Abstract
To better track the planned output (forecast output), energy storage systems (ESS) are used by wind farms to compensate the forecast error of wind power and reduce the uncertainty of wind power output. When the error compensation degree is the same, the compensation interval is not unique, different compensation intervals need different ESS sizing. This paper focused on finding the optimal compensation interval not only satisfied the error compensation degree but also obtained the max profit of the wind farm. First, a mathematical model was proposed as well as a corresponding optimization method aiming at maximizing the profit of the wind farm. Second, the effect of the influencing factors (compensation degree, electricity price, ESS cost, and wind penalty cost) on the optimal result was fully analyzed and deeply discussed. Through the analysis, the complex relationship between the factors and the optimal results was found. Finally, the comparison between the proposed and traditional method was given, and the simulation results showed that the proposed method can provide a powerful decision-making basis for ESS planning in current and future market.
Highlights
Wind power as a kind of high-quality renewable energy, has developed rapidly in the recent years.With the increasing penetration of wind power, it generates a large quantity of clean electricity for the power grid
In [14], a mixed distribution was proposed based on Laplace and normal distribution to model forecast errors, a probabilistic method was proposed to determine the optimal size of Energy storage systems (ESS) for wind farms in electricity markets
Based on the previous works, in this study, we proposed optimal planning of the ESS sizing in several new ways
Summary
Wind power as a kind of high-quality renewable energy, has developed rapidly in the recent years. In real-time dispatching, the ESS can store or release energy to balance the corresponding power error when the real-time power value of a wind farm is different from the planned output. In terms of compensating wind power forecast error, there are many references discussing this top from different aspects [7,8]. The authors of [9] optimized the capacity of energy storage devices by controlling the wind power forecast error within a certain range to minimize the cost of energy storage equipment. In [14], a mixed distribution was proposed based on Laplace and normal distribution to model forecast errors, a probabilistic method was proposed to determine the optimal size of ESS for wind farms in electricity markets. This work provided a powerful decision-making base for ESS planning in current and future markets
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