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• Study the vision of engaging networked data centers into distributed energy markets. • Propose a decision-making model to optimize the bidding decisions and energy resource operations of networked data centers in multiple geo-distributed local energy markets. • Conduct comprehensive numerical simulations to validate the benefit the cloud service provider can obtain from local energy markets. Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs' cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems. [ABSTRACT FROM PUBLISHER]

Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems. [ABSTRACT FROM PUBLISHER]

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS). [ABSTRACT FROM PUBLISHER]

• New security-constrained OPF model for a microgrid considering tie-line switching. • New Benders decomposition based solution algorithm to solve the OPF problem. • Stochastic optimization method with probabilistic modeling to address uncertainty. With the rapid development of microgrid, its tie-line switching from grid-connected to islanded mode is a topic worth discussing for considering both main grid resilience and microgrid security. In this paper, a stochastic security-constrained optimal power flow (OPF) method is proposed to deal with these conditions under high uncertainties. Firstly, a linear load flow model and a backward forward sweep algorithm are applied to present microgrid power flow with reduced computing burdens. Secondly, with consideration of tie-line switching from grid-connected to islanded operation mode, a security-constrained OPF problem for a microgrid is proposed to minimize operating cost and by optimizing microturbine setpoints and load shedding coefficient. To promise stable islanded operation after disconnection from the main grid, a Benders decomposition method is developed to decouple the OPF problem into a grid-connected master problem and an islanded sub-problem and then solve them iteratively with Benders cuts to guarantee microgrid security after tie-line switching. Last, a stochastic optimization method with probabilistic modelling is adopted to address the uncertainty issue caused by renewable energy sources and loads. The proposed stochastic security-constrained OPF method has been verified with high computing efficiency and robust security via comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

• Review the state-of-the-art research works of integrated energy systems of data centers and smart grids. • Propose future integration scenarios for data centers and smart grids. • Analyze the challenges of implementing integrated energy systems of data centers and smart grids. Cloud computing platforms are critical cyber infrastructures in modern society. As the backbone of cloud systems, data centers act as large energy consumers in today's power grids. The integration of on-site renewable energy sources and energy storage systems further transforms data centers to be energy prosumers (producers-and-consumers). As a result, optimizing data centers' energy production and consumption, and exploiting their potential of actively engaging in the external grid's planning, operation, and control has been drawing increasing attention in the last few years. This paper conducts a comprehensive review of the state-of-the-art research efforts on integrated energy systems of data centers and smart grids. A taxonomy of such integration scenarios is provided. Consequently, this paper identifies several future application scenarios of integrating data centers and smart grids, which serves as a roadmap towards future research. This article is expected to provide a useful reference for researchers and engineers in the areas of energy systems and cloud computing. [ABSTRACT FROM AUTHOR]

A new criterion to detect the stator single-phase-to-ground fault for Powerformer is proposed in this paper, which is based on the direction of leakage currents, magnitude of leakage currents, and fault point energy dissipation. New fault characteristics of Powerformer are analyzed and a novel criterion is put forward. The proposed approach detects the ground fault by analyzing the direction, magnitude, and energy of leakage current, which is the difference of zero-sequence current fault component between the neutral and the terminal of Powerformer. The fundamental component and the third-harmonic component of leakage current are combined to realize 100% coverage of fault detection for the stator windings of Powerformer. Simulation results have shown that, under different fault conditions, the new scheme can distinguish internal faults from external faults reliably, and can detect in which machine a stator single-phase-to-ground fault is occurring even if the fault resistance is at 5 kΩ. [ABSTRACT FROM PUBLISHER]

This paper focuses on modeling and oscillatory stability analysis of a wind turbine with doubly fed induction generator (DFIG). A detailed mathematical model of DFIG wind turbine with vector-control loops is developed, based on which the loci of the system Jacobian's eigenvalues have been analyzed, showing that, without appropriate controller tuning a Hopf bifurcation can occur in such a system due to various factors, such as wind speed. Subsequently, eigenvalue sensitivity with respect to machine and control parameters is performed to assess their impacts on system stability. Moreover, the Hopf bifurcation boundaries of the key parameters are also given. They can be used to guide the tuning of those DFIG parameters to ensure stable operation in practice. The computer simulations are conducted to validate the developed model and to verify the theoretical analysis. [ABSTRACT FROM PUBLISHER]

