scholarly journals Optimization and Visualization Tools for Situational Awareness in Highly Renewable Power Systems

2020 ◽  
Author(s):  
Paul Cuffe
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Situational Awareness ◽  
Short Term ◽  
Renewable Power ◽  
Output Fluctuations ◽  
System Operator ◽  
Visualization Tools ◽  
Near Term

<div>As submitted to IEEE EnergyCon 2020<br></div><div><br></div><div><br></div><div>Abstract:<br></div><div><br></div><div>This paper proposes new tools for predicting and visualising the plausible near term shifts in branch loading that may arise due to output fluctuations from renewable generators. These tools are proposed to enhance situational awareness for control room operators, by providing early warnings of where bottlenecks may manifest in a transmission system. For predicting plausible branch loading shifts, a linear optimal power flow formulation is presented which uses a novel objective function to characterise the maximum loading a branch could be exposed to in the short term. This analysis therefore identifies which branches could become overloaded due to shifts in output from volatile generators. Equivalently, these branches can be seen as congestion bottlenecks which may cause curtailment of renewable generation. To allow the system operator to maintain awareness of such potentialities, these congestable branches are highlighted on a system diagram which is drawn to explicitly portray the electrical distance between components in the network.</div><br>

scholarly journals Optimization and Visualization Tools for Situational Awareness in Highly Renewable Power Systems

2020 ◽  
Author(s):  
Paul Cuffe
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Situational Awareness ◽  
Short Term ◽  
Renewable Power ◽  
Output Fluctuations ◽  
System Operator ◽  
Visualization Tools ◽  
Near Term

<div>As submitted to IEEE EnergyCon 2020<br></div><div><br></div><div><br></div><div>Abstract:<br></div><div><br></div><div>This paper proposes new tools for predicting and visualising the plausible near term shifts in branch loading that may arise due to output fluctuations from renewable generators. These tools are proposed to enhance situational awareness for control room operators, by providing early warnings of where bottlenecks may manifest in a transmission system. For predicting plausible branch loading shifts, a linear optimal power flow formulation is presented which uses a novel objective function to characterise the maximum loading a branch could be exposed to in the short term. This analysis therefore identifies which branches could become overloaded due to shifts in output from volatile generators. Equivalently, these branches can be seen as congestion bottlenecks which may cause curtailment of renewable generation. To allow the system operator to maintain awareness of such potentialities, these congestable branches are highlighted on a system diagram which is drawn to explicitly portray the electrical distance between components in the network.</div><br>

scholarly journals Alleviation of VAr Impact on Critical Loading Margin with Redispatch in Deregulated Power Systems

2018 ◽  
Vol 7 (1.8) ◽  
pp. 130
Author(s):  
Venkateswarlu A N ◽  
Tulasi Ram ◽  
S S ◽  
Sangameswara Raju.P
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Short Term ◽  
Critical Loading ◽  
Power Loading ◽  
System Operator ◽  
The Stability ◽  
Continuous Power

The stability management under deregulated environment has become typical task to the system due to random nature of load pattern and generation schedules. In addition, the regular uncertainties in system operation like line outage, generator outage or change in loading level are also causing to change in stability as well as security margins significantly. In order to manage transmission system security, the system operator can go for redispatch as a short term solution. In this article, an attempt is made to clear reactive power loading (VAr) impact on voltage instability margin or Critical Loading Margin (CLM). An Interior Point –Optimal Power Flow (IP-OPF) is applied to make system secured under (N-1) line contingencies. Using this secured schedule, the CLM is computed using Continuous Power Flow (CPF) for the two operating scenarios i.e., without VAr and with VAr loading on the system. The case study is simulated on IEEE 14-bus test network and outcome is validating that, the redispatch can also be apt for CLM enhancement even under contingencies as short term solution for stability management in real time.  

scholarly journals Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

2020 ◽  
Vol 12 (2) ◽  
pp. 518
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Hongbo Li ◽  
Yi Yang ◽  
Abebe Tilahun Tadie ◽  
...  
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Power Sources ◽  
Renewable Power ◽  
Point Estimate Method ◽  
Optimal Power ◽  
Simulation Results ◽  
Point Estimation Method

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

scholarly journals Tie-Line Reserve Power Probability Margin for Day-Ahead Dispatching in Power Systems with High Proportion Renewable Power Sources

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4742
Author(s):  
Yue Chen ◽  
Zhizhong Guo ◽  
Abebe Tilahun Tadie ◽  
Hongbo Li ◽  
Guizhong Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Prediction Error ◽  
Error Probability ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Sources ◽  
Renewable Power ◽  
Optimal Power ◽  
Tie Line

In power systems with a high proportion of renewable power sources (PSHPRPSs), the power constraints of the tie-line may limit the ability of the reserve power to accommodate uncertain power generation, resulting in difficulties for the grid power balance. As uncertain power generation cannot be predicted accurately and in accordance with the law of probability and statistics, it is necessary to use a probability model to calculate the uncertain power of the tie-line. Here, day-ahead prediction error probability optimal power flow (DPEPOPF) is proposed to calculate the tie-line reserve power probability margin (TRPPM) in day-ahead dispatching. In day-ahead dispatching, TRPPM is reserved for real-time dispatching to accommodate uncertain power generation, so as to avoid tie-line power congestion. This study classifies the area of the grid based on the principle of area control error accommodation, and the DPEPOPF is divided into two categories: An inter-area day-ahead prediction error probability optimal power flow mathematical model, and an intra-area day-ahead prediction error probability optimal power flow mathematical model. The point estimate optimization algorithm was implemented in MATLAB 8.3.0.532 (R2014a) to calculate the TRPPM. The simulation results verify the accuracy of the model and effectively avoid power congestion of the tie-line.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

scholarly journals SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3164 ◽  
Author(s):  
Yuwei Chen ◽  
Ji Xiang ◽  
Yanjun Li
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Additional Term ◽  
Transmission Losses ◽  
Stability Margins ◽  
Optimal Power ◽  
Second Order Cone ◽  
Convex Problem ◽  
Generation Costs

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results.

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