Search

His current research interests include operation, management and cyber security analysis of smart grids, microgrid, smart city, electric vehicles, artificial intelligence, and machine learning. In recent years, due to the grid modernizations, high penetration of renewable energies, and using smart sensors in the main structure of the power grids, a large amount of data has been generated that can potentially lead to the complexity of the network. His current research interests include power system operation, reliability, resiliency, renewable energy sources, cybersecurity analysis, machine learning, smart grids, and microgrids. TOPIC 1: OPTIMAL OPERATION AND MANAGEMENT OF MULTI-MICROGRIDS USING BLOCKCHAIN TECHNOLOGY Paper 1 by Misagh Dehghani Ghotbabadi et al. investigates the optimal operation of a networked microgrid from the reliability perspective in a correlated atmosphere for the wind generators using an advanced machine learning technique. [Extracted from the article]

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of electric vehicles (EVs), the EV charging station (EVCS) and power distribution have become a coupled physical system. A multi‐objective planning model is developed herein for the sizing and siting of EVCSs and the expansion of a power distribution network with high wind power penetration. The objectives of the planning model are to minimise the total cost of investment and energy losses of the distribution system while maximising the total captured traffic flow. The uncertainties associated with wind power sources are considered. Additionally, the uncertainties in EV daily charging loads are also important concerns in the optimisation of the planning model. To model the EV load uncertainties, a recent scenario generation (SG) method is adopted. Further, a multi‐objective optimisation tool, Multi‐Objective Natural Aggregation Algorithm (MONAA), is introduced to obtain the final solutions of the planning model. The simulations based on coupled 54‐node distribution network and 25‐node traffic network systems are conducted to verify the efficiency of the proposed model and the effectiveness of SG‐based MONAA. [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 advancement in sensing technologies and infrastructure allows real‐time condition monitoring on wind turbines (WTs), which helps improve the power generation efficiency, lower the maintenance costs of wind farms (WFs). Practically, the real‐time measurements could be unavailable at the Supervisory Control and Data Acquisition end due to unintended events such as sensor faults and communication loss, which significantly depreciates the condition monitoring and fault detection performance. Aiming to mitigate the missing data impact on data‐driven WF applications, this study develops a robust anomaly detection approach for WT fault detection using a denoising variational autoencoder. In presence of missing measurements, the proposed approach can not only sustain high fault detection performance but also recover the missing data as an auxiliary function. The proposed approach is tested on a realistic offshore WF and compared with other autoencoder variants and traditional anomaly detection methods. The testing results verify the outstanding robustness of the proposed approach against missing data events and demonstrate its great potential in missing data recovery. [ABSTRACT FROM AUTHOR]

As residential PV generation penetrates in the low‐voltage distribution network (LVDN), the unbalance issue may be intensified due to the asymmetry of generations/loads in different phases. Phase‐reconfiguration device (PRD), which can reconfigure connected phases of residential customers, provides an effective method to address this issue. Noting that although the benefit brought by PRDs can vary if they are placed at different locations in the network, little literature has been reported on this topic. To bridge the research gap, this paper presents a novel method to optimally place PRDs in an LVDN aiming at minimizing the power unbalance running through the distribution transformer. The problem considers both installation, operational constraints, and boils down to a challenging mixed‐integer non‐convex programming problem, which is then reformulated as an efficient solvable mixed‐integer linear programming problem. Moreover, operational constraints are relaxed with slack variables penalized in the objective function, which makes sure a feasible solution is always available without or with minimal operational violations. Case studies based on a modified IEEE system and a practical system in Australia demonstrate that an efficient strategy can be provided to address the unbalance issue while improving the network's power supply qualities. [ABSTRACT FROM AUTHOR]

