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The Global Consortium for the Classification of Fungi and fungus-like taxa is an international initiative of more than 550 mycologists to develop an electronic structure for the classification of these organisms. The members of the Consortium originate from 55 countries/regions worldwide, from a wide range of disciplines, and include senior, mid-career and early-career mycologists and plant pathologists. The Consortium will publish a biannual update of the Outline of Fungi and funguslike taxa, to act as an international scheme for other scientists. Notes on all newly published taxa at or above the level of species will be prepared and published online on the Outline of Fungi website (https://www.outlineoffungi.org/), and these will be finally published in the biannual edition of the Outline of Fungi and fungus-like taxa. Comments on recent important taxonomic opinions on controversial topics will be included in the biannual outline. For example, ‘to promote a more stable taxonomy in Fusarium given the divergences over its generic delimitation’, or ‘are there too many genera in the Boletales?’ and even more importantly, ‘what should be done with the tremendously diverse ‘dark fungal taxa?’ There are undeniable differences in mycologists’ perceptions and opinions regarding species classification as well as the establishment of new species. Given the pluralistic nature of fungal taxonomy and its implications for species concepts and the nature of species, this consortium aims to provide a platform to better refine and stabilise fungal classification, taking into consideration views from different parties. In the future, a confidential voting system will be set up to gauge the opinions of all mycologists in the Consortium on important topics. The results of such surveys will be presented to the International Commission on the Taxonomy of Fungi (ICTF) and the Nomenclature Committee for Fungi (NCF) with opinions and percentages of votes for and against. Criticisms based o

The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less; rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.

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.

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.

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.

This paper proposes an operational reliability assessment approach of photovoltaic (PV) inverters considering a voltage/VAR control (VVC) function. The approach aims to quantify the reliability degradation and estimate the lifetime of PV inverters when they are utilized for the VVC function. Firstly, an inverter based VVC model considering uncertain profiles of PV power generation and loads is developed and it is solved by a stochastic optimization method. Then, a long-term reliability assessment procedure of the inverters utilized in the VVC model is proposed. To assess the reliability, a power loss model considering the VVC results is developed and a modified lifetime model based on Coffin-Manson model and manufacturing information is proposed. The proposed VVC model and inverter reliability assessment approach are verified on a 33-bus distribution network. The simulation results reveal the long-term impacts of additional utilization in the VVC function on operational reliability and lifetime of PV inverters.

This paper proposes a multi-timescale coordinated operation approach which coordinates the combined heat and power (CHP) plant and wind farm operation in a deregulated day-ahead heat market and a real-time balancing electricity market. This approach aims to optimize profits of the CHP-wind farm portfolio by considering energy sale in the day-ahead heat market and penalty in the real-time two-price balancing market. Multiple uncertainties of heat load, wind power generation, day-ahead electricity price, up- and down-regulation prices are taken into account for the optimal operation. A stochastic optimization method is applied to solve the proposed coordinated operation problem. The proposed multi-timescale coordinated operation approach is simulated on a real-life system in Copenhagen Denmark where an energy company owns both a CHP plant and a wind farm. The simulation results verify that the proposed method can achieve a profitable operation in the markets and a highly robust operation against the multiple uncertainties.

This paper proposes a novel nested restoration decision system (NRDS), which aims to minimize unused capacity of distributed generation (DG) for service restoration due to contingency during islanding stage. The proposed algorithm is based on the concept of nested microgrid formation, where the network control schemes are framed on a layered structure. The power-sharing strategy between neighboring microgrids in the network is from outer to inner microgrid loads, where the outermost microgrid exchanges power with utility grid only. The first stage refers to pre-fault scenario, which finds a solution for networked microgrid distributed generation as the initial setting. Furthermore, the second stage calculates additional redistribution requirements based on respective microgrid's deficiency using a solution index matrix. The proposed control has been evaluated on a networked microgrid system, while performing islanding.

Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as 'highly responsive users' and 'less responsive users' based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator 'SimHouse' are designed to validate the proposed system.

