• Energy Research

<p>This industry-oriented paper presents an overview and in-depth analysis of previous and current situations of power system frequency response and control in Australia. The evaluation of different services provided by different electricity market players under the supervision of the Australian Energy Market Operator (AEMO) as an independent system operator provides lessons and an understanding of the current operation status with its real challenges and opportunities from frequency stability and security perspectives. Based on the evaluation of the current situation and future national planning, a number of research gaps and industrial technical issues are identified, and some perspectives on future research directions are presented to help move the power system transformation toward almost 100% renewable and more secure energy systems.</p>

Abstract This paper proposes a novel and hybrid intelligent algorithms to directly modelling prediction intervals (PIs), as an accurate, optimum, reliable and high efficient wind power generation prediction intervals (PIs) are developed by using extreme learning machines (ELM) and self-adaptive evolutionary extreme learning machines (SAEELM). Given significant of uncertainties existed in the wind power generation, SAEELM is the state-of-the-art technology to estimate and quantify the potential uncertainties that may result in risk facing the power system planning, economical operation, and control. In SAEELM, a single hidden layer extreme learning machine is constructed, where the output weight matrix is optimised by using the self-adaptive differential evolution (DE) optimisation method. Also, selecting and adjusting the control parameters and generation strategies involved in differential evolution algorithm to minimises the developed objective cost function. Different case studies using Australian real wind farms have been conducted and analysed. By comparing the statistical analysis and results to other models and methods, e.g. artificial neural networks (ANN), support vector machines (SVM), and Bootstrap, therefore, the proposed approach is an efficient, accurate, robust, and reliable for dealing with uncertainties involved in the integrated power systems, and generation of high-quality PIs. Moreover, the proposed SAEELM based algorithm has a better generalisation than other methods and has a high potential for practical applications.

Transient stability and short-term voltage stability have successively attracted the attention of electric power industry. This paper proposes a novel systematic approach for dynamic VAR planning to improve short-term voltage stability level and transient stability level. The dynamic VAR planning problem is formulated as a multi-objective optimization (MOO) model with objectives including investment cost, short-term voltage stability level, and transient stability level. To reduce the complexity of the proposed MOO model, K -means clustering-based severe contingencies selection and global sensitivity analysis-based potential buses selection are employed, leading to a simplified MOO model. The combination of a surrogate modeling technique called support vector regression and the multi-objective evolutionary algorithm (MOEA) are then used to solve the simplified MOO model, considering both the accuracy of models and the optimization computation cost. This combination makes it feasible to perform multiple runs of MOEAs for weakening the effect of the MOEA's randomness to optimal results and offering more diverse Pareto-optimal solutions for decision makers. Simulations are carried on the IEEE 39-bus system and a real power grid of China, illustrating that our methodology is reliable with high efficiency.

Load model is one of the most important elements in power system simulation and control. Based on more than 20 years of practice using load characteristic recorders in the measurement-based load modeling, this paper proposes the expectation composite load model to predict unseen data. The methods for load bus classification and parameter identification are also provided. The generalization capability of the proposed load modeling is validated by the measured load dynamics and system dynamics during two three-phase short circuit tests of the NE power grid in China. In order to evaluate the measurement-based load model, the probabilistic collocation method (PCM) is applied to quantitatively analyze uncertainties of the simulation responses raised by the parameters.

Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisi-ble to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner as-suming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM so-lar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to cap-ture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant fea-tures can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.

Compared with a wide-area grid, an isolated power system (IPS) is usually operated under persistent disturbances and is vulnerable to external disturbances. The reliability of an IPS is often achieved through reconfiguration and emergency controls, which lead to variable topologies of an IPS. A fast and efficient stability criterion is required to ensure the stability of an IPS with changing structures. This paper proposes a stability criterion for dynamic systems based on local input-to-state stability (LISS) and local input-to-output stability (LIOS) properties of subsystems. The proposed stability criterion decomposes the stability analysis of the whole system into analyzing its subsystems' stability and connections. Since the LISS/LIOS properties of subsystems are completely decoupled and can be analyzed offline, the proposed stability criterion has the potential for online stability analysis through checking several algebraic inequalities. This method has good adaptability for topology changes and switching operations of subsystems. Algorithms for estimating subsystems' LISS/LIOS properties are also presented. Case studies on a typical isolated power system verify the proposed stability criterion.

Capacity market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.

To address the uncertain renewable energy in the day-ahead optimal dispatch of energy and reserve, a multi-stage stochastic programming model is established in this paper to minimize the expected total costs. The uncertainties over the multiple stages are characterized by a scenario tree and the optimal dispatch scheme is cast as a decision tree which guarantees the flexibility to decide the reasonable outputs of generation and the adequate reserves accounting for different realizations of renewable energy. Most importantly, to deal with the “Curse of Dimensionality” of stochastic programming, stochastic dual dynamic programming (SDDP) is employed, which decomposes the original problem into several sub-problems according to the stages. Specifically, the SDDP algorithm performs forward pass and backward pass repeatedly until the convergence criterion is satisfied. At each iteration, the original problem is approximated by creating a linear piecewise function. Besides, an improved convergence criterion is adopted to narrow the optimization gaps. The results on the IEEE 118-bus system and real-life provincial power grid show the effectiveness of the proposed model and method.

In this paper, a mixed integer linear programming (MILP) formulation for robust state estimation (RSE) is proposed. By using the exactly linearized measurement equations instead of the original nonlinear ones, the existing mixed integer nonlinear programming formulation for RSE is converted to a MILP problem. The proposed approach not only guarantees to find the global optimum, but also does not have convergence problems. Simulation results on a rudimentary 3-bus system and several IEEE standard test systems fully illustrate that the proposed methodology is effective with high efficiency.