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[Display omitted] The itinerant ferromagnetism can be induced by a van Hove singularity (VHS) with a divergent density of states at Fermi level. Utilizing the giant magnified dielectric constant ε r of SrTiO 3 (111) substrate with cooling, here we successfully manipulated the VHS in the epitaxial monolayer (ML) 1T-VSe 2 film approaching to Fermi level via the large interfacial charge transfer, and thus induced a two-dimensional (2D) itinerant ferromagnetic state below 3.3 K. Combining the direct characterization of the VHS structure via angle-resolved photoemission spectroscopy (ARPES), together with the theoretical analysis, we ascribe the manipulation of VHS to the physical origin of the itinerant ferromagnetic state in ML 1T-VSe 2. Therefore, we further demonstrated that the ferromagnetic state in the 2D system can be controlled through manipulating the VHS by engineering the film thickness or replacing the substrate. Our findings clearly evidence that the VHS can serve as an effective manipulating degree of freedom for the itinerant ferromagnetic state, expanding the application potentials of 2D magnets for the next-generation information technology. [ABSTRACT FROM AUTHOR]

The state-of-the-art centralized computing framework applied to the optimal market dispatch of energy storage systems (ESS) aggregates data from local ESS units for training on the cloud server due to the limited computing resources on edge. However, this approach poses several challenges, including the lack of joint optimization for multiple ESS units, susceptibility to single point of failure and attacks, and inadequate data privacy protection for ESS owners. This study proposes an adaptive personalized federated reinforcement learning (FRL) for multiple-ESS optimal dispatch in various electricity markets with electric vehicle and renewable energy, achieving both the joint optimization of multiple ESSs and avoiding the degraded performance of FRL's local model. Under an adaptively ESS-related differential privacy protection, local devices and the cloud server are specialized to form multiagent deep reinforcement learning (DRL) model for bidding energy, regulation, and third-party services and update the global models, respectively. Given the adaptability of personalization layer to different agents and clients, an adaptive personalization method is developed by calculating the number of personalization layers with the relative loss of each agent and client in the iteration process. The case study shows that the adaptive personalized FRL outperforms conventional FRL, DRL and optimization algorithms. [Display omitted] • FRL-based algorithm for multiple-ESS optimal market dispatch. • Multiagent DRL model for bidding energy, regulation, and third-party services. • Adaptive personalization layers with the relative loss of each agent and client. • Adaptively ESS-related differential privacy method. [ABSTRACT FROM AUTHOR]

Hydrogen as a low-carbon clean energy source is experiencing a global resurgence and has been recognized as an alternative energy carrier that can help bring the world to a carbon neutral future. However, getting to scale is one of the main challenges limiting the growth of the hydrogen economy. In particular, the high cost of transporting green hydrogen is bottlenecking the international trading and wider adoption of hydrogen for global carbon natural objectives. In order to explore incentives for the global hydrogen economy and develop new pathways towards the carbon neutral future, the concept of hydrogen credit is proposed by this research and a framework of trading hydrogen credits similar to carbon credits in the international market is established. This research aims to contribute to the overall uptake of green hydrogen financially rather than relying on the physical production, transportation, and storage of hydrogen. Case studies are presented to demonstrate the feasibility and efficiency of the proposed hydrogen credit framework, as well as the great potential of a global hydrogen credit market. • The concept of hydrogen credit is proposed the first time. • The green hydrogen credit system is closely coupled with the carbon credit market. • A framework of trading hydrogen credits is designed to stimulate hydrogen economy. • This work contributes to the global uptake of green hydrogen financially. • It provides a new pathway towards the net-zero emission/carbon neutral future. [ABSTRACT FROM AUTHOR]

