Showing papers in "Iet Generation Transmission & Distribution in 2019"

Review on microgrids protection

Abstract: Microgrid, which is one of the main foundations of the future grid, inherits many properties of the smart grid such as, self-healing capability, real-time monitoring, advanced two-way communication systems, low voltage ride through capability of distributed generator (DG) units, and high penetration of DGs. These substantial changes in properties and capabilities of the future grid result in significant protection challenges such as bidirectional fault current, various levels of fault current under different operating conditions, necessity of standards for automation system, cyber security issues, as well as, designing an appropriate grounding system, fast fault detection/location method, the need for an efficient circuit breaker for DC microgrids. Due to these new challenges in microgrid protection, the conventional protection strategies have to be either modified or substituted with new ones. This study aims to provide a comprehensive review of the protection challenges in AC and DC microgrids and available solutions to deal with them. Future trends in microgrid protection are also briefly discussed .

Adaptive protection combined with machine learning for microgrids

Abstract: This paper presents a rule-based adaptive protection scheme using machine-learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so-called hybrid artificial neural network and support vector machine (ANN-SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision-making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively.

Short-term power load forecasting based on multi-layer bidirectional recurrent neural network

Abstract: Accurate power load forecasting is of great significance to ensure the safety, stability, and economic operation of the power system. In particular, short-term power load forecasting is the basis for grid planning and decision making. In recent years, machine learning algorithms have been widely used for short-term power load forecasting. Specifically, long short-term memory (LSTM) and gated recurrent unit (GRU) are tailored to time series data. In this study, a multi-layer bidirectional recurrent neural network model based on LSTM and GRU is proposed to forecast short-term power load and is validated on two data sets. The experimental result shows that the proposed method is superior to the competition winner in the precision of forecasting on the European Intelligent Technology Network competition data. On power company data in Chongqing, considering the differences of the seasonal load, the hourly peak load of different types of load data is used for experiments. The authors separately forecast the seasonal load and compare it with LSTM, support vector regression and back propagation models. The results of the comparison show the priority of the proposed method in terms of forecasting accuracy as compared to the adopted models.

Transformation of microgrid to virtual power plant – a comprehensive review

Abstract: To provide continuity of balancing demand and generation, renewable sources will be more active than today in near future due to the tendency of massive investment on renewable energy sources (RESs) by countries. However, due to the uncertain and intermittent nature of RESs, RESs would create problems on power system operations such as power quality, efficiency, stability and reliability. Owing to having problems with RESs integration, virtual power plant (VPP) has introduced to make this integration smooth without compromising the grid stability and reliability along with offering many other techno-economic benefits. This study reviews structures, types, architecture and operations of VPP along with the status of present implementations worldwide. The types of VPP are introduced in details with the optimisation algorithm used with each type. In addition, VPP is linked with the most of the components in power systems such as distributed generation, active prosumers, transmission system operator and distribution system operator, grid services such as fault ride through, reactive power control as well as with the help of technology such as communications, control and optimisations. This study gives a comprehensive outline of transforming microgrid to VPP that is useful for researchers, consumers, prosumers and utility operators.

Comprehensive review on structure and operation of virtual power plant in electrical system

Abstract: Constrained by low capacity and volatility, the rapid growth of distributed energy resources are obviously slowdown resulting in consumption difficulty and investment obstacle. As an effective integration and management technology, virtual power plant (VPP) becomes a suitable cornerstone of renewable energy future development. Based on current scientific research, this study intends to provide a detailed review of VPP from an internal perspective (e.g. energy resources' integration and operation) to the external aspect including participation in electricity market. In accordance with market diversity, different bidding strategy optimisation problems of VPP are formulated and their corresponding mathematical solution methods are literately reviewed. To extract characteristics of VPP, a comparison between energy management techniques (e.g. VPP, microgrid, active distribution network, and load aggregator) is conducted, where advantages and defects of VPP are also concluded. Meanwhile, realistic deployments of VPP in European electricity markets are elaborated to verify its feasibility and applicability. To better accommodate future development of renewable energy, a flexible structure and effective management mechanism of VPP should be built leading to its technical innovation and management reform. Possessed with the threefold development orientation, VPP will undoubtedly improve the utilisation and management of renewable energy sources as a coordinated and operational entity.

