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“…Although coal-based or quick-response generators can be run as spinning reserves for solving these kinds of problems, these generators are polluting the environment massively. Therefore, energy storage systems (ESSs) have currently been installed in the smart grid to smoothing the generators' power output [23]. Several research works have been carried out on the configuration and development of sufficient energy storage facilities for power system flexibility, reliable operation, and management [24].…”

Section: Introductionmentioning

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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“…Electricity generators are responsible to supply sufficient generation capacity for the worst-case demand, and therefore strive to limit the power demand during peak times for smoother demand profiles [1,2] Electricity supply is a recurrent concern, particularly in developing countries that experience capacity constraints and limited reserve margins [3,4,5,6]. This challenge is compounded by peak demand periods of the residential load sector coinciding with the peak demand periods experienced by the supply grid [1,7,8].…”

Section: Introductionmentioning

confidence: 99%

Comfort, peak load and energy: Centralised control of water heaters for demand-driven prioritisation

Roux

Apperley

Booysen

2018

Energy for Sustainable Development

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Recent advances in smart grid technology enable new approaches to address the problem of load control for domestic water heating. Since water heaters store energy, they are well-suited to load management. However, existing approaches have focused on the electrical supply side, ignoring the obvious link between the user and the grid: individual hot water consumption patterns. This paper proposes a load spreading approach in which water heaters compete for access to the heating medium. The proposed smart grid solution takes grid load limits, real-time temperature measurements, water usage patterns, individual user comfort, and heater meta-data into consideration. The scheduler only turns on the heaters with the highest level of need, but limits the number of on heaters to ensure that the grid load stays below a set limit for a set time. The method is evaluated by simulation against various heater set temperature levels, and for various load limits, and compared with ripple control and actual consumption measured in a field trial of 34 water heaters. The proposed algorithm reduces the load from 62kW to 20, 30, 40, and 50kW (vs. 106kW for full ripple control). The resulting number of unwanted cold events is fewer than for ripple control, and only slightly more than no control, while reducing the total energy by 14% from a user-optimised natural experiment.

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Duck curve problem solving strategies with thermal unit commitment by introducing pumped storage hydroelectricity & renewable energy

Cited by 20 publications

References 8 publications

Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration

Model Predictive Control Approach for Optimal Power Dispatch and Duck Curve Handling Under High Photovoltaic Power Penetration

Energy Storage System Analysis Review for Optimal Unit Commitment

Comfort, peak load and energy: Centralised control of water heaters for demand-driven prioritisation

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