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A practical approach for probabilistic short-term generation forecast of a wind farm is proposed in this paper. Compared to the deterministic wind generation forecast, the probabilistic wind generation forecast can provide important wind generation distribution information for operation, trading, and some other applications. The proposed approach is based on Sparse Bayesian Learning (SBL) algorithm, which products probabilistic forecast results by estimating the probabilistic density of the weights of Gaussian kernel functions. Furthermore, since the wind generation time series exhibits strong non-stationary property, a componential forecast strategy is used here to improve the forecast accuracy. According to the strategy, the wind generation series is decomposed into several more predictable series by discrete wavelet transform (DWT), and then the resulted series are forecasted using SBL algorithm respectively. To fulfill multi-look-ahead wind generation forecast, a multi-SBL forecast model is constructed in the context. Tests on a 74-MW wind farm located in southwest Oklahoma demonstrate the effectiveness of the proposed approach.

Abstract A robust economic dispatch (ED) considering automatic generation control (AGC) with affine recourse process is proposed in this paper. The approach co-optimizes the base points and participation factors of the AGC units using preemptive goal programming and robust optimization while considering the uncertain nodal power injections and the network constraints. The proposed approach is realized by two steps. The aim of the first step is to maximize the system effective acceptable disturbance range (EADR) while minimize the generation costs and reserve costs with respect to the obtained EADR in the second step. The novelty of the approach is as follows: (a) The security of the power system is optimized by maximizing the system EADR. The approach can obtain a solution which can cover the disturbance as much as possible even when the system does not have enough adjustable capacity to cover it all. The obtained nodal EADR can quantitatively represent the anti-disturbance capability of a node. (b) The economics of the system is significantly improved by minimizing the generation costs and reserve costs while the constraint of EADR requirement is respected. (c) The conservative level of the solution can be tuned according to the user’s requirements. A simplified one-step linear model is also deduced. The effectiveness and validity of the proposed approach are demonstrated by a 6-bus system, the IEEE 118-bus system, and a real 445-bus system.

Abstract The interdependences between electric power systems and transportation systems are becoming increasingly tight as the market share of electric vehicles keeps booming. Therefore, improving the operation performance of power system and transportation system as an integrated system becomes a critical issue. This paper investigates the coordination between electric vehicle charging stations and electric vehicles assuming they are managed by non-cooperative stakeholders. The operation strategies of electric vehicle charging stations can be optimized by solving a mixed integer linear optimization model while an integer optimization model offers the optimal operation strategy of electric vehicles considering travel route selections. A method for coordinating electric vehicle charging stations and an electric vehicle aggregator without exchanging or sharing private information is proposed. A marginal price based co-ordination model is used for representing the gameplay between two non-cooperative stakeholders. The proposed formulation uses charging pricing policy as an effective tool to coordinate electric vehicle charging stations and electric vehicles, considering travel route selections of electric vehicles. We propose an iterative algorithm for obtaining the optimal solution of proposed marginal price based co-ordination model. A numerical case is provided to validate the merits of proposed model, as a 78.3% total cost reduction can be achieved by adopting the proposed model, comparing to three intuitive approaches.

Effective control of spinning reserve (SR) is important for economic advanced dispatch (AD). In the conventional AD methods, the SR allocation based on deterministic criterion is hard to keep system response risk within a specified level. So, in this paper, expected demand not supplied (EDNS) is introduced to AD as an index for response risk, and a given EDNS limit is treated as a constraint of AD. The resulted AD with equal response risk model forms a {0, 1} mixed integer programming (MIP) problem. Additional constraints for {0, 1} variables are then used to transform the {0, 1} MIP problem to quadratically constrained quadratic program (QCQP) problem. The decoupled primal-dual interior point method is then used to achieve solutions. Test results show the efficiency of the proposed method.

The implementation of greenhouse gases (GHGs) emission policies has altered the economic fundamentals of electrical power systems. Economic dispatch (ED) which works as an important part of daily operation planning of power system should also take the new emission control policies into consideration. This paper proposes a new solution for economic dispatch problem which takes cap-and-trade emission constraints into account by including emission affection factors to reflect the influence of emission constraints on unit cost curves. The proposed method can treat units with piecewise continuous and continuous fuel cost curves harmoniously. Case study is used to demonstrate the effectiveness of the new algorithm. The compromise between economy and environment is also analyzed.