One of the basic components of future distribution networks is renewable energy resources (RER). The uncertainty in power production of renewable resources such as wind and solar as well as load is another characteristic of such networks. Conventional power flow methods may not be suitable for active distribution networks such as microgrids. In this study, a heuristic load flow method considering the effects of intermittent behaviour of RERs and load is modelled in probabilistic load flow (PLF) algorithm. The method is suitable for both radial and weakly meshed distribution networks with RER for operation and planning of microgrids. Imperialist competitive algorithm (ICA) as heuristic-based optimisation algorithm is applied to solve the PLF. Based on PLF technique, calculated parameters of the system such as bus voltages and feeders’ current are extracted as random variables. A modified version of IEEE 33-bus test system with RER is used to evaluate efficiency and capability of the algorithm. Results are compared with Monte Carlo simulation method. The probability density function and cumulative distribution function (CDF) of some network variable are compared. Based on the results, the presented approach can solve the PLF problem regardless of the type of distribution network.

Current differential protection is widely used as the main protection in UHV/EHV (Ultra/Extra High Voltage) transmission lines with advantages of simple principle, high reliability and inherent phase-selection capacity. However, as the voltage level and line length increase, the performance of the current differential protection is severely restricted by distributed capacity current. Aimed at this problem, a rapid extraction method of single-phase active current is proposed in this paper. The method first uses single PLL (phase-locked loop) to track the phase of the corresponding fundamental voltage. Then the current signal is transformed from the abc static coordinate system to the pq rotating coordinate through transformation matrix. By filtering out the high-frequency AC signal on p axis with low-pass filter, the active current component remaining being in the same direction with voltage phase can be obtained. On this basis, a novel active current differential protection method is proposed, with the advantages of high sensitivity and strong capacity of enduring transition resistance without requirement for the acquisition of line impedance parameters. Finally, the proposed method is tested by a series of dynamic simulations through PSCAD and experiment. The simulation results show that the method has merits of effectiveness, robustness and rapidity.

Existing system restoration strategies are primarily based on operators’ experience with no specific decision support tools. Restoration of interconnected grids in Europe is decentralised; restoration planning is limited to within the area of control for each national transmission system operator (TSO). Although coordination of restorative actions between TSOs is encouraged, few agreements exist. This study proposes a decision support methodology for system operators and restoration planners for restoration of interconnected grids. Solutions to optimal and coordinated utilisation of tie lines (TLs) are proposed. The proposed tool facilitates collaboration between TSOs affected by wide area blackouts. The decision support tool allows to adaptively develop restoration strategies based on system conditions, available resources and operating constraints. Optimisation algorithms are proposed to determine the optimal utilisation of TLs and/or black-start (BS) units to crank non-BS units. Optimal power flow problems are formulated for utilisation of TLs. They offer solutions for neighbouring TSOs to determine the extent to which TLs can be used without violating constraints. The proposed decision support tool is tested with IEEE 39-bus system.

The unified power flow controller (UPFC) is a component that belongs to the flexible AC transmission systems devices family which has been deeply studied under different conditions of operation within the power network. One of its main applications is on damping oscillations in power systems. In this research, the UPFC non-linear interaction with the power system is studied through the use of modal series method, which is a kind of analytical tool that has the ability to characterise the non-linear effects because of small perturbations. The analysis is based on non-linear contribution of UPFC variables when controller parameters are varied; time-domain analysis and non-linear interaction indices are calculated in order to quantify the non-linear effect of UPFC connected to a synchronous machine-infinite busbar test system.

Rapid advancements pertaining to measurements and computational technology have brought a paradigm shift for operational architecture of power grids across the globe. Self-healing, a vital operational feature of emerging power grids, necessitates real-time identification and localisation of transmission line faults for the entire power network. This study proposes a novel support vector machine-based fault localisation methodology to precisely identify and localise all types of transmission line faults occurring at any location in the power grid based on phasor measurement unit (PMU) measurements. Detection of fault is achieved through PMU measurements only from a single generator bus for the entire grid. Bus associated with fault, faulty branch and location of fault in faulty branch are calculated using fast Fourier transform analysis of variations pertaining to equivalent voltage phasor angle (EVPA) and equivalent current phasor angle (ECPA). The proposed methodology has been validated through extensive case studies for Western System Coordinating Council (WSCC)-9 and IEEE-14 bus systems. The main contribution of the proposed methodology is that the fault location information can significantly contribute to system protection center for restoration of the line within shortest time span and initiate appropriate wide area control actions to maintain stability.