Deploying the cloud energy storage system (CESS) is an economic and efficient way to store excess photovoltaic generation and participate in demand response without personal investment on pricy energy storage equipment. It is a shared battery energy storage system (BESS) for local residential and small commercial consumers, which is designed and controlled by the CESS operator. Based on the profit purpose, the CESS operator not only pursues the most economic operating strategy, but also tries to minimize the total investment on the design stage. This paper considers the investment on the batteries, power conversion system, reactive power compensation equipment and the cost including battery degradation cost and operation cost. The electricity price uncertainty and the voltage deviation of the CESS node caused by power exchange are also considered. Moreover, the cases of a largely centralized energy storage system and multiple distributed energy storage systems are all modelled. Finally, an original robust cooptimization model is transferred to a mixed integer linear programming model (MILP) and solved in GAMS. Numerical results based on historical data from 300 residential consumers in Australia present that the battery degradation cost and price uncertainty can't be neglected. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The speed regulating differential mechanism (SRDM) enables grid‐connected wind turbines (WTs) to generate constant‐frequency electric power without fully‐ or partially‐rated converters. In this study, the authors present a self‐stabilising SRDM (SS‐SRDM) consisting of a planetary gear train (PGT), a differential mechanism, and a constant speed motor for WTs to dislodge the high‐cost speed sensors, servo motor and the complicated control system from the electrical controlled SRDM. The kinematic principles and the four‐axis dynamic characteristics of SS‐SRDM are studied. An effective parameter configuration method for tuning the speed ratios of five key connection units is also proposed and used to obtain a PGT with optimal structural parameters. The detailed simulation model of a 3 MW WT with SS‐SRDM is established and then validated through physical experiments. Results show the satisfactory accuracy of the built model (maximum steady‐state errors in output shaft and speed regulating shaft are, respectively, 0.76 and 1.95%). Finally, case studies and verification work of the proposed methods are carried out, in which the rotational speed, power, speed error, and power ratio among wind rotor, SRM, and synchronous generator are obtained. Case studies well‐illustrate the availability of parameter design and the effectiveness of speed‐regulations of the proposed SS‐SRDM method. [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]

Notable benefit can be brought by combined operation of coupled electricity‐heat system (CEHS) and be enhanced by introducing independent thermal energy storage (ITES). With prevalent constant‐flow variable temperature (CF‐VT) control strategy of heat network, explicitly formulating CEHS with ITES leads to mixed‐integer non‐convex programming. Although the problem can be reformulated as a mixed integer second‐order cone programming (MISOCP) problem and solved by commercial solvers, improving the computation efficiency still needs more effort. Here, several alternative solution techniques, based on either reformulation or approximation methods, are studied and compared with the original MISOCP formulation. The computation efficiencies as well as on the solution accuracies of various solution techniques or a combination of them are investigated and analysed based on two constructed systems. Simulation results reveal that appropriately selecting formulations of electric and heat networks can effectively improve the performance of solving the original problem. [ABSTRACT FROM AUTHOR]

To harmonise the operation of renewable and conventional power generation, the radial distribution network of islanded microgrids (MGs) has been regionalised into two types of regions namely conventional generation dominant regions (CGRs) and renewable generation dominant regions (RGRs). RGRs will operate on constant frequency control methodology and CGRs will operate using conventional control techniques. A framework starting from the planning stage where sensitivity analysis‐based approach has been implemented for regionalisation and then optimisation of distributed generation (DG) units has been carried out for regionalised MGs (RMGs) is proposed in this study. At operation stage, a short‐term dispatch framework has been proposed based on the forecasted wind power generation scenarios with a battery energy storage system in the RMGs. A power exchange strategy with a scheduled pattern between the regions has also been developed to make the scheme more dispatchable and reliable. Multi‐objective optimisation algorithms have been developed for DGs planning and power dispatch in RMGs. The performance of the proposed RMGs has been studied by implementing it with four different modes of operation by converting IEEE 15‐bus radial distribution system into an islanded MG. [ABSTRACT FROM AUTHOR]

Microgrids have been recognised as one of the most promising but challenging research topics over the last decade. The optimal energy scheduling problem is regarded as the most essential aspect in the tertiary level control in microgrids. However, most existing centralised or distributed scheduling models only focus on the logical and dynamical feature of microgrids' operation or the non‐linear power flow constraint, which would overact the system performance without considering appropriate unit commitment requirements. Moreover, applying decomposition and iteration technics to complex scheduling problems would encounter convergence issues. To address this concern, this study presents a mixed integer linear reformulation to characterise the operation of different controllable devices and convex relaxation techniques to cope with non‐linear power flow constraints, leading to a mixed integer second‐order cone programming framework in a concordant yet computationally efficient pattern, capturing non‐linear, logical and dynamical properties of the optimal energy scheduling problem in microgrids. The effectiveness of the proposed framework is validated on the IEEE 33‐bus distribution network with both grid‐connected and islanded modes. [ABSTRACT FROM AUTHOR]

