Abstract
With the rapid growth of renewable energy generation, it has become essential to give a comprehensive evaluation of renewable energy integration capability in power systems to reduce renewable generation curtailment. Existing research has not considered the correlations between wind power and photovoltaic (PV) power. In this paper, temporal and spatial correlations among different renewable generations are utilized to evaluate the integration capability of power systems based on the copula model. Firstly, the temporal and spatial correlation between wind and PV power generation is analyzed. Secondly, the temporal and spatial distribution model of both wind and PV power generation output is formulated based on the copula model. Thirdly, aggregated generation output scenarios of wind and PV power are generated. Fourthly, wind and PV power scenarios are utilized in an optimal power flow calculation model of power systems. Lastly, the integration capacity of wind power and PV power is shown to be able to be evaluated by satisfying the reliability of power system operation. Simulation results of a modified IEEE RTS-24 bus system indicate that the integration capability of renewable energy generation in power systems can be comprehensively evaluated based on the temporal and spatial correlations of renewable energy generation.
Highlights
To cope with the impending fossil energy crisis, environmental pollution, and the greenhouse effect, renewable energy is being developed rapidly, especially with regards to power generation.Given the superiorities of clean energy and its low marginal price, renewable energy generation, including wind power and photovoltaic (PV) power, has occupied a larger and larger proportion of power system generation in recent years
Where C(·) represents the copula function; Fpv (·) and Fwind (·) are the marginal cumulative distribution functions (CDFs) of PV and wind power, respectively; e pv and ewind stand for the random variables of PV and wind power forecast errors, respectively; and F(·) is the joint CDF of the random variables, that is, in this case it is the joint CDF of PV and wind power
Step 1: Sample V groups of the two-dimensional uniform random variables PV and wind power forecast errors based on the joint CDF estimated by the Gaussian copula model in (4)
Summary
To cope with the impending fossil energy crisis, environmental pollution, and the greenhouse effect, renewable energy is being developed rapidly, especially with regards to power generation. Most existing research either focuses on penetration capability analysis with individual renewable resources or combinations of multiple renewable resources without considering their correlations in time and space. It is possible for outputs of wind generation and PV generation to have large temporal and spatial correlations because of their shared dependence on weather forecasts. A novel approach for evaluating the integration capability of renewable energy generation in transmission systems is proposed based on temporal and spatial correlations. An optimization model with the objective of maximizing the loss of load probability (LOLP) index is established based on the aggregated renewable generation distribution corresponding to the varying installed capacities of wind farms and PV power farms. The optimal installed capacities of wind power and PV power is shown to be able to be achieved by solving the optimization model
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