Distributed static series compensator (DSSC) is a member of the family of distributed flexible AC transmission system devices that can be attached directly to conductors of transmission lines. DSSCs are able to compensate for and adjust the line reactance; thus, they could be widely used in stability-limited transmission systems. Owing to economic matters, the optimal placement of DSSCs is of a great interest from practical viewpoints. This study presents an approach to find the optimal locations of DSSCs to enhance the system loadability and reliability using the DC load flow model. The optimisation problem is solved using the mixed-integer linear programming. A compromise between improving the system loadability and reliability is fulfilled and a wide range of non-dominated optimal solutions is calculated. The final solution is then selected using the fuzzy satisfying method. Numerical studies are conducted on the IEEE reliability test system (IEE-RTS).

Practically, loads are found to be voltage dependent and reconfiguration in distribution systems changes the node voltage profile. Hence reconfiguration affects the load behaviour. In this scenario, the traditional approach of defining energy efficient operation as mere loss minimisation does not give truly energy efficient results. Therefore this study defines energy efficiency as a multi-variable function in terms of energy efficiency defining parameters and develops an energy efficiency performance index, in order to identify an energy efficient configuration. The proposed approach of reconfiguration gives more flexibility to utilities in operating the distribution system with improved power quality and reliability, and enables them to actively participate in a competitive deregulated environment. In this study, voltage dependent loads are modelled in the form of load combinations to represent the real world loads at a bulk delivery point of the distribution system.

An accurate algorithm for locating faults on two-terminal transmission lines is presented in this study. The presented method does not require values of line parameters to locate the fault; therefore variation of line parameter values does not affect its accuracy. The method utilises synchronous post-fault measurements of currents and voltages in both terminals and is based on the distributed time-domain representation of transmission lines. Unlike conventional methods, fault location is converted to an optimisation problem. Then, this problem is solved by genetic algorithm to find the accurate location of fault. Furthermore, modifying the problem to be solved by the Newton optimisation method is discussed in this study. Simulation results performed by ATP/EMTP verify high accuracy of the presented method, and reveal that it is not significantly affected by fault resistance, fault type, fault inception angle and source impedance. Moreover, effect of measurement errors and synchronisation errors on the accuracy of presented method have been analysed which verify the efficiency of the algorithm in these conditions.

Voltage unbalance propagation is an important aspect in relation to the voltage unbalance management process which aims to maintain acceptable voltage unbalance levels in the power system. The IEC Technical Report IEC/TR 61000-3-13:2008 incorporates the effects of voltage unbalance propagation in the voltage unbalance emission allocation methodology by introducing the voltage unbalance transfer coefficient, in order to evaluate the influence made by background voltage unbalance at the point of evaluation. However, no comprehensive approaches exist in the literature to evaluate these coefficients and the IEC work follows some approximations, which are deduced based on simulations and practical measurements. Recent work completed on voltage unbalance emission assessment at the post-connection stage of unbalanced installations allows separation of the voltage unbalance emission contribution made by upstream/surrounding unbalance sources as a constituent component of the resultant voltage unbalance emission level at the point of evaluation. These new methodologies implicitly deal with the important aspects of voltage unbalance propagation and allow the quantification of coefficients associated with voltage unbalance propagation, which is the main thrust of this study. The theoretical work completed by considering different types of loads in radial and interconnected networks is supplemented by the simulation results.

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non-deterministic components using the OHD matrix, including discrete impulse dictionary ( I ), cosine dictionary ( C ), sine dictionary ( S ) and the ℓ₁-norm optimisation algorithm. Then, the hard-thresholding, uniform threshold dead-zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries Ψ = [ I C S ] and Ψ = [ I C ], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE-1159-PQE and GIM–PQE databases and generated using the Microgrid model. The results show that the method with dictionary Ψ = [ I C S ] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

This study focuses on developing an integrated transmission and distribution framework that couples the two sub-systems together with due consideration to conventional demand flexibility. The proposed framework ensures accurate representation of the system resources and the network conditions when modelling the distribution system in the transmission optimal power flow and vice-versa. It is further used to develop an accurate pricing mechanism (distribution-based location marginal pricing), which is reflective of the moment-to-moment costs of generating and delivering electrical energy, for the distribution system. By accurately modelling the two sub-systems, the authors can improve the economic efficiency and the system reliability, as the price sensitive resources can be controlled to behave in a way that benefits the power system as a whole.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non-linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference-based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33-node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

This study presents a new approach to detecting and classifying voltage disturbances in electrical distribution systems based on wavelet transform and artificial immune algorithm. This proposal unifies the negative selection artificial immune algorithm with the discrete wavelet transform concept. Thus, the measurements obtained in a distribution substation by the supervisory control and data acquisition acquisition system are transformed into the wavelet domain. Afterward, a negative selection artificial immune system realises the diagnosis, identifying and classifying the abnormalities. The principal application of this tool is to aid the system operation during faults as well as to supervise the protection system. To evaluate the performance of the proposed method, two distribution systems were modelled in EMTP software: an 84-bus test system and a 134-bus real system. The results show a good performance, emphasising the precision of the diagnosis.

