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This paper presents a compressive introduction to the development of a simulation platform for smart multi-terminal DC micro-grid (sMTdc), this tool will facilitate the development of DC network to support a low-carbon sustainable electricity supply system. The simulation platform uses a compressive set of high-resolution (1-min) sub-models: generation (photovoltaic, wind power), demand (individual...
This paper proposes a hybrid approach utilizing particle swarm optimization along with dynamic programming to solve unit commitment problem based on the profit under the deregulated power market. In the deregulated market, power and reserve prices are important factors in the decision process for unit commitment scheduling and offer freedom to utilities to schedule their generators to produce less...
In an electricity market, suppliers are more concerned with maximizing their profit and minimizing risk. To achieve this, optimal bidding strategy of a supplier has been formulated as a Bi-Level Multi Objective Optimization Problem (BLMOOP), in which lower level problem represents market clearing process by System Operator (SO) for minimization of customers' payments. Upper level problem is a multi...
The placement and sizing of Distributed Generators (DGs) can be formulated as a nonlinear optimization problem to maximize the benefits from its placement, while minimizing its size. This paper proposes a voltage stability index based method for the DG placement and a meta-heuristic technique based on Multi-Objective Particle Swarm Optimization (MOPSO) to provide Pareto optimal solutions. The MOPSO...
Many technical areas in power systems require the simultaneous optimization of multiple and often conflicting objective functions with complicated non-linear constraints. The recent studies on multi-objective evolutionary computation methods have shown that the population-based stochastic algorithms are the most attractive approaches for this class of problems. Moreover, these methods can be efficiently...
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