Multi-Objective Stochastic Optimization for Determining Set-Point of Wind Farm System

Abstract

Due to the uncertainty in output power of wind farm (WF) systems, a certain reserve capacity is often required in the power system to ensure service reliability and thereby increasing the operation and investment costs for the entire system. In order to reduce this uncertainty and reserve capacity, this study proposes a multi-objective stochastic optimization model to determine the set-points of the WF system. The first objective is to maximize the set-point of the WF system, while the second objective is to maximize the probability of fulfilling that set-point in the real-time operation. An increase in the probability of satisfying the set-point can reduce the uncertainty in the output power of the WF system. However, if the required probability increases, the set-point of the WF system decreases, which reduces the profitability of the WF system. Using the proposed method helps the WF operator in determining the optimal set-point for the WF system by making a trade-off between maximizing the set-point of WF and increasing the probability of fulfilling this set-point in real-time operation. This ensures that the WF system can offer an optimal set-point with a high probability of satisfying this set-point to the power system and thereby avoids a high penalty for mismatch power. In order to show the effectiveness of the proposed method, several case studies are carried out, and the effects of various parameters on the optimal set-point for the WF system are also analyzed. According to the parameters from the transmission system operator (TSO) and wind speed profile, the WF operator can easily determine the optimal set-point using the proposed strategy. A comparison of the profits that the WF system achieved with and without the proposed method is analyzed in detail, and the set-point of the WF system in different seasons is also presented.

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

“…That is why combining power plants that have a more flexible output, for example, gas turbine, benefiting from Plug-in Electric Vehicles (PEV) if a moderate portion of PEV owners are convinced to provide service [133] and using diesel generators in combination with other less intermittent RES can reduce output fluctuations of them [134]. In order to decrease the added reserve capacity due to the presence of wind energy, [135] proposed a multi-objective stochastic optimization to provide wind farm operators with an optimal set-point of the wind farm. In this study, the two objectives are maximising the wind farm set-point and the probability of fulfilling the determined set-point in the real-time operation, leading to the reduction of the uncertainty of the output power.…”

Optimal Sizing of Energy Storage System for Operation of Wind Farms Considering Grid-Code Constraints

“…Wind data is assumed to follow the Weibull distribution [28,29]. Equations ( 1) and (2) show the formula of probability density function (PDF) and cumulative distribution function (CDF) of the Weibull distribution, respectively.…”

“…The rated power is 10 MW with a minimum set-point of 1 MW. The maximum ramp-up and ramp-down in two consecutive intervals are 2 MW [28]. As mentioned earlier in Section 3.2, the value of α, β in the objective function (6) represents the weight of each grid-code constraint in the WF operation.…”

Wind Put Barrier Options Pricing Based on the Nordix Index

“…Weather derivatives are used to manage the economic consequence of non-catastrophic weather events on companies' performance [1][2][3][4][5][6][7][8][9][10][11][12][13]. Given there is no standardised pricing model for weather derivatives, recent studies have developed different pricing models using underlying indices derived from climatic variables like temperature [2,4,5,8,[10][11][12][14][15][16], irradiance [17], rainfall [3,6] and wind [18][19][20][21].…”