In this study, an advanced control strategy is proposed for hybrid energy storage systems (HESS) to smooth wind power generation fluctuations. Compared with the limited performance of solo energy storage system, the HESS, composing of lithium‐ion battery (LiB) and a flywheel energy storage system (FESS), can comparatively show improved flexibility and adaptivity. A novel sequence control scheme for the HESS is proposed in this study to improve the overall economic and smoothing performances. Specifically, based on variable‐interval reference outputs, the control horizon of the HESS is statistically determined. Afterwards, a sequence control scheme for the HESS is presented to improve the internal collaboration of HESS media. Especially when the LiB and FESS are synchronously charging/discharging to smooth wind power, the charge/discharge power are optimally distributed between the LiB and FESS via an optimiser to minimise the equivalent cost during each control cycle. Case studies are conducted to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the rapidly growing integration of wind power generation (WPG), it is of great importance for an operator to grasp the ability of the power system to accommodate uncertain WPG. This study proposes two probabilistic methods to assess such capability of a power system based on the level of data availability. If the probability distribution type (PDT) of wind power prediction error (WPPE) is known, the total accommodation probability is calculated as the sum of a fully guaranteed probability and a partially guaranteed probability. The former one leads to a tri‐level max‐max‐min optimisation problem which is solved via a dichotomy procedure, and the latter one can be obtained based on the geometrical analysis of the dispatchable region of WPG. If the PDT of WPPE is not exactly known, the authors tackle the problem via a data‐driven uncertainty quantification method. More precisely, they consider a family of ambiguous probability distributions around the empirical distribution described by historical data in the sense of Wasserstein metric. The probability of failure in the worst‐case distribution is calculated from a linear programming. The proposed method is tested on modified PJM‐5 and IEEE‐118 bus systems. Comparison with the traditional Monte Carlo Simulation method demonstrates its efficacy and efficiency. [ABSTRACT FROM AUTHOR]

Shared electric vehicle (EV) is an emerging component across interdependent power and transportation networks that affect economic, environmental and societal benefits. Studies on how to collaboratively enable the optimal planning of these interacted networks is less explored. In this work, a joint distribution network expansion planning framework integrated with a shared EV charging station is proposed. One of the objectives is to minimise overall investment cost considering operational waiting cost of shared EV charging service and power loss, while the other aims to maximise the utilisation of charging infrastructures. To overcome the difficulties in solving the multi‐objective mixed‐integer non‐linear problem, Tchebycheff decomposition based evolutionary algorithm is modified to find the Pareto front showing the trade‐offs between goals above. Besides, stochastic traffic assignment model is used to obtain traffic distribution and travel demand. The final optimal solution is decided by the fuzzy satisficing method. The performance of the proposed approach is evaluated on a 54‐node test system. Sensitivity analysis is performed to assess the impact of key parameters from the perspectives of transportation and distribution network sectors. The numerical results demonstrate the capability and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

With development of two‐way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air‐conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K‐medoids‐based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the development of synchronised measurement technique, online dynamic security assessment (DSA) is of great significance to prevent power system blackout. Recently, based on the phasor measurement unit (PMU) data, intelligent data‐driven techniques have been rapidly developed for online DSA owing to their fast decision speed, less data requirement, and decision rule discovery ability. The interpretable decision rules provide useful information for preventive control and post‐event auditing. However, in case of incomplete measurement events, such as PMU failure, communication loss, and phasor data concentrator failure, the accuracy and the transparency of the intelligent models can be significantly impaired by such incomplete data. To overcome this problem, this paper proposes a robust white‐box model that can sustain DSA accuracy and survive the model interpretability and transparency against incomplete PMU data. The proposed model is tested on New England 39‐bus system and demonstrates higher robustness over existing methods. [ABSTRACT FROM AUTHOR]

