Showing papers in "Iet Renewable Power Generation in 2020"

Critical Review on PV MPPT Techniques: Classical, Intelligent and Optimisation

Abstract: Maximum power extraction from the photovoltaic (PV) system plays a critical role in increasing efficiency during partial shading conditions (PSC's). The higher cost and low conversion efficiency of the PV panel necessitate the extraction of the maximum power point (MPP). So, a suitable maximum power point tracking (MPPT) technique to track the MPP is of high need, even under PSC's. This study gives an extensive review of 23 MPPT techniques present in literature along with recent publications on various hardware design methodologies. MPPT classification is done into three categories, i.e. Classical, Intelligent and Optimisation depending on the tracking algorithm utilised. During uniform insolation, classical methods are highly preferred as there is only one peak in the P–V curve. However, under PSC's, the P–V curve exhibits multiple peaks, one global MPP (GMPP) and the remaining are local MPPs. Hence, Intelligent and Optimisation techniques came into limelight to differentiate the GMPP out of all LMPPs. Every MPPT technique has its advantages and limits, but a streamlined MPPT is drafted in numerous parameters like sensors required, hardware implementation, tracking in PSC's, cost, tracking speed and tracking efficiency. This present study aimed to address the advancement in this area for further research.

Review of hierarchical control strategies for DC microgrid

Abstract: This work presents an extensive review of hierarchical control strategies that provide effective and robust control for a DC microgrid. DC microgrid is an efficient, scalable and reliable solution for electrification in remote areas and needs a reliable control scheme such as hierarchical control. The hierarchical control strategy is divided into three layers namely primary, secondary and tertiary based on their functionality. In this study, different methods of primary control for current and voltage regulation, secondary control for error-correction in voltage and current, power sharing in a microgrid and microgrid clusters and tertiary control for power and energy management with a primary focus on minimal power loss and operational cost in a DC microgrid system are reviewed in-depth. Along with this, the advantages and limitations of various control structures like centralised, decentralised, distributed are discussed in this study. After a comparative study of all control strategies, the optimum control schemes from the author's point of view are also presented.

Solar irradiance resource and forecasting: a comprehensive review

Abstract: With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

Abstract: The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy-based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two-area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG-WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

Illustration of demand response supported co-ordinated system performance evaluation of YSGA optimized dual stage PIFOD-(1 + PI) controller employed with wind-tidal-biodiesel based independent two-area interconnected microgrid system

Abstract: This study proposes an earliest approach toward coordinated frequency stabilisation of wind turbine driven generator-tidal power generation-biodiesel driven generator-micro-turbine generator-based islanded two-area interconnected microgrid system with demand response support (DRS) mechanism. A recent bio-inspired optimisation technique, named yellow saddle goatfish algorithm (YSGA) is employed to optimally tune the controller gains. The comparative dynamic performance of conventional proportional–integral–derivative (CPID), fractional order (FO) PID, dual-stage PIFOD-one plus PI [PIFOD-(1 + PI)] controllers’ parameters optimised by several algorithmic tools such as particle swarm optimisation, firefly algorithmic tool, salp swarm technique and YSGA clearly designates the superiority of YSGA-PIFOD-(1 + PI) controller under different scenarios (considering the real-time recorded wind and load data) in terms of change in frequency, tie-line power fluctuation and objective function. Furthermore, the impact of the DRS mechanism in both areas is analysed first time under real-time wind and load disturbances. Finally, the rigorous sensitivity analysis of YSGA-optimised PIFOD-(1 + PI) controller has been conducted with the variation of wind turbine driven generator gain, ±30% change in synchronising tie-line factor, frequency bias value, microgrid system time constant and + 30% change in loading magnitude without retuning the optimal base condition values.

Fast pyrolysis of LERDADEs for renewable biofuels

Abstract: The lipid-extracted residues of Desmodesmus sp. cultivated in anaerobically digested effluents (LERDADEs) were determined in real time using a thermogravimetric analyser and pyrolysis-gas chromatography–mass spectrometry coupling technology over a range of temperature (300–800°C). The composition analysis results indicated that LERDADE has potential for energy application due to its high carbon content and relatively low nitrogen content. The main decomposition temperature of LERDADE was 319.9°C, at which up to 68.4% of the mass was lost. The fast pyrolysis of LERDADE at 800°C produced the maximum yield (36.6%) of bio-oil compared with 29% at 700°C. However, the number of harmful pollutants (polycyclic aromatic hydrocarbons and nitrogen compounds) released at 800°C (41.7%) was much higher than that released at 700°C (28.3%), which caused a relative increase of 32.1%. Considering the reasonably high bio-oil production and minimum pollutant emission, a lower temperature (∼700°C) was found to be optimum for producing biofuel from LERDADE.