Due to the development of new technologies, change of generation mix and appearance of newly formed energy supply hubs, there is a large year‐on‐year change in the marginal loss factors in power systems. Since any change of marginal loss factors could have significant impacts on payment of loads and profitability of generators, it is necessary to carry out a comparative study on the loss factor‐based locational marginal pricing methods. Considering that a systematic comparison of various locational marginal pricing methods has not been reported in existing publications, this work presents a comparative study of the loss factor‐based locational marginal pricing methods that are widely adopted in electricity markets. Advantages and disadvantages of each locational marginal pricing method are explored in detail, and could serve as references in selecting appropriate locational marginal pricing methods in practice. The selected five locational marginal pricing models are tested in two standard power systems, that is, the IEEE 5‐bus and 39‐bus systems. Then, through numerical experiments and detailed analysis, key findings about the reference point dependency of loss factors, accuracy of loss estimation, load payment, generation income, and market settlement surplus are summarised and elaborated. It is found that marginal loss factors‐based locational marginal pricing methods tend to produce a higher market settlement surplus and can lead to a lower generation income than other locational marginal pricing methods. [ABSTRACT FROM AUTHOR]

The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process. [ABSTRACT FROM AUTHOR]

The growth in residential air-conditioning is a primary contributor to electric utility critical peak load causing millions of dollars spent on extra network infrastructure to cater for these peak times. This paper aims to provide an attempt to coordinate multiple groups of aggregated air-conditioners for distribution network loading management. Through limited communication to exchange information among neighboring aggregators, the proposed dispatch strategy shares the required active power curtailment among aggregators, maintaining room temperatures to keep occupants comfort in the meanwhile. Three case studies and sensitivity analysis are conducted to show the performance of the proposed scheme. The results show that it can provide technical and economic benefits to both participating residents and network operators. [ABSTRACT FROM AUTHOR]

In this letter, a distributed economic model predictive control (EMPC) strategy is proposed for economic load dispatch and load frequency control of interconnected power systems. A general economic cost function is used in the EMPC to calculate the control sequence directly optimizes the economic performance of the system without using a hierarchical control structure, thus achieving both the real-time optimization and transient optimization. A cooperation-based distributed EMPC algorithm is proposed and the asymptotic stability is proven. Simulation results reveal the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Advances in metering and two‐way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

This research presents a novel approach for optimal active and reactive power control using permanent magnet synchronous generator with fully rated converter (PMSG‐FRC). The power output variations and wind energy intermittent performance create significant challenges for power system integration, operation, and control. A fast and reliable sensorless optimal wind speed (WS) control system‐based extreme learning machines and complete ensemble empirical mode decomposition with adaptive noise is developed. Also, an approximate entropy‐based complexity measure is used for online WS estimation. The fuzzy controller system is used for calculating the optimal wind turbine (WT) speed, power, and torque, and then adjusts WT by the optimal speed value. Moreover, the wind farm (WF) active and reactive power controller is utilised for obtaining the active and reactive power reference values for every region/feeder. The system is applied and integrated to power system grid by using IEEE standard models, and the speed of WT is adjusted by the required active power level and the estimated WS value to increase the maximum power capture from WF. The simulations results using different cases studies and power system voltage levels have confirmed the validity and accuracy of the proposed control algorithms for practical applications and demonstrated excellent performance for power system integration. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks. It aims to utilize multiple devices to counteract uncertain voltage fluctuation and deviation. In the hourly timescale (first stage), capacitor banks and transformer tap changers are scheduled before stochastic renewable output and load variations are realized. In the 15-min timescale (second stage), inverters that interface the renewable energy resources provide var support to supplement the first-stage decision after uncertainty is observed. The coordination is model as a two-stage stochastic programming problem with scenario reduction. It is then converted to a deterministic mixed-integer quadratic programming equivalence model and solved by commercial solvers combined. Compared with existing methods, the proposed volt/var control can achieve lower expected energy loss and can sustain a secure voltage level under random load demand and renewable power injection. The proposed method is verified on the IEEE 33-bus distribution network and compared with existing practices. [ABSTRACT FROM PUBLISHER]

