In: 2006 IEEE PES transmission and distribution conference and exposition Latin America, VenezuelaĬhaturvedi KT, Pandit M, Srivastava L (2008) Self organizing hierarchical particle swarm optimization for nonconvex economic dispatch. Perth, Australia, vol IV, pp 1942–1948Ĭhen CH, Yeh SN (2006) Particle swarm optimization for economic power dispatch with valve-point effects. In: Proceedings of IEEE international conference on neural networks (ICNN’95). Kennedy J, Eberhart RC (1995) Particle swarm optimization. IEEE Trans Evol Comput 7(1):83–94īouktir T, Slimani L, Belkacemi M (2004) A genetic algorithm for solving the optimal power flow problem. Sinha N, Chakrabarti R, Chattopadhyay PK (2003) Evolutionary programming techniques for economic load dispatch. IEEE Trans Power Appar Syst PAS-87:1866–1876
IEEE Trans Power Appar Syst PAS-92:2015–2023ĭommel HW, Tinney WF (1968) Optimal power flow solutions. Reid GF, Hasdorf L (1973) Economic dispatch using quadratic programming. IEEE Trans Power Appar Syst PAS-98:837–848 Stott B, Marinho JL (1979) Linear programming for power system network security applications. Marcel Dekker, New YorkĪlomoush M (2003) Derivation of UPFC DC load flow model with examples of its use restructured power systems. Shahidehpour M, Alomoush M (2001) Restructured electrical power systems: operation, trading, and volatility. Vasant PM (2012) Meta-heuristics optimization algorithms in engineering, business, economics, and finance. Also, the proposed algorithm in this paper has been tested on IEEE 14-BUS, IEEE 30-BUS, IEEE 57-BUS network, and the results show improvements on the OPF problem. Finally, the output of the proposed GA-GSF algorithm would include values of buses voltages, lines losses, injected power to buses, power passing through lines, total generation cost, and generators’ profits.
#Calculate lmp full#
In this paper, genetic algorithm (GA) has been employed to solve these equations, and in order to improve the performance of GA in its structure, generating scaling factor (GSF) has also been used for simultaneous calculations of power passing through in transmission lines so that by gaining some knowledge on the capacity of transmission lines, in addition to optimal power flow becoming real, we could determine the electricity price by uniform market pricing or LMP methods depending on using the full capacity of lines and generating power of the units, and thus, we can calculate the profit of generators. Since power flow equations are nonlinear, they should be solved using numerical and repetition-based methods. An important factor in reviewing the performance of generation units and calculating their profit is the calculation of their locational marginal price (LMP), and this depends on our knowledge on the capacity of transmission lines and optimal power flow (OPF) based on reality by which we aim to minimize the total cost of the generators, solve the congestion of transmission lines, and hence, reduce the price of electricity in the market.
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