Optimal Load Flow with Steady-State Security

Alsaç, O.; Stott, B.

doi:10.1109/tpas.1974.293972

Cited by 1,314 publications

(632 citation statements)

References 8 publications

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“…The IEEE-30 bus system has 6 generator buses, 24 load buses and 41 transmission lines of which four branches are (6-9), (6-10) , (4)(5)(6)(7)(8)(9)(10)(11)(12) and (28-27) -are with the tap setting transformers. The real power settings are taken from [1]. The lower voltage magnitude limits at all buses are 0.95 p.u.…”

Section: Simulation Resultsmentioning

confidence: 99%

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Jeyanthy¹,

Devaraj²

2010

IJCEE

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Abstract-This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. An improved genetic algorithm which permits the control variables to be represented in their natural form is proposed to solve this combinatorial optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point number and integers are used. The proposed method has been tested on IEEE 30 bus system and has resulted in loss which is less than the value reported earlier and is well suited for solving the mixed integer optimization problem.Index Terms-Modal analysis, optimal reactive power dispatch, loss minimization, genetic algorithm. I. INTRODUCTIONOptimal reactive power dispatch problem is one of the difficult optimization problems in power systems. The sources of the reactive power are the generators, synchronous condensers, capacitors, static compensators and tap changing transformers. The problem that has to be solved in a reactive power optimization is to determine the required reactive generation at various locations so as to optimize the objective function. Here the reactive power dispatch problem involves best utilization of the existing generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the loss and to enhance the voltage stability of the system. It involves a non linear optimization problem. Various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [1-2], Newton method [3] and linear programming [4][5][6][7].The gradient and Newton methods suffer from the difficulty in handling inequality constraints. To apply linear programming, the input-output function is to be expressed as a set of linear P. Aruna Jeyanthy is with the Electrical and Electronics Engineering Department, N.I. College of Engineering Kanyakumari District, India (corresponding author to provide phone: 91 9443174253. email: arunadarwin@yahoo.com) D.Devaraj is with the Electrical and Electronics Engineering Department, Kalasalingam University, Srivilliputhur, India.functions which may lead to loss of accuracy. Recently global optimization techniques such as genetic algorithms have been proposed to solve the reactive power flow problem [8,9]. A genetic algorithm is a stochastic search technique based on the mechanics of natural selection. In this paper, genetic algorithm is used to solve the voltage constrained reactive power dispatch problem. The proposed algorithm identifies the optimal values of generation bus voltage magnitudes, transformer tap setting and the output of the reactive p...

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Section: Simulation Resultsmentioning

confidence: 99%

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Jeyanthy¹,

Devaraj²

2010

IJCEE

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“…In defender response model, reactive power is ignored when calculating power flow in electricity network. Therefore, constraints for power system are modified in (17)- (21).…”

Section: Defender Response Modelmentioning

confidence: 99%

“…2 is consist of 6 thermal units including 3 fossil units and 3 natural gas-fired units, 41 branches, 4 transformers and 20 demand sides. Data for buses, branches and load demands are from [21]. Characteristics for generator locations, costs and limits are taken from [22].…”

Section: Ieee 30-bus Systemmentioning

confidence: 99%

Robust optimization for improving resilience of integrated energy systems with electricity and natural gas infrastructures

Hao

Wang

Jiang

2018

J. Mod. Power Syst. Clean Energy

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The integration of natural gas in electricity network requires a more reliable operating plan for increasing uncertainties in the whole system. In this paper, a threestage robust optimization model is proposed for resilient operation of energy system which integrates electricity and natural gas transmission networks with the objective of minimizing load curtailments caused by attacks. Nonconvex constrains are linearized in order to formulate the dual problem of optimal energy flow. Then, the proposed three-stage problem can be reformulated into a two-stage mixed integer linear program (MILP) and solved by Benders decomposition algorithm. Numerical studies on IEEE 30-bus power system with 7-node natural gas network and IEEE 118-bus power system with 14-node natural gas network validate the feasibility of the proposed model for improving resilience of integrated energy system. Energy storage facilities are also considered for the resiliency analysis.

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“…The share of the active power from each generator is equal to the proportion of active power generation defined in the IEEE30 standard model [14] which is shown in Table 5.…”

Section: Load Profile and Electricity Pricementioning

confidence: 99%

“…This paper proposed a novel dispatching strategy which is applied to the system for the charging and discharging of parked EVs within the IEEE30 [14] network while considering their capacity, state of charge (SoC), vehicle movement within the system, the electricity prices and the requirements of the driver and power system operator.…”

Section: Introductionmentioning

confidence: 99%

Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes

Kiaee

Cruden

Sharkh

2015

J. Mod. Power Syst. Clean Energy

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The storage capacity of the batteries in an electric vehicle (EV) could be utilised to store electrical energy and give it back to the grid when needed by participating in vehicle to grid (V2G) schemes. This participation could be a source of revenue for vehicle owners thus reducing the total charging cost of their EVs. A V2G simulator has been developed using MATLAB to find out the potential cost saving from participation of EVs in V2G schemes. A standard IEEE30 network has been modelled in the simulator which uses the MATPOWER engine to undertake power flow analysis. A novel control algorithm has been developed to take advantage of the difference between the selling and buying electricity prices by charging and discharging EVs at the appropriate time. Two scenarios are simulated to compare the total charging cost of EVs with or without the utilisation of V2G technology within the power system assuming a total of 5000 EVs. The results of the simulation show that the applied control strategy with V2G is able to reduce the charging cost of EVs by 13.6 % while satisfying the minimum requirement for state of charge (SoC) of the EV batteries to complete their next journey.

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Optimal Load Flow with Steady-State Security

Cited by 1,314 publications

References 8 publications

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Optimal Reactive Power Dispatch for Voltage Stability Enhancement Using Real Coded Genetic Algorithm

Robust optimization for improving resilience of integrated energy systems with electricity and natural gas infrastructures

Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes

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