2018
DOI: 10.1016/j.apenergy.2018.05.031
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Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations

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Cited by 48 publications
(11 citation statements)
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“…Fast respond to such fluctuations requires the generation fleet flexibility with the object of having a balance between generation and consumption with minimum system operation cost. Higher power grid operational flexibility could be achieved by system operation improvement [2][3][4], using fast start resources [5], using emerging flexible resources [6,7], and improving grid infrastructure. Practically, in order to improve system operation, designing new markets, using new models and algorithms in the process of unit commitment [8,9] and modeling the uncertainty of renewable energy sources are the main concerns [10].…”
Section: Motivation and Problem Descriptionmentioning
confidence: 99%
“…Fast respond to such fluctuations requires the generation fleet flexibility with the object of having a balance between generation and consumption with minimum system operation cost. Higher power grid operational flexibility could be achieved by system operation improvement [2][3][4], using fast start resources [5], using emerging flexible resources [6,7], and improving grid infrastructure. Practically, in order to improve system operation, designing new markets, using new models and algorithms in the process of unit commitment [8,9] and modeling the uncertainty of renewable energy sources are the main concerns [10].…”
Section: Motivation and Problem Descriptionmentioning
confidence: 99%
“…Non-convex problems are difficult to solve and have high computational time, and the optimal solution is also difficult to achieve [14], [15]. The non-convex CHP operating region can be divided into convex multi-regions and formulated and solved as a mixed integer linear programming (MILP) [15][16][17][18][19][20][21], by Lagrangian relaxation methods [21][22][23][24], or by heuristic techniques [25], [26]. A MILP model was developed for optimization of a power system [15].…”
Section: Earlier Studiesmentioning
confidence: 99%
“…The algorithm solved the problem faster than other algorithms and found better global optimum. Economic optimization of a multi-timescale power system was performed by a MILP model [16]. They considered costs of operation, power ramping and unit commitment, and formed piecewise linear costs curves.…”
Section: Earlier Studiesmentioning
confidence: 99%
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“…Currently, the scale of existing utility-scale battery energy storage capacity is still relatively low compared to installed wind and solar capacities as the return of energy storage investment is inadequate due to the high upfront costs and the lack of flexible and efficient schemes for storage utilization [5,6]. While demands for flexibility (such as time shift [7], congestion relief [8], and ramping [9]) supplied by energy storage will become increasingly pervasive [10,11], they are intermittent and distributed-varying across both time and location-and thus usually result in a low utilization rate if the energy storage system is deployed at a fixed location. For example, in a time-shift application, the energy storage system will operate only when electricity prices reach extremes as a result of very high or low renewable generation and/or electricity demand and stay idle most of the time [12].…”
Section: Introductionmentioning
confidence: 99%