This paper proposes a novel adaptive droop control strategy for multi-terminal HVDC (MTHVDC) network to provide frequency support and power sharing via a distributed consensus algorithm. The droop coefficient is adaptively tuned by an effective integration of optimization algorithms and frequency consensus method. The optimization control aims to minimize generation cost, frequency deviation cost, and converter losses. Furthermore, the coordination between the consensus-based droop controller and the optimization technique is established by a feedback loop of coupling gain solution method, which is based on coupling gain matrix. The proposed control is successfully applied to a four-terminal MTHVDC system and modified New England 39-bus system on PSCAD. Power sharing is further improved by adding an adjustment factor based on frequency deviation with coupling gain matrix, which redirects power flow in ac and dc sub-grids. In addition, the loss reduction of voltage source converters is presented to demonstrate the proposed control performance.

The increasing adoption of multiway communications in the advanced metering infrastructure (AMI) of the energy Internet, which is known as the Internet-based smart grid, raises a new question about the security of customers' sensitive data and how the data can be protected from growing cyber attacks such as side-channel and false data injection attacks. The dynamic nature of remote connect/disconnect of components in the AMI also brings new types of security threats. To achieve secure multiway communications and remote connect/disconnect of components, the AMI requires a key exchange protocol (KEP) that meets a number of its security requirements such as confidentiality, integrity, availability, identification, authentication, and access control. In this context, in this article we present a KEP that uses an ideal crypto functionality and an ideal AMI key exchange functionality based on universal composability, which allows modular design and analysis of cryptographic protocols. The former functionality enables AMI components or users to perform authenticated cryptographic operations, while the later functionality enables the users to meet the AMI security requirements before generating a shared secret session key, which can be used in an ideal manner. We carry out experiments to validate the performance of our protocol, and the results show that our protocol offers better performance benefits compared to the existing related protocols and is suitable for the Energy Internet. We further demonstrate the usefulness of our ideal functionalities as a security reinforcement for a widely used KEP, namely the Elliptic Curve Diffie-Hellman.

This study deals with the development of an optimal power scheduling controller for energy management of distributed energy resources in the microgrid system. The developed optimized controller is implemented using lightning search algorithm to overcome the uncertainties of microgrid energy management and to provide an optimum power delivery to loads with minimum cost. The primary objectives of the proposed optimized controller are to: (i) develop an optimized controller for microgrids energy management, (ii) minimize the total operating cost of the distributed energy resources units, (iii) reduce the environmental emission, and (iv) solve the complicated constraint optimization problems. The proposed optimization algorithm is implemented in the modified IEEE 14-bus test system to optimize the microgrid power management schedule. The optimized controller is executed based on the real load varying conditions recorded in Perlis, Malaysia. It is observed that the optimized controller successfully reduced the amount of power consumption from 971.65 MW to 364.3 MW which in turn saving cost of RM 265432.06. The proposed scheduling optimized controller performance is compared with the recent reported work of backtracking search algorithm optimization for validation. Result shows that the lightning search algorithm based MG controller produced a cost-effective system with 62.5% of cost saving and 61.98% of carbon dioxide emission reduction which is much higher compared to with MG and backtracking search algorithm based MG optimization, respectively. The effectiveness of the proposed approach outperformed other techniques in terms of minimum total operating cost of distributed energy resources and solving complicated constraints in optimization problems.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

