A risk-aware electricity retailer may alleviate concern about wholesale pool-price volatility through coordinated demand response (DR) trading with aggregators who act as intermediaries between end-users and the market operator (MO). This article proposes cost-efficient integration of DR into electricity markets using a bi-level optimisation framework. In the upper-level, the retailer's problem is to maximise expected payoff, i.e. revenues earned by selling energy to end-users minus the expected cost of purchasing from the wholesale energy pool and the DR aggregators. The evolving mean reverting volatility in pool electricity prices is captured as a stochastic jump-diffusion process. The conditional value-at-risk (CVaR) measure is explicitly incorporated into the problem to limit the risk of payoff loss due to the price volatility. The lower-level problem involves the aggregator's strategic bidding offer in which the primary objective of the MO is to minimise the DR transaction cost. In the DR offer setting, the conflicting economic interest to increase the aggregator's payoff is captured. A Lagrangian relaxation method with associated Karush Kuhn Tucker (KKT) optimality is used to solve these problems. The simulation results consider plausible case studies and provide the effectiveness of the proposed market model.

With the accelerating advances in renewable energy technologies and growing penetration of these sources as distributed generation units in the power systems and utility networks, designing controllers has become more complicated. Model order reduction is a proper way to ease this process. In this study, an order reduction procedure based on the residualisation method is designed to reduce the order of the system. The major advantage of the residualisation method is its capability in preserving steady-state gain of the system. This characteristic increases the accuracy of the approximated system, which is demanded in designing the controller. The model of a slightly modified IEEE 14 bus test system with penetration of photovoltaic resources is implemented and reduced through the proposed procedure. Simulation results show the better performance of the residualisation-based method in the steady state compared with three other widely used methods.

In this study, a dynamic state estimation (DSE) approach is proposed for power systems based on particle filter (PF) and ant colony optimisation for continuous domains (ACO_R). Usually, the Kalman-based estimators with Gaussian noise assumption are utilised for DSE. However, this assumption is questionable for real power system data. Although PF methods offer a potential solution for this issue, the basic PF algorithm is time-consuming and suffers from sample impoverishment problem and degeneracy of the propagated samples. In this study, the search capability of ACO_R is utilised as an adaptive probability density function estimator to overcome these shortcomings and reduce the required particle numbers. The proposed approach minimises the computational effort by reducing the required particle numbers. The ninth-order model of the synchronous generator has been applied in this study. The performance of the proposed method is investigated through a two-area-four-machine test system as well as the IEEE 39-bus (New England) test system and it is compared with several advanced Kalman filter-based and PF-based approaches. The simulation results obtained for different case studies demonstrate the effectiveness and robustness of the proposed method against noises, abrupt state changes, and gross measurement errors.

The authors found that aggregation of same constant power loads (CPLs) in the parallel connection may possibly cause oscillation instability of a DC microgrid. In this study, they carry out analysis to reveal the mechanism about why the danger of oscillation instability may be brought about by the aggregation of CPLs in parallel connection. By assuming that the dynamic models of N CPLs in parallel connection are identical, analysis in this study proves that aggregated representation of the CPLs is equivalently comprised of N dynamically independent subsystems. Oscillation modes of aggregated CPLs may possibly move toward the right on the complex plane when the number of CPLs, N , increases. They propose a method to estimate the direction of movement of oscillation modes when N varies without having to conduct the modal solution of full-order state matrix. Thus, the proposed method can detect the danger of oscillation instability caused by aggregation of CPLs in parallel connection and estimate the maximum number of CPLs when the oscillation instability may occur. They present two examples DC microgrids with aggregated CPLs in parallel connection to evaluate the analytical conclusions. Application of the proposed method in planning the grid connection of CPLs is demonstrated.

