Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

Shargh, Saeid; ghazani, B. Khorshid; Mohammadi‐Ivatloo, Behnam; Seyedi, Heresh; Abapour, Mehdi

doi:10.1016/j.renene.2016.02.064

Cited by 113 publications

(59 citation statements)

References 44 publications

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“…This paper presents a two-stage solution approach to slove the problem of MOPF for AC/DC grids with VSC-HVDC. The use of multi-objective evolutionary algorithms (MOEAs) for MOPF has been deeply studied in [4][5][6][7]. However, these methods cannot be applied directly to such AC/DC grids, since VSC-HVDC has not been considered in their models.…”

Section: A Contribution Of This Papermentioning

confidence: 99%

“…As a result, the conventional mono-objective OPF may not be adequate to analyze the increasingly complex and stressed power systems [4]. For this purpose, multi-objective OPF (MOPF) has become a hot topic in the field of OPF, since it adapts to the utilities' actual needs in coordinating multiple different weight-or even conflicting operational objectives [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process

et al. 2018

Energy

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A two-stage solution approach for solving the problem of multi-objective optimal power flow (MOPF) is proposed for hybrid AC/DC grids with VSC-HVDC. First, a MOPF model for hybrid AC/DC grids is built to coordinate the economy, voltage deviation and environmental benefits. Then, a two-stage solution approach, incorporating decision analysis into optimization process, is presented to solve the model. The first stage of the approach is consisted of a multi-objective particle swarm optimization algorithm with a hybrid coding scheme employed to find multiple Pareto-optimal solutions. The second stage will have the obtained solutions clustered into different groups using fuzzy c-means (FCM) clustering, and then the 'best' compromise solutions are obtained by calculating the priority memberships of the solutions belonging to the same groups via grey relation projection (GRP) method. The novelty of this approach lies primarily in incorporating the FCM-GRP based decisions analysis technique into MOPF studies, thereby assisting decision makers to * Corresponding author. E-mail address: liyang@neepu.edu.cn (Yang Li). automatically identify the 'best' operation points. The effectiveness of the proposed approach is verified based on the test results of the IEEE 14-and 300bus systems.

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Section: A Contribution Of This Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process

et al. 2018

Energy

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“…Uncertainties reflect the lack of accurate information on the values of parameters, system components, and measurements. Continuous changes of parameters, unmodelled dynamics, and unprecise measurements, are the main sources of uncertainty in the power systems [34][35][36]. Load, as the most uncertain variable, plays an important role in the performance of the microgrid system.…”

Section: Uncertainties In the Proposed Modelmentioning

confidence: 99%

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

et al. 2019

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In microgrids a major share of the energy production comes from renewable energy sources such as photovoltaic panels or wind turbines. The intermittent nature of these types of producers along with the fluctuation in energy demand can destabilize the grid if not dealt with properly. This paper presents a multi-agent-based energy management approach for a non-isolated microgrid with solar and wind units and in the presence of demand response, considering uncertainty in generation and load. More specifically, a modified version of the lightning search algorithm, along with the weighted objective function of the current microgrid cost, based on different scenarios for the energy management of the microgrid, is proposed. The probability density functions of the solar and wind power outputs, as well as the demand of the households, have been used to determine the amount of uncertainty and to plan various scenarios. We also used a particle swarm optimization algorithm for the microgrid energy management and compared the optimization results obtained from the two algorithms. The simulation results show that uncertainty in the microgrid normally has a significant effect on the outcomes, and failure to consider it would lead to inaccurate management methods. Moreover, the results confirm the excellent performance of the proposed approach.The major share of the energy production in microgrids usually comes from renewable energy sources such as photovoltaic panels (PV) or wind turbines (WT). The intermittent nature of such units can cause fluctuation and power imbalance in the system. Hence, energy management in microgrids considering the uncertainties in generation/demand has always been of paramount importance to researchers in this field [3-6]. State-of-the-ArtResearch on all aspects of microgrids has become very popular in the recent past. In this review we will concentrate on optimization objectives in microgrids only since this is the topic of this paper.Reference [3] presents a review of existing optimization objectives, tools, and solution approaches used in microgrid energy management. In [6], a summary of various uncertainty quantification methods along with a comparative analysis on utilized communication technologies are presented. An energy management solution based on a Bayesian optimization algorithm (BOA) is proposed by [7] in which the optimization problem is formulated without a closed-form objective function expression, and solved using a BOA-based data-driven framework. In [8], a two-layer predictive energy management system (EMS) for microgrids was proposed. The upper layer reduces the total operational cost and the lower layer try to remove the forecast fluctuations. In [9], an optimal scheduling of a standalone microgrid under system uncertainties is proposed. This model uses a dynamic programming method to solve a single-objective optimization problem to reduce the operation and emission costs.Flexibility and cooperative behavior of multi-agent systems (MASs) make them appropriate candidates for ener...

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“…Statistical models for wind speed do not follow normal distribution but rather Weibull distribution. Many studies show that wind speed can be modeled as 2‐parameter Weibull distribution . In this paper, variation of wind speed, u w , is modeled using Weibull probability density function as Equation , where, u w is the wind speed, and A w and k w are the scale and shape factors, respectively.…”

Section: Proposed Stochastic Strategymentioning

confidence: 99%

“…Many studies show that wind speed can be modeled as 2-parameter Weibull distribution. [32][33][34] In this paper, variation of wind speed, u w , is modeled using Weibull probability density function as Equation 24, 35,36 where, u w is the wind speed, and A w and k w are the scale and shape factors, respectively. The generated power of a wind turbine, P m , is approximated by Equation 25.…”

Section: Uncertainties Of Loads and Wind Productionsmentioning

confidence: 99%

Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

Jabari

Seyedi

Ravadanegh

et al. 2017

Int Trans Electr Energ Syst

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Summary Nowadays, occurrence of severe contingencies may cause an interconnected power system to lose stability and lead to the partial or wide‐spread cascading failures. Hence, intentional islanding is the last countermeasure to mitigate the system vulnerability and avoid the catastrophic wide area blackout. This paper proposes a novel probabilistic splitting strategy for generating all possible islanding solutions and evaluating different static and dynamic constraints in reduced power system graph. The proposed stochastic scenario generation algorithm investigates the steady‐state stability of all partitions in each generated solution taking into account the uncertainties of loads and wind farms. Multi‐objective binary imperialistic competitive algorithm is then developed to find the optimum line switching points that minimizes load‐generation mismatch and probability of islands' partial blackout, maximizes voltage stability security margin, and satisfies the slow coherency, connectivity, voltage, and the transmission capacity constraints. Monte Carlo simulation and point estimation method are applied in the stochastic programming model to investigate the islands' stability, calculate the optimization error, and determine the critical stressed transmission lines and PQ‐busses under uncertain operating condition. The validity and speed of the proposed approach are revealed using simulation on IEEE 39‐bus standard system.

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Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties

Cited by 113 publications

References 44 publications

Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process

Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process

An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties

Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

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