The integration of large‐scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security. The conventional TEP models have not fully addressed the cost and the feasibility of corrective control (CC) actions such as generation rescheduling and load curtailment under contingencies. Moreover, the optimality of CC depends on the pre‐contingency state, the post‐contingency state, as well as the existence and viability of the involved CC actions. In this study, first the authors have given the explicit definition of CC risk index (CCRI), which evaluates the expected system performance under a set of contingencies (i.e. risk of incurring security issues). With the authors' improvement, the CCRI is now mathematically tractable and may have wide applications to TEP problems. Afterwards, the authors have proposed a multi‐objective TEP framework with tradeoffs between cost and risk. A relatively new yet superior multi‐objective evolutionary algorithm called the multi‐objective evolutionary algorithm (MOEA)/D is introduced and employed to find Pareto optimal solutions. The proposed model is numerically verified on the modified IEEE RTS 24‐bus and 118‐bus systems. According to the simulation results, the proposed model can provide information regarding variants of risks and coordinate the optimum planning and DR solutions. [ABSTRACT FROM AUTHOR]

Due to low electricity rates at nighttime, home charging for electric vehicles (EVs) is conventionally favored. However, the recent tendency in support of daytime workplace charging that absorbs energy produced by solar photovoltaic (PV) panels appears to be the most promising solution to facilitating higher PV and EV penetration in the power grid. This paper studies optimal sizing of workplace charging stations considering probabilistic reactive power support for plug-in hybrid electric vehicles (PHEVs), which are powered by PV units in medium voltage (MV) commercial networks. In this study, analytical expressions are first presented to estimate the size of charging stations integrated with PV units with an objective of minimizing energy losses. These stations are capable of providing reactive power support to the main grid in addition to charging PHEVs while considering the probability of PV generation. The study is further extended to investigate the impact of time-varying voltage-dependent charging load models on PV penetration. The simulation results obtained on an 18-bus test distribution system show that various charging load models can produce dissimilar levels of PHEV and PV penetration. Particularly, the maximum energy loss and peak load reductions are achieved at 70.17% and 42.95% respectively for the mixed charging load model, where the system accommodates respective PHEV and PV penetration levels of 9.51% and 50%. The results of probabilistic voltage distributions are also thoroughly reported in the paper. [ABSTRACT FROM AUTHOR]

As a result of the power industry restructuring and the increasing integration of large-scale renewable energy, power transmission expansion planning (TEP) has become a complicated decision-making process requiring risk analysis. This paper proposes a chance constrained approach to TEP. The probabilistic load curtailment degree is quantified by a capped load curtailment probability, which is incorporated into our multi-stage TEP model. Moreover, the system dynamic performance including security and stability is also realistically considered in our model through an iterative procedure. To enhance the computational efficiency, probabilistic optimal power flow (POPF) is adopted in conjunction with an evolutionary algorithm (EA). In case studies, different TEP approaches are compared, and effects of different parameter settings are also investigated. According to the simulation results, our model can not only provide information regarding risks, but also help network planners make trade-offs to select the most flexible and cost-effective planning schemes. [ABSTRACT FROM PUBLISHER]

Overhead cables are widely applied in power distribution and transmission networks due to their financial and geographical merits. But since the environment issue has become a general concern, negative impacts of overhead cables are no longer negligible. Although overhead transmission lines are gradually being replaced by underground cables for the purpose of improving the security and stability of power transmission system, a large number of overhead cables are still in use. If buildings are built below the high voltage cables, people who stay inside will be adversely affected by power frequency magnetic fields. In order to solve this issue, our paper studies the distribution of magnetic field produced by a 500 kV cable, and proposes a shielding method to reduce the indoor field intensity. In this research, field distribution is analyzed through simulation, and the maximum indoor field intensity is calculated and compared with safety limits in the guide rule of limitation. Shielding method proposed in this paper provides good protection performance and halves the indoor intensity of magnetic field produced by the 500 kV overhead cable. [ABSTRACT FROM AUTHOR]

Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or dynamic (short‐term) voltage stability criterion, this study proposes a multi‐objective programming (MOP) model to simultaneously minimise (i) investment cost, (ii) unacceptable transient voltage performance, and (iii) proximity to steady‐state voltage collapse. The model aims to find Pareto optimal solutions for flexible and multi‐objective decision‐making. To account for multiple contingencies and their probabilities, corresponding risk‐based metrics are proposed based on respective voltage stability measures. Given the two different voltage stability criteria, a strategy based on Pareto frontier is designed to identify critical contingencies and candidate buses for STATCOM connection. Finally, to solve the MOP model, an improved decomposition‐based multi‐objective evolutionary algorithm is developed. The proposed model and algorithm are demonstrated on the New England 39‐bus test system, and compared with state‐of‐the‐art solution algorithms. [ABSTRACT FROM AUTHOR]

Natural gas is an important fuel source in the power industry. Electricity and natural gas are both energy that can be directly consumed. To improve the overall efficiency of the energy infrastructure, it is imperative that the expansion of gas power plants, electricity transmission lines and gas pipelines can be co-planned. The co-planning process is modeled as a mixed integer nonlinear programming problem to handle conflicting objectives simultaneously. We propose a novel model to identify the optimal co-expansion plan in terms of social welfare. To evaluate the robustness of plans under different scenarios, the flexibility criterion is used to identify each plan’s adaptation cost to uncertainties, such as demand growth, fuel cost and financial constraints, etc. We developed a systematic and comprehensive planning model to understand, develop and optimize energy grids in order to reach higher social welfare, and is therefore of great importance in terms of supporting and guiding investment decisions for the power and gas industry. Meanwhile, we use the sequential importance sampling (SIS) to perform scenario reduction for achieving a higher computational efficiency. A comprehensive case study on the integrated IEEE 14-bus and a test gas system is conducted to validate our approach. [ABSTRACT FROM PUBLISHER]

Traditional security-constrained unit commitment (SCUC) considers only static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium following a large disturbance, such as transient stability. This paper proposes a tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach. The problem is modeled without explicit differential-algebraic equations, reducing the problem size to one very similar to a conventional SCUC. The whole problem is decomposed into a master problem for UC and a range of subproblems for steady-state security evaluation and transient stability assessment (TSA). Additional constraints including Benders cut and so-named stabilization cut are generated for eliminating the security/stability violations. The extended equal-area criterion (EEAC) is used for fast TSA and analytically deriving the stabilization cut, wherein multiple contingencies having common instability mode can be simultaneously stabilized by one cut. The proposed approach is demonstrated on the New England 10-machine system and the IEEE 50-machine system, reporting very high computational efficiency and high-quality solutions. [ABSTRACT FROM PUBLISHER]

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework. [ABSTRACT FROM PUBLISHER]

A new approach for power system transient stability preventive control is proposed by performing trajectory sensitivity analysis on the one‐machine‐infinite‐bus (OMIB) equivalence of multi‐machine systems. Exact instability time/angle are determined from the equivalent OMIB power–angle curve; the trajectory sensitivity is calculated at the instability time and the transient stability of the multi‐machine system is controlled by constraining the OMIB's angle excursion at the instability time to that of the critical OMIB which corresponds to the marginally stable condition of the system. The required preventive control action (generation rescheduling) can be efficiently solved via a linear programming model with the OMIB trajectory sensitivities‐based constraints. Simulation results on the New England 10‐machine 39‐bus system and a 285‐machine and 1648‐bus system validate its effectiveness and superiority over previous trajectory sensitivity applications to this problem. [ABSTRACT FROM AUTHOR]

Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for optimal placement of dynamic VAR support against short-term voltage instability. The problem is formulated as a multi-objective optimization model minimizing two conflicting objectives: 1) the total investment cost and 2) the expected unacceptable short-term voltage performance subject to a set of probable contingencies. STATCOM is employed for its stronger dynamic VAR support capability. Indices for quantifying the short-term voltage stability and the related risk level are proposed for problem modeling. Candidate buses are selected based on the concept of trajectory sensitivity. Load dynamics are fully considered using a composite load model containing induction motor and other typical components. A relatively new and superior multi-objective evolutionary algorithm called MOEA/D is introduced and employed to find the Pareto optimal solutions of the model. The proposed method is verified on the New England 39-bus system using industry-grade models and simulation tool. [ABSTRACT FROM AUTHOR]