This study presents a novel pulse-closing vacuum switch (PCVS) that is used for catenary fault detection and location. First, as the application background of PCVS, the principle theory of the fault detection and locating system is briefly introduced. Then, the PCVS is analysed and designed based on the detailed analysis of its working process and principle. To validate the performance of the designed PCVS, the multi-physical domain co-simulation numerical model is implemented using the ANSYS Maxwell and ANSYS Simplorer. Finally, a PCVS prototype is developed with permanent magnet closing and repulsive force opening to verify the simulation results. The simulations and experiments both approve that the contact time between the moveable contact and static contact can be controlled to be <6 ms. This meets the pulse-closing technology requirement for the fault detection and locating system.

Unit restarting after a blackout is the basis of the overall restoration process for a power system. A reasonable restarting sequence of units may speed up the restoration process and reduce economic loss. In view of the situation that there are usually several black-start sources in a large-scale power system, a global optimisation method for unit restarting considering black-start zone partitioning is proposed in this study. On the basis of the multiple time-step modelling framework, the optimisation problem is formulated as a mixed integer linear programming model, with the objective functions to maximise the total-weighted MWh output and minimise the links between the subsystems under various operational constraints. The hierarchical method based on generalised Benders decomposition is applied to decompose the optimisation problem into one master problem and two sub-problems: (i) determination of the unit restarting sequence considering only the grouping of units and (ii) rationality check of unit grouping and check of operational constraints based on power flow calculation. The master problem and sub-problems are alternately and iteratively solved until the optimal solution is obtained. The results of New England 10-unit 39-bus system and a practical power system in southwest China verify the validity of the proposed method.

Optimal transmission switching (OTS) is widely used in mitigating transmission congestion and reducing power losses. However, with large-scale renewable energy integration to the grid, making a decision for the randomness characteristic of renewable energy (e.g. wind and solar power) is complicated in OTS. In this study, a novel stochastic optimal power flow-based point estimation method (PEM) is presented to model the uncertainties of wind power and load in OTS. Polynomial normal transformation is introduced to handle the correlations between random variables, and percentile matching method is employed to obtain the coefficients of polynomial normal transformation, which can avoid the integral operation. Moreover, the expected value of power flow obtained by the PEM is applied to the presented model to control the line overload risk. Two indexes, namely, population sample mean and mean of population sample standard deviation, are proposed to investigate the effect of correlations on OTS strategies. The proposed model is finally transformed into a mixed-integer second-order cone programming to consider bus voltage and reactive power, and the modified IEEE RTS 24-bus system and IEEE 118 system are presented to test the model.

This work presents a new hybrid multiverse optimisation (HMVO) methodology to solve the optimal accommodation of distributed generations (OADGs) problem in power distribution networks. The HMVO method is formulated by incorporating the space transformation search (STS) technique, and piecewise linear chaotic map-based optimisation method. STS avoids the prior convergence and increases the possibility to find the solutions near the optimal global point. In contrast, the chaotic optimiser is extremely good at ergodicity and stochasticity, used around the transition of the exploration–exploitation phase. The objective functions to be optimised are as follows: minimisation of electrical energy losses, minimisation of overall node voltage deviation, maximisation of overall voltage stability margin, and minimisation of energy not served. Analytical hierarchy process method is employed to optimise the objective function weights, as the optimisation problem is solved by weighted sum method. The proposed methodology is demonstrated on radial power distribution networks of IEEE 33-bus and INDIAN 85-bus. The effectiveness of the proposed HMVO method is validated by comparing the results with studied algorithms and algorithms presented in the literature and found to be promising. Further, the OADG problem under uncertain environment is also investigated for long-term planning using self-adaptive polyhedral deterministic set.

This study develops a multi-objective approach to pursue an optimal reactive power dispatch (ORPD) of power systems equipped with wind farm (WF) integrations. The preliminary control variables include shunt compensators, synchronous condensers, and under-load tap changers (ULTCs). Innovatively, reactive power provision capability of WFs, beyond their active power contributions, is scheduled besides the variables above. Seemingly, a double operating area is adopted for WF within which it injects active power towards the main grid. Concurrently, it could inject or absorb reactive power, which impacts technical metrics of the power system operation. The main purpose is then establishing a compromise between the power loss and voltage deviation minimisation and also reducing the number of ULTC tap variations and operations. To handle the multi-objective feature of the proposed model, the global criterion method is deployed. The established ORPD model represents a multi-objective non-linear mathematical problem, which is converted to a mixed-integer linear programming format. By this way, the computational burden of the problem is reduced to maintain an acceptable accuracy of the obtained results. Detailed discussions are provided to assess the performance of the proposed model.

