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 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 Local Market for TSO and DSO Reactive Power Provision Using DSO Grid Resources

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3442
Author(s):  
Fábio Retorta ◽  
João Aguiar ◽  
Igor Rezende ◽  
José Villar ◽  
Bernardo Silva
Keyword(s):  
Real Time ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Time Interval ◽  
Local Market ◽  
Distribution Grid ◽  
Power Profile ◽  
System Operator

This paper proposes a near to real-time local market to provide reactive power to the transmission system operator (TSO), using the resources connected to a distribution grid managed by a distribution system operator (DSO). The TSO publishes a requested reactive power profile at the TSO-DSO interface for each time-interval of the next delivery period, so that market agents (managing resources of the distribution grid) can prepare and send their bids accordingly. DSO resources are the first to be mobilized, and the remaining residual reactive power is supplied by the reactive power flexibility offered in the local reactive market. Complex bids (with non-curtailability conditions) are supported to provide flexible ways of bidding fewer flexible assets (such as capacitor banks). An alternating current (AC) optimal power flow (OPF) is used to clear the bids by maximizing the social welfare to supply the TSO required reactive power profile, subject to the DSO grid constraints. A rolling window mechanism allows a continuous dispatching of reactive power, and the possibility of adapting assigned schedules to real time constraints. A simplified TSO-DSO cost assignment of the flexible reactive power used is proposed to share for settlement purposes.

scholarly journals Reactive Power Management Considering Stochastic Optimization under the Portuguese Reactive Power Policy Applied to DER in Distribution Networks

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4028 ◽  
Author(s):  
Abreu ◽  
Soares ◽  
Carvalho ◽  
Morais ◽  
Simão ◽  
...  
Keyword(s):  
Power Management ◽  
Distribution System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Renewable Energy Sources ◽  
Distribution Network ◽  
Real Data ◽  
Distribution Networks ◽  
System Operator

Challenges in the coordination between the transmission system operator (TSO) and the distribution system operator (DSO) have risen continuously with the integration of distributed energy resources (DER). These technologies have the possibility to provide reactive power support for system operators. Considering the Portuguese reactive power policy as an example of the regulatory framework, this paper proposes a methodology for proactive reactive power management of the DSO using the renewable energy sources (RES) considering forecast uncertainty available in the distribution system. The proposed method applies a stochastic sequential alternative current (AC)-optimal power flow (SOPF) that returns trustworthy solutions for the DSO and optimizes the use of reactive power between the DSO and DER. The method is validated using a 37-bus distribution network considering real data. Results proved that the method improves the reactive power management by taking advantage of the full capabilities of the DER and by reducing the injection of reactive power by the TSO in the distribution network and, therefore, reducing losses.

Optimal Location of TCSC, TCPS, and SVC Devices for Solving OPF and ORPD Problem Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 244-273
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Phase Shifter ◽  
Reactive Power ◽  
Test System ◽  
Optimal Location ◽  
Thyristor Control ◽  
Evolutionary Optimization Techniques

The secure operation of power system has become a topmost issue in today's largely complicated interconnected power systems. This chapter presents the implementation of grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) approaches to find the optimal location of various FACTS devices such as thyristor control series compensator (TCSC), thyristor control phase shifter (TCPS), and static VAR compensator (SVC) to solve optimal power flow (OPF) and optimal reactive power dispatch (ORPD) in power system. In this chapter, a standard IEEE 30-bus test system with multiple TCSC and TCPS and SVC devices are used for different single and multi-objective functions to validate the performance of the proposed methods. The simulation results validate the ability of the HCRO to produce better optimal solutions compared to GWO, TLBO, BBO, KHA, and CRO algorithms.

FACTS Devices Allocation for Power Transfer Capability Enhancement and Power System Losses Reduction

2013 ◽  
Vol 2 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Peerapol Jirapong
Keyword(s):  
Tabu Search ◽  
Power Systems ◽  
Power Loss ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Evolutionary Programming ◽  
Optimal Placement ◽  
Facts Devices ◽  
Transfer Capability

In this paper, a hybrid evolutionary algorithm (HEA) is proposed to determine the optimal placement of multi-type flexible AC transmission system (FACTS) devices to simultaneously maximize the total transfer capability (TTC) and minimize the system real power loss of power transfers in deregulated power systems. Multi-objective optimal power flow (OPF) with FACTS devices including TTC, power losses, and penalty functions is used to evaluate the feasible maximum TTC value and minimum power loss within real and reactive power generation limits, thermal limits, voltage limits, stability limits, and FACTS devices operation limits. Test results on the modified IEEE 30-bus system indicate that optimally placed OPF with FACTS by the HEA approach could enhance TTC far more than those from evolutionary programming (EP), tabu search (TS), hybrid tabu search and simulated annealing (TS/SA), and improved evolutionary programming (IEP) algorithms, leading to much efficient utilization of the existing transmission systems.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

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.

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