This paper focuses on the super/subsynchronous operation of the doubly fed induction generator (DFIG) system. The impact of a damping controller on the different modes of operation for the DFIG-based wind generation system is investigated. The coordinated tuning of the damping controller to enhance the damping of the oscillatory modes using bacteria foraging technique is presented. The results from eigenvalue analysis are presented to elucidate the effectiveness of the tuned damping controller in the DFIG system. The robustness issue of the damping controller is also investigated. [ABSTRACT FROM AUTHOR]

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia's electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high. • An energy sharing platform is presented for renewable distributed generation. • An independent energy management strategy can be customized for each participant. • The call auction model of stock exchange is cited in the energy sharing platform. • The maximum transaction volume is applied in the call auction model. • The award of energy sharing can be self-ruled by participants. [ABSTRACT FROM AUTHOR]

• A private reliability service is proposed and can be traded in the electricity market. • A novel group double side auction method for market clearing is designed and verified. • Dynamic elasticities considering real-time preference are applied in this model. • The model provides the advised price interval for customers. • Real historical data of customers in Australia are used to verify the proposed model. Due to the rapid development of distributed renewable generation, emerging prosumers are encouraged to participate in the energy market. Meanwhile, blackout events, planned and unplanned outages due to weather, component failures and other causes, can also impact users' experience. To maintain reliability and improve users' experience under outage conditions, it is possible to transform the service reliability from a public good (compulsory and uniform) to a private good (self-selection). In this paper, a new restore mechanism based on private reliability service is proposed, the reliability service is no longer uniform. Customers with a higher reliability requirement are willing to pay to maintain their desired consumption in outage conditions. The customers are classified by dynamic elasticity considering historical data and real-time customer preferences. Then the advised unit price, trading amount and reliability level are provided to customer. Once they submit this information to market operator, the price and transaction amount is matched in each group, and floated average clearing price is applied to obtain the bidding results. The trading mechanism is proved to satisfy incentive compatibility, and the transaction process for a specific area in Australia is analyzed in a case study to verify the proposed model. [ABSTRACT FROM AUTHOR]

• A framework solution for multi-timescale coordinated operation of CHP and wind power. • Day-ahead and real-time uncertainties are handled by stochastic optimization. • The solution is applied to a real-life case in Denmark. • Comparative analysis between coordinated operation and independent operation. • Comparative analysis between stochastic and deterministic methods. 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. [ABSTRACT FROM AUTHOR]

• Integrated planning of Internet data centers and battery energy storage systems. • Coupled cyber-physical modelling of Internet data centers and a smart grid. • Multi-objective modelling of the planning task. Modern power grids have been becoming complex cyber-physical systems integrated with distributed energy sources and information and communication facilities. With prevalence of cloud computing, geo-distributed, networked data centers have become an integrated part of modern grids. The coupling impact between data centers and smart grids thus becomes an important consideration. This paper proposes an integrated planning scheme that optimally determines the locations and capacities of interconnected Internet data centers and battery energy storage systems in a smart grid. The model is formulated as a multi-objective optimization problem, in which both computational performance metrics of Internet data centers and operational criteria of the grid are coordinately considered as three inter-related but conflict objectives; the coupling impact between the cyber and energy resources are modelled. An advanced evolutionary algorithm – Multi-Objective Natural Aggregation Algorithm is used to solve the model. Extensive case studies are conducted to demonstrate the reasonability and effectiveness of the proposed integrated planning method. [ABSTRACT FROM AUTHOR]