Corrective security‐constrained optimal power flow (CSCOPF) considers the use of corrective control to remove system security violations in the post‐contingency state. Its optimality not only depends on the pre‐contingency state, but also the post‐contingency state as well as the involved corrective control actions. This study first gives a comprehensive review on the relevant OPF models and then proposes a hybrid method to solve the CSCOPF problem. It makes use of the evolutionary algorithms to randomly search the maximum feasible region and state‐of‐the‐art OPF solution technique (interior‐point method) to provide deterministic solutions in the found region. The two interact iteratively to progressively approach the final solution. The proposed method is verified on the IEEE 14‐bus and 118‐bus systems. Comparison studies show that (i) CSCOPF can better balance the security and economy and (ii) the hybrid method is overall superior (in solution quality, robustness and convergence characteristic) over the single evolutionary algorithm. Parallel processing is applied to speed‐up the computations. [ABSTRACT FROM AUTHOR]

Preventive control (PC) and corrective control (CC) are complementary actions in protecting large power systems against the risk of blackout. This paper addresses the optimal coordination between PC and CC via a preventive–corrective security-constrained optimal power flow model. The objective is to minimize the total expected-security-control cost which is the sum of the costs of PC and CC considering the probability of the contingencies. The constraints include system operating limits in pre- and post-contingency states, the existence of the post-contingency short-term equilibrium, and the coupling constraints on the CC actions. To solve the model, a hybrid computational strategy combining evolutionary algorithm and interior-point method is developed. The solution process consists of globally searching the critical feasible region and locally optimizing the operating variables in the found region, the two procedures interact iteratively to progressively tighten the solution region leading to the final solution. The proposed model and computational strategy are demonstrated on the IEEE 14-bus and 118-bus test systems. To speed up the computation, parallel processing of the approach is implemented. [ABSTRACT FROM PUBLISHER]

Event‐driven load shedding is an effective countermeasure against voltage collapse in power systems. Conventionally, its optimisation relies on sensitivity‐based linear methods, which, however, could suffer from unrealistic assumptions and sub‐optimality. In this study, an alternative approach based on parallel‐differential evolution (P‐DE) is proposed for efficiently and globally optimising the event‐driven load shedding against voltage collapse. Working in a parallel structure, the approach consists of candidate buses selection, voltage stability assessment (VSA) and DE optimisation. Compared with conventional methods, it fully considers the non‐linearity of the problem and is able to effectively escape from local optima and not limited to system modelling and unrealistic assumptions. Besides, any type of objective functions and VSA techniques can be used. The proposed approach has been tested on the IEEE 118‐bus test system considering two cases for preventive control and corrective control, respectively, and compared with the two existing methods. Simulation results have verified its effectiveness and superiority over the compared methods. [ABSTRACT FROM AUTHOR]

Maintaining transient stability is a basic requirement for secure power system operations. Preventive control deals with modifying the system operating point to withstand probable contingencies. In this article, a decision tree (DT)-based on-line preventive control strategy is proposed for transient instability prevention of power systems. Given a stability database, a distance-based feature estimation algorithm is first applied to identify the critical generators, which are then used as features to develop a DT. By interpreting the splitting rules of DT, preventive control is realised by formulating the rules in a standard optimal power flow model and solving it. The proposed method is transparent in control mechanism, on-line computation compatible and convenient to deal with multi-contingency. The effectiveness and efficiency of the method has been verified on New England 10-machine 39-bus test system. [ABSTRACT FROM AUTHOR]

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]

Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. In this paper, a framework for optimal design of battery charging/swap stations in distribution systems based on life cycle cost (LCC) is presented. The battery charging/swap station models are developed to compare the impacts of rapid-charging stations and battery swap stations. Meanwhile, in order to meet the requirements of increased power provided during the charging period, the distribution network should be reinforced. In order to control this reinforcement cost, stations should be placed at appropriate places and be scaled correctly. For optimal cost-benefit analysis and safety operation, the LCC criterion is used to assess the project and a modified differential evolution algorithm is adopted to solve the problem. The proposed method has been verified on the modified IEEE 15-bus and 43-bus radial distribution systems. The results show that battery swap station is more suitable for public transportation in distribution systems. [ABSTRACT FROM PUBLISHER]

