Optimisation of cost and load flattening for a distribution network is attempted in this work. The objective function is described in terms of energy cost, emissions, real power losses, and load flattening. The solution is envisaged in terms of hourly scheduling of distributed generations (DGs), distributed battery energy storage systems (D-BESSs), and plug-in hybrid electric vehicles (PHEVs). An investigation in the reformulation of the cost of energy is carried out to eliminate the solutions involving excessive charging and discharging of BESSs/D-BESSs and PHEVs. It is demonstrated that simultaneous optimisation of cost, emissions, real power losses, and load flattening cannot be effectively solved as a weighted sum objective function. An -constraint method is applied to obtain the optimal scheduling to achieve cost optimisation, load flattening, and minimisation of emissions from the utility point of view. A case of decentralised multi-agent optimisation problem is also formulated and compared. It is observed that the combination of the scheduling of DGs, BESSs/D-BESSs, grid-to-vehicle/vehicle-to-grid can successfully be used to significantly reduce the system peak demand along with system cost, losses and emissions.

Natural gas, as a clean fossil energy, has been widely used in power generation system by converting chemical energy into electrical energy through gas turbines. However, the natural gas pipeline network system (NGPNS) is comprised of many equipments, and the failure of any equipment will probably cause the outage of gas turbines. Therefore, this study proposes an analytical method which can incorporate the uncertainty of NGPNS into the reliability assessment of integrated energy system. Firstly, on the basis of an analytical technique, NGPNS's operating states and the corresponding state probabilities are simulated. Secondly, a state transferring and equivalence technique is proposed for transferring the unreliability of NGPNS to the involved outage of gas turbines. Thirdly, an equivalent reliability model of gas turbine is established for taking into account the uncertainty of NGPNS. Finally, the NGPNS is connected to the Roy Billinton Test System through the coupling of gas turbines between electrical and gas networks. The results of case studies verify the correctness and effectiveness of the proposed algorithm.

Modular multilevel converter (MMC) has great prospects in voltage source converter-based high-voltage direct current (HVDC) transmission. Although the typical half-bridge MMC can bring huge economic benefits, it is unable to clear dc fault. An MMC based on arm transfer (AT-MMC) and corresponding timing control are presented to effectively solve this drawback. Compared with the half-bridge MMC, the AT-MMC adds the arm-transferring branch to the each phase unit of the converter and adds the current-breaking branch and the energy-absorbing branch to the dc line. Through these branches cooperating with each other, the fault current is cleared. Referring to the half-bridge MMC with the same voltage grade, the AT-MMC has a good economy and its additional device cost is low and additional conduction loss is very small. A model of 51-level MMC–HVDC system is built in RT-LAB OP5600 to verify the effectiveness of the proposed solution.

The electrical energy required by the remote communities can be supplied efficiently and effectively using a decentralised renewable energy source (RES). However, the stochastic variations in power output of RESs, i.e. solar photovoltaic and wind turbine, and deviations in demand make it difficult to preserve the balance between generation and demand which may jeopardise the stable operation of the system. Moreover, in the islanded systems the lack of inertia due to the replacement of conventional power plants with inverter-based sources cause undesirable influence on the frequency of the supply. Generally, various energy storage systems (ESSs) are proposed in such a grid to overcome this problem. This study investigates the implications of the hybrid ESS (HESS) on the frequency regulation (FR) of an islanded system. Battery ESS and a supercapacitor has been used to form a HESS for the islanded power system. Analysis has been conducted in MATLAB/Simulink simulation platform. Simulation results demonstrate the efficacy of the proposed HESS in FR of the islanded system.

The present study proposes a corrective method of electric system overload provided that the wind farm is integrated into the distribution system, taking into account the congestion cost. The authors attempted to mitigate the overload and to monitor flow over transmission lines. Unified power flow controller device was the first suggestion utilised to solve this problem, then due to its extremely fast training and the excellent generalisation performance, extreme learning machine algorithm is employed. The fundamental point is the transmission line alleviation. In addition, other targets are realised: the load shedding avoidance, minimisation of losses and congestion cost. This study is also designed to utilise PowerWorld Simulator and MATLAB software to demonstrate methods for relieving transmission overloads. The accuracy of the proposed approach has been tested for Algerian (Adrar) 22-bus system. Obtained results showed an improvement in power system behaviour. Simulation results are exposed, discussed and compared at the end of this study.

Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near-optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large-scale distribution network. The results show that the proposed technique is able to consistently attain near optimal-solutions.

In recent years, the use of interconnected multi-area systems has increased significantly. To operate these systems efficiently, a proper coordination approach among different areas is required. With this context, a novel decentralised optimal dispatch algorithm is proposed in this study. The centralised economic dispatch model is decoupled into a series of sub-problems for different areas, allowing the independent decision of each area without a central operator. The detailed information of each area is not required for information exchange. To better estimate the impact of each area's decision on other areas, the approximate value functions of the tie-line powers are added into the local objective function of the sub-problem. This technique makes the decentralised solution of the proposed algorithm more explicit and rational compared with most existing distributed algorithms. Besides, the proposed technique does not require parameter tuning and has good convergence performance. Numerical simulations on several test systems and a real power system demonstrate that the proposed algorithm is favourable in terms of accuracy, adaptability, and computational efficiency.

Considering the high spatial complexity of signal processing methods and the low diversity between the base classifiers in the ensemble classifier, a new method with optimal multi-resolution fast S-transform (OMFST) with low spatial complexity and rotation forest (ROF) was proposed. Firstly, Gini importance of features is evaluated by random forest (RF), and the sequence forward search method was adopted for feature selection. Then, the intermediate matrix was constructed on the optimal feature set. The results of inverse fast Fourier transform in the main frequency points of signals based on OMFST are compressed in time domain. The intermediate matrix was used to extract features for power quality (PQ) disturbances recognition. Finally, the ROF was adopted to encourage simultaneously individual accuracy and diversity within the ensemble. The optimal ROF was applied to identify 17 kinds of PQ disturbance signals. The simulation results show that the new method can effectively compress the time–frequency matrix of the existing S-transform (ST) method; the space complexity of the ST modular matrix is reduced significantly and has higher accuracy. Besides, the results of the experiment with real PQ data prove that the new method was effective for practical industrial applications.

Owing to the intermittency of the renewable generation and constantly changing load demand, the battery energy storage systems (BESSs) have become a required solution to future power systems. In this study, the BESS is modelled as an agent. The authors propose an event-trigger finite-time consensus approach to maintain the supply–demand balance and minimise the total power loss associated with charging/discharging inefficiency. The proposed consensus algorithm can guarantee all BESSs to converge to an optimal operation point in finite-time. A situation where the charging power of each individual BESS has a constraint is considered. Furthermore, event-triggered feedback is considered to reduce the frequency of agents’ communicating with their neighbours. The communication bandwidth and energy can be saved corresponding to the goal of economic requirement. Finally, some simulation cases are further presented to illustrate the effectiveness of the scheme.

This work proposes a novel relative electrical distance measure that provides information of coupling between voltage and apparent power between two buses in power systems. Relative electrical distance measure is derived from the bus admittance matrix which can be obtained in real time using Phasor Measurement Units. Based on the relative electrical distance measure, in this work, an isoperimetric clustering based algorithm for partitioning power systems into voltage–apparent power coupled areas is proposed. The advantage of the partitioning algorithm proposed in this work is that large networks can be represented as a weighted graph with number of vertices equal to number of generators in the system which is much lesser than the size of system, thereby reducing the computational effort for partitioning. Isoperimetric clustering technique along with k-means is then applied to the graph to obtain voltage–apparent power coupled areas. Simulations carried out on New England 39-bus system and IEEE 118-bus system demonstrate the effectiveness of the proposed methodology for partitioning the system into voltage–apparent power coupled areas, subject to changes in the operating condition of the system. The quality of clustering is analysed and compared with Cheeger inequality bounds, which ensures that power system is well partitioned.