• Proposing an energy-based transient stability constrained unit commitment model. • Considering sensitivity of generation level and inertia on stability margin. • Proposing cutset energy function methodology without loss of network structure. • Developing a state transition MILP formulation for the unit commitment model. Rapid growth of load demand and concurrently system inertia deterioration put power systems at risk of transient instability. Therefore, operation of power systems as bulk complex systems must be secured against transient instability not only in hourly optimal power flow studies, but also in daily unit scheduling. To address this challenge, in this paper, secure and economic operation of power system is ensured through a Transient Stability-Constrained Unit Commitment (TSCUC) model employing Benders Decomposition (BD) technique. The proposed TSCUC model consists of one master problem determining committed units and two distinct sub-problems verifying the steady state impacts of single outages, and transient stability criteria, respectively. This paper proposes a state transition formulation for master problem as a mixed integer linear programming optimization problem preserving both compactness and tightness of the problem. A structure-preserving transient stability assessment approach called cutset energy function method is developed to assess the transient stability of the system for each configuration of the committed units, under a set of probable contingencies. Several case studies on a dynamic test system are demonstrated to validate the efficacy of the proposed TSCUC algorithm. Finally, the proposed method performance is compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A sharing platform framework with energy and capacity sharing of hybrid energy storage system (HESS) is proposed. • Detailed technic-economic analysis has been conducted. • The characteristics of electrical and thermal demands are captured, in order to assist the energy management of HESS. • Flexibility from multi-energy systems studied is including electricity, heat and gas sector and energy storage. • Energy sharing and flexibility of end-users are also considered. There is already a large amount of energy storage system (ESS) and demand response potential in the power, heat and gas system, which can be used to promote a cost-effective transition to low-carbon and renewable energy. This paper proposes an energy sharing platform to effectively integrate power, thermal and gas systems of different sizes to balance the fluctuation of renewable energy and improve the system reliability. In a potential application in the hospitality industry, hotels can jointly share and rent ESS. This sharing platform uses a hybrid energy storage system (HESS), comprising BESS and thermal energy storage system (TESS). Unlike BESS, TESS is cost-effective and can be provided by hot water tanks as short-term energy storage. The capacity and energy sharing method of the hybrid BESS and TESS system is provided, including the detailed rental model of HESS in the operation economy. Moreover, the coupled energy supply chain is also considered, the combined utilization of thermal, gas and electricity sharing is achieved. The proposed model is evaluated on the IEEE 18-bus system with the real historical data in Australian energy market, and the cost-benefit analysis is also provided. [ABSTRACT FROM AUTHOR]

Levelized cost of electricity (LCOE) is widely used to compare the cost of different electricity generation technologies. However, with the increasing penetration of variable renewable energy (VRE), it is inappropriate to use traditional equations to calculate the LCOE for non-dispatchable VRE due to its intermittent nature. Therefore, this paper reviews LCOE for renewable generation considering aspects such as improving traditional methods for evaluating and reducing the LCOE of VRE. The existing methods for improving the accuracy of the traditional LCOE have been divided into four aspects: investment-related cost, operating-related cost, expressions for plant performance and the uncertainty and risk-related costs. The review summarizes the existing studies from academic articles and technical reports and proposes suggestions for future research studies. • Comprehensive Levelized cost of electricity (LCOE) methods review. • LCOE evaluation from cost, plant performance, uncertainty and risk. • Summary of existing methods for improving the LCOE estimation accuracy of variable renewable energy (VRE). • Compare and summarize the existing VRE LCOE methods in academic papers and technical reports. [ABSTRACT FROM AUTHOR]

Various challenges are acknowledged in practical cases with high wind power penetration. Fault ride-through (FRT) capability has become the most dominant grid integration requirements for the wind energy conversion system worldwide. The high voltage ride-through (HVRT) and low voltage ride-through (LVRT) performance play a vital role in the grid-friendly integration into the system. In this paper, a coordinated HVRT and LVRT control strategy is proposed to enhance the FRT capability of the permanent magnet synchronous generator (PMSG)-based wind turbine generators (WTG). A dual-mode chopper protection is developed to avoid DC-link overvoltage, and a deadband protection is proposed to prevent oscillations under edge voltage conditions. The proposed strategy can ride through different levels of voltage sags or swells and provide auxiliary dynamic reactive power support simultaneously. The performance of the proposed control scheme is validated through various comparison case tests in PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

