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The integration of industrial batteries with photovoltaic applications is a common practice to charge the batteries using solar energy. Long-duration flow batteries are useful in dealing with the intermittency of renewable energy sources and offer a great opportunity for total fossil fuel replacement. In this study, the effects of different battery operation time and load profiles on the temperature dynamics of a containerised vanadium flow battery system are modelled and simulated for a range of locations and seasons to identify active cooling or heating requirements that might be needed to maintain safe operating temperatures. This paper explores and analyses the stack, tank, and container temperature dynamics of 6 h and 8 h containerised vanadium flow batteries (VFBs) during periods of higher charge and discharge current using computer simulations that apply insulation with passive or active hybrid cooling thermal management where needed to keep the battery temperature within a safe operating range under a range of climate conditions. According to the simulation results, when adopting the hybrid cooling strategy as described in the case study, for a 30 kW–240 kWh VFB system with ambient temperatures fluctuating between 25 °C and 45 °C, the monthly electricity consumption of the air conditioning system, calculated using average power, can be maintained at a relatively low level of approximately 330 kWh. By employing an air conditioning system with an airflow rate of 0.2 m3/s and a suitable thermal management strategy, it is sufficient to keep an 8 h system operating within a safe temperature range when the ambient temperature is between 15 °C and 35 °C. This study presents the first application of our previously developed containerised VFB thermodynamic model to explore the necessity of active cooling or heating in PV (photovoltaic) applications across different geographical locations and seasons. This analysis provides valuable insights for battery designers and manufacturers to understand the performance of containerised battery systems under various climate conditions. Furthermore, this paper is the first to apply this model for simulating 6 and 8 h batteries and to adopt a hybrid thermal management strategy. The simulation data offer guidance on whether active cooling or heating is required for industrialised vanadium batteries with capacities exceeding 6 h. [ABSTRACT FROM AUTHOR]

While high volume of power electronic converters with renewable energy are being integrated into the grid, low system strength of grid causes reduction in system inertia and loss of synchronization with power converters. This paper proposes a novel interactive control scheme of voltage source converter (VSC) connected to weak grid. Firstly, a grid synchronization approach is performed using VSC side voltage transformed on rotating reference frame with respect to grid voltage, which is carried out based on lowest short circuit ratio (SCR) as approved by utilities such as Australian Energy Market Operator’s system strength report. The transformed voltage is then fed into the active power and reactive power control through PLL. Secondly, the grid synchronization approach through PLL is made interactive by adding virtual synchronous generator (VSG) control into the outer loop, which generates virtual inertia to mimic the synchronous generators of a power system. The design of the VSG control parameters is realized based on small signal stability analysis. Furthermore, the control method is evaluated as per the National Electricity Rules clauses S5.2.5.1-S5.2.14 under AEMO and National Service Providers of Australian strict Industry guidelines. The time-domain simulation results are conducted on PSCAD platform. [ABSTRACT FROM AUTHOR]

With the development of a new smart load device, the electric spring (ES), the operation of distribution systems with high renewable penetration becomes more flexible. The ESs can be installed at noncritical loads for grid support. This paper proposes a two-level voltage management scheme to optimize the voltage profiles of the network, especially at chosen critical buses. In the upper level, the tap positions of load tap changer and capacitor banks switching are optimized to prevent the voltages along the feeder from being out of limits. The model predictive control technique is applied to handle the uncertainties in renewable energy and demand. In the lower level, the responsibility of maintaining the voltages of the critical buses is shared among the ES in a distributed way via consensus control which is suitable for systems with limited communication and calculation capabilities. The proposed management scheme is verified on a modified IEEE 15-bus distribution network. The results show that different voltage regulation devices can work together to maintain the voltage of critical buses by sharing the responsibility in the proposed scheme. [ABSTRACT FROM PUBLISHER]

Along with the increasing penetration of renewable energy, distribution system power flow may be significantly altered in terms of direction and magnitude. This will make delivering reliable power, on demand, a major challenge. In this paper, a novel battery energy storage system (BESS) based energy acquisition model is proposed for the operation of distribution companies (DISCOs) in regulating price or locational marginal price (LMP) mechanisms, while considering energy provision options within DISCO controlled areas. Based on this new model, a new battery operation strategy is proposed for better utilization of energy storage system (ESS) and mitigation operational risk from price volatility. Meanwhile, optimal sizing and siting decisions for BESS is obtained through a cost-benefit analysis method, which aims at maximizing the DISCO's profit from energy transactions, system planning and operation cost savings. The proposed energy acquisition model and ESS control strategy are verified on a modified IEEE 15-bus distribution network, and risk mitigation is also quantified in two different markets. The promising results show that the capacity requirement for ESS can be reduced and the operational risk can also be mitigated. [ABSTRACT FROM PUBLISHER]