Maiden application of SSA-optimised CC-TID controller for load frequency control of power systems

Abstract: This study describes a maiden application of salp swarm algorithm (SSA) optimised cascade tilt-integral-derivative controller (CC-TID) for frequency and tie-line power control of interconnected power systems (IPSs). The proposed controller includes both the merit of the cascade control algorithm and fractional-order calculus. In the proposed CC-TID controller, TID controller is used as a slave controller and a proportional-integral (PI) controller served the role of master controller. To elucidate the effectiveness, the designed controller has employed to, initially, two-area IPS followed by four-area IPS. SSA is used to pursue the optimum settings of the CC-TID controller via the minimisation of area control error. Performances of CC-TID controller is obtained and compared with I-, PI-, PI-derivative-, cascade PI-PD, and TID controllers. Simulation results, apparently, show that SSA: CC-TID controller exhibits superior performance compared with other controllers described above. Moreover, the performance of SSA is evaluated and compared with differential evolution and flower pollination algorithm in terms of convergence profile, power density, and statistical results. Finally, time-varying step load perturbation and random load perturbation are applied to confirm the robust behaviour of SSA: CC-TID controller.

Multi-objective synergistic planning of EV fast-charging stations in the distribution system coupled with the transportation network

Abstract: The adoption of environmentally friendly electric vehicles (EVs) is increasing over the years due to the increased awareness of energy and environmental challenges. However, the current growth is slow because of lack of proper charging infrastructures. This study proposes a multi-objective synergistic planning model of an EV charging station considering the interaction between the distribution system and transportation networks as the fast charging of EVs affects the operation of both networks. The developed model minimises the power losses and voltage deviation of the distribution system and maximises the EV flow served by the fast charging station (FCS) simultaneously taking into account permissible waiting time and service radius of FCS. Multi-objective grey wolf optimiser (MOGWO) algorithm is used to obtain the non-dominated solutions and fuzzy satisfaction-based decision-making method is employed to reach final planning scheme. The effectiveness of the proposed model is investigated on the IEEE 123-bus distribution system coupled with a 25-node transportation network. The influence of different objectives, service radius and waiting time on the planning of FCS is also explored. Results reveal that the developed method can provide rational siting and sizing of FCS and it is also found that proper service radius and waiting time provide more convenience to the customer.

Proficient load-frequency regulation of demand response supported bio-renewable cogeneration based hybrid microgrids with quasi-oppositional selfish-herd optimisation

Abstract: 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.

Optimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid

Abstract: An AC-DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC-DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC-DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC-DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power-flow technique, and by modelling uncertainties by Hong's two-point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max-min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6-bus AC-DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi-objective GA indicates that the optimised solution lies on the Pareto-front, thereby validating the proposed technique.

Bi-level network reconfiguration model to improve the resilience of distribution systems against extreme weather events

Abstract: When a natural disaster occurs in a distribution network, a widespread power interruption may occur for a few days or weeks. This study presents a bi-level optimisation-based model for reconfiguration of the distribution network to improve the resilience of electricity distribution network against severe weather events such as storm and hurricane with the aim of minimising the cost of load outage. To achieve this, a model is first presented for evaluating the vulnerability of distribution network poles to estimate the damages imposed by the threat. Then, in the first level, according to the forecasting of possible failed lines and based on the predicted wind speed before the storm, a network reconfiguration strategy is employed to minimise the expected cost of load outage. In the second level, a new reconfiguration is carried out to restore the system loads and minimise the cost of load outage after the storm. The proposed model is then applied to a standard 33-bus radial distribution system using the GAMS software. The simulation results demonstrate the effectiveness of the proposed model in increasing network resilience and highlight the importance of network reconfiguration in the face of extreme natural disasters.