Carbon emission from the burning of fossil fuel has resulted in global warming. Climate change and global warming are among the most complex issues requiring immediate solutions. Microgrid (MG) based on renewable energy sources (RESs) can be used to reduce the carbon intensity of electricity and achieve the global decarbonization goal by 2050. Optimizing the size of the energy storage system (ESS) can ensure the sustainable, resilient, and economic operation of the MG. Thus, key features of the optimal ESS, including methods and algorithms of ESS sizing, power quality, reliability, connection mode, and public policy enforcement for low-carbon emission, must be identified. Existing literature mostly focuses on the cost-effective optimal sizing method based on capacity minimization, which overlooks other issues. This work reviews the features of optimal ESS sizing methods and algorithms, their characteristics, and the scenarios between ESS and decarbonization in MG applications to address their shortcomings. ESS characteristics on storage type, energy density, efficiency, advantages, and issues are analyzed. This review highlights details of ESS sizing to optimize storage capacity, reduce consumption, minimize storage cost, determine the optimal placement and mitigate carbon emission for decarbonization. The analyses on the understanding of decarbonization in relation to the use of ESS in MG scenarios are explained rigorously. Existing research gaps, issues, and challenges of ESS sizing for next-generation MG development are also highlighted. This review will strengthen the efforts of researchers and industrialists to develop an optimally sized ESS for future MGs that can contribute toward achieving the decarbonization goal.

Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are required to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple scenarios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model.

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.

Energy flow calculation (EFC) plays an important role in steady-state analysis of multi-energy systems (MESs). However, the independent management of sub-energy systems (subsystems) poses a considerable challenge to solve the high-order nonlinear energy flow model due to the limited information exchange between these subsystems. In this article, a fixed-point based distributed method is proposed for EFC in an electricity-gas-heating system. Firstly, the mathematical modeling of each subsystem with coupling units is introduced. Then, two information exchange structures among subsystems are presented as sequential and parallel structures. Based on the fixed-point theorem, novel distributed sequential and parallel methods for EFC are proposed to calculate energy flow distribution in MESs. In our proposed method, the EFC in subsystems is implemented by the individual system operators, with limited information exchange between subsystems. Therefore, the information privacy of subsystems can be preserved in this solution process. Moreover, the convergence of the proposed method is guaranteed, and the sufficient conditions for the convergence are presented. Lastly, simulations on a MES demonstrate the effectiveness of the proposed method and the quantified superiority over the existing methods in computation time, accuracy and reliability.

© 2020 IEEE. Partial discharge (PD) may be one of the most common defects widely existing in power systems. If such conditions are left unattended, they can eventually develop into breakdowns., causing significant interruption of services, huge financial lost and serious safety problems. Owing to the large number of electrical components existing across electric equipment on every level within power system, timely detection and proper diagnosis of PD defects are usually quite challenging. The development of artificial intelligence and data analytics has provided new opportunities for early detection and diagnosis of such defects, which can lead to maintenance scheduling optimization and improved system reliability. This paper proposes to build an automated non-invasive online PD diagnosis system that is designed to provide effective classifications of different PD conditions based on deep learning technique. The system incorporates with the use of non-invasive PD monitoring via ultrasonic sensing., deep learning models and advanced feature extraction techniques. Through a comprehensive laboratory case study, it is shown that our proposal is significantly better than traditional PD diagnosis with expert knowledge. More specifically, the proposed deep neural network models with advance feature extraction can provide the best overall PD diagnosis performance, while the proposed convolutional neural network structure can also give comparable supreme results without complex feature extraction.

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

Microgrid transition between standalone and grid-connected modes is a promising alternative to provide the grid with increasing flexibility and availability. However, transition smoothness relies heavily on control topologies and corresponding parameters, which thus remains challengeable. Existing microgrid transition strategies have two major deficiencies: 1) Inverter control mode alters subjected to microgrid operating mode, for instance, the inverter in current control will switch to voltage control when microgrid disconnects to the utility grid; 2) Controller parameters are selected based on practice and experience, where a systematic and efficient approach does not exist. Motivated by these limitations, in this paper, an autonomous control strategy is proposed for microgrid smooth state transitions. It is highlighted in the following aspects: 1) The cascaded control strategy enables smooth state transition within a single control structure, which permits controller independent of mode switching; 2) Nonlinear-Simplex based algorithm is interfaced with electromagnetic transient simulations, searching for optimal controller parameters in order to minimize voltage deviation in a chain of microgrid events. The effectiveness of the control framework is validated with simulations in PSCAD/EMTDC and RTDS.