This study measures the long-term electric energy data of an electric vehicle (EV) charging station, and uses these field measurements to analyse and construct the load behaviour model of an EV charging station. According to the statistics of field measurements, the state of charge of EV battery pack at the start of charging and the number of EVs starting charging in each time interval can be modelled using Poisson distribution, Roulette wheel selection and uniform distribution. The load behaviour of an EV charging station can be modelled and analysed accordingly. To validate the accuracy of the proposed load behaviour model, a portion of field measurements is reserved for load behaviour model verification. The proposed load behaviour model can also be used to analyse the impacts of EV charging stations on distribution systems. Test results show that the load behaviour model proposed in this study has higher accuracy and the impacts of EV charging stations on distribution systems can be analysed more effectively and precisely.

Electricity price forecasting has become the focus of considerable interest in a deregulated energy market. In this study, regression tree-based models: classification and regression trees, Bagging and Random Forests have been built and used to identify the variables dominating the marginal price of the commodity as well as for short-term (one hour and day ahead) electricity price forecasting for the Spanish–Iberian market. Different prediction models are proposed including the main features of the market such as load, hydro and thermal generation and from available, wind energy production, of strategic interest in the Spanish market. In addition other explanatory variables are considered as lagged prices, as well as hour, day, month and year indicators. In the study, hourly data from 2000–2011 corresponding to 22 variables have been used. The results show the effectiveness of the proposed ensemble of tree-based models which emerge as an alternative and promising tool, competitive with other existing methods.

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid.

This initiative work addresses the comparative performance assessment of a novel quasi-oppositional harmony search (QOHS) algorithm and internal model control (IMC) method, in the environment of automatic generation control. These two techniques are applied to a single-area non-reheat and reheat turbine with and without droop characteristic, and four-area hydro-thermal interconnected power system under various operating conditions. Later on, robustness analysis of both the two test systems is carried out by varying the speed regulating parameters, time constants of governor, turbine, power system and the gain of power system in the range of ±50% in case of single-area test system. In four-area test system, the same is carried out with the consonance of step load perturbation in different control area at distinct time-interval. Simulation results show that the proposed QOHS algorithm offers better dynamic control with robust performance as compared with IMC based approach. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic (SFL) is applied to obtain on-line dynamic responses of the studied power system model. Moreover, time-domain simulation of the investigated four-area test system reveals that the proposed QOHS-SFL based intelligent controller yields on-line, off-nominal controller parameters, resulting in on-line optimal dynamic response profile.

Power grid operation centres have been separately developed and operated in various domains, such as the transmission, distribution and marketing domains. As the need for cooperation among domains increases in the smart grid environment, operation of the grid based on separated domains reveals serious problems, such as heterogeneous data presentation. For reliable operation of the smart grid, it is essential to integrate data from separated domains in real time and provide seamless analytics without increasing the security risk. This study presents a cross-domain approach to grid operation that supports cross-domain monitoring, seamless analytics and simulation based operation action recommendation. The results of the experiment show that the presented system monitors the whole grid state and supports seamless analytics with acceptable overhead.

Offshore wind plant will play an increasingly important role in future decades if ever-stringent requirements of energy security and low carbon emissions are to be met. Although some analyses of the DFIG impact on system stability have previously been reported, none are based on a large network representing a real system, or the large network is simply not publicly available. This paper describes one such suitable equivalent dynamic model for stability studies based on the UK transmission system. The methodology for appropriate control system design and adjustment of the parameters under different dispatch conditions is presented. Two important aspects contributing to future system stability are studied in detail, namely maximum value of the rate of change of frequency and transient stability. Following National Grid future energy scenarios, a number of detailed case studies are presented to quantify the impact of key influencing factors including the size of the largest generating unit for n−1 contingency, amount of primary system response, and frequency dependency of load. The study concludes that none of the individual factors can provide a complete solution and that careful cost benefit analysis is needed to determine the proper mix of services and reinforcements needed in the future.

A hybrid NanoGrid (NG) can be considered as the power supply system, for future community houses. In this study, the operation and control of an NG are presented. The NG consists of an AC bus and a DC bus, interconnected through a tie-converter (TC). All AC loads and distributed energy resources (DERs) of the community houses are connected to the AC bus while all their DC loads and DERs are connected to the DC bus. The NG has an adequate generation capacity to supply its loads in off-grid status as well as the capability to exchange power with the grid. The TC exchanges the power between two buses and regulates the voltage in both buses, in the case of off-grid operation. The NG dynamics, with the proposed structure and developed primary and secondary control algorithms are validated through extensive computer simulations in PSCAD/EMTDC.