The use of inertia emulation in a microgrid has been growing as a promising control approach to maintain the bus voltage and frequency. An inertia emulation-based control technique is proposed for a DC microgrid supplying AC loads in this study. The proposed work includes: (i) the DC bus voltage regulation using virtual inertia control and economic power management by a current sharing algorithm in cascade with a restoration control; (ii) AC load bus voltage and frequency regulation using virtual excitation emulation and inertia emulated control that utilises the emulated inertia and DC bus voltage. Further, it also provides inertia support to the grid. The coordinated current sharing algorithm manages the current (power) sharing between the grid and battery pack of electric vehicles based on electricity price and state of charge of the battery. The cascaded restoration control eliminates the steady-state error in the DC bus voltage. Finally, the DC microgrid is simulated under various operating conditions to verify the performance of the proposed control approach. The simulation results reveal that the objectives are achieved successfully.

A horizontal time decomposition strategy to reduce the computation time of security-constrained economic dispatch (SCED) is presented in this study. The proposed decomposition strategy is fundamentally novel and is developed in this paper for the first time. The considered scheduling horizon is decomposed into multiple smaller sub-horizons. The concept of overlapping time intervals is introduced to model ramp constraints for the transition from one sub-horizon to another sub-horizon. A sub-horizon includes several internal intervals and one or two overlapping time intervals that interconnect consecutive sub-horizons. A local SCED is formulated for each sub-horizon with respect to internal and overlapping intervals’ variables/constraints. The overlapping intervals allow modelling intertemporal constraints between the consecutive sub-horizons in a distributed fashion. To coordinate the subproblems and find the optimal solution for the whole operation horizon distributedly, accelerated auxiliary problem principle is developed. Furthermore, the authors present an initialisation strategy to enhance the convergence performance of the coordination strategy. The proposed algorithm is applied to three large systems, and promising results are obtained.

This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network.

Power of constant impedance load is proportional to the square of bus voltage magnitude (SBVM). A solution space for a set of power balance equations (PBEs), constructed using SBVMs, has lower-order terms and less non-linearity. Such solution space yields faster solution. This work proposes a set of rectangular PBEs which uses SBVMs in conjunction with bus phase angles to relate branches power flow, a pair of real and reactive power equations for each branch for sending/receiving ends. Using an incidence matrix, branches power flows are related to bus powers with two set of equations for real and reactive powers. Another set of equations to relate rectangular form of voltages at generators to their voltage magnitudes, the total number of equations equals four times branches number, and two times buses number, less the two for unknown powers of slack bus. While the equations number is more than the node-based PBEs, order of Jacobian's terms is significantly lower. Testing the proposed algorithm on numerous case studies, up to a 9241-bus real system, showing the algorithm is precise, has superior convergence features, scales well for real systems and is up to thrice as fast as the rectangular node-based load flow for some cases.

The transition of the electric power system to reach sustainability goals leads to new market conditions with larger uncertainties. This constitutes new challenges and opportunities for new as well as for existing market players such as retailers. In a future more volatile and unpredictable market, financial risk management becomes an important element for such actors in order to achieve viable businesses. Different instruments can be applied for this purpose, where demand response can contribute in the short-term to manage risks related to price variations and imbalance costs. This study contributes to the enhancement of retailer's businesses by presenting a stochastic optimisation model exploring the possibility to apply demand response to control financial risk exposure. The model considers trading and demand response scheduling for different customer clusters, generating optimal trading volumes for day-ahead markets while also considering the possibility to trade intra-day. The optimisation considers uncertainties in prices and loads as well as imbalance settlement costs. Risk management is integrated into the model by applying conditional value-at-risk as risk measures. The developed model has also been applied in a case study with data from the Swedish and Nordic electricity market together with simulated load profiles for different customer clusters.

