High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk ...
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High penetration of renewable energy has instigated stochastic power injection at interconnection between transmission system (TS) and distribution system (DS). This paper delves into the intricate collaborative risk based stochastic dispatching challenges due to contingencies and the integration of renewable sources in the TS and DS. A framework of risk constrained probabilistic problem (RCPC) is presented, which is divided into probabilistic coordination (PC) and risk-based stochastic security-constrained unitcommitment (RSSCUC). The PC is a stochastic linear problem and RSSCUC is a stochastic mixed integer non-linear problem. Therefore, the probabilistic analytical target cascading along with polynomial chaos expansion (PCE) has been utilized for solving the PC problem for coupled TS and DS. Here, PCE has provided the proposed algorithm with the needed capability to present stochastic shared variables as coefficients for PC. Further, the benders decomposition al-gorithm has been utilized for solving RSSCUC, by dividing it into master problem and sub problem to generate feasibility cuts. The proposed technique reduces the computational burden on the system, as it requires single stochastic coordination problem for all scenarios as an alternative of each coordination problem for each scenario and polynomial coefficients instead of multiple scenarios for each shared variable between TS and DS. Different case studies have been performed utilizing the 6-bus system and IEEE 118-bus system as TSs, 7-bus, 9-bus, 85-bus and 69-bus systems as DSs. Results depict the efficacy of the proposed method.
As a matter of fact, the soaring penetration of distributed energy resources (DERs), mainly those harvesting renewable energies (REs) such as wind and solar, is concomitant with environmentally friendly concerns. This...
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In this paper, a methodology to solve unitcommitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing ...
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In this paper, a methodology to solve unitcommitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing the Probability Distribution Function (PDF) of the output power of thermal generators, energy not supplied, excess of electricity, Generation Cost (GC), and Spinning Reserve (SR). The obtained ED solution is combined with Priority List (PL) method in order to solve UC problem probabilistically, giving especial attention to the probability of providing a determined amount of SR at each time step. Three case studies are analysed;the first case study explains how PDF of SR can be used as a metric to decide the amount of power that should be committed;while in the second and third case studies, two systems of 10-units and 110-units are analysed in order to evaluate the quality of the obtained solution from the proposed approach. Results are thoroughly compared to those offered by a stochastic programming approach based on mixed-integer linear programming formulation, observing a difference on GCs between 1.41% and 1.43% for the 10-units system, and between 3.75% and 4.5% for the 110-units system, depending on the chosen significance level of the probabilistic analysis. (C) 2015 Elsevier Ltd. All rights reserved.
An electricity generation system adequacy assessment aims to generate statistically significant adequacy indicators given projected developments in, i.a., renewable and conventional generation, demand, demand response...
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An electricity generation system adequacy assessment aims to generate statistically significant adequacy indicators given projected developments in, i.a., renewable and conventional generation, demand, demand response and energy storage availability. Deterministic unitcommitment (DUC) models with exogenous reserve requirements, as often used in today's adequacy studies to represent day-to-day power system operations, do not account for the contribution of operating reserves to the adequacy of the system. Hence, the adequacy metrics obtained from such an analysis represent a worst-case estimate and should be interpreted with care. In this paper, we propose to use a DUC model with a set of state-of-the-art probabilistic reserve constraints (DUC-PR). The performance of the DUC-PR model in the context of adequacy assessments is studied in a numerical case study. The Expected Energy Not Served (EENS) volume obtained with the DUC model is shown to be a poor estimate of the true EENS volume. In contrast, the DUC-PR methodology yields an accurate estimate of the EENS volume without significantly increasing the computational burden. Policy makers should encourage adopting novel operational power system models, such as the DUC-PR model, to accurately estimate the contribution of operating reserves to system adequacy.
In this paper, a probabilistic unit commitment model by incorporating wind energy and battery energy storage is developed, as well as a methodology based on forward dynamic programming with the aim of solving the prob...
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ISBN:
(纸本)9781538654903
In this paper, a probabilistic unit commitment model by incorporating wind energy and battery energy storage is developed, as well as a methodology based on forward dynamic programming with the aim of solving the problem. To test the model, a test system with four thermal generating units was used, including a wind farm and an energy storage system. The results of the simulation demonstrate the applicability and good performance of the proposed method and consequent decrease in pollution by using less fossil fuels. Besides, it shows the advantages of using energy storage systems to help at reducing the cost of unitcommitment and make the system less susceptible to the uncertainties arising from the inclusion of wind power units in the power system.
This paper proposes a probabilistic unit commitment (PUC) to evaluate the transmission-related applications of energy storage systems for wind power integration. Statistical distributions are used to stochastically mo...
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ISBN:
(纸本)9781479964154
This paper proposes a probabilistic unit commitment (PUC) to evaluate the transmission-related applications of energy storage systems for wind power integration. Statistical distributions are used to stochastically model the wind speed and load. A (2m+1)-point estimate method incorporates the power system uncertainties into the PUC. The IEEE 24-bus system is used as a benchmark to test the proposed method for different scenarios. The distributed storage system is introduced as a solution to decrease the incremental congestion cost of the co-located wind and centralized storage system.
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