Application of modified sine cosine algorithm to optimally design PID/fuzzy-PID controllers to deal with AGC issues in deregulated power system

Abstract: The focus of this work is to analyse the performance of automatic generation control (AGC) of two area four units interconnected deregulated power system for implementing modified sine cosine algorithm (MSCA) to design the conventional PID and fuzzy-PID (FPID) controllers. Each area consists of thermal generating unit with reheat turbine and a gas generating unit. Thermal generating units of both areas are employed with appropriate generation rate constraint. At first, sine cosine algorithm (SCA) is modified to implement MSCA technique and superiority of MSCA is proved over SCA using four benchmark functions. Then the work is extended to design the conventional PID and FPID controllers using both the algorithms. Three different situations of deregulated markets are considered. For the design of optimal controller gains integral time absolute error is taken as the objective function. To analyse the dynamic behaviour of the deregulated power system undershoot, overshoot and settling time are considered as transient parameters. After the analysis, it is found that MSCA-based FPID controller exhibits a better transient response in terms of less undershoot, overshoot and settling time. Robustness analysis is performed by varying some of the important parameters of the power system to show the proposed FPID controller is less sensitive to parametric variations.

Robust bidding strategies and scheduling of a price-maker microgrid aggregator participating in a pool-based electricity market

Abstract: This study presents a model for the activities of the price-maker microgrid aggregator (MGA). In this model, an MGA is considered to aggregate several microgrids (MGs) and be in charge of obtaining an optimal bidding strategy for MGs as well as scheduling their resources and demand. Two price-maker strategies (the marginal and non-marginal strategies for players) are proposed and the robust scheduling and optimal transactions of a price-taker MGA are also obtained in order to analyse different bidding behaviour of MGA. A robust optimisation is used in this model in order to capture uncertainties associated with renewable generation in the worst-case situation. Accordingly, the robust solution is obtained for the optimal scheduling of an MGA participating in the pool-based day-ahead electricity market. The proposed robust bidding strategies and scheduling of a price-maker MGA are obtained considering a hypothetical test system and the results are compared with the bidding strategy and robust scheduling of a price-taker MGA. The results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA. Also, using the proposed model for the price-maker MGA increases the profits of MGs.

A novel constraint and non‐standard characteristics for optimal over‐current relays coordination to enhance microgrid protection scheme

Abstract: Over the last few decades, the inverse over-current protection scheme has been one of the most common schemes utilised in protecting the distribution networks (DNs). Although over-current relays (OCRs) give an excellent protection in extreme failure situations, but continuous integration of distributed generation (DG) and the multi-looped structure of modern DN has made it difficult for the existing industrial OCR's to secure suitable coordination between the different OCR's. The problem of coordinating over-current schemes in the presence of DG needs a complementary between the optimisation techniques employed and the limitation of the manufactured relays existed in the DN for proper discrimination between OCRs. This study investigates the influence of excessive fault current due to DG penetration on conventional IEC characteristics. A novel constraint had been proposed to be added to the formula of the coordination problem – taking into consideration – the limitation of conventional IEC tripping characteristics utilised in nowadays numerical relays. Furthermore, a new non-standard tripping characteristic is suggested to increase the applicability of the optimisation techniques with the existing OCR's. A benchmark IEC microgrid is implemented in ETAP Package. The genetic algorithm is employed to investigate the effectiveness of the two proposed approaches to the conventional optimisation techniques in different operational modes.

Robust model for optimal allocation of renewable energy sources, energy storage systems and demand response in distribution systems via information gap decision theory

Abstract: Considering increasing distributed energy resources and responsive loads in smart grid paradigm, this study proposes a new approach for robust hourly energy scheduling of distribution systems at the presence of severe uncertain renewable energy sources (RES). Wind and photovoltaic power generations are considered as the RESs. The aim is to minimise the total energy procurement cost, while considering the participation of RESs, by their optimal allocation in the network. The inherent uncertainty of RESs is handled via information gap decision theory. One of the features of the proposed model is to consider the impact of demand response and energy storage system as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the proposed model handles the uncertainty of multiple RESs in a way that maximum tolerable uncertainty of RESs is achieved for a given worsening of total energy procurement cost. The proposed model is formulated as a mixed integer nonlinear optimisation problem and is implemented in general algebraic modelling system environment. The model is applied on the IEEE standard 33-bus radial test system, and the obtained results substantiate that the utilisation of ESS and DR can reduce the impact of RESs' uncertainty on the energy cost.