An electrical power system is intended to transmit power from generation to load. To do so, it needs ancillary services to guarantee a certain level of reliability and quality. With the emergence of DC grids, it becomes necessary to ask what ancillary services they may themselves require, and how DC grids should be required to interact with the remainder of the system. This study defines a first basic set of ancillary services for High Voltage Direct Current (HVDC) grids which did not exist so far, and it shows that this development could affect the ancillary services in AC grids. This study describes how the origin of ancillary services can change if an HVDC grid is added to the existing AC grids and gives an overview of existing definitions for ancillary services.

The combined use of modal and balanced truncations methods is proposed for model order reductions. To efficiently combine these methods, a stopping criterion based on spectral energy concepts is also proposed. This criterion was implemented into the code of the widely known subspace accelerated dominant pole algorithm (SADPA), designed to compute a set of dominant poles and associated residues of transfer functions from large-scale, sparse, linear descriptor systems. The resulting enhanced SADPA code automatically stops once the computed set of dominant poles and associated residues is sufficient to build a modal reduced order model (ROM) whose energy content approaches that of the complete model within a specified tolerance and considering a frequency window of interest. The number of dominant poles in this set is much smaller than the number of poles of the full system model. Hence, their state-space realisation usually has a small enough dimension for the efficient application of the square root balanced truncation method. This new method, named hybrid modal-balanced truncation, produces ROMs whose order are much smaller than that of the modal ROMs and, most importantly, can also be applied to unstable models.

This study proposes a method for designing advanced power distribution system (PDS) including distributed generations, using a combination of fundamental loop generator and multi-objective seeker-optimisation algorithm (MOSOA). The proposed approach reduces the searching space using fundamental loop generator technique to obtain initial feasible solutions which is further improved by SOA to generate new set of solutions with improved aptitude. The proposed methodology uses a contingency-load-loss-index for reliability evaluation, which is independent of the estimation of failure rate and fault repair duration of feeder branches. This planning strategy includes distribution automation devices such as automatic reclosers (RAs) to enhance the reliability of PDS. The proposed algorithm generates a set of non-dominated solution by simultaneous optimisation of two conflicting objectives (economic cost and system reliability) using Pareto-optimality-based trade-off analysis including a fuzzy-operation to automatically select the most suitable solution over the Pareto-front. The performance of the proposed approach is assessed and illustrated on 54-bus and 100-bus PDS, considering realtime design practices. Extensive comparisons are made against some well-known and efficient MO algorithms such as fast non-dominated sorting genetic algorithm-II, MO particle-swarm-optimisation and MO immunised-particleswarm-optimisation. Simulation results show that the proposed approach is accurate and efficient, and a potential candidate for large-scale PDS planning.

Parallel power system restoration (PPSR) restores isolated sections (islands) of the network in parallel, thus the overall restoration process is accelerated. These islands are defined during the preparation stage of PPSR as part of a sectionalising strategy (SS). During this process, it is important that the operators only use updated post-blackout system information. This study proposes a new methodology based on the ‘cut-set’ matrix defined in graph theory, which can identify a shortlist of suitable SSs that satisfy the critical PPSR constraints in a few minutes. This short list of SSs, not identified in previous works, can be presented to the operators to help them select a restoration plan that is tailored to the specific changes in topology and asset availability that the blackout has caused. The methodology is illustrated using the IEEE 9-bus system, and validated using the IEEE 118-bus and the Polish 3375-bus system to demonstrate the efficiency of the new approach for large-scale networks. Multiple case studies are developed to demonstrate the adaptability of the methodology to different system conditions, for example, the unavailability of assets. In every case, the methodology quickly identified a number of SSs that create suitable islands for parallel restoration.

Although protection failures have critical influence on the reliability of power systems, the methodology of assessing composite power system reliability including protection failures has not gone far enough yet. In this study, a Bayesian network (BN)-based analytical methodology is proposed for modelling and analysis of the impact of protection system failures on bulk power system reliability. Initially, basic BN model of composite power system reliability is constructed based on its minimal cutsets (MCs) and logical relationships between components, MCs and system failure. Then, different failure modes of protection system and the interactions among components caused by protection system failures are conveniently incorporated into the basic BN model and the reliability calculations. By using the presented method, several restrictive assumptions, implicit in the other methods, can be removed. Moreover, applying BN provides additional capabilities at modelling and analysis levels. The proposed method is applied to the IEEE reliability test system and the results demonstrate that the proposed method is effective and is flexible in applications.