The national electricity market (NEM) of Australia is reforming via the rapid uptake of variable renewable energy (VRE) integration concurrent with the retirement of conventional synchronous generation. System strength has emerged as a prominent challenge and constraint to power system stability and ongoing grid connection of VRE such as solar and wind. In order to facilitate decarbonization pathways, Australia is the first country to evolve system strength and inertia frameworks and assessment methods to accommodate energy transition barriers, and other parts of the world are now beginning to follow the same approach. With the evolvement of the system strength framework as a new trending strategy to break the transition barriers raised by renewable energy project development and grid connection studies, this paper provides a high-level overview of system strength, covering such fundamental principles as its definition, attributes, and manifestations, as well as industry commentary, cutting-edge technologies and works currently underway for the delivery of a secure and reliable electricity system with the rapid integration of inverter-based resources (IBRs) in the NEM grid. The intent of this study is to provide a comprehensive reference on the engineering practices of the system strength challenge along with complementary technical, regulatory, and industry perspectives. [ABSTRACT FROM AUTHOR]

• An improved method considering correlated stochastic variables is proposed for PLF. • A twice-permutation technique is proposed to ensure the desired correlations. • Singular value decomposition extends the scope of the method. As the increasing integration of large-scale renewable energy sources in power systems, the stochastic characteristics of loads and renewable energy systems become much more complex and impacts power systems much more than ever. Probabilistic load flow analysis is a powerful tool to discover the stochastic characteristics of power systems. There are two important issues for probabilistic load flow analysis based on Monte Carlo simulation: (i) How to generate random samples with the specific distribution and correlation; and (ii) how to make the simulation method to work well even when the correlation matrices are not positive definite. In order to handle the two issues, Nataf transformation combined with Latin hypercube sampling and singular value decomposition method is proposed for solving probabilistic load flow problems with correlated variables in this paper. By using the singular value decomposition (SVD), the proposed method works well even when the correlation matrices are not positive definite. And the twice-permutation technique based on SVD ensures that the samples have the desired correlations. The investigation on modified IEEE 14-bus system and modified IEEE 118-bus system shows that the proposed method is accurate and efficient. [ABSTRACT FROM AUTHOR]

• A risk-averse deep learning method for real-time emergency load shedding. • Deep learning algorithm to enhance prediction accuracy on load shedding strategy. • A shedding amount inference model to speculate the stability after load shedding. • A risk-averse loss function to minimize the risk of load under-cutting events. • Experiments on multiple power systems to verify the practical values of the method. Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method. [ABSTRACT FROM AUTHOR]

Renewable energy resources and transport electrification have become essential components of modern and future distribution planning. Despite the impetus of economic and environmental benefits, the uncertainty brought in poses challenges. In this paper, we propose an integrated expansion planning framework based on a multiobjective mixed-integer nonlinear program. The aim is to minimize the net present value of investments considering feeder routing, substation alterations and construction while maximizing the utilization of proposed charging stations. Distributed generation and load uncertainties, recast in two-stage stochastic programming, are tackled with a scenario generation and reduction technique using a probabilistic approach with Kantorovich metrics. The final number of the scenarios are validated with an alternative clustering method. A flow-based location–allocation theory with user equilibrium traffic assignment model is exploited to site charging stations. The sizing problem is determined using continuous-time Markov chain modeling. The proposed framework is solved with a multiobjective Tchebycheff decomposition-based evolutionary algorithm and tested on a modified 54 bus distribution network and 25 transportation node system. Numerical results demonstrate the capability of the proposed method. Distribution planning authorities can benefit from the presented approach to make intertemporal investment decisions while maintaining the quality of system performance. • Optimal Integrated distribution plan coupled with road electricity is identified. • Uncertainty is handled using scenario generation and reduction approach. • The final branches of scenario tree are verified by k-means clustering method. • Stochastic modeling with Markov Chain solves charging station planning problem. [ABSTRACT FROM AUTHOR]

Networking of microgrids has received increasing attentions in recent years, which requires the uncertainty management associated with variations in the system. In this paper, a two-stage energy management strategy is developed for networked microgrids under the presence of high renewable resources. It decomposes the microgrids energy management into two stages to counteract the intra-day stochastic variations of renewable energy resources, electricity load and electricity prices. In the first stage (hourly time scale), a hierarchical hybrid control method is employed for networked microgrids, aiming to minimize the system operation cost. The mean–variance Markowitz theory is employed to assess the risk of operation cost variability due to the presence of uncertainties. In the second stage (5-min time scale), the components in microgrids are optimally adjusted to minimize the imbalance cost between day-ahead and real-time markets. Simulation study is conducted on an uncoordinated microgrids system as well as on the proposed networked system. According to the simulation results, the proposed method can identify optimal scheduling results, reduce operation costs of risk-aversion, and mitigate the impact of uncertainties. [ABSTRACT FROM AUTHOR]