Double-sided ring topology has been widely applied in collector system planning for large-scale offshore wind farms (OWF), owing to its high reliability against cable faults. For collector systems with multiple substations, the conventional topology designs isolate the operation of substations, which requires high transformer capacity within each substation to satisfy the redundancy need. This paper first investigates the economic and reliability benefits of multi-substation incorporation (CSI), and then proposes a new double-sided ring topology with CSI for large-scale OWF collector systems. It encourages the transformer capacity sharing among multiple substations to improve the economic efficiency of collector system and in the meantime preserve the reliability advantage of ring topology. To achieve the optimal topology design, Fuzzy C-means (FCM) clustering method is adopted to locate substations and allocate turbines, and the Two-Phase Clark and Wright's saving algorithm is modified and applied to optimize the cable connection for the proposed topology so as to minimize the overall collector system cost. The proposed topology is tested and compared with traditional isolated topologies on two benchmark collector systems. The test results verify that the proposed the highest economic efficiency while maintaining the collector system reliability.

This paper proposes an effect orientated approach to enhance the short-term voltage stability for wind-penetrated power systems. The low system stability with high uncertain renewable energy is addressed by coordination of network and non-network solutions, i.e., STATCOM placement and UVLS respectively in this paper. The proposed model considers the uncertainties of power systems, including operating points load dynamics and multiple contingencies. Unlike other methods that try to minimize the cost, the proposed method aims to maximize the voltage stability enhancement with a fixed cost. The high computing burden is alleviated by a net flow-based scenario reduction algorithm. Compared with the conventional approaches, this paper has three major contributions: 1) Device placement is coordinated with demand response. 2) Operating points and type 4 wind turbines are considered. 3) An effect maximizing DP algorithm with a robust scenario reduction method is applied. The effectiveness and efficiency of the proposed method is demonstrated on the New England 39-bus test system.

In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted voltage deviation index (LVDI) to quantify network voltage deviation. Secondly, this paper proposes a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously. Then, a multi-objective adaptive robust optimization method is proposed to obtain robust Pareto solutions under uncertainties. Accordingly, solution algorithms based on different multi-objective programming algorithms and a column-and-constraint generation algorithm are developed and systematically compared. The proposed method is verified through comprehensive tests on the IEEE 123-bus system and simulation results demonstrate high effectiveness of the LVDI, high efficiency of the solution algorithms and full operating robustness of the proposed VVC method against any uncertainty realization.

In order to achieve sustainable development goals, modern power systems need to achieve a low-carbon transition. Retirement and replacement of aging coal-fired power plants (CFPP) is the major part of electricity network transition. However, the sudden retirement of large aging CFPP could lead to power supply or reserve shortages and generally, it takes several years to complete the replacement process. This paper proposes a low-carbon electricity network transition model, helping to plan aging CFPP retirement and renewable power plants installation as well as network augmentation. The trade-off decisions are found among three conflicting objectives including cost, risk and carbon emission. Moreover, the carbon emission flow (CEF) model is applied in the planning process to calculate carbon emissions from the demand side, in order to assess the efficacy of the low-carbon transition. A relatively new multi-objective natural aggregation algorithm (MONAA) is introduced and applied to find the optimal solution. The proposed model is verified on a modified IEEE 24-bus RTS system and a modified IEEE 118-bus system. According to the numerical results, the proposed model can achieve carbon mitigation, cost and risk benefits and provide a roadmap to guide the energy network transformation towards a low-carbon economy.

To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an 'event-based' model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system.

Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multienergy microgrids (iMEMGs). In addition, combined cooling, heat, and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories, and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization.