• Proposing an energy-function-based approach to assess the transient stability of the economic operating point of the power system. • Developing a transient stability criterion to solve system swing equations only until the fault clearing time, that improves the computational efficiency of the transient stability constrained OPF problem significantly. • Utilizing Benders decomposition technique to handle the transient stability constraints as parallel and separate sub-problems. • Promoting the transient stability constrained Optimal Power Flow to estimates the system critical clearing time with an acceptable level of accuracy without any network reduction. Security Constrained Optimal Power Flow (SCOPF) is traditionally performed to obtain the optimal generation levels to achieve the minimum generation cost under operational constraints in normal and contingency conditions. In this paper the SCOPF problem with DC load flow equations is extended to consider the transient stability margin of the generating units as a heuristic decomposition algorithm. However, the proposed algorithm is compatible with any SCOPF formulation. Due to computational complexities of security constraints and transient stability assessment, Benders decomposition algorithm is utilized to handle the transient stability margin of generating units as separate sub-problems. A transient energy function is developed to assess the transient stability of generating units under credible faults using the cut-set energy function criterion. In case of violation of the required transient stability margin, Benders cuts are constructed and added to the master problem. The proposed decomposed Transient Stability Constrained OPF (TSCOPF) is converged in few iterations. The developed TSCOPF algorithm is implemented on the IEEE 39-bus dynamic test system to validate its accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Several electricity tariffs have emerged for Demand Side Management (DSM) and residential customers are faced with challenges to choose the plan satisfying their personal needs. Electricity Plan Recommender System (EPRS) can alleviate the problem. This paper proposes a novel EPRS model named EPRS with Electrical Instruction-based Recovery (EPRS-EI), which is a dual-stage model consisting of feature formulation stage and recommender stage. In the feature formulation stage, matrix recovery with electrical instructions is applied to recover appliance usages, and the recovered data is set as features representing customers' living patterns. In the recommender stage, Collaborative Filtering Recommender System (CFRS) based on K-Nearest Neighbors (KNN) and adjusted similarity is applied to recommend personal electricity plans to customers based on the above features. Different from other EPRS models, EPRS-EI is the first model utilizing matrix recovery methods and similarity computation with electrical instructions. With these electrical instructions, the proposed model is able to utilize more explicit features and recommend more personalized plans. We then apply EPRS-EI to predict the testing customers' preference for electricity plans. Simulation results on recovering electricity data and their applications in EPRS confirm the effectiveness of the proposed methods in comparison to state-of-the-art methods, with 93.56%–94.85% customers correctly recommended. • Proposing an electricity plan recommender system (EPRS) with matrix recovery. • Appliance classification avoids trivial changes in recovering appliance usages. • Total electricity usages are set as a recovery instruction in recovering data. • Novel matrix recovery could recover up to 38.15% more data. • 93.56%–94.85% of users can obtain effective electricity plans with novel EPRS. [ABSTRACT FROM AUTHOR]

• Consensus-based control of distributed generators to minimize frequency deviation. • Consensus-based control of interlinking converters to align AC subgrid frequencies. • Modifications on Backward/Forward Sweep-based three-phase AC-DC power flow analysis. • Accurate, scalable and generic method for networked islanded hybrid microgrids. This paper proposes the incorporation of distributed and decentralized control models of generators and converters into the power flow analysis of islanded hybrid microgrids (IHMG). The absence of a slack bus in an islanded microgrid presents challenges to its frequency and voltage regulation as well as power flow calculation. While droop mechanism as the primary control has been well incorporated into load flow analysis, distributed secondary control has only recently been developed for islanded microgrids. This paper proposes a distributed secondary control for a) generators in an AC subgrid of an IHMG and b) interlinking converters in networked IHMGs. Both the distributed and droop control are incorporated into a three-phase power flow calculation based on a modified Backward Forward Sweep method. The algorithm has been tested on several case studies, and the power flow results have been verified against a time-domain simulation. The proposed method proves to be accurate and useful for the planning and design of three-phase networked IHMGs. [ABSTRACT FROM AUTHOR]

• The planning model reflects the interactions among multiple factors in reality. • Robust chance constraints are formulated to address the uncertain charging demands. • Moment-based information is utilized to construct ambiguity set for uncertainties. • The chance constraints are reformulated into mixed integer linear constraints. • The modeling results are validated by experiments through real world applications. In this paper, a bi-level programming model is established to address the planning issues of fast-charging stations in electrified transportation networks with the consideration of uncertain charging demands. The capacitated flow refueling location model is considered in the upper level to minimize the planning cost of fast-charging stations while the traffic assignment model is utilized in the lower level to determine the spatial and temporal distribution of plug-in electric vehicle flows over entire transportation networks. Such bi-level model unveils the inherent relationship among charging demands, electrical demands and the spatial and temporal distribution of plug-in electric vehicle flows. Robust chance constraints are formulated to characterize the service abilities of fast-charging stations under distribution-free uncertain charging demands, where the ambiguity set is constructed to estimate the potential values of the uncertainties based on their moment-based information, such that the robust chance constraints can exactly be reduced to mixed integer linear constraints. By introducing new variables, the bi-level model is then reformulated into a single-level mixed integer second-order cone programming model so as to be solved via off-the-shelf solvers, which guarantee the optimality of the solution. A case study is conducted to illustrate the effectiveness of the proposed planning model, which reveals three critical factors that significantly impact the planning outcomes. [ABSTRACT FROM AUTHOR]

