Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulated Electricity Markets

<span lang="EN-US">Ancillary services in interconnected networks are crucial to secure real power transfers, maintaining network reliability, improving power quality as well as network stability. Reactive power has always been of special importance as one of the most substantial ancillary services required to control the voltage in power grid. This paper presents a fresh reactive power pricing approach by embedding variable transformer taps into voltage ampere reactive (var) pricing method. To carry out the resultant optimization problem with a set of complicating constraints, the genetic algorithm takes part and guarantees the global optimal solution. The simulation results show that when transformer tap works as a control variable, the total cost of reactive power can be decreased. However, the incorporation of transformer taps in the pricing model can slightly complicates voltage profiles as the sensitivity of bus voltages to the reactive power variations in the system is increased. Further, the findings reiterate that by optimal position of transformer taps, technically, the security index of the system can be enhanced while the var price for end-users (lowest purchase cost for independent system operator) being more reasonable. To simulate the proposed reactive pricing method, the standard IEEE 30-bus system is employed to analyze the proposed method.</span>

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“…The reactive power generated at bus i must be in the range of maximum and minimum reactive power generated by the generator stated in (9).…”

Section: Problem Formulationmentioning

confidence: 99%

Optimal reactive power pricing with transformer variable taps using genetic algorithm

Kor¹,

Zeynal

Zakaria

et al. 2023

IJECE

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“…In this market model, active power market is cleared first and then by using these results the reactive power market is cleared next. Generally, in any competitive electricity market, the problem of active power dispatch is formulated by using the cost minimization or social welfare maximization [15]. In this market model, two objective functions, i.e., fuel cost (FC) minimization and social welfare maximization (SWM) are considered.…”

Section: Market Model 1: Conventional/sequential Market Clearingmentioning

confidence: 99%

Social welfare maximization based optimal energy and reactive power dispatch using ant lion optimization algorithm

2021

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In this paper an optimal energy and reactive power dispatch problem is solved by using the ant lion optimization (ALO) algorithm by considering the total cost minimization and social welfare maximization (SWM) objectives. Two different market models are proposed in this work, i.e., conventional/sequential market clearing and the proposed/simultaneous market clearing. In each market model, two objectives, i.e., total cost minimization and SWM are considered. The conventional social welfare (SW) consists the benefit function of consumers and the cost function of active power generation. In this paper, the conventional SW is modified by including the reactive power cost function. The reactive power cost calculation is exactly same as that in the conventional practice. The most important difference is that instead of doing cost calculation in post-facto manner as in conventional practice, simultaneous approach is proposed in this work. The scientificity and suitability of the proposed simultaneous active and reactive power methodology has been examined on standard IEEE 30 bus test system.

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“…Infeasible solutions are penalized by applying a constant penalty to those solutions, which violate feasibility in any way. The penalized objective function would then be the unpenalized objective function "J" plus a penalty (for a minimization problem) [26]. The violated functional operating constraints are incorporated as penalties in objective function.…”

Section: Enhanced Genetic Algorithms (Ega)mentioning

confidence: 99%

Optimal power flow based congestion management using enhanced genetic algorithms

Kim

Salkuti

2019

IJECE

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<p>Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.</p>

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Multi-Objective based Optimal Energy and Reactive Power Dispatch in Deregulated Electricity Markets

Cited by 6 publications

References 18 publications

Optimal reactive power pricing with transformer variable taps using genetic algorithm

Optimal reactive power pricing with transformer variable taps using genetic algorithm

Social welfare maximization based optimal energy and reactive power dispatch using ant lion optimization algorithm

Optimal power flow based congestion management using enhanced genetic algorithms

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