Real power loss minimisation of smart grid with electric vehicles using distribution feeder reconfiguration

Abstract: The ongoing and anticipated growth in the use of electric vehicles (EVs) in transportation brings new opportunities for the development of new smart grids. EVs can transfer power from vehicle-to-grid (V2G) to potentially contribute towards improving grid functionality and stability. Coordinated charging/discharging of EVs is a possible solution to the challenges imposed by random charging and potential ways to extract benefits from the V2G functionality of EVs in the grid. Furthermore, coordinated scheduling can be augmented with effective operative tools to improve the operation of system. Towards addressing this goal, this study proposes a two-stage optimisation to investigate the impact on the distribution system in terms of losses and voltage when distribution feeder reconfiguration (DFR) is employed with different scheduling strategies of EVs. In the first stage, the optimal charging/discharging schedule of EVs is developed on the basis of the technical and economic objective. A genetic algorithm-based approach is used to model EV demands. The DFR problem is solved in the second stage with a new, improved version of a grey wolf optimisation algorithm considering the optimal EV load demand obtained from the first stage. The efficacy of proposed methodology is demonstrated on 69-bus distribution system and 118-bus distribution system.

Improved power management control strategy for renewable energy-based DC micro-grid with energy storage integration

Abstract: This study presents an improved power management control strategy of a hybrid direct current (DC) micro-grid (MG) system consisting of photovoltaic cell, wind turbine generator, battery energy storage (BES), fuel cell (FC), and electrolyser. Based on the voltage and state of charge of BES, FC, and electrolyser, the proposed control scheme improved the dynamics of the DC-link voltage and contributes a better power management between each generation/source and load. A gain control technique is implemented in the grid-side inverter controller to regulate the modulation index and improving the voltage stability of the DC-link. Furthermore, the PI-controller gains of BES are tuned dynamically based on the deviation in voltage and its derivative using Takagi–Sugeno-fuzzy control to enhance the transient response of the voltage. For a reliable operation of the DC MG under standalone or prolonged islanding mode of operation, a priority-based load shedding algorithm is proposed for maintaining proper power coordination between different energy sources and storage devices. Owing to smoother and faster voltage response, the proposed control schemes can comply with the grid code requirements of the changing configuration of the modern renewable energy integrated DC MG. The effectiveness of the proposed control strategy is tested by comparing the existing scheme through MATLAB/Simulink ® .

Unified two-stage reconfiguration method for resilience enhancement of distribution systems

Abstract: Fast load restoration based on network reconfiguration is a key step to enhance the resilience of distribution systems (DSs). For DSs equipped with remote-controlled switches (RCSs), the reconfiguration can be completed promptly. However, when the branch is equipped with manual switches only, it cannot be operated immediately, and its post-event state is determined by its pre-event state. Considering these facts, this study proposes a unified two-stage reconfiguration method for the resilience enhancement of DSs. In the pre-event stage, the allocation of RCSs and the reconfiguration of the network are determined to prepare the system for a set of possible fault scenarios caused by upcoming extreme weather. In the post-event stage, network reconfiguration based on the placed RCSs is performed for fast load restoration to minimise the loss of load expectation (LOLE). A new mathematical expression is proposed to ensure the radial operation of DSs with islanded DSs and islanded microgrids. To improve the computational efficiency, the scenario decomposition algorithm is employed to decompose the proposed model into several subproblems, which can be solved in parallel. The results show that the proposed method can significantly reduce the LOLE caused by extreme weather events, thus enhancing the resilience of DSs.