In this study, an extinction angle predictive (EAP) control strategy is proposed to mitigate commutation failure (CF) in high voltage direct current (HVDC) systems. The proposed strategy works effectively during both AC system faults and commutation voltage distortions. At first, an index is put forward to evaluate the prospective commutation area drop and quantify the upcoming commutation stress considering the commutation mechanism. Then, the extinction angle is predicted based on the detected commutation area drop incident. Further, the predicted extinction angle is introduced into the HVDC inverter control to design the EAP control strategy, ensuring that the CF can be prevented due to a sufficient commutation margin. The advantage of the proposed strategy is that it evaluates the upcoming commutation stress based on the transient commutation voltage waveform. Thus, a better sensitivity for commutation voltage distortion can be ensured. The proposed strategy is simulated in the PSCAD/EMTDC platform and the results are compared with that of the conventional schemes, verifying the performance of the proposed strategy in mitigating the CF during AC faults and voltage distortion.

Fault current limiter (FCL) is a kind of device with current-dependent impedance that has low impedance in normal state of power system, and high impedance during fault condition. Among all classes of FCLs, the saturated iron-core FCL (SICFCL) is practically the most favourable one nowadays. In this study, a numerical method for design and cost optimisation of SICFCLs is presented. It uses a set of constraints to ensure the desirable operation of the SICFCL based on its electrical and magnetic characteristics. For numerical calculations and optimisations, GAMS software has been used. Suitability of the method is approved by design, simulation and test of a 3500 VA prototype. Magnetic field distributions are simulated using COMSOL Multiphysics and performance of the designed SICFCL is evaluated in a power system hardware model. Simulation and experimental results show the satisfying agreement with predicted results by the proposed numerical method.

Aiming at the power sharing among converter stations and the frequency stability of weak AC system connected by converter stations in multi-terminal voltage source converter high-voltage direct current (VSC-HVDC) system, a coordinated control strategy without communication is proposed, which can realise the bidirectional active power support between DC system and weak AC system. The strategy divides the voltage of DC system and the frequency of weak AC system into open-time operation area and time-limited operation area, respectively. When DC voltage and AC frequency are in the open-time operation area, respectively, unbalanced power in the system can be flexibly allocated according to the power margins of converter stations, which reduces DC voltage fluctuation and the influence of DC voltage errors (line length and other factors) among the converter stations on power regulation. When the AC frequency is in the time-limited operation area, the DC system can provide power support for the weak AC system to maintain frequency stability. When the DC voltage is in the time-limited operation area, the weak AC system loses part of the frequency quality to provide short-term power support for the DC system to maintain the stability of VSC-HVDC system as far as possible. The simulation results have verified the feasibility and effectiveness of the proposed control strategy.

The self-scheduling horizon is an important schedule parameter in the self-scheduling problem. A more reasonable self-scheduling horizon can lead to higher benefits of the wind farm (WF) and large-scale compressed air energy storage (CAES) combined system. However, very few studies have been reported about the optimisation of self-scheduling horizon for a WF paired with a CAES plant. In this study, a rolling day-ahead self-scheduling framework for a WF and CAES combined system is first proposed. After that, the self-scheduling horizon optimisation model is developed in the formulation of a bilayer stochastic chance-constrained optimisation problem. The proposed model is converted into its equivalent deterministic linear formulation and then is solved. Based on the developed model, the impacts of self-scheduling horizon on the profit of the combined system are analysed. Numerical simulation results indicate that the profit of the combined system increases after using the optimal self-scheduling horizon, and the profit increment is more obvious with the increase of the CAES's energy storage capacity.

Integration of renewable energies such as wind and solar with an energy storage system (ESS) in a distribution network is the interest of current studies in power system engineering. Wind and battery ESS (BESS) are known for their complement and efficient approaches into the distribution networks. The promising of renewable energies for wind and solar in Afghanistan is a motivation for stepping up the power sector of the country by enhancing the power quality as well as self-dependency in electricity production. In this study, a multi-objective optimisation technique, non-dominated sorting genetic algorithm II (NSGA-II) is proposed for an extensive distribution network in Kabul city considering technical, environmental, and financial control schemes for the network improvement. Three different scenarios with various objective functions are deemed to evaluate their impact on decision variables and network parameters. Furthermore, optimum allocation of the wind turbine and charge/discharge scheduling of BESS are revealed with improvement in performance of the power system. Simulations are deployed in MATLAB^® with its application on developed 162-bus real-distribution network to demonstrate the effect of different objective function arrangements in each scenario as well as confirming the robustness of the proposed approach.