The smart distribution system architecture provides value‐based control techniques that facilitate bi‐directional power flows and energy transactions. Although consensus and understanding continue to develop around peer‐to‐peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two‐stage of power scheduling. The proposed model is verified on the modified IEEE 15‐bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk‐seeking operation strategy is recommended if wind power penetration increases. [ABSTRACT FROM AUTHOR]

Coordinated reactive power regulation is always a critical issue when it comes to a system with a high penetration level of wind energy. This study provides a distributed control scheme for wind farm reactive power regulation, aiming to coordinate the reactive power reference among wind farm clusters. Within the limited communication among neighbouring clusters, fair reactive power generation sharing is achieved. Moreover, the reactive power capability of the wind turbine (WT) is utilised to cooperate with reactive power compensation devices. The characteristics of the collector system are analysed to improve the voltage profile. Instead of averagely assigning the generation order to each WT, the reference is assigned based on voltage sensitivity. Case studies are carried out to validate the performance of the proposed control scheme. [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 study presents a two‐stage planning framework of the battery energy storage system (BESS) and micro‐turbine (MT) in a microgrid. In the first stage, the optimal allocation decisions are made before the actual realisation of the operational uncertainties. In the second stage, the optimal operation strategies are made for the microgrid by minimising the costs paid to the main grid, fuel cells, MTs, BESSs and controllable loads (CLs). The hot water system and interruptible load are considered as CLs. Their mathematical models are built to investigate their roles in smoothing renewable energy. In addition, efforts are made to keep the linearity of the formulated optimisation problem, and the backward scenario reduction method is adopted to further enhance the computational efficiency. The modified IEEE 33‐bus radial system is used as a microgrid to verify the effectiveness of the proposed approach for both islanded and grid‐connected microgrids. Sensitivity analysis has been conducted to compare the economics and robustness of the identified solutions. [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]

Home energy management system (HEMS) provides an effective solution to assist residential users in dealing with the complexity of dynamic electricity prices. This study proposes a new HEMS in contexts of real‐time electricity tariff and high residential photovoltaic penetrations. First, the HEMS accepts user‐specified residential energy resource operation restrictions as inputs. Then, based on the forecasted solar power outputs and electricity prices, an optimal scheduling model is proposed to support the decision making of the residential energy resource (RES) operations. For the scheduling of heating, ventilating, and air conditioning system, an advanced adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. For the controllable appliances, the 'user disturbance value' metric is proposed to estimate the psychological disturbances of an appliance schedule on the user's preference. The proposed scheduling model aims to minimise the future 1 day energy costs and disturbances to the user. A new biological self‐aggregation intelligence inspired metaheuristic algorithm recently proposed by the authors (a natural aggregation algorithm) is applied to solve the model. Extensive simulations are conducted to validate the proposed method. [ABSTRACT FROM AUTHOR]

Wide‐area measurement system (WAMS) has been widely deployed in modern power systems to achieve data‐driven dynamic security assessment (DSA). Considering the unavailability of measurement data during unintentional events, a robust classification model for phasor measurement unit (PMU)‐based DSA is proposed. The proposed approach identifies the observability of different PMU combinations in the system and an ensemble of random vector functional link networks is trained with observability‐constrained feature subsets to ensure its viability under any PMU missing condition. Meanwhile, the available operating features are comprehensively utilised by the proposed classification model to support accurate DSA under both normal and abnormal data collection conditions. The proposed classification model is tested on New England 39‐bus system with high accuracy and strong robustness. [ABSTRACT FROM AUTHOR]

Deployment of battery energy storage (BES) in active distribution networks (ADNs) can provide many benefits in terms of energy management and voltage regulation. In this study, a stochastic optimal BES planning method considering conservation voltage reduction (CVR) is proposed for ADN with high‐level renewable energy resources. The proposed method aims to determine the optimal BES sizing and location to minimise the total investment and operation cost considering energy saving achieved by CVR, while satisfying system operational constraints in the presence of stochastic renewable power generation. The uncertainty of load composition is also modelled through scenario analysis. The proposed planning scheme is tested in a modified IEEE 15‐bus system and 43‐bus radial system, respectively. The numerical results validate that the combination of CVR and BES can achieve more energy savings. [ABSTRACT FROM AUTHOR]