Optimal sizing of an AC-coupled hybrid power system considering incentive-based demand response

Abstract: This study investigates the impact of incentive-based demand response on the optimal economic sizing of hybrid power systems for a remote area in South Australia. The hybrid power systems are modelled as AC-coupled system with various power generation and energy storage systems including diesel generators, solar photovoltaics, wind turbines, battery storages and flywheels. Operating reserve requirements are introduced to ensure a specified reliability with variable renewable energy generation and consumer loads. Incentive-based demand response is introduced to allow a reduction in customer loads, up to a maximum value, during peak load events. Customers receive a financial benefit as an incentive for the total demand response energy reduction. Active power operation of four different power system configurations is modelled over one year in hourly time steps. The study uses real data for customer demand, wind speed, solar insolation and ambient temperature profiles in a specific location. The hybrid power systems with demand response are optimised to minimise the system net present cost in project lifetime (20 years) using a particle swarm optimisation algorithm. Sensitivity analysis of levellised cost of energy for various values of the maximum demand response power and the incentive payments are also carried out.

Cost-effective sizing of an AC mini-grid hybrid power system for a remote area in South Australia

Abstract: This study determines the optimal capacity of various components of an AC mini-grid hybrid power system in a remote area of South Australia. A range of generation and storage technologies, such as diesel generators, wind turbines, solar photovoltaic arrays, battery banks, and flywheels are considered. A minimum system operating reserve is maintained to ensure a certain degree of reliability. Four different configurations of hybrid power systems are analysed in hourly time-steps, over a year, using real data for system load, solar insolation, ambient temperature, and wind speed. Particle swarm optimisation approach is used to optimise the capacity of various components of each hybrid system to obtain the minimum net present cost (NPC) over a 20-year system lifespan. Suitable capital, operating, and maintenance costs in Australian context are considered in evaluating the system. Various simulation results, such as annual energy balances, NPCs, levelised cost of energy and cash flow analysis obtained for each optimised system are carefully analysed and discussed. The modelling and results confirms the feasibility and economical replacement of the diesel power system with those of hybrid power systems which use renewable energy supply.

On the advance of SFCL: a comprehensive review

Abstract: This study aims to provide a comprehensive review of various superconducting fault current limiters (SFCLs) configurations. Regarding the applied technology, the different types of SFCLs are classified into three groups including quench-type SFCL, non-quench-type SFCL, and composite-type SFCL. Resistive-type SFCL, hybrid-type SFCL, magnetic-shielded iron-core-type SFCL, superconducting fault current limiting transformer, transformer-type SFCL, flux-lock-type SFCL, saturated iron-core-type SFCL, and resonance-type SFCL are some of the investigated structures. The structures are fully surveyed in terms of operating principle, technical feasibility, and recent advancements. The study is performed based on published information in papers, reports, and other available online documents. It is expected that this study provides a fundamental foundation for researchers and companies to become familiar with different types of SFCLs. It would be useful to help further studies about the development and the implementation of SFCL for real power systems.

Robust N–k CCUC model considering the fault outage probability of units and transmission lines

Abstract: Robust optimisation has been applied to the dispatching problem considering fault outage of power systems. The traditional fault outage uncertainty set are too conservative. To solve this problem, a robust contingency constrained unit commitment (CCUC) model considering the outage probability of units and transmission lines is proposed. The proposed model has the following characteristics: (i) the outage probability of units is determined by normal operation time and fault repair time, whereas the outage probability of transmission lines is determined by length and load rate; (ii) based on the probability criterion of unit outage contingencies, the outage probabilities of units and transmission lines are both incorporated into the modelling of uncertainty sets; (iii) the robust CCUC model obtains UC decision of base case and the UC decision under uncertainty scenarios can adjust the output of units by spinning reserve capacity to cope with stochastic fault outage scenarios. The proposed robust CCUC model is solved with Benders-column and constraint generation (C&CG) method combined with Benders decomposition method and C&CG method. Simulations on the modified IEEE-RTS-79 system and IEEE-RTS-96 system demonstrate the effectiveness and reliability of the proposed robust CCUC model.