Under a multi-energy environment, the conventional demand response programmes in smart-grid will be evolving to the concept of integrated demand response (IDR). In order to examine such an effect, this study presents a new methodological framework to conduct the probabilistic energy flow (PEF) analysis of integrated energy systems, while considering the potential effects of IDR. For this aim, a new price-elasticity-based IDR model is developed. As distinct from existing works, this study explicitly focuses on the stochastic characteristics of IDR programme and a novel Z-number-based technique is introduced to handle the relevant uncertainties that involved. The proposed Z-number model can not only capture the inherent randomness in demand-side performance but also account for the potential impact of information availability/reliability on the representation of IDR. Since the presence of Z-numbers makes the authors model include different types of uncertain representations, the fuzzy expectation technique and centroid method are jointly used to make them manageable under the same framework. A comprehensive algorithm based on the cumulant method and an optimisation scheme is employed to solve this PEF problem with consideration of IDR. The proposed framework is illustrated through numerical studies and simulation results confirm its effectiveness in actual implementations.

The bipolar modular multi-level converter-based multi-terminal high voltage direct current (MTDC) grid offers an attractive solution to cope with the increased renewable power generation integration and power transfer capability. However, the N − 1 outage of direct current transmission lines and converters may result in overloads of other components and renewable power generation congestion. This study presents a power shift-based optimal corrective control method to guarantee the operational security of the system with bipolar MTDC grid embedded. First, the principle of power shift in the bipolar MTDC grid is analysed. The grid features in isolated positive and negative poles and can provide up to 50% transmission redundancy. Following an N − 1 contingency, certain power can be shifted from the faulty pole to the healthy pole or between different converter stations. Second, two optimisation models are, respectively, established for the MTDC and alternating current grids to address the power shift-based optimal corrective control. The proposed method guarantees operational security of the system while the amount of renewable power generation accommodation by the MTDC grid is maximised. Finally, the method is applied to the real-life Zhangbei ±500 kV four-terminal bipolar high-voltage direct current grid. Numerical results and electromagnetic transient simulation verify the effectiveness of the presented method.

Considering financial, ecological and also reliability challenges in planning and operation of microgrids is an indispensable issue. On the other hand, an appropriate design of microgrids in planning stages has a major impact on the daily operation of the system. According to these matters, in this study, a hierarchical decision-making framework has been presented for both planning and operation of microgrids with wide ranges of means and concepts. The proposed model has been formulated as a bi-level optimisation problem in which every level optimises its own objectives independently, however, in interaction with each other. The upper level (leader) problem, which is related to the planning of microgrids, minimises the utility's demand, investment, and emission costs, while the lower level (follower) problem minimises the operation and maintenance costs through the implementation of an energy management system. The mentioned model is a non-linear bi-level problem, which is transformed into a linear single-level problem through Karush–Kuhn–Tucker conditions. Moreover, the contingency based energy management, demand response programme and uncertain nature of renewable resources have been taken into account. Finally, the proposed method has been applied to a typical microgrid and its results are compared with the weighted-sum multi-objective approach to depict its efficiency.

A modified hybrid energy storage system (HESS) consisting of a double-screw expander-generator (DSEG) and a supercapacitor is proposed here to accommodate wind power fluctuations. The dynamic matrix control (DMC) algorithm is applied to improve the operating performance of the HESS. The reference track of the exchanging active power of the supercapacitor is pre-planned based on wind turbine output, load demand, and the predictive DSEG output. Meanwhile, a linearisation and state of charge (SOC) management strategy for the supercapacitor is proposed to avoid excessive charging-discharging. During the predictive control process, the DSEG input is continually adjusted by means of rolling optimisation combined with feedback correction until the output of the HESS approximates its expectation. Case studies show that the supercapacitor efficiently eliminates the power hysteresis of the DSEG and the DSEG eliminates the intrinsic error caused by the linearisation of the supercapacitor output in turn, indicating that the control effect of the HESS is improved.