The increasing penetration of wind power (WP) and demand response (DR) programs into modern power systems poses more challenges on transmission expansion planning (TEP). To ensure the economical, secure and reliable operations of power systems, this study presents a risk‐averse TEP framework. Instead of using the deterministic security criterion, an insecurity risk cost (RC) is proposed to provide network planners with the insight into the problem, options and future implications in decision making. Specifically, this RC can quantify the system security degree, considering the probability and the severity of contingencies. Meanwhile, the economic value of DR is modelled and incorporated into the optimal operation solutions. Moreover, to enhance the computational efficiency, an iterative solution algorithm based on the Benders decomposition is developed to solve the formulated TEP problem. The proposed approach is numerically verified on the Garver's 6‐bus, IEEE 24‐bus RTS, and 2383‐bus polish systems. Case study results demonstrate that the proposed approach can effectively investigate the impacts of large‐scale integration of WP and DR on system operations and planning. Moreover, the proposed risk‐averse approach is economically efficient and more robust to stochastic variations. [ABSTRACT FROM AUTHOR]

Wind power is one of the promising renewable energy resources to achieve energy sustainability. Its growth poses increasing financial and technical challenges to power systems, mainly due to the wind intermittency. Existing transmission networks may require infrastructure investment, in order to maintain the economical, secure and reliable operations of power systems with increasing wind power penetration. This study proposes a stochastic transmission expansion planning (TEP) framework to assess the impacts of wind power penetration and demand response incorporation. A risk constraint on load curtailment is introduced and its effect on TEP solutions is investigated. Also, to reduce the complexity and size the formulated TEP problem, a decomposition‐based approach is adopted. According to the numerical results on the Garver's six‐bus and the modified IEEE 30‐bus systems, the solutions of TEP are subject to the variation of wind power characteristics and cannot be determined straightforward. Hence risk‐analysis should be carried out to guide transmission investment with increased flexibility. The incorporation of wind power uncertainty increases the total cost and expected energy not supplied, which can be mitigated by the proposed TEP approach. [ABSTRACT FROM AUTHOR]

For cross‐regional transmission projects, the two‐part transmission pricing mechanism is suggested so as to promote the sustainable development of cross‐regional electricity trading. In the two‐part transmission pricing mechanism, appropriately determining the capacity charging ratio (CCR) is an important issue not well solved. Given this background, a Z‐number‐based risk‐minimised negotiation model is developed for a transmission company and a power purchaser to achieve an agreeable CCR under incomplete information. The uncertainty distribution of the future annual electricity transmission quantity is first estimated by the Z‐number‐based multiple Z‐valuations; and then, the benefit and risk loss measured by the well‐established conditional value at risk (CVaR) are analysed for the participating two parties. Subsequently, the negotiation model where each negotiator is to minimise its risk loss under a given lowest acceptable benefit constraint and the estimations of the opponent's risk tolerance and negotiation strategy is presented to determine the optimal offer. Finally, the ± 500 kV Xiluodu−Guangdong direct current (DC) transmission project in the southern region of China is employed to demonstrate the basic characteristics of the proposed model. [ABSTRACT FROM AUTHOR]

Demand side management is one of the key topics of smart grids. This study integrates the service computing paradigm in smart grid domain and proposes a demand side personalised recommendation system (PRS). The proposed PRS employs service recommendation techniques to infer residential users' potential interests and needs on energy saving appliances, and then it recommends energy saving appliances to users, therefore potentially creating opportunities to save energy for the grid. The proposed approach starts by applying a non‐intrusive appliance load monitoring (NILM) method based on generalised particle filtering to disaggregate the end users' household appliance utilisation profiles from the smart meter data. Based on the NILM results, several inference rules are applied to infer the preferences and energy consumption patterns, and to form the user profile. In parallel, information retrieval techniques are applied to extract keywords from the textual appliance advertisements (Ads), and to define the appliance profile. Finally, the similarity measurement method is applied to compare the user profile and appliance profile, to rank the appliance Ad, and to make the recommendations. Experiments are conducted to validate the proposed system. [ABSTRACT FROM AUTHOR]