Microgrids can effectively integrate distributed generation (DG) to supply power to local loads. However, uncertainties from renewable DG and loads may lead to increased operating costs or operating constraint violations. To solve these issues, this paper proposes a two-stage coordination approach of price-based demand response (PBDR) and battery energy storage systems (BESSs) to minimize the total operating cost and enhance operational reliability. In the first stage, day-Ahead PBDR is scheduled, aiming to shift loads to improve renewable energy utilization efficiency. Considering limited prediction horizon of uncertainties when dispatching BESSs, hourly state of charge (SoC) limits are also optimized over the whole-day horizon with consideration of BESS degradation cost in the day-Ahead stage. Then in the second stage, the BESSs are dispatched hourly within the optimized SoC limits to track uncertainty realization and compensate the first-stage PBDR decisions. Furthermore, a two-stage interval optimization (TSIO) method is proposed to formulate the problem. Accordingly, a solution algorithm is developed to coordinately solve the two operation stages under the uncertainties. The proposed coordination approach is verified with uncertainty realization scenarios. The results indicate the high energy utilization efficiency and strong operational reliability of the proposed coordination approach.

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.

With a large number of inverter-interfaced renewable power generation, fault-induced delayed voltage recovery (FIDVR) events have become a serious threat to power system stability assessment. This study proposes a novel data-driven method based on probabilistic prediction, ensemble learning, and multi-objective optimisation programming (MOP) to rapidly predict the FIDVR severity index for real-time FIDVR assessment. Distinguished from the existing single machine learning (ML) algorithm data-driven method, the proposed method combines different randomised learning algorithms to acquire a more diversified ML outcome. The probabilistic prediction models the uncertainties existing in the prediction process, which quantifies the prediction confidence over a progressive observation window. Besides, the FIDVR can be evaluated through the time-adaptive framework to achieve the best FIDVR speed and accuracy with the MOP framework. The simulation results on the New England 10-machine 39-bus system display its preponderance over the single ML, and also demonstrate its better speed and accuracy performance in FIDVR assessment.

A key feature of electricity generation in a distribution network is manifested by renewable generation with zero marginal cost. Existing market mechanisms are likely to fail in supporting such decentralized transactions while providing a reasonable price signal to compensate for the investment cost of renewable generators. Given this background, this paper first describes an average pricing market (APM) mechanism for pricing zero marginal cost renewable generation outputs in the distribution network. Then, a decentralized formulation of the APM mechanism is derived using the alternating direction method of multipliers (ADMM). Convergence of the decentralized mechanism can be guaranteed under some mild conditions for parameter setting. Finally, case studies are carried out to demonstrate the presented market mechanism. Simulation results show that the problem of always bidding a zero price by renewable generators in some existing markets can be avoided. The presented method also provides a solution for organizing decentralized electricity transactions in the distribution network and can converge to similar results with those obtained by the centralized one, with a relative error less than 5%.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.

Owning to electricity market deregulation, residential customers now enjoy the freedom to choose their preferred electricity retailers. This paper investigates the application of recommender system, a fast-developing technique in machine learning, into the task of recommending electricity plans for the individual residential customer. Based on a collaborative filtering strategy, an electricity plan recommender system (EPRS) is developed. By providing easily obtainable data of some household appliances, residential customers of the EPRS are recommended with predicted ratings of different plans, which can provide effective guidance to customers in the selection of suitable plans and proper tariffs. Different numerical tests are carried out to evaluate the performance of the EPRS. The EPRS outperforms other strategies in the accuracy of recommendation result and is verified to be a promising solution to electricity plan recommendation task.

This paper proposed a mathematical framework for establishing a fully decentralized distribution market by using the alternating direction method of multipliers (ADMM) method, where no cooperation between market participants is needed. Notably, the proposed decentralized formulation of distribution market is derived based on the centralized nodal pricing model that is widely adopted in existing electricity markets. Therefore, under the proposed market, merits of nodal pricing can still be retained and contribute to the enhancement of the operating efficiency of a distribution network. Under some mild conditions on the choice of parameters, convergence of the decentralized algorithm can be guaranteed. Finally, case studies are carried out to demonstrate the feasibility and efficiency of the developed models and algorithms.

© 2019 IEEE. This paper proposes a method to support the voltage of remote (critical) buses in islanded microgrids based on the consensus control of distributed generations (DG) output reactive power. In addition to primary droop control which ensures equal load sharing among DGs, secondary distributed control is employed to mitigate voltage drop/rise of remote buses. Consensus is reached when all DGs agree on their reactive power contribution, proportional to their capacity. The amount of the required reactive power is obtained from Backward Forward Sweep (BFS) power flow analysis, which has been modified to calculate the power flow of unbalanced islanded microgrids more accurately. The proposed algorithm's performance is evaluated on several case studies. It proves to be accurate and effective in regulating voltage level of remote (critical) buses in islanded microgrids.

