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With the growing penetration of distributed renewable energy, distributed control approaches are widely utilized in voltage control of active distribution networks (ADNs), suffering from limited applicability for control devices or heavy communication burden. This paper develops an innovative distributed voltage control strategy of ADNs with global sensitivities (DVC-GS), which integrates network information from a global optimization view to coordinate energy storages, PV inverters and the OLTC with little communication and computing time. The global sensitivities of voltage violations across all buses with respect to nodal voltage and active/reactive power injection are formulated to quantify the ability of each controllable resource for alleviating voltage violations in the entire ADN. The back-and-forth communication combined with the modified DistFlow model is newly developed for the real-time update of global sensitivities at each bus with high accuracy and no iteration. Then the coordinated control of various devices is implemented on basis of global sensitivities to effectively ensure voltage security of ADNs. Comprehensive simulations based on modified 11-bus and 123-bus systems demonstrate the significant efficiency and superiority of the global sensitivities and DVC-GS. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed renewable energy, peer-to-peer (P2P) transactions among small scale consumers become possible. However, it remains challenging to maintain the operational security of distribution networks in P2P transactions. This paper proposes a novel integrated transaction and operation model to realize P2P transactions while ensuring the security constraints of distribution network simultaneously. The Vickrey-Clarke-Groves mechanism is used to solve the conflict between interests of prosumers in the P2P transactions and the operational requirements of distribution network. A zero-sum P2P settlement method is designed to quantify the expense among prosumers indicating their contributions to the safety operation of distribution network by controlling the flexible demand. A distributed algorithm based on sharing form alternating direction method of multipliers is developed for the P2P trading problem to derive transaction solutions of prosumers binding the power injection limits of connected buses. Numerical case studies based on IEEE 13-bus distribution network verify the effectiveness of the proposed model in motivating energy trading among prosumers against operational security violations of the distribution network and reducing the entire energy usage cost. [ABSTRACT FROM AUTHOR]

As an efficient tool for uncertainty quantification of renewable energy forecasting, prediction intervals (PIs) provide essential prognosis to power system operator. Merely improving the statistical quality of PIs with respect to calibration and sharpness cannot always contribute to the operational value for specific decision-making issue. In this paper, the cost-oriented prediction intervals are firstly proposed to achieve the joint improvement of forecasting quality and decision performance. In order to bridge the gap between forecasting and decision, a novel cost-oriented machine learning (COML) framework is established, which unifies nonparametric renewable power PI construction and decision-making. Formulated as a bilevel programming model, the COML minimizes the operational costs of decision-making process by adaptively adjusting the quantile proportion pair of PIs resulting from extreme learning machine based quantile regression. The hierarchical optimization model of the COML is equivalently simplified as a single level nonlinear programming problem. Then an enhanced branch-and-contract algorithm with innovative bounds contraction strategy is devised to efficiently capture the optimum of the single level problem with bilinear nonconvexity. Numerical experiments based on actual wind farm data simulate the online forecasting and decision process for wind power offering. Comprehensive comparisons verify the substantial superiority of the proposed COML methodology in terms of forecasting quality, operational value, as well as computational efficiency for practical application. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, accurate and efficient probabilistic optimal power flow (POPF) calculation becomes more and more important to provide decision support for secure and economic operation of power systems. This paper develops a novel nonparametric probabilistic optimal power flow (N-POPF) model describing the probabilistic information by quantiles, which avoids any parametric probability distribution assumptions of random variables. A novel critical region integral method (CRIM) which combines the multiparametric programming theory and discrete integral is proposed to efficiently solve the N-POPF problem. In the CRIM, the critical region partitioning algorithm is firstly introduced into the POPF model to directly establish the mapping relationship from wind power to optimal solutions of the POPF problem. Besides, a discrete integral method is developed in the CRIM to achieve the probability convolution calculation based on quantiles. Comprehensive numerical experiments verify the superior performance of the proposed CRIM in estimation accuracy and computational efficiency, and demonstrate that N-POPF model significantly improves the accuracy of uncertainty analysis. In general, the proposed N-POPF model and CRIM form a new framework of POPF problem for power system analysis and operation. [ABSTRACT FROM AUTHOR]

