Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Hojjat, Mehrdad; Javidi, Mohammad Hossein; Yazdanpanah, Dariush

doi:10.3906/elk-1306-88

Cited by 3 publications

(4 citation statements)

References 20 publications

(40 reference statements)

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“…The SQG method was first introduced by Ermoliev in 1976 [49]. This method combines the Monte Carlo simulation (MCS) [50] and the recursive sampling algorithm, and was first used to solve non‐linear stochastic programming problems with continuous probability distributions. The main feature of this method, compared to other gradient mapping programming methods, is that both the objective function and its constraints can be non‐linear and non‐convex.…”

Section: Optimisation Algorithmmentioning

confidence: 99%

Optimal scheduling of the energy storage system in a hybrid micro‐grid considering uncertainties, using the stochastic quasi‐gradient method

2023

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Energy storage and renewable sources play a unique role in the future advances of smart grids. In this article, the optimal scheduling of the energy storage system in a hybrid microgrid is presented considering the uncertainties of the renewable generations and the load. The optimisation problem in this article is non‐linear and non‐convex, therefore conventional optimisation methods such as linear programming (LP) are unable to solve this problem. On the other hand, because of parameters uncertainty, special considerations are required to simulate these parameters. In this regard, a new optimisation algorithm that can solve the non‐linearity and non‐convexity of the objective function is proposed based on the Stochastic Quasi‐Gradient optimisation Method (SQGM). Moreover, the uncertainties of the wind, PV generation, and the load are modelled. Different optimisation algorithms: the conventional Stochastic Dynamic Programming (SDP), the Stochastic Dual Dynamic Programming (SDDP) and the proposed SQGM are compared. A 9‐bus benchmark system with distributed generation units is used to evaluate the optimisation strategies.

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Section: Optimisation Algorithmmentioning

confidence: 99%

Optimal scheduling of the energy storage system in a hybrid micro‐grid considering uncertainties, using the stochastic quasi‐gradient method

2023

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“…Esfahani and Yousefi proposed an algorithm to minimize the congestion clearing time for CM [11]. Hojjat et al in 2016 considered power system uncertainties and proposed a chance-constrained programming method for CM [12]. In another study, Sarwar and Siddiqui mitigated congestion taking into account the locational marginal pricing differences for the placement of distributed generations (DGs) in the most congested zones [13].…”

Section: Literature Surveymentioning

confidence: 99%

Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

Paul¹,

Kumar²,

Agrawal³

et al. 2019

Turk J Elec Eng & Comp Sci

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The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The potency of the proposed method is tested on the 39-bus New England System and the IEEE 30-bus system and 118-bus system, and the outcomes achieved with the GSA outperform the results reported with other algorithms.

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“…In the recent literature, stochastic TEP problem can be solved either robust optimization [6][7][8], two or multi-stage stochastic optimization [9][10][11][12][13] and chance constrained programming [4,5,[14][15][16][17][18][19]. In this study, we work with chance constraints under TEP optimization, which ensures the system constraints will be satisfied with a pre-specified probability.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], a three-line overload risk index is considered under TEP optimization with load, wind, and N-1 contingency uncertainties, and the proposed formulation gives a comprehensive risk control model. In [16], an equivalent linear model for stochastic congestion management is proposed using chance constraints on transmission lines. In [17], the authors quantified the probabilistic load curtailment degree by a capped load curtailment probability, which is incorporated into the multi-stage TEP model.…”

Section: Introductionmentioning

confidence: 99%

Stochastic AC Transmission Expansion Planning: A Chance Constrained Distributed Slack Bus Approach With Wind Uncertainty

Mir

Karatepe

2022

IEEE Access

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Integrating more renewable energy sources into the grid leads to a more vulnerable power system and presents challenges for power system planners. This paper proposes a probabilistic overload constraint based AC transmission expansion planning model. In terms of economic dispatch, generation adequacy and network constraints are assessed using a probabilistic participation factor power flow. Generation participation factors, which are handled in the AC power flow equations, are used to manage mismatch power that comes from uncertainties as well as transmission loss of the system in a distributed slack bus concept. Combinations of load, wind and N-1 contingency uncertainties are evaluated with Monte Carlo Simulation and the loading limits for the existing and candidate lines and violations on bus voltages are enforced as probabilistic constraints using chance constraint programming. The investment decisions taken under various operating conditions are re-evaluated under a new set of wind power uncertainty that differs from those utilized in the optimization process, and the resulting TEP decisions are analyzed from a risk perspective. The proposed method allows for the uncertain operating conditions to be easily and accurately adapted to the ACTEP optimization. The optimization results show that the participation factor power flow is a promising tool for evaluating probabilistic operating conditions, which can be used to explore the possibility of violations under different uncertainty conditions. INDEX TERMSChance constrained programming, distributed slack bus, participation factor, transmission expansion planning, wind power. NOMENCLATURE 𝑖, 𝑘 Bus no 𝑐 Contingency 𝛽 Not overload probability 𝑁 Total number of buses Ω Total set of candidate lines Ω 𝐶 Total set of contingency 𝑃 𝐺𝑖 Real power output of a generator at bus i 𝑃 𝐷𝑖 Load at bus i 𝑃 𝐼𝑀𝐵 Imbalance power 𝐶 𝑖 Quadratic cost function of generator at bus i 𝐶 𝑖 (𝑃 𝐺𝑖 ) Total generation cost 𝑎 𝑖 , 𝑏 𝑖 , 𝑐 𝑖 Cost coefficients of generator at bus i 𝑃 𝐺𝑖_𝑠𝑐ℎ Scheduled real power output of a generator at bus i 𝑃 𝐺𝑖_𝑢𝑝𝑑 Updated real power output of a generator at bus i 𝑃𝑓 𝑖 Participation factor of generator at bus i 𝑃 𝑖 Injected active power at bus i 𝑄 𝑖 Injected reactive power at bus i 𝑃 𝑠𝑝,𝑖 Specified (known) active power at bus i 𝑄 𝑠𝑝,𝑖 Specified (known) reactive power at bus i ∆𝑃 Mismatch active power vector ∆𝑄 Mismatch reactive power vector 𝑉 𝑖 Voltage magnitude at bus i 𝜃 𝑖 Voltage angle at bus i 𝑉 𝑖 𝑐 Voltage magnitude at bus i at contingency operating condition c 𝜃 𝑖 𝑐 Voltage angle at bus i at contingency operating condition c 𝑔 𝑖𝑘 Conductance of line i-k 𝑏 𝑖𝑘 Susceptance of line i-k 𝑏 𝑖𝑘 𝑠ℎ

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Stochastic congestion management considering power system uncertainties: a chance-constrained programming approach

Cited by 3 publications

References 20 publications

Optimal scheduling of the energy storage system in a hybrid micro‐grid considering uncertainties, using the stochastic quasi‐gradient method

Optimal scheduling of the energy storage system in a hybrid micro‐grid considering uncertainties, using the stochastic quasi‐gradient method

Optimal rescheduling of real power to mitigate congestion using gravitationalsearch algorithm

Stochastic AC Transmission Expansion Planning: A Chance Constrained Distributed Slack Bus Approach With Wind Uncertainty

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