Abstract
Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.
Highlights
The direct cost of the j-th wind power plant in terms of scheduled power is modelled as follows, CWd,j (PWs,j) = dw,jPWs,j where dw,j and PWs,j represent the direct cost coefficient and scheduled wind power associated with the j-th wind power generators (WPGs), respectively
CASE STUDIES AND RESULTS FOR IEEE-30 BUS SYSTEM we verify the effectiveness of our proposed optimisation framework and the chosen grey wolf optimisation technique (GWO) algorithm, using the modified IEEE-30 bus system introduced earlier
Numerical results using the GWO approach are compared with those obtained by genetic algorithms (GA) [11], particle swarm optimisation (PSO) [12], crow search algorithms (CSA) [13], artificial bee colony (ABC) [14] and success history-based adaptive differential evolution (SHADE)-SF [24]
Summary
These aim to find an optimal solution for the power system without modifying the original cost function [8]. The cost functions and associated optimisation algorithms that have been developed for OPF to date, have either relied on linear approximations or, when using modern heuristic techniques, have seen unsolved challenges in relation to the exploration and exploitation phases. Novel contributions are made in three main areas: the new objective functions for OPF; the use of the GWO approach to optimise objective functions both in small and medium-scale systems; and a simulation based investigation for selected case study examples to demonstrate the benefit of the proposed approach in terms of operation cost, computational time and scalability.
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