Deploying shunt capacitor banks in distribution systems can effectively reduce the power loss and provide additional benefits for system operation. In practice, the power loss on distribution transformers can account for a considerable portion of the overall loss. This paper proposes a method for optimal placement of capacitor banks to the distribution transformers to reduce power loss. The capacitor bank locations are considered at the low-side of transformers. The net present value (NPV) criterion is adopted to evaluate the cost benefit of the capacitor installation project. First, an explicit formula for directly calculating the power loss of radial distribution systems is derived. Then, the optimal capacitor bank placement is formulated as a mixed-integer programming (MIP) model maximizing the NPV of the project subject to certain constraints. The model is suitable for being solved by commercial MIP packages, and the operational control of the capacitor banks to maximize the power loss reduction can be simply achieved by local automatic switching according to VAR measurements. The proposed method has been practically applied in the Macau distribution system, and the simulation results show that the proposed method is computationally efficient, and a considerable positive NPV can be obtained from the optimal capacitor bank placement. [ABSTRACT FROM PUBLISHER]

Artificial Neural Network (ANN) has been recognized as a powerful method for short‐term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient‐based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high‐quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre‐defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on‐line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state‐of‐the‐art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis. [ABSTRACT FROM PUBLISHER]

A hybrid method combining classical deterministic programming technique and evolutionary algorithm (EA)-enhanced stochastic optimization strategy is proposed for more effective and flexible computation of transient stability-constrained optimal power flow (TSC-OPF). The method consists of searching the maximum TSC-feasible solution region with EA and deterministically optimizing an operating point in the found region via a conventional OPF solution. Stability constraints are exactly treated by a rigorous transient stability assessment (TSA) procedure, where complex system model and multi-contingency can be readily considered and stability margin is applied as the transient stability index (TSI) to avoid over-stabilizing. Benefiting from both the EA and the deterministic programming, the proposed method can continuously approach the global optima in a robust, reliable and fast-convergent way. The method is verified on the New England test system and a dynamic equivalent system of a real-world large power grid. Comparing with existing methods, it has provided more economic solutions and better adaptability to multi-swing instability and multi-contingency stabilizing. It can also be a unified approach for solving other stability constrained-OPFs. [ABSTRACT FROM PUBLISHER]

There has been continuous development of techniques for assessing the transient stability of power systems in the uncertain environment. In this paper, a novel framework for stochastic transient stability assessment is proposed. The basic theory of stochastic calculus is first introduced to form the mathematical basis of the proposed approach. A stochastic power system model based on stochastic differential equations (SDEs) is proposed to take into account the uncertain factors such as load levels and system faults. We then present a detailed discussion on the numerical methods for solving SDEs. The stochastic Euler and Milstein schemes are introduced. The concept of strong convergence is also introduced to evaluate their accuracy. The proposed approach is tested with comprehensive case studies to validate its effectiveness. [ABSTRACT FROM PUBLISHER]

False data injection attack (FDIA) is a major threat in wide-area monitoring systems. Being able to differentiate FDIA from normal grid contingencies is a paramount necessity for a grid operator to decide the correct response on a critical prompt basis as well as reduce the overall FDIA's false alarms. Two FDIA's characterization algorithms are developed in this paper. The first is based on the principal component analysis (PCA) while the second is based on the canonical correlation analysis (CCA). Both algorithms are developed in an online platform to reduce the computational complexity. The various designed test cases demonstrate a promising FDIA characterization performance utilizing both algorithms. The testing results of three machine learning-based classifiers indicate that the proposed FDIA's characterization algorithms provide better classification models than conventional PCA-based characterization algorithm with CCA illustrating advanced characterization and detection results. [ABSTRACT FROM AUTHOR]