Recent developments of MPPT techniques for PV systems under partial shading conditions: a critical review and performance evaluation

Abstract: This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × V oc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications.

MPPT in PV systems using ant colony optimisation with dwindling population

Abstract: Ant colony optimisation has been tailored to suit maximum power point tracking (MPPT) in photovoltaic (PV) systems and is presented in this study. Artificial ants are deployed in the solution space and are made to forage and the ants which find better sources of food are retained while ants fail to search effectively are deleted from the population. The greedy search of potential ants for better food location leads to identification of higher power peaks in the PV system. The concept is modelled suitably and MPPT curves in a few PV configurations are simulated and found to be promising. Experiments were also conducted to show the veracity of the new method.

Short-term wind power forecasting based on two-stage attention mechanism

Abstract: Wind power is usually closely related to the meteorological information around the wind farm, which leads to the fluctuation of wind power and makes it difficult to predict precisely. In this study, a wind power forecasting model based on long-short-term memory network two-stage attention mechanism is established. The attention mechanism is extensively employed to weigh the input feature and strengthen the trend characteristic of wind power. The intermittency and volatility feature of the wind are efficiently mitigated, and the prediction accuracy is improved significantly. Besides, quantile regression and kernel density estimation are combined with the proposed model to predict the wind power interval and the probability density. These two parameters are important information for ensuring security and stability while accessing to the electricity grid. The simulation results on the actual wind power dataset verify the higher prediction accuracy of the proposed model compared with other machine learning methods.

Energy management strategy for solid-state transformer-based solar charging station for electric vehicles in smart grids

Abstract: This study introduces a type of solid-state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic-assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real-time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real-time power allocation which improves the efficiency of the photovoltaic-based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

PV hosting capacity of LV distribution networks using smart inverters and storage systems: a practical margin

Abstract: This study proposes an approach to evaluate a practical margin for photovoltaic (PV) generation hosting capacity (HC) of low voltage distribution networks. This HC is determined considering the randomness of the connection points and is supposed to be the maximum value of PV penetration up to which the utility can authorise any interconnection without performing additional case studies. Smart inverter control strategies and battery storage systems are used to avoid costly network expansion solutions. The simulations are performed using actual solar radiation data and residential demand profiles. The results show an increase in the network HC, bringing benefits by deferring network investments such as conductors and asset upgrades.

ANN-based robust DC fault protection algorithm for MMC high-voltage direct current grids

Abstract: Fast and reliable protection is a significant technical challenge in the modular multilevel converter (MMC)-based DC grids. The existing fault detection methods suffer from the difficulty in setting protective thresholds, incomplete function, insensitivity to high-resistance faults and vulnerable to noise. This study proposes an artificial neural network (ANN)-based method to enable DC bus protection and DC line protection for DC grids. The transient characteristics of DC voltages are analysed during DC faults. On the basis of the analysis, the discrete wavelet transform is used as an extractor of distinctive features at the input of the ANN. Both frequency-domain and time-domain components are selected as input vectors. A large number of offline data considering the impact of noise is employed to train the ANN. The outputs of the ANN are used to trigger the DC line and DC bus protections and select the faulted poles. The proposed method is tested in a four-terminal MMC-based DC grid under PSCAD/EMTDC. The simulation results verify the effectiveness of the proposed method in fault identification and the selection of the faulty pole. The intelligent algorithm-based protection scheme has good performance concerning selectivity, reliability, robustness to noise and fast action.

Chaotic sine–cosine algorithm for chance-constrained economic emission dispatch problem including wind energy

Abstract: One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine–cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.