With the improvement of traffic electrification, the coupling between power and transportation systems becomes more and more intensified. In this paper, electrified railway is firstly introduced into power system to support frequency regulation under high penetration of renewable energy sources. A novel frequency regulation framework consisting of day-ahead capacity estimation, frequency control parameters dispatch, and real-time response is constructed for electrified railway to provide primary frequency control support. Motion equations of electric trains under regulation are developed in detail. To fulfill the frequency regulation requirement and ensure punctual arrival, a multi-train joint response strategy is proposed to coordinate different electric trains for primary frequency control. The frequency regulation capacity estimation problem and frequency control power dispatch problem are established based on the multi-train joint response strategy. A novel sequential cutting plane algorithm is developed to linearly relax the nonlinear capacity estimation problem and power dispatch problem with ordinary differential equation constraints and iteratively tighten the relaxation area to improve solution optimality with desirable efficiency. Comprehensive numerical studies verify the effectiveness of the proposed primary frequency control framework, indicating the high potential of electrified railway to support frequency regulation of modern power systems. [ABSTRACT FROM AUTHOR]

Distributed storage systems (DESSs) are widely utilized to regulate voltages in active distribution networks with high penetration of volatile renewable energy. In this paper, the distributed multi-energy storage systems (MESSs) are integrated into the active distribution network to enhance the capability of voltage regulation by exploiting interactions among multi-energy loads. A novel distributed control strategy based on back-and-forth communication (BFC) framework is developed to optimally coordinate multiple MESSs for multi-time-step voltage regulation. To achieve the independent control of MESSs at each bus, the BFC is proposed to determine the global sensitivities of voltage violation in the whole network with respect to the power injection at each bus in only one-step iteration. These sensitivities updated in real time during BFC quantify the capability of the single MESS to mitigate voltage fluctuations across all buses, which can be used to individually inform the operation of each MESS. Distinct from traditional distributed control methods that achieve voltage regulation in a single timeslot, the proposed control strategy regulates MESSs in a multi-time-step manner to alleviate anticipated voltage violation in advance. The simulation based on a modified 11-bus radial distribution network demonstrates the effectiveness of the proposed distributed control strategy. [ABSTRACT FROM AUTHOR]

Combined heat and power dispatch (CHPD) is utilized for energy-efficient coordination of integrated electricity and heat systems. By incorporating district heating networks (DHNs) into CHPD, the thermal storage capability of network pipelines can be exploited to increase the operational flexibility for high penetration of renewable energy. This article proposes a flexibility evaluation method based on a generalized thermal storage model to systematically characterize and quantify the flexibility of DHNs in CHPD. In particular, systematic flexibility metrics are introduced to define the parameters of the proposed thermal storage model, including heat ramping capability, heat input limits, and heat storage capacities. A direct quantification method is proposed to explicitly derive these metrics without extensive simulations required in traditional methods. Besides, four different control modes of district heating systems are identified and modeled according to the controllability of mass flow rates and supply temperature at heat sources. A simplified CHPD model is developed based on sequential linear programming and a lumped heat loss model to enhance the computational efficiency. Comprehensive case studies validate the effectiveness of the proposed method in evaluating the flexibility of DHNs in CHPD. It is demonstrated that the flexibility of DHNs in CHPD can be exploited as far as possible in the control mode with adjustable mass flow and supply temperature. [ABSTRACT FROM AUTHOR]

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies. [ABSTRACT FROM AUTHOR]

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems. [ABSTRACT FROM PUBLISHER]

Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems. [ABSTRACT FROM PUBLISHER]

This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness, without the prior knowledge of forecasting errors. The proposed approach has been proved to be highly efficient and reliable through preliminary case studies using real-world wind farm data, indicating a high potential of practical application. [ABSTRACT FROM PUBLISHER]