With the advancement of energy storage technologies, installing an energy storage system (ESS) in a distribution network has become a new solution to accommodate more and more distributed renewable generations. In this study, the optimal allocation of distributed ESSs is studied to maximise the benefit of the distribution system operator. The ESS allocation problem is divided into two stages: the mixed integer investment problem as the first stage and the optimal operation problems considering daily charging/discharging schedule of ESSs as the second stage. To tackle the uncertainties of distributed generation output and base load, typical days (scenarios) are firstly obtained by the clustering method and thereafter the operation problems include a number of scenarios, each with the corresponding possibility. Then each second stage problem is relaxed to a second-order cone programming model. To efficiently solve the whole problem considering multiple scenarios, the generalised Benders decomposition (GBD) algorithm is adopted, which is further accelerated by relaxing and rebinding integer constraints. Numerical experiments are conducted on a 17-bus test system to demonstrate the effectiveness of the proposed method. Additionally, comparisons between different algorithms are performed to verify the merits of the proposed acceleration method with respect to the original GBD and the branch-and-bound algorithm.

High intermittent wind generation necessitates integration of bulk energy storage systems (ESSs) for maintaining security and reliability in power system operation. Considering this, stochastic security constrained unit commitment (SCUC) including compressed air energy storage (CAES) as bulk ESS for high wind penetration and with wind uncertainty modelling is addressed. Network constraints for pre- and post-line contingency are modelled using DC power flow. Injection sensitivity factors (ISFs) are conventionally used in power flow equations which, however, make N − 1 network security constrained formulation huge and computationally demanding for the proposed stochastic model. Therefore, this study proposes a line outage distribution factor (LODF) to reduce the number of coefficients of post contingency DC power flow equations. This is a compact formulation with the lower computational requirement. Wind uncertainty is modelled as probable scenarios. The proposed SCUC is a complex mixed integer linear programming problem and solved using Benders decomposition technique for IEEE 30-bus and 118-bus system. Simulation results to analyse the impact of CAES, wind uncertainty and line contingency with ISF and LODF on overall operation costs, CAES scheduling, wind curtailment, locational marginal price and computational time. Results show that the proposed model is computationally efficient for system operation under high wind penetration.

To provide reliable technical support for the power grid to accommodate the increasing electric vehicle (EV) penetration, various charging strategies have been studied in the literature. Considering the difficulties of the device-level implementation and the limitations of the current EV charger technology, this study focuses on co-ordinating the charging start time (i.e. the time instant when each EV starts charging). To avoid excessive reduction of the adjustable range of the charging start time, this study applies a symbolic–graphic combination principle to address this problem. This involves formulating the valley-filling charging problem as a rectangle packing problem, which is equivalent to divide the valley-filling problem into two sub-problems: filling the load valley and optimising the valley-filling effect. Furthermore, a Zero-Gap Lowest Horizontal line (ZGLH) algorithm is proposed to fill the load valley without producing any undesirable gaps. In addition, a genetic algorithm (GA) is employed to further optimise the valley-filling effect by regulating the scheduled sequence. The proposed ZGLH algorithm can guarantee the charging start time to be continuously regulated within the controllable range rather than discretely regulated as in other works, and it does not add extra computational overhead. The effectiveness of the optimal charging strategy was quantitatively verified by several simulation cases.

