Abstract
This study tackles a risk-limiting scheduling problem of non-renewable power generation for large power systems, and addresses potential violations of the security constraints owing to the volatility of renewable power generation and the uncertainty of load demand. To cope with the computational challenge that arises from the probabilistic constraints in the considered problem, a computationally efficient solution algorithm that involves a bisection method, an off-line constructed artificial neural network (ANN) and an on-line point estimation method is proposed and tested on the IEEE 118-bus system. The results of tests and comparisons reveal that the proposed solution algorithm is applicable to large power systems in real time, and the solution obtained herein is much better than the conventional optimal power flow (OPF) solution in obtaining a much higher probability of satisfying the security constraints.
Highlights
There is a growing interest in utilizing renewable energy such as wind and solar as the power generation sources to overcome the global climate change induced by carbon emission [1,2,3,4].the economic incentives based demand response has prevailed recently to improve the efficiency of electricity utilization and reduce carbon emission [5]
The intermittency of the electricity supplied by renewable energy sources and the uncertain load demand caused by demand response in a modern power system require some traditional power system operations to consider these uncertainties [7,8]
In the OPFPRB (Equation (3)) and conventional optimal power flow (COPF) problem (Equation (1)), the values of the cost coefficients ai, bi and ci ∀i ∈ G \WG are randomly selected from the intervals (6.78, 74.33), (8.3391, 37.6968) and
Summary
There is a growing interest in utilizing renewable energy such as wind and solar as the power generation sources to overcome the global climate change induced by carbon emission [1,2,3,4].the economic incentives based demand response has prevailed recently to improve the efficiency of electricity utilization and reduce carbon emission [5]. There is a growing interest in utilizing renewable energy such as wind and solar as the power generation sources to overcome the global climate change induced by carbon emission [1,2,3,4]. To integrate the distributed renewable energy sources and increase the participation of demand response, it is necessary to transform the traditional electricity grid into a smart grid [6]. The intermittency of the electricity supplied by renewable energy sources and the uncertain load demand caused by demand response in a modern power system require some traditional power system operations to consider these uncertainties [7,8]. Dvorkin et al [9] used a hybrid stochastic/interval approach to tackle the transmission-constrained unit commitment problem with uncertainties of wind power generation and load demand. The schedule of non-renewable power generation in a large power system with uncertain power generation and load demand is under consideration
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