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With the increasing deployment of wide-area monitoring systems (WAMS) and phasor measurement units (PMUs), along with artificial neural network (ANN) and highperformance distributed computation technique for smart grid and smart metering environment, online dynamic security assessment (DSA) plays a key role for early unstable event detection on power system security. It is especially important at a post-fault operation that the timing by DSA to detect an unstable event is critical to emergency remedial control action. However, excessive update training is one of the constraints for ANN to be effectively performed at pre-fault and post-fault operations on online DSA. This paper describes how transfer learning is successfully employed to shorten the training time for online DSA. It also helps to improve the validation accuracy if the training dataset of scratch ANN model is insufficient. Besides, a new approach of using the densely connected convolutional network with kernel principal component analysis (KPCA) is proposed to eliminate the traditional step of dimensionality reduction. [ABSTRACT FROM AUTHOR]

In the trend of multi‐energy integration development and the intellectualisation for demand‐side, the application of integrated energy system (IES) contained demand response is of great significance to the economic operation of energy systems. The energy hubs are widely studied as effective tools for integrating regional energy in the IES. With the expansion of schedulable devices, the mode of multiple energy hubs (MEHs) cooperative operation is proposed in this study. Moreover, in order to adjust the periodic loads, the authors further study the responsiveness of electric vehicles, air‐conditioning and energy storages according to time‐of‐use price incentive mechanisms, and the total operation cost is minimised. Meanwhile, the eco‐friendly target is achieved by the pollutant trading market. Furthermore, due to the complementary characteristic of MEHs, the economics allocation issues are also carefully considered where the cost and benefit of each participant are allocated by cooperative game theory to make them satisfied with the profit brought by cooperation. The simulation takes three hubs cooperation as an example to verify the effectiveness of the proposed methods. The results show that the total profit increased by 53.54% and the peak load is reduced by 27.06% after optimal operation, and the modified propensity to disrupt verifies the fairness of allocation. [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]

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]

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 rollinghorizon 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]

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]

Aimed to clarify the correlation characteristics between the internal partial discharge (PD) in the negative direct current (DC) gas‐insulated system (GIS) and gas pressure, and to establish an insulation fault diagnosis method based on decomposition component analysis, this study on PD decomposition of sulphur hexafluoride (SF6) was conducted under different pressures on the basis of constructing a SF6 decomposition experimental platform under DC PD. A stainless steel needle‐plate electrode was used to simulate the insulation defects of metal protrusions in GIS. SOF2, SO2 F2, CO2, SO2, and CF4 data generated from SF6 decomposition were obtained under different pressures. The variation law of component concentration and effective characteristic ratio with pressure were analysed in detail. In addition, a mathematical formula for pressure and decomposition components was deduced according to gas micro‐ionisation theory. The effective content and formation rate were defined to validate the relationship. Results show that the concentration of SF6 decomposition components decreased gradually with increasing pressure with a strong regularity. Furthermore, the relationship between pressure and component concentration derived from the theory can explain the relationship between the effective content of SF6 decomposition components and the effective formation rate and pressure variations. [ABSTRACT FROM AUTHOR]

Wide-area measurement system (WAMS) has been widely deployed in modern power systems to achieve datadriven 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]

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]

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]

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]

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]