Parallel flexible AC transmission systems (FACTS) devices affect the performance of protection relays and conventional phasor-based fault location schemes in transmission lines. This study focuses on both multi-terminal and parallel-compensated lines, not investigated simultaneously in previous works. An algorithm based on deep neural networks is proposed for fault location in a three-terminal transmission line with the presence of parallel FACTS device. The line model and fault occurrence are simulated in SIMULINK and features are extracted from voltages at the three terminals by wavelet transform. The generated features are used to train a deep neural network which determines faulted line section and fault distance simultaneously. The adopted intelligence-based approach has the advantage of not requiring pre-knowledge of line specifications, FACTS devices modelling and the uncertainty in compensator parameters. A large number of fault scenarios are investigated. The faulted section is recognised correctly in 100% of test cases. The algorithm performance is acceptable for both symmetrical and unsymmetrical fault types, small fault inception angles and high fault resistance. The accuracy of fault location is improved compared to previous schemes (total mean error of 0.0993%). The proposed algorithm provides an accurate, fast and robust tool for fault location in parallel-compensated three-terminal transmission lines.

In this study, the authors present a chance-constrained economic dispatch model for power systems with the integration of wind power. In the proposed model, the automatic generation control (AGC) regulation is adopted to compensate for the power variations caused by the forecast errors of wind power and load. Moreover, the power flow limits and the transient stability constraints under contingencies are formulated as chance constraints through the security region (SR) approach. On the basis of the hyper-plane characteristics of the boundaries of SR, the chance constraints of power flow limits and transient stability limits are converted to equivalent deterministic linear inequalities. The proposed method can assure that there is enough reserve capacity in the dispatch solution to accommodate the power variations caused by wind power and load, and the branch power flow limits and the transient stability constraints under uncertain power injections and contingencies are satisfied with a high probability. Numerical tests show that the proposed method has well-convergence property and computational efficiency and it serves as a useful tool for power dispatchers to identify a balance between economics and robustness of power system operation.

Sensitivity, selectivity, speed of operation and reliability of the conventional current differential protection schemes are affected and limited by the distributed shunt capacitance of high-voltage long transmission lines. In this study, a new pilot protection algorithm has been proposed based on the computation of change in shunt line admittance ‘Y’ during dynamic conditions using synchronised data. Therefore, it does not require a separate compensating technique to nullify the effect of line charging current in the fault detection. This scheme can quickly discriminate the internal faults and also detects the faulty phase. Also, it has an advantage of estimation of an accurate fault location. The performance of the proposed algorithm is evaluated on 400 kV, 50 Hz two-bus system as well as IEEE-39 bus 345 kV system using EMTDC/PSCAD and MATLAB simulation tools. The simulation results confirm the proposed algorithm can effectively discriminate the faults, independent of fault location, fault resistance, type of fault, series compensation lines and source impedance. Finally, a hardware experiment using National Instruments (NI) based LabVIEW platform is carried out to check the practical feasibility of the proposed algorithm.

Parameter estimation of synchronous generation systems helps system operators develop control schemes to enhance system stability and reliability. However, the input signals used in the online estimation are measured with errors and their direct usage may produce biased parameters. To handle this problem, an online parameter estimation method considering input errors is proposed. A Lagrange multiplier-based input variable selection method is further proposed to reduce the scales of estimation problem and improve the computational efficiency. In order to avoid ill-conditioned problem resulted from input error over-fitting, a Tikhonov regularisation approach is applied and the optimal regularisation parameter is determined via cross-validation. In the case study, the numerical experiments and real-world results validate the effectiveness and advantages of the proposed method.

In this study, simultaneous generation and transmission expansion planning is presented in the presence of wind farms. Short-circuit current constraint is included in the problem, and the impact of wind farms on short-circuit current is evaluated. This study presents a novel approach for reducing short-circuit current in composite generation and transmission expansion planning. In this way, new units and lines are determined such that the short-circuit currents of substations are less than the defined limit. In addition, an optimal level of short circuit is determined and the total cost is reduced. The proposed method is applied to a real case study, and its performance is demonstrated by comparison with other methods.