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]

Conventional approaches for measurement‐based load modelling uses measured voltage (V) as the input to calculate the load model output – P and Q powers. This assumes that the P and Q response is a function of V. In fact, there is an inherent interaction among the three variables (P, Q, and V) especially when motor load occupies a large proportion. With increased penetration of wind power, the fault‐induced dynamic voltage response is becoming a serious concern given the low‐voltage‐ride‐through (LVRT) requirement of wind turbines. This paper firstly shows that given different load model parameters, the V responses can vary significantly. Then, an improved method is proposed for more accurate measurement‐based load modelling. The proposed method incorporates the V response into the load model output, therefore is able to accurately reflect the dynamic voltage trajectories. Given the load model parameters, the system responses of P, Q, and V are all simulated via time‐domain simulations using industry‐grade software, and problem is to search the load model parameters to minimise the fitting error between the simulated and measured system responses. A trajectory sensitivity index is used to identify the well‐conditioned parameters, and a parallel‐differential evolutionary algorithm is designed to solve the model. [ABSTRACT FROM AUTHOR]

System overloading is becoming a critical issue in distribution system due to outdated infrastructure and growing electricity demand. Although renewable‐based distributed generation is a promising solution to relieve this issue, its intermittency and uncertainty impose significant challenges on system operations. This study attempts to coordinate networked energy storage systems (NESSs) to manage network loading in distribution networks. The NESS can act as a buffer to absorb surplus energy during high generation periods and serve the demand during peak load periods. A consensus‐based dispatch strategy is proposed to coordinate NESSs. Through limited communication among neighbouring NESSs, required active power curtailment is shared. The mathematical formulation of a state‐of‐charge weighting factor is introduced to improve the efficiency of NESSs. A sensitivity study is also conducted to demonstrate the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

In recent years, the battery energy storage system (BESS) has been considered as a promising solution for mitigating renewable power generation intermittencies. This study proposes a stochastic planning framework for the BESS in distribution networks with high wind power penetrations, aiming to maximise wind power utilisation while minimise the investment and operation costs. In the proposed framework, the uncertainties in wind power output and system load are modelled by the Monte–Carlo simulation, and a chance‐constrained stochastic optimisation model is formulated to optimally determine the location and capacity of BESS while ensuring wind power utilisation level. Then, the Monte–Carlo simulation embedded differential evolution algorithm is used to solve the problem. Simulation studies performed on a 15‐bus radial distribution system prove the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This study proposes a two‐stage operational planning framework for the short‐term operation of the virtual power plant (VPP). In the first stage, a stochastic bidding model is proposed for the VPP to optimise the bids in the energy market, with the objective to maximise its expected economic profit. The imbalance costs of the VPP are considered in the bidding model. In the second stage, a model predictive control (MPC)‐based dispatch model is proposed to optimise the real‐time control actions. In the real‐time dispatch model, the real‐time information of the resources is continuously updated, and the deviations between the actual energy output and the contracted energy over the MPC control horizon are minimised. The simulation results prove the efficiencies of the proposed method. [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]

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]

Intelligent system (IS) using synchronous phasor measurements for transient stability assessment (TSA) has received continuous interests recently. For post‐disturbance TSA, one pivotal concern is the response time, which was reported in the literature as a fixed value ranging from 4 cycles to 3 s after fault clearance. Since transient instability can develop very fast, there is a pressing need for faster response speed. This paper develops a novel IS to balance the response speed and accuracy requirements. A set of classifiers are sequentially organised, each is an ensemble of extreme learning machines (ELMs), whose inputs are post‐disturbance generator voltage trajectories and outputs are the classification on the stable/unstable status of the post‐disturbance system and an evaluation of the credibility of the classification. A self‐adaptive TSA decision‐making mechanism is designed to progressively adjust the response time, such that the IS can do the classification faster, thereby allowing more time for emergency controls. The ELM ensemble classifiers can also be updated by on‐line pre‐disturbance TSA results due to its very fast learning speed. Case studies on the New England system and IEEE 50‐machine system have validated the high efficiency and accuracy of the IS. [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]

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]

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]