Recently, game theory has been used to design optimized strategies for defending an electric power system against deliberate attacks. In this paper, we extend the current static model to a more generalized framework which includes several interaction models between defenders and attackers. A new criterion of reliable strategies for defending power systems has been derived. In addition, two allocation algorithms have been developed to seek reliable strategies for two types of defense tasks. The new criterion and algorithms are complementary to current security criteria and can provide useful information for decision-makers to protect their power systems against possible targeted attacks. Numerical simulation examples using the proposed methods are given as well. [ABSTRACT FROM PUBLISHER]

Abstract: Power grids have been studied as a typical example of real-world complex networks. Different from previous methods, this paper proposes a hybrid approach for structural vulnerability analysis of power transmission networks, in which a DC power flow model with hidden failures is embedded into the traditional error and attack tolerance methodology to form a new scheme for power grids vulnerability assessment and modeling. The new approach embodies some important characteristics of power transmission networks. Furthermore, the simulation on the standard IEEE 118 bus system demonstrates that a critical region might exist and when the power grid operates in the region, it is vulnerable to both random and intentional attacks. Finally, a brief theoretical analysis is presented to explain the new phenomena. [Copyright &y& Elsevier]

Abstract: Electric power networks have been studied as a typical example of real-world complex networks. Traditional models for structural vulnerability analysis appear to be all based on physical topological structure. In this paper, we depict a typical power network as a weighted graph based on electrical topology by introducing its bus admittance matrix, which embodies the important characteristics of power networks in a much more realistic structure. Furthermore, the numerical simulation for both the traditional dynamical model and the proposed electrical topological model are investigated based on the IEEE 300 bus system respectively. The comparison demonstrates that the improved model is more precise and highly efficient for the analysis of structural vulnerability of power networks. [Copyright &y& Elsevier]

Deregulation of the retail electricity market has led to the emergence of an increasing number of electricity plans with competitive rates. Electricity customers now have more flexibility in choosing an electricity provider and electricity plan based on individual consumption needs. In this paper, a feature engineering hybrid collaborative filtering-based electricity plan recommender system (FECF-EPRS) is proposed for helping the customer get the right electricity plan. This system is composed of three-segment models for missing feature estimation, feature crosses construction, and electricity plan recommendation. It only takes easy-to-obtain household appliance usage features as inputs and outputs ratings for different plans. Through the test of real electricity market data, the FECF-EPRS shows a greater improvement in terms of recommendation accuracy, which can provide more accurate recommendations to customers and more reasonable pricing references for retailers. • Developing an electricity tariff recommender system for customers with different household energy consumption patterns. • Proposing a missing feature value filling method based the link between different appliances. • Introducing a Feature Crosses method to enhance recommendation accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Power system real time security assessment is one of the fundamental modules of the electricity markets. Typically, when a contingency occurs, it is required that security assessment and enhancement module shall be ready for action within about 20min time to meet the real time requirement. The recent California black out again highlighted the importance of system security. This paper proposed an approach for power system security assessment and enhancement based on the information provided from the pre-defined system parameter space. The proposed scheme opens up an efficient way for real time security assessment and enhancement in a competitive electricity market for single contingency case. [Copyright &y& Elsevier]

Abstract: This paper proposes a transmission and wheeling pricing method based on the monetary flow tracing along power flow paths: the monetary flow–monetary path method. Active and reactive power flows are converted into monetary flows by using nodal prices. The method introduces a uniform measurement for transmission service usages by active and reactive powers. Because monetary flows are related to the nodal prices, the impacts of generators and loads on operation constraints and the interactive impacts between active and reactive powers can be considered. Total transmission service cost is separated into more practical line-related costs and system-wide cost, and can be flexibly distributed between generators and loads. The method is able to reconcile transmission service cost fairly and to optimize transmission system operation and development. The case study on the IEEE 30 bus test system shows that the proposed pricing method is effective in creating economic signals towards the efficient use and operation of the transmission system. [Copyright &y& Elsevier]

Abstract: Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes, which may be tens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far, most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply–demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour, electricity demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. [Copyright &y& Elsevier]