This study presents a distributed collaborative transmission expansion planning (TEP) algorithm for interconnected multi-regional power systems. The proposed algorithm is a multi-agent-based TEP. A local TEP is formulated for each region (agent) with respect to the region's local characteristic and interactions (i.e. tie-line flows) with its neighbours. Nodal power balances at border buses are modified to model the interactions. Realistic planning constraints and objectives such as budget constraints, operational costs, N − 1 security criterion, and uncertainties are modelled in the local TEPs. The information privacy is respected as each local planner needs to share limited information related to cross-border tie lines with other planners. To coordinate the local planners, a two-level distributed optimisation algorithm is proposed based on the concept of analytical target cascading (ATC) for multidisciplinary design optimisation. While the upper level solves the local TEPs in parallel, the lower level seeks to coordinate neighbouring regions. The lower-level problem is further replaced in the upper-level optimisation by Karush–Kuhn–Tucker conditions to relax the need for any form of central coordinator. This makes the proposed ATC-based TEP a fully parallelised distributed optimisation algorithm. An initialisation strategy is suggested to enhance the performance of the distributed TEP.

Unsecured operation of backup distance relay during an external fault results in removing a needed energy link and can jeopardise overall system stability. This problem that is well known as distance relay overreach is very likely during coupling capacitor voltage transformer (CCVT) transients mainly under high system impedance ratio (SIR) conditions. To tackle this challenging problem, a new methodology consisting of three stages is proposed in this study. It is capable of solving the overreaching problem which occurs under high SIR conditions, without sacrificing the operation speed of distance relay for inside the first zone faults. Comprehensive simulation studies are carried out to test the proposed methodology. The obtained results demonstrate its feasibility and effectiveness for practical applications in the power system.

A framework to quantify the value of model enhancements (VOMEs) in transmission planning models is proposed and applied to a case study of the large-scale, long-term planning of the Western Electricity Coordinating Council (WECC) system. The VOME, which is closely related to the concept of the value of information from decision analysis, quantifies the probability-weighted improvement in the system performance resulting from changes in decisions that result from model enhancements. The WECC case study shows that it is practical to quantify VOME and illustrates the type of insights that can be obtained. The values of four types of model enhancements are compared. The results show major benefits from considering long-run uncertainty using multiple scenarios of technology, policy, and economics; these benefits are as much as 14% of total benefits of new transmission built in the first ten years. However, less benefit is obtained from more temporal granularity, more complex network representations, and inclusion of unit commitment constraints and costs. This framework can be applied to quantify the VOMEs in any planning context, such as integrated resource planning.

This study investigates a multi-status simulation method based on event-driven for a smart distribution network (SDN). By switching a steady simulation model and a dynamic simulation model, the typical events of the SDN with various time constants can be simulated within the same simulation framework. The aim of the proposed method is to make full utilisation of the advantages of conventional simulation methods when considering the actual operation process of the SDN with many typical events so that the simulation results can approach the actual situation. To ensure that the operating status and corresponding simulation models of the SDN could be switched correctly and automatically, a simulation engine is proposed and implemented based on a finite state machine. Multi-status simulation experiments on IEEE 33-bus and an actual 10-kV distribution network validate the proposed multi-status simulation method.

With increasing penetration of distributed generations, the importance of the voltage stability assessment of distribution networks has been increased. Considering this situation, a probabilistic model is proposed to evaluate the voltage stability of distribution network integrating wind turbine (WT) units. With this intention, a probabilistic voltage stability index (PVSI) of a radial distribution system with wind power generation is presented. This index investigates the probabilistic risk of voltage collapse for all the buses of system considering uncertainty of WTs. Therefore, the most sensitive bus to the voltage collapse can be identified. The problem model is based on the catastrophe theory that can find the bifurcation point of system. Three-point estimation method is employed to calculate the statistical moments of voltage and PVSI of nodes. Moreover, to estimate the cumulative distribution function of output random variables, the Cornish−Fisher series are used. The performance of the probabilistic index is tested on the IEEE 69-bus radial distribution system where different load models are considered. The results demonstrate that the PVSI can accurately predict the voltage instability condition of the system.

