Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids’ operating security. Therefore, it is significant to provide the accurate ranges of power grids’ variables, which can be used by the operators to guarantee the power grid’s operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods.
In order to solve the problem of the influence of time-varying green certificate on the operation benefit of virtual power plant, promote the full interaction between virtual power plant and large power grid, and improve the new energy consumption capacity, a virtual power plant economic dispatching model based on time-varying green certificate was proposed. The biggest difference between the time-variant green certificate and the traditional green certificate is that the green certificate of new energy generation is related to the transaction price of electricity in this period, so that the green certificate can reflect the cost paid by the market subject to consume new energy in this period. The influence of time-variant green card on the operation mode of virtual power plant is analyzed. By introducing the green card revenue function based on time-varying green card into the optimization model, a virtual power plant economic scheduling model with time-varying green card was constructed. The simulation results show that the virtual power plant will use adjustable resources with relatively high cost to promote the consumption of new energy and obtain higher green license income. The results show that the time-varying green card is helpful to improve the market participants' new energy consumption price tolerance, thus promoting the new energy consumption capacity.
With the rapid development of economy and technology, large-scale integrated energy buildings account for an increasing proportion of urban load. However, the randomness of EV owner behaviors, electricity price and outdoor temperature have brought challenges to the energy management of integrated energy buildings. This paper proposes a stochastic dynamic programming-based online algorithm to address the energy management of integrated energy buildings with electric vehicles and flexible thermal loads under multivariate uncertainties. First, an online energy management framework is established, which is further formulated as a multi-stage stochastic sequential decision-making problem. To address the complexities of the problem, a novel stochastic dynamic programming is employed to develop a distributionfree, computationally efficient, and scalable solution. By using extensive training samples, the algorithm is trained offline to learn how to deal with multivariate uncertainties and get the approximate optimal solution, which no longer depends on intraday forecast information. Numerical tests demonstrate the effectiveness of the proposed algorithm compared with other online algorithms in terms of optimality and computation efficiency.INDEX TERMS Stochastic dynamic programming, online algorithm, energy management, integrated energy building, multivariate uncertainties
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