Salp swarm algorithm-based optimal control scheme for LVRT capability improvement of grid-connected photovoltaic power plants: design and experimental validation

Abstract: Contribution of Photovoltaic (PV) systems is rapidly growing and great attention is given to the design of PV controllers to enhance both the performance of PV systems and the low voltage ride through (LVRT) capability during abnormal operational conditions. This article presents a novel application of the salp swarm algorithm (SSA) in order to optimally tune the PV controllers to enhance the LVRT of grid-connected PV systems. Enhancement of LVRT is indicated in percentage undershoots or overshoots, settling time and steady-state error of voltage response. A control strategy is applied to the DC-DC converter to obtain a maximum power point tracking operation through a proportional-integral (PI)-based open fractional voltage control. The grid side inverter controls both the point of common coupling voltage and the DC-link voltage through PI-based cascaded-voltage control. To get PI controller parameters that guarantee the optimum design of the controllers, the fitness function is optimized by using the SSA. The proposed optimal control scheme is tested under various fault scenarios and compared with other conventional optimization-based PI controllers to examine its validity under PSCAD environment. The effectiveness of the optimal control scheme is verified by comparing the simulation results with the practical results of the PV system.

Improved virtual synchronous generator control to analyse and enhance the transient stability of microgrid

Abstract: In recent years, the integration of renewable energy resources (RESs) into the power system is growing rapidly, and it is necessary to analyse and evaluate the effect of RES on transient stability of the power system. In this study, centre of inertia (COI) concept is implemented to analyse and evaluate the integration effects of an auxiliary damping control-based virtual synchronous generator (VSG) consisting an improved governor. The impact of VSG integration is divided into synchronous generator (SG) linked parts and COI associated parts. Due to VSG integration into the power system, the significant elements which disturb the COI dynamic motion and rotor dynamics of SG are examined in detail. Different cases are considered to evaluate the effectiveness of the proposed method, i.e. VSG's different integrating location and different power capacities. It is observed in simulation results that COI dynamic motion and rotor dynamics of SG are positively affected by VSG integration, and transient stability improves significantly.

Increasing operational flexibility of integrated energy systems by introducing power to hydrogen

Abstract: Owing to the limited operating regions of combined heat and power (CHP) units, the operation of integrated energy systems suffers from low flexibility, low-cost efficiency, renewable curtailment etc. Meanwhile, as the capacity of renewable energies keeps growing and integrating into power systems, various methods, such as installing electric boilers to enable electricity-heat conversion, have been developed to absorb excessive renewables and increase system operation flexibility. To further increase the system operation flexibility, this study explores the possibilities of utilising electrolysers and hydrogen storage tanks to enable electricity-hydrogen-heat conversion. To better visualise the enhanced flexibility, this study presents extra flexibilities from electric boilers, electrolysers and hydrogen tanks as the equivalent operating region expansion for CHP units. In this study, the system is modelled as a mixed-integer optimisation problem which balances the electricity, heat and hydrogen demands in a 24-hour period. A 6-bus test system is used in the case studies to illustrate the effectiveness of implementing electrolysers and hydrogen storage tanks. The optimisation results show the application of hydrogen energy improves the system operation flexibility, reduces wind curtailment, thereby decreasing fuel consumption and carbon emission.

Grid forming control scheme for power systems with up to 100% power electronic interfaced generation: a case study on Great Britain test system

Abstract: The penetration of power electronic interfaced generation (PEIG) is expected to reach up to 65% in some parts of the European power system by 2030 (at least during some hours of the year). Under such grid conditions, system security challenges are observed with frequency stability, voltage stability and undamped converter control interactions being among the most important issues. This study presents a short-term voltage stability assessment of the Great Britain synchronous area under EMT modelling assumptions. The study provides a mapping of system stability and identifies the critical penetration level of PEIG that instabilities are observed. In addition, an application of a grid forming control scheme (namely the enhanced direct power control) is proposed as a mitigation option which is applied here on full-converter interfaced wind power plants (type-4). The simulation results reveal that the application of the grid forming control to a part of the total wind power generation fleet can mitigate the instabilities observed, while enabling the system operation with 100% PEIG.

