Stochastic dynamic economic emission dispatch with unit commitment problem considering wind power integration

Abstract

SummaryThis paper establishes a probabilistic scenario-based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, taking into account wind power uncertainty. To solve the stochastic UC problem, this paper presents a probabilistic scenario analysis approach to find the unit scheduling solution considering all the original scenarios under a predetermined probability level. And a reduced scenario set can be obtained by the simultaneous backward method. Consequently, the SDEED problem is decomposed into a number of deterministic multiobjective dispatch problems. For each scenario, an enhanced multiobjective particle swarm optimization (EMOPSO) algorithm integrated with the handling strategy for system operation constraints is proposed to produce the Pareto optimal solutions. Furthermore, the available reserve capacity by the UC solution is verified. The simulation results demonstrate that the proposed probabilistic scenario-based and EMOPSO approach is practicable for solving SDEED-UC from the perspectives of system operation economy, emission, and reliability simultaneously.

LSA-GA: A Hybrid Algorithm for Solving Economic Emission Dispatch Problem

“…This paper sets up a probabilistic situation based system for the stochastic unique monetary emanation dispatch with unit responsibility (SDEED-UC) issue, considering wind power vulnerability. The reenactment results show that the proposed probabilistic situation based and EMOPSO approach is practicable for settling SDEED-UC from the viewpoints of framework activity economy, discharge, what's more, unwavering quality all the while [2].…”

Joint generation and reserve scheduling of wind‐solar‐pumped storage power systems under multiple uncertainties

“…In practical applications, wind power output is generally got based on wind speed prediction . As was addressed in previous works, the forecasting error of wind speed Δ v can be regarded as a Gaussian distribution random variable with a mean of 0 and a standard deviation of σ v , ie, Δ v ~ N (0, σ v 2 ), but it should be mentioned that the proposed approach in this paper based on Gaussian distributed forecast error is general enough to be applicable with any probability distribution of wind speed. According to the basic knowledge of probability theory, real wind speed v is then regarded as a Gaussian distribution random variable with a mean of v and a standard deviation of .…”

“…Rewrite (18), (19), (20), and (40) in matrix form, W = W F + λε, S = S F + λμ, D = D F + λζ and 3 μ can be obtained, substituting them into (44) and (45), the following can be obtained:…”

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

“…To address the uncertainties caused by RDGs, robust optimization and stochastic‐programming framework have gained a great amount of popularity for decision makings in power systems since the traditional deterministic is incapable of dealing with the uncertainties. Having been extensively studied over the past decade, the achievements of stochastic optimization include applications in bidding strategy in electricity market, RDG investment, and unit commitment . Despite offering a perfect exemplification for optimization problems under the situation of uncertainty, the stochastic optimization still faces certain challenges as it requires accurate probability distributions of uncertain factors and careful selection of scenario reductions when complex DN‐operation details need to be considered.…”

“…Having been extensively studied over the past decade, the achievements of stochastic optimization include applications in bidding strategy in electricity market, 18 RDG investment, 19 and unit commitment. 20 Despite offering a perfect exemplification for optimization problems under the situation of uncertainty, the stochastic optimization still faces certain challenges as it requires accurate probability distributions of uncertain factors and careful selection of scenario reductions when complex DN-operation details need to be considered. As another powerful method to manage uncertain data, robust-optimization theory is becoming popular in diverse decision-making problems.…”