This study proposes a bi-level stochastic framework to address optimal scheduling of energy hub (EH) in a pool-based short-term market considering electrical–thermal–water demands. EH acts as an independent price-maker producer in a day-ahead electricity market aiming to maximise its profit. The market settlement mechanism is constructed as the pay-at-market-clearing price (MCP), where each producer/consumer is paid at the MCP. The problem model is formulated as a bi-level optimisation approach in a stochastic environment, in which the upper level defines the profit maximisation of the proposed strategic producer, whereas the lower-level expresses the dispatch cost of the considered power grid. This results in a problem formulation with mathematical equilibrium constraints which is transformed into a new mixed-integer linear programme based on Karush–Kuhn–Tucker conditions. A stochastic framework based on unscented transform is developed to model the high uncertainties of EH water demand, EH thermal demand, EH electric demand, generators and loads submitted price to the market. The simulation results on the IEEE test system advocate the effectiveness and appropriate performance of the proposed strategic EH producer in the electricity market and its effect on the locational marginal prices of buses in a transmission-constrained market.

Demand side participation is a fundamental property of modern power distribution networks. Time-of-use pricing is a common strategy to persuade customers to shift a part of their consumption to low consumption hours. In flat time-of-use pricing, it is not the price that changes over a period, it is the cost of energy. The aim of this paper was to quantify impact of cost elasticity of demand on investment plans over a multi-stage horizon, where the time-of-use pricing was taken into account. On one hand, time-of-use pricing was modeled with three load levels as peak, medium-peak and off-peak. On the other hand, the tendency of customers to participate in reduction of demand was modeled where it shows rate of response of customers to change in cost of energy and it determines maximum demand response penetration. So, it was examined how time-of-use pricing and responsive customers can postpone the investments. Mixed integer linear programming method was utilized to solve the optimal distribution expansion planning problem. Simulations were performed on an 18-node distribution network. Results revealed effectiveness of the proposed model and showed that consideration of cost elasticity of demand in expansion planning, changes optimal configuration of network and significantly influences total costs.

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.

In a complex interconnected network, due to dynamic interaction between the AC networks, during occurrences of faults, the successful relay operation is threatened. For security and reliability of the power system network, a fast and accurate protection scheme is of great importance. This study presents a protection scheme for multiple fault detection at bus/line and its type in a wide area network. If a fault occurs on a line, followed by another fault at the same/different line, before the clearance of former fault, the proposed scheme is capable of detecting such multiple fault events. The scheme applies processed signal information for its time–frequency representation using Smoothed Pseudo Wigner–Ville distribution, followed by Hilbert transform and calculation of indices to interpret the fault events. It is shown that faulted bus, faulted line, time instant, and types of faults can be easily identified with low computational burden. The scheme is validated on the signals simulated on Kundur's model, IEEE 39 bus system and verified with signals on real-time digital simulator for different fault conditions; fault resistance and fault location on the line. The scheme can be successfully applied on signals available from phasor measurement units for wide area network protection.

This study proposes distributed energy management approach for charging multi-class electric vehicles (EVs) in community microgrids. The energy management problem is implemented in real-time and represented by a non-cooperative Stackelberg game for the power distribution inside the microgrid. In this game, a battery energy storage system is chosen as a leader and the EVs are designated as followers. The charging power distribution among EVs is tackled in the two cases of ‘plenty of power’ and ‘lack of power’. The challenging case of ‘lack of power’ occurs when the total charging power is insufficient to meet the need of each EV, such as when weather conditions are unfavourable. A priority factor is included in the EV utility functions to address charging priorities of different classes of EVs in practical scenarios. A consensus-based distributed algorithm is developed later to iteratively reach the Nash equilibrium, i.e. final charging power distribution, among EVs with different charging priorities. Both simulation and experimental results show that the charging power is properly distributed when the predefined charging priorities are followed, particularly in the case of a ‘lack of power’.