Optimal power flow based congestion management using enhanced genetic algorithms

Abstract

<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>

Artificial fish swarm optimization algorithm for power system state estimation

“…The equation for power flowing over a transmission line is clearly not a linear function of the voltage magnitude and phase angle at each end of line. Therefore, the h(x) functions will be nonlinear functions; except for a voltage magnitude measurement where h(x) is simply unity times the particular xi that corresponds to the voltage magnitude being measured [25][26].…”

Quantification of operating reserves with high penetration of wind power considering extreme values

“…In [10], the authors proposed an approach based on a model to clear a network-constrained electricity market with offers made by wind generators. In [13] the optimal power flow approach is addressed to minimize the rescheduling including total congestion cost minimization. In [14] the authors propose a dispatching model to consider simultaneously wind power and demand response.…”

“…The quantification of reserves under high penetration of wind power has been addressed in various electricity markets with different approaches, for instance, PJM (Pennsylvania, New Jersey and Maryland) in US classifies ancillary services as regulating and load-following reserves [15] [13]. The operating reserves deals with the variability and intermittency of power wind from a power dispatching point of view, in fact, in [16], the authors affirm about the operational problems associated with renewable sources.…”

Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques

“…ANFIS and other method were used to calculate the short-term demand forecasting of a multi-carrier energy system and to optimize its energy flow [14]. There is other methods that were used in power system, for example a modified models of PSO algorithm was used to get the estimation solution of power system state [15], in reference [16], the authors used the continuous Newton's for power flow analysis, reference [17] proposed the firefly algorithm for solving load flow problem and authors of paper [18] discussed the optimal power flow based congestion management using enhanced genetic algorithms. Reference [19] proposed a study about strengths, solutions and limitations of ANFIS.…”