• Small-signal stability analysis, frequency scan, time domain simulations are carried out to analyse the mechanism and characteristics of such SSI. • A real SSO incident is reported with its field measured data analysed. Replication of this incident in PSCAD/EMTDC is conducted to validate the consistency with theoretical analysis. • Composite short circuit ratio (CSCR) and relative short circuit ratio (RSCR) are developed to investigate characteristics of SSI with two different WPPs existing in the wind farm. Recently, sporadic power oscillations in subsynchronous frequency range were detected in several permanent magnet synchronous generator (PMSG)-based wind farms in Xinjiang, China. Given no existence of fixed series compensation in the system, it can be concluded that this is a new type of subsynchronous interaction (SSI) and different from the previous subsynchronous control interaction (SSCI) occurred in doubly-fed induction generator (DFIG)-based wind farms. Therefore, the characteristics and mechanism behind this phenomenon are so ambiguous that further clarification is needed. To investigate this new type of subsynchronous oscillations (SSO) problem, a real SSI incident is reported, and the equivalent model is established based on multi-machine equivalent aggregation. Refined frequency scan and small-signal analysis are carried out to analyse the mechanism and characteristics of such SSI. Time-domain simulations are conducted to replicate the incident. The results show that the interactions between PMSG-based wind farms and weak grids can trigger negative-resistance and capacitive-reactance effects, which leads to unstable power oscillations. To further investigate the influence of different wind power plants (WPPs), two short-circuit ratio metrics are developed, and time-domain simulations are conducted based on semi-aggregation. The potential hazards of this SSI and its mitigation strategies are also discussed and proposed. [ABSTRACT FROM AUTHOR]

• Review on combined heat and power plant (CHP) operation is presented. • Technical flexibility and characteristics of CHP technologies are summarized. • Applications of CHP flexibility in integrated energy system (IES) are discussed. • Cost- and price-based operation strategies of CHP in recent research are reviewed. • Future technical and operational flexibility of CHP and IES is summarized. With an increasing penetration of renewable energy, combined heat and power (CHP) plants play an important role in connecting different energy sectors of an integrated energy system (IES). Technical flexibility is an essential ability for CHPs to provide secure and economic operation of the IES, as well as to implement a profitable arbitrage in different markets. Operation strategies (OSs) are adopted to utilize the existing technical flexibility in an optimal way. The delivered flexibility is evaluated in terms of the system performance and operation profits. In this paper, a literature review on technical flexibility and characteristics of different CHP technologies is summarized. Moreover, the applications of CHP system flexibility in the IES are discussed from the perspectives of plant owners, system operators and demand side. Literature classified by cost-based and price-based OSs is studied for system operators and generation plant owners respectively. Characteristics of the OSs in terms of the CHP modeling, optimization objectives, constraints and algorithms, etc. are discussed and compared. The study is beneficial to achieve more economic operation of the IES and more profitable operation of the CHP system by system operators and plant owners. Future research work regarding this topic is investigated in the last part. [ABSTRACT FROM AUTHOR]

In a false data injection attack (FDIA), an adversary stealthily compromises measurements from electricity grid sensors in a coordinated fashion, with a view to evading detection by the power system bad data detection module. A successful FDIA can cause the system operator to perform control actions that compromise either the physical or economic operation of the power system. In this letter, we consider some implications for FDIAs arising from the late 2015 Ukraine Blackout event. [ABSTRACT FROM AUTHOR]

The active power control (APC) of variable-speed wind turbines (VSWT) is increasingly required to support the power system operation and control. Besides control strategies, the power reference also exerts a significant influence upon APC performance. Due to the VSWT instability, the optimistic reference which is usually determined according to the conventional available wind power (AWP) based on maximum power point, cannot be sustainably maintained over the dispatch period. This may results in frequent interruption of power reference tracking. On the other hand, the conservative reference will easily lead to excessive pitch regulation. In this letter, the turbine stability-constrained available wind power is defined and analyzed to sustain the power generation of scheduled wind turbines at a maximum level. Simulation results show that the VSWT dispatched according to the proposed AWP can maintain stable power generation with less pitch action and therefore better support secure and economic power system operations. [ABSTRACT FROM AUTHOR]