• Energy Research

To settle a large-scale integration of renewable distributed generations (DGs), it requires to assess the maximal DG hosting capacity of active distribution networks (ADNs). For fully exploiting the ability of ADNs to accommodate DG, this paper proposes a robust comprehensive DG capacity assessment method considering three-phase power flow modelling and active network management (ANM) techniques. The two-stage adjustable robust optimization is employed to tackle the uncertainties of load demands and DG outputs. With our method, system planners can obtain the maximum penetration level of DGs with their optimal sizing and sitting decisions. Meanwhile, the robust optimal ANM schemes can be generated for each operation time period, including network reconfiguration, on-load-tap-changers regulation, and reactive power compensation. In addition, a three-step optimization algorithm is proposed to enhance the accuracy of DG capacity assessment results. The optimality and robustness of our method are validated via numerical tests on an unbalanced IEEE 33-bus distribution system.

This paper presents a cold-start linear branch flow model named modified DistFlow. In modified DistFlow, the active and reactive power are replaced by their ratios to voltage magnitude as state variables, so that errors introduced by conventional branch flow linearization approaches due to their complete ignoring of the quadratic term are reduced. Based on the path-branch incidence matrix, branch power flows and nodal voltage magnitudes can be obtained in a non-iterative and explicit manner. Subsequently, the proposed modified DistFlow model is applied to the problem of reactive power optimization and network reconfiguration, transforming it into a mixed-integer quadratic programming (MIQP). Simulations show that the proposed modified DistFlow has a better accuracy than existing cold-start linear branch flow models for distribution networks, and the resulting MIQP model for reactive power optimization and network reconfiguration is much more computationally efficient than existing benchmarks.

As large scale distributed energy resources are integrated into distribution networks, coordinated dynamic economic dispatch (DED) for integrated transmission and distribution networks is becoming essential. In this paper, we describe a transmission and distribution network coordinated DED model and propose an efficient decentralized method to solve this problem using multi-parametric quadratic programming. In the proposed method, only boundary variables are exchanged between transmission and distribution networks so that the privacy between different operators can be guaranteed. A revised procedure is developed based on the characteristics of the distribution network problem, which can significantly accelerate the iteration. An effective method for dealing with degeneracy in multi-parametric programming is also proposed. Numerical tests demonstrate that the proposed method can achieve a global optimal solution and that the coordinated DED method can provide additional economic benefits compared with the isolated economic dispatch method. Furthermore, the computational performance of the proposed method is far superior to some popular decentralized methods in terms of both iteration number and computation time.

To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes. Since the long-term RES prediction involves significant errors, we need online configure the frequency control parameters of RESs in a rolling manner to improve the operation economics under the premise of stabilizing system frequency. To address this concern, this paper proposes a frequency constrained stochastic look-ahead power dispatch (FCS-LAPD) model to formulate the frequency control parameters of RESs and Energy Storage Systems (ESSs) as scheduling variables, which can optimally allocate the virtual inertia and droop coefficient of RESs and ESSs. In this FCS-LAPD model, the uncertainties of RESs are characterized using Gaussian Mixture Model (GMM). The required reserves are determined by frequency control parameters, and the reserve cost coefficients are adjusted properly to allocate the reserves according to the predicted power generation. Due to the nonlinearity of the frequency nadir constraint, a convex hull approximation method is proposed to linearize it with guaranteed feasibility. The proposed FCS-LAPD is ultimately cast as an instance of quadratic programming and can be efficiently solved. Case studies on modified IEEE 24-bus system and a provincial power system in China are conducted to show the effectiveness of the proposed model.

Multi-area state estimation (MASE) is indispensable for coordination among independent system operators in interconnected systems. In this paper, we propose an adaptive distributed quasi-Newton (A-DQN) algorithm with an optimal step length tuning strategy for MASE in electric power systems. To address the nonlinearity of the measurement model, nonlinear MASE is factorized into two linear estimation stages with a nonlinear transformation in between. A-DQN is then applied to solve the two linear estimation stages in which the decomposed solution for optimal step length is derived in closed form by exploiting the problem structure. With this method, each area performs a local state estimation with limited information exchange with its neighbors; thus, no central coordinator is needed. Based on a peer-to-peer communication paradigm, this method provides accurate estimation results and considers coupling among areas while the privacy and independence of each area remain well-preserved. Numerical tests demonstrated that the proposed algorithm outperforms MASE methods based on state-of-the-art algorithms such as the alternating direction method of multipliers, the matrix-splitting-based distributed Newton method, and the existing distributed Broyden–Fletcher–Goldfarb–Shanno algorithm.