Fault ride through (FRT) capability is an essential practice as per the present grid code demands for grid-connected renewable energy-based distributed energy resources. Studies on FRT capability for grid-connected hybrid systems are rarely found. This study considers a wind energy conversion system and a fuel cell system interconnected at a common dc bus. It proposes a new feed-forward-based FRT control scheme for the inverter control where new current references in dq-axis frame are derived by tracking the positive sequence power. The newly derived references are fed forward to the input of the current regulator of the voltage source inverter. Second, fuzzy logic-based current controllers are suggested to improve the tracking capability of the current references in the inverter control scheme so as to enhance the FRT capability of the hybrid system as a whole. The proposed feed-forward-fuzzy control scheme for achieving an enhanced FRT capability is compared with the conventional dq current control and feed-forward FRT control for various grid voltage sag tests, where the performance of the combined feed-forward-fuzzy control is found better. The validation of the proposed FRT control scheme is performed in MATLAB-Simulink environment.

As coherency of generators decreases, the risk of rotor angle instability increases, especially under severe contingencies. The slow coherency as a network characteristic may be controlled by the locations of committed generators. Unit commitment (UC) problem is conventionally carried out regarding operational and network constraints. In this study, a two-step strategy is developed to promote the slow coherency via the network constrained UC (NCUC) model on a daily horizon. First, conventional NCUC is executed. The most important generators with both economic and coherency merits are then determined as representative generators. In the second step, the Slow Coherency Based Unit Commitment (SCBUC) is re-optimisedaccording to the results obtained from the first step, using a multi-objective function. The first part of the multi-objective function is devoted to the cost of generation, start-up, and shutdown of generators. The goal of the second part of the multi-objective function is to maximise the coherency between the committed generators to reach a transient stability margin. The proposed model is converted to a mixed integer linear programming model. The performance of the proposed method of promoting transient stability is investigated using the dynamic IEEE 118-bus test system.

This is an earliest attempt to study the effective regulation of load-frequency oscillations due to the penetration of renewable generations in bio-renewable cogeneration based hybrid microgrids with demand response (DR) support considering optimal utilisation of resources. The work is a maiden attempt to derive the linearised model of a medium-sized linear-Fresnel-reflector type solar-thermal power unit for load-frequency study in the proposed wind/micro-hydro/biogas/biodiesel generator-based hybrid microgrids, modelling suitable DR strategies for both isolated and interconnected modes. The proposed systems are simulated using MATLAB/Simulink for coordinated source/demand-side management, proposing a novel quasi-oppositional selfish-herd optimisation algorithm in both the modes, incorporating real-time recorded solar/wind data and realistic random loads. Firstly, the oscillations due to renewable-penetrations are reduced efficiently in the isolated microgrid incorporating biodiesel generator and DR supports. Then the study is further extended for interconnected two-unequal hybrid microgrids considering resource availabilities. The system responses are compared in four extreme scenarios of source variations, as well as three variations of DRs without retuning the controllers to study the adaptability of the proposed system. Finally, the system frequency oscillations are regulated satisfactorily by DR support for both the modes.

Here, the authors study the optimal coordination of electric vehicles (EVs) in a multi-microgrid (MMG) system with respect to a given time-of-use (TOU) price trajectory over a multi-time period. The authors firstly formulate a class of EV charging/discharging coordination problems for each individual microgrid (MG) to minimise the electricity cost of this MG, while the implemented strategy may result in high variations of the total demand and even build new peaky demands. To mitigate these negative effects, the authors build an aggregate optimisation problem with quadratic cost function under certain bounds on the electricity costs of each MG. The authors further propose a decentralised method for the underlying optimisation problem and verify the convergence of the system to the optimal strategy with a logarithmic convergence rate. Furthermore, the authors consider the power exchange capacity between the MMG system and the main grid, and present a decentralised algorithm to obtain an optimal strategy that minimises the system cost under this capacity constraint. Also, the convergence, the optimality, and the convergence rate of the proposed algorithm are shown.