Inertia emulation control technique based frequency control of grid-connected single-phase rooftop photovoltaic system with battery and supercapacitor

Abstract: With the increasing penetration of renewable energy sources in the power system, the power electronic inverters are widely used to interface with the grid, which will reduce the inertia of the power system. This study proposes ancillary inertial service from single-phase rooftop solar photovoltaic (PV) based inverter to the grid. The inertia emulation control technique transforms the behaviour of inverter like a synchronous generator under power imbalances. A hybrid energy storage system consisting of battery and supercapacitor (SC) has been connected at the DC bus to take care of the variability in PV output power and load fluctuations. The SC absorbs/injects the fast-varying power and provides the inertial response to arrest the frequency deviation and battery charges/discharges to bring back the frequency to the nominal value. Real-time simulation is carried out to test the system behaviour for different operating conditions using OPAL-RT 5700.

GMPPT by using PSO based on Lévy flight for photovoltaic system under partial shading conditions

Abstract: The photovoltaic (PV) system contemplated in the study displays multiple peaks on power-voltage ( P - V ) curve under partial shading condition (PSC) results in a complicated maximum power point tracking (MPPT) process. Conventional MPPT algorithms work in an effective manner under uniform irradiance conditions. However, these algorithms are unable to track the global peak effectively under different irradiance conditions. In this study, a velocity of particle swarm optimisation-based Levy flight (VPSO-LF) for Global MPPT of PV system under PSCs is proposed. For the changes in irradiance, when verified with VPSO-LF, tracking time and a number of iterations are fewer to reach the global peak of PV array. It also minimises the number of tuning parameters of the velocity of particle swarm optimisation (PSO). The proposed technique is simulated in MATLAB/SIMULINK as well as experimentally validated. It is observed that the results obtained using VPSO-LF is superior to conventional PSO and hill-climbing algorithm under different patterns of PV array.

Comprehensive review of a linear electrical generator for ocean wave energy conversion

Abstract: Ocean waves are an abundant source of energy. This energy of the ocean can be converted into useful electrical energy using electrical generators. Linear generators have received tremendous attention in energy harvesting technology due to its unique ability to convert the energy without any intermediate converter. The principal objective of this study is to present the various types of linear electrical generators which have been investigated so far for direct drive ocean wave energy conversion and describe the working principle of each type with the difference. In this study, after a brief description of the basic electrical generator, various types of generators, including the linear and flat generators available in the literature are reviewed and discussed based on the design configuration of different types of magnet arrangements. Linear generators have been compared in terms of core type, flux path, the location of PMs, etc. The research gaps have been identified and future research directions have been suggested.

Uncertain wind power forecasting using LSTM-based prediction interval

Abstract: Estimating prediction intervals (PIs) is an efficient and reliable way of capturing the uncertainties associated with wind power forecasting. In this study, a state of the art recurrent neural network (RNN) known as long short-term memory (LSTM) is used to produce reliable PIs for one-hour ahead wind power uncertainty forecast using the non-parametric lower upper bound estimation framework. Two realistic hourly stamped wind power data sets are obtained and by using mutual information and false nearest neighbours techniques, the data are made suitable for model inputs. A novel comprehensive objective function consisting of the coverage probability, the average width of the PIs, symmetricity and variational synchronicity is developed to train the LSTM model using intelligent optimisation techniques. The standard of the PIs generated for the test set as well as for different seasons are evaluated based on the indices used to design the objective function for model training, with one of them being modified. The performance of the proposed LSTM model is found to outperform typical RNN models like Elman, non-linear auto-regressive with exogenous models and other benchmarking models while tested on the real-world data sets.

Highly sensitive microgrid protection using overcurrent relays with a novel relay characteristic

Abstract: Following the high penetration of synchronous generators (SGs) in the power network, optimal overcurrent coordination improvement under faulty conditions has become a crucial problem. To reduce the overcurrent relay operating time, a new overcurrent relay curve is proposed in this study. Then, the overcurrent coordination problem is overcome by using a robust combinatorial optimisation method. Additionally, SG sizing and location is performed to verify the merits of both the proposed relay curve and the applied optimisation algorithm. The proposed relay curve performance is compared with other non-standard relays characteristic available in the literature for a standard microgrid. Then, the proposed relay curve is applied to both the 8-bus transmission and the 33 kV distribution portion of the 30-bus IEEE standard power test systems. Then, the SG transient stability for different fault locations is analysed. Finally, an accurate comparison between the proposed relay curve and standard/non-standard curves available in the literature is provided by applying the same optimisation method and network topology. The simulation results confirm the superiority of the proposed relay curve.