Voltage/VAR regulation (VVR) implemented by capacitor banks (CBs), on-load tap changers (OLTCs), and photovoltaic (PV)-associated inverters is effective to enhance voltage stability and reduce power loss. On the other hand, conservation voltage reduction (CVR) has been widely adopted in distribution networks to reduce load demand. Existing works mainly focus on each of them. This paper proposes a VVR method with CVR, forming a multi-objective optimisation problem which minimises (i) voltage collapse proximity indicator (VCPI), (ii) load demand, and (iii) power loss. Besides, PV power generation as uncertainty significantly impairs the VVR results. To deal with the uncertainty issue, this paper proposes a rolling-horizon framework to determine VVR and applies Taguchi's orthogonal array testing (TOAT) to model the uncertain PV power generation, achieving rolling-horizon-based multi-objective robust VVR. The proposed model is tested and demonstrated on the IEEE 33-bus and 69-bus systems, and simulation results verify that the proposed method can support robust solutions of the multi-objective VVR problem for decision-making.

This paper proposes a coordinated multilayer control strategy for the energy management (EM) of grid-connected ac microgrids. The strategy predicts the customer's load demand and the photovoltaics (PV) power generation for a day-ahead EM. It utilizes the PV power generations and the bidirectional energy transactions from electric vehicles and battery storage to provide a combined response for load support. The system also predicts any uncertainties in customers demand and power generations and implements day-ahead precautionary measures to tackle that uncertainty. Two different prediction strategies are used, autoregressive moving average and artificial neural networks, and their performances for a day-ahead EM are investigated. The reference power from the tertiary layer EM is sent to the local controllers for power regulation at the inverter level. Additionally, the varying power output reference obtained from a day-ahead EM is classified into the slow, medium, and fast variations. The performance of the local controller employed in the interfacing grid-connected three-phase inverter is tested during the above-mentioned power reference variations. The total harmonic distortion incorporating a moving window technique is calculated for the ac output current during each class of power variations over a day.

As one of the most complex and largest dynamic industrial systems, a modern power grid envisages the wide-area measurement protection and control (WAMPAC) system as the grid sensing backbone to enhance security, reliability, and resiliency. However, based on the massive wide-area measurement data, how to realize real-time short-term voltage stability (STVS) assessment is an essential yet challenging problem. This paper proposes a hierarchical and self-adaptive data-analytics method for real-time STVS assessment covering both the voltage instability and the fault-induced delayed voltage recovery phenomenon. Based on a strategically designed ensemble-based randomized learning model, the STVS assessment is achieved sequentially and self-adaptively. Besides, the assessment accuracy and the earliness are simultaneously optimized through the multiobjective programming. The proposed method has been tested on a benchmark power system, and its exceptional assessment accuracy, speed, and comprehensiveness are demonstrated by comparing with existing methods.

© 2018 Elsevier B.V. With the significant improvement in deployment of Internet of Things (IoT) into the smart grid infrastructure, the demand for cyber security is rapidly growing. The Energy Internet (EI) also known as the integrated internet-based smart grid and energy resources inherits all the security vulnerabilities of the existing smart grid. The security structure of the smart grid has become inadequate in meeting the security needs of energy domains in the 21st century. In this paper, we propose a cyber security framework capable of providing adequate security and privacy, and supporting efficient energy management in the EI. The proposed framework uses an identity-based security mechanism (I-ICAAAN), a secure communication protocol and an Intelligent Security System for Energy Management (ISSEM) to certify security and privacy in the EI. Nash Equilibrium solution of game theory is applied for the evaluation of our proposed ISSEM based on security events allocation. The formal verification and theoretical analysis show that our proposed framework provides security and privacy improvement for IoT-based EI.