Day-ahead electric vehicle aggregator bidding strategy using stochastic programming in an uncertain reserve market

Abstract: Electric vehicle (EV) as dynamic energy storage systems could provide ancillary services to the grids. The aggregator could coordinate the charging/discharging of EV fleets to attend the electricity market to get profits. However, the aggregator profits are threatened by the uncertainty of the electricity market. In this study, an EV aggregator bidding strategy in the day-ahead market (DAM) is proposed, both reserve capacity and reserve deployment are considered. The novelty of this study is that: (i) The uncertainty of the reserve developments is addressed in terms of both time and amount. (ii) Scenario-based stochastic programming method is used to maximise the average aggregator profits based on one-year data. The proposed method, jointly considers the reserve capacity in the DAM and the reserve deployment requirements in the real-time market (RTM). (iii) The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. (iv) An owner-aggregator contract is designed, which is used to mitigate the economic inconsistency issue between the EV owners and the aggregator. Results verify the performance of the proposed strategy, that is the average aggregator profits are guaranteed by maximising reserve deployment payments and mitigating the penalties in RTM and thus the reserve deployment requirements uncertainty is well managed.

Adaptive droop control for balancing SOC of distributed batteries in DC microgrids

Abstract: To guarantee efficient and stable operation of a microgrid, overcharging and unbalanced state of charge (SOC) of batteries have to be mitigated. An adaptive droop control scheme is proposed in this study to provide energy management between distributed batteries having unbalanced SOC and different capacities. The proposed approach suggests using a droop factor proportional to the n th order of SOC in the charging mode and inversely proportional to the n th order of SOC in the discharging mode. Moreover, a modification is proposed to adapt the performance taking into account the battery relative capacity. The relative capacity of an individual battery is defined as the ratio between the maximum capacity of all batteries and the battery capacity. The proposed adaptive droop control scheme is investigated and analysed based on an accurate model of dc microgrid incorporating lithium-ion battery, photovoltaic, permanent-magnet synchronous generator-based wind energy system and constant power loads. Several operating conditions were considered to validate the proposed control approach. The proposed control scheme has been succeeded in keeping the dc bus voltage within limits and equalising SOC of batteries, either having similar or having different capacities.

Optimally coordinated dispatch of combined-heat-and-electrical network with demand response

Abstract: A multi-energy microgrid (MEMG) aims to simultaneously supply electricity and thermal energy to customers for higher energy utilisation efficiency. In this study, a system-wide coordinated operation method for MEMGs is proposed to dispatch different components including generation resources and flexible loads. In the coordination method, the coupling constraints of electrical and heat network, dynamic characteristics of heat network as well as the power flow constraints are comprehensively modelled. Besides, the price-based demand response and indoor temperature control strategy are used as demand response for more flexible operation of the combine electrical and thermal networks. The coordination model is formulated as a mixed-integer linear programming problem and tested on 33-bus and 69-bus MEMGs. Simulation results verify the advantages of the proposed method over existing methods.

Benchmark of machine learning algorithms on capturing future distribution network anomalies

Abstract: The conventional distribution network is undergoing structural changes and becoming an active grid due to the advent of smart grid technologies encompassing distributed energy resources (DERs), aggregated demand response and electric vehicles (EVs). This establishes a need for state estimation-based tools and real-time monitoring of the distribution grid to correctly apply active controls. Although such new tools may be vulnerable to cyber-attacks, cyber-security of distribution grid has not received enough attention. As smart distribution grid intensively relies on communication infrastructures, the authors assume in this study that an attacker can compromise the communication and successfully conduct attacks against crucial functions of the distribution management system, making the distribution system prone to instability boundaries for collapses. They formulate the attack detection problem in the distribution grid as a statistical learning problem and demonstrate a comprehensive benchmark of statistical learning methods on various IEEE distribution test systems. The proposed learning algorithms are tested using various attack scenarios which include distinct features of modern distribution grid such as integration of DERs and EVs. Furthermore, the interaction between transmission and distribution systems and its effect on the attack detection problem are investigated. Simulation results show attack detection is more challenging in the distribution grid.

