• Energy Research

The increasing penetration of photovoltaic has been reshaping the electricity net load curve, which has a significant impact on power system operation and short-term dispatch scheduling. Accurate short-term net load forecasting is essential to ensure reliable and economical operations of a power system. Nonetheless, most of the existing net load forecasting approaches are mostly focused on net load forecasting at household, distribution or microgrid level, but not at grid system-wide level. They also suffer from low accuracy due to the presence of uncertainties on high-frequency fluctuations in the net load. This paper proposes a new improved two-stage net load forecasting method at grid system-wide level. Firstly, it contributes to eliminate the high-frequency components with insignificant amount of energy from the original net load by using the “Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-ICEEMDAN” technique. Then, net load decomposition outcomes form the inputs of a computationally efficient and accurate “Long Short-Term Memory-LSTM” network algorithm to produce an accurate day-ahead forecasting, which lays out the foundation of day-ahead power dispatch scheduling. The superiority of the suggested algorithm was confirmed by comparing the obtained results against five other algorithms that use different empirical based decomposition techniques along with Back Propagation (BP) or LSTM. Statistical metrics, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were computed to show the model accuracy. The validity of the proposed method is verified by the net load data of “South West Interconnected System” power network in Western Australia, which refers to the total demand on the conventional generators made up of the consumers’ actual demand plus system losses, minus the solar power harnessed by the rooftop PV panels installed within the grid system. Achieving a very high day-ahead net load forecasting accuracy of 96.67% confirms our hypothesis on ICEEMDAN's ...

Renewable energy resources, especially wind power, are expected to provide a considerable portion of the world energy requirements in the near future. Large-scale wind power penetration impacts the electricity industry in many aspects and raises a number of technical challenges for the electricity network. A day-ahead network-constrained market clearing formulation is proposed which considers demand side resources. The proposed approach can provide flexible load profile and reduce the need for ramp up/down services by the conventional generators. This method can potentially facilitate a large penetration of wind power by shifting the wind power generation from the off-peak periods to the high-peak hours. The validity of the proposed approach has been verified using the IEEE 30 bus and 57 bus test systems.

In this paper, we propose an integral data rectification strategy for phasor measurement units (PMUs) in multi-area power systems, comprising local data processing and central data recovery modules. The local data processing is designed for the purpose of detecting and eliminating false PMU measurements, which is performed in a decentralized manner, based on our previously developed dynamic state estimation technique; whereas the data recovery is performed in a centralized manner at the control center, based on a newly proposed adaptive-phasor approach. The recovered PMU measurements are then utilized in a low-frequency oscillation damping enhancement scheme, which is achieved by a modified proportional-integral (PI) power system stabilizer (PSS) mechanism embedded in the automatic voltage regulator structure of a synchronous generator. Control parameters of the PI-PSS are optimized by maximizing the critical damping ratio of the power system. This paper is intended to make a contribution to the need of high-quality data transmission in modern power grids with contemporary measuring technologies.

AbstractAncillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances.

In this paper, an intelligent economic dispatch (ED) model integrating wind energy, carbon tax, and battery energy storage system (BESS) is developed. BESS is incorporated with wind generation to reduce fluctuation of wind energy output. To verify the suitable storage size for the Australian power grid, a sensitivity analysis is performed with different levels of BESS. Carbon tax is also considered to reduce carbon emissions in the proposed ED scheme. A hybrid computational framework based on quantum-inspired particle swarm optimization (QPSO) is proposed to achieve faster and better optimization performance, and its viability demonstrated on a simplified 14-generator model of the Australian power system using a set of case studies. The proposed dispatch model can minimize the generating cost and enhance renewable power consumption capacity.

In this paper, a novel Dynamic State Estimation–current feedback with STATCOM control scheme is proposed for the mitigation of voltage fluctuation of Solid Oxide Fuel Cell (SOFC) power station connected to the complex power grids during electrical faults. The proposed control scheme is compared to two existing control strategies and shows its superiority in alleviating voltage flickers and deviations as well as protecting the internal membranes of the fuel cells. Since SOFC internal dynamic states are able to closely reflect the transience and dynamic behavior of SOFC, using them in controller designs can generate better regulations of the state-related internal voltage of SOFC than other methods. STATCOM is also utilized in this study to mitigate the voltage oscillations induced by unavoidable voltage fluctuations during electrical faults. The power system with proposed control strategy is proven to be stable through linear analysis. The acquisition of the useful internal dynamic states is realized using unscented Kalman filter algorithm based state estimator. The success of incorporating estimated states into the development of control strategies is conducive to the designs and implementations of new control schemes for power systems and also the applications of interdisciplinary control theories.

This paper presents an evolutionary approach for optimizing the topology of rural electrical distribution networks. The primary objective of this project is to determine if the rural distribution network for a case study has expanded in an optimal manner through finding the shortest weighted path between network customers, thereby establishing the cost. Currently, there are large portions of the distribution network assets in rural areas that are nearing end of life and will need to be replaced in the near future. This presents the opportunity to redesign the routing of the network through the consideration of all customers, with the expectation that the length of the network and thus the level of investment will be reduced. The minimum spanning tree (MST) and genetic algorithm (GA) are used to compute the optimized path throughout a constraint weighted area. The results indicate that the optimized path of the network produces a considerable reduction in the total cost.

This paper presents an enhanced control and energy management strategy for the remote isolated power system. The presented control method includes the hybrid energy storage system control with a supercapacitor voltage restoration loop. This method improves the transient response and extends the battery lifespan while maintaining the SC voltage at a defined range of operation. To maximize the usage of renewable energy and reduce the diesel generator's frequent turn-on and off operation, the novel battery state of charge stop control is included in the energy management system. This improves the lifetime of diesel generators and reduces maintenance costs. In the proposed control method, a stabilizing control method is also introduced which helps in a seamless transition from grid-connected mode to islanded operating mode. The effectiveness of the proposed EMS and control method is validated by real-time simulation studies. ; Published version ; This work is partially funded by 020 Future Battery Industries CRC Microgrid Battery Deployment project.