The global resurgence of hydrogen as a clean energy source, particularly green hydrogen derived from renewable energy, is pivotal for achieving a carbon-neutral future. However, scalability poses a significant challenge. This research proposes innovative business models leveraging the low-emission property of green hydrogen to reduce its financial costs, thereby fostering its widespread adoption. Key components of the business workflow are elaborated, mathematical formulations of market parameters are derived, and case studies are presented to demonstrate the feasibility and efficiency of these models. Results demonstrate that the substantial costs associated with the current hydrogen industry can be effectively subsidized via the implementation of proposed business models. When the carbon emission price falls within the range of approximately 86–105 USD/ton, free access to hydrogen becomes a viable option for end-users. This highlights the significance and promising potential of the proposed business models within the green hydrogen credit framework. • Business models for trading green hydrogen credits are designed the first time. • It provides innovative financial solutions to decrease the cost of green hydrogen. • Incentives of utilizing green hydrogen are created using proposed business models. • The adoption of proposed trading framework can stimulate global hydrogen economy. • This work accelerates the progress towards a carbon-neutral future. [ABSTRACT FROM AUTHOR]

Presents a general method for the computation of different power system small signal stability conditions. Conditions included in signal stability; Location of characteristic points of system parameters; Dependence of point obtained on initial variables and numerical methods.

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method. [ABSTRACT FROM AUTHOR]

Residential load forecasting has been playing an increasingly important role in modern smart grids. Due to the variability of residents’ activities, individual residential loads are usually too volatile to forecast accurately. A long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address such volatile problem. It is shown that the forecasting accuracy can be notably improved by including appliance measurements in the training data. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset. [ABSTRACT FROM PUBLISHER]

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be adequately addressed. To mitigate unbalances in LVDN, phase-switching devices (PSDs) and static var compensator (SVC) are two equipment that is cost-effective and efficient. However, most existing research on operating PSDs is based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem after developing an SVC model for dispatch purpose. Compared with existing work, the proposed method aims at minimizing current unbalance based on their phasor values and takes the network's operational requirements into account. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on two modified IEEE systems and a practical Australian LVDN demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN. [ABSTRACT FROM AUTHOR]

High‐level wind power penetration has significantly changed the power system's static and dynamic characteristics, which tends to decrease the overall system transient instability. This study proposes a multi‐objective preventive‐emergency coordinated control method against transient instability in the presence of uncertain wind power generation. This method optimally coordinates preventive control (PC) and emergency control (EC) to simultaneously minimise the total control cost and the expected stability margin. PC includes synchronous generator dispatch and wind farm power curtailment to reduce the overall percentage of renewable power to a safe level. EC adopts an emergency demand response scheme, in which the demand is immediately reduced for stabilising post‐contingency system and avoiding wide‐spread blackout. Wind power uncertainty is modelled through Taguchi's testing scenarios. The proposed method was verified on both New England 39‐bus system and Nordic‐32 system using industry‐grade dynamic models and simulation software. Simulation results verified that the proposed method can optimally balancing system control cost and system expected stability margin. [ABSTRACT FROM AUTHOR]

Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets. [ABSTRACT FROM AUTHOR]

In this letter, a phase reformulation-based sliding framework is proposed for microgrid to adequately decouple control interacts cross the quadrature axes. This is implemented by formulating another virtual coordinate that rotates with the synchronism frequency, while the original coordinate rotates with the reference frequency. The deviation during transients between coordinates is thus to be eventually mitigated by the intersected controller. The case study in PSCAD/EMTDC shows that, with the sliding framework, frequency and voltage fluctuations can be dramatically reduced. [ABSTRACT FROM AUTHOR]

This paper details a nonlinear Sliding Mode Control (SMC) based Sub-Synchronous Resonance (SSR) mitigation method for Doubly Fed Induction Generator (DFIG) based wind turbines interconnected to series compensated transmission systems. The proposed method is used to control the rotor side converter to mitigate SSR while ensuring decoupled torque and reactive power control ability of the DFIG is maintained. The proposed method is implemented on the detailed DFIG wind turbine model provided in the RTDS platform for validation via simulations. Results obtained indicate the controller is successful at damping SSR when tested under varying compensation levels and wind speeds. [ABSTRACT FROM AUTHOR]