Decentralised-distributed hybrid voltage regulation of power distribution networks based on power inverters

Abstract: In modern power distribution networks, voltage fluctuations and violations are becoming two major voltage quality issues due to high-level penetration of stochastic renewable energies (e.g. wind and solar power). In this study, a hybrid control strategy based on power inverters for voltage regulation in distribution networks is proposed. Firstly, a decentralised voltage control is designed to regulate voltage ramp-rate for mitigating voltage fluctuations. As a beneficial by-product, the var capacity from the inverters become smoothed. Then, a distributed voltage control is developed to fairly utilise the var capacity of each inverter to regulate the network voltage deviations. Furthermore, once there is a shortage of var capacity from inverters, on-load tap changers control will supplement to provide additional voltage regulation support. The simulation results on IEEE 33-bus distribution network with real-world data have validated the effectiveness of the proposed voltage regulation method.

Controller to enable the enhanced frequency response services from a multi-electrical energy storage system

Abstract: The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steady-state domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

Line maintenance within transmission expansion planning: a multistage framework

Abstract: Maintenance in transmission networks is an economical way to reduce upgrading network costs without decreasing its reliability. Hence, new studies regarding transmission expansion planning (TEP) must take into account the effects of maintenance in order to obtain realistic and economic expansion investment plans. This work presents a novel framework for multistage TEP, considering line maintenance, i.e. the expansion cost of the transmission system, network losses, costs of old-line replacement and maintenance, cost of newly constructed line maintenance, and cost of replaced line maintenance, are simultaneously optimised. The advantage of this approach is the fact that the lifetimes of the lines that are replaced, retained, and added to the transmission system are changing during the expansion horizon. These lifetimes have an impact on the maintenance expenses. Annual maintenance costs are also affected by the inflation rate. Hence, both the lifetime and inflation rate roles are integrated into the proposed model. The robustness and effectiveness of the model are tested on the IEEE 24-bus test system, using a particle swarm optimisation algorithm. The results show that the proposed formulation finds more economic investment plans for TEP when compared with those found using static formulations considering the maintenance available in specialised literature.

Generation maintenance scheduling in virtual power plants

Abstract: In an active network, as a virtual power plant (VPP), periodic maintenance of distributed generators (DGs) is critically vital for the reliable operation of the power system. To prevent unexpected failure of DGs and avoid deterioration of the grid's reliability, coordination of maintenance scheduling is indispensable. In this study, maintenance management of a VPP is proposed for scheduling the planned outage of DGs, in order to preserve their useful lifespan. In addition to conventional DGs and the upstream power grid, renewable generation including wind turbines and photovoltaic systems, energy storage systems, and curtailable loads are considered as components of the VPP. The proposed maintenance scheduling scheme provides different advantages in viewpoints of cost and reliability. Moreover, risk management is also investigated to lower the risk of maintenance scheduling due to the uncertainty in price in an energy market by adopting the conditional value at risk as a measure of risk. The overall cost is minimised considering the power loss in the grid as well as the security constraints such as DGs operational constraints, voltage magnitude, and transmission lines’ power flow limit. The effectiveness of the proposed scheme is illustrated using numerical studies with short- and long-term scheduling.

Microgrid planning considering the resilience against contingencies

Abstract: Increasing the redundancy of distribution lines and increasing the penetration of distributed energy resources (DERs) both help microgrids ride through contingencies. However, it remains a challenging problem for how to coordinate these two measures for minimising the deployment cost while guaranteeing a pre-specified degree of resilience in case of contingencies. Accordingly, this study proposes a novel microgrid planning model to site and size candidate sets of DERs and distribution lines in close coordination, which is mathematically equivalent to a two-stage robust optimisation problem. In particular, the resilience level of microgrid operations is quantified and maintained such that the load loss is constrained within a given bound under any realisation of N – k contingencies. The proposed model also incorporates a practical strategy to maintain the radial topology of islanding network sections in any N – k contingency. Finally, numerical experiments based on two microgrid test systems are performed. The results show that the system resilience is adaptively enhanced through optimally placing DERs and distribution lines compared with the conventional economics based model. Moreover, the employed robust method is at least ten times faster than the reliability-based method in identifying the worst contingency.