Grid balancing with a large-scale electrolyser providing primary reserve

Abstract: As the share of renewable energy sources increases, the grid frequency becomes more unstable. Therefore, grid balancing services will become more important in the future. Dedicated devices can be installed close to the point where off-shore wind farms are connected to the transmission grid on land. There, they can be used to attenuate power variations, reduce congestion and offer grid balancing. The provision of these ancillary services can create considerable additional economic revenue. In this study, the provision of the primary reserve by means of a large hydrogen electrolyser of 25 MW is investigated for the specific case of the Belgian transmission system. The revenue of the provision of the frequency containment reserve (FCR) is analysed on a techno-economic model, including capital costs, operational costs, the revenue of the generated hydrogen and oxygen products and the ancillary service income. The revenue depends strongly on the contracted power band. Therefore, it is optimised to yield maximum revenue. The results show that providing FCR creates considerable additional revenue. Therefore, a large electrolyser can be a good candidate to buffer excess renewable energy into green gas while simultaneously providing the grid support.

Progress in control and coordination of energy storage system-based VSG: a review

Abstract: Virtual synchronous generator (VSG) is an important concept toward frequency stabilisation of the modern power system. The penetration of power electronic-based power generation in power grid reduces the total inertia, and thus increases the risk of frequency instability when disturbance occurs in the grid. VSG produces virtual inertia by injecting appropriate active power value to the grid when needed. This virtual inertia can stabilise the grid frequency in case of a power imbalance between generation and loads or any disturbances that affected frequency stability. Its intensive research can see the importance of VSG in inertia control and various intelligent controller techniques. Owing to the importance of VSG in the modern power grid, this study provides a comprehensive review on the control and coordination of VSG toward grid stabilisation in terms of frequency, voltage and oscillation damping during inertia response. A review on the type of energy storage system used for VSG and their benefits is also presented. Finally, perspective on the technical challenges and potential future research related to VSG is also discussed in this study.

Stochastic framework for planning studies of energy systems: a case of EHs

Abstract: The substantial presence of different uncertainties in energy systems highlights the need for probabilistic analysis of operational and planning studies. Motivated by this fact, a new stochastic planning framework for energy hubs (EHs) is presented in this study based on the probability transformation concept. In the proposed framework, a measure of relative-likelihood impact is developed to estimate the importance of uncertainty sources in the studies, which, in turn, can remarkably reduce the overall complexity of stochastic problems. Step-by-step algorithm for implementation of the proposed framework on planning studies of an EH is also addressed in this study. Three different case studies are introduced, and the results provide some insightful information regarding the impact of different uncertainty sources in the system.

Distributed droop control of dc microgrid for improved voltage regulation and current sharing

Abstract: DC microgrid is becoming popular because of its high efficiency, high reliability and connection of distributed generation with energy storage devices and dc loads. The main objective in the dc microgrid is to keep the dc bus voltage constant and equalise per unit current sharing among converters. The conventional droop control is used to equalise per unit current sharing similar to reactive power sharing in an ac microgrid. Nevertheless, the problem in conventional droop control is that equal current leads to a reduction of dc bus reference voltage and voltage regulation becoming unequal across each node due to unequal line resistance drop. The proposed controller works on adaptive droop and voltage shifting technique, which equalises the current sharing whether line resistances are similar or not and controls each output voltage to follow the respective bus reference voltage. The isolated dc–dc converters are used to simulate and validate the proposed control technique.

Interdependence of electricity and heat distribution systems coupled by an AA-CAES-based energy hub

Abstract: With the development of economy and the growth of industrial demands, the peak-valley difference of electric load is ever increasing, calling for the deployment of energy storage units. Advanced-adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology and exhibits various advantages in fast response, long service time, low environmental impact and so on. It also has the potential in combined heat-and-power production because heat is a by-product when air is compressed. This study presents a novel AA-CAES-based energy hub and its mathematical formulation considering the pressure behaviours and mass flow rate variations of AA-CAES, and further envisions its business model for the transaction with a power distribution system and a heating system under time-of-use price. A bilevel game-theoretical model is developed to capture the interaction between the two infrastructures through the integrated demand response of the energy hub and investigate the equilibrium state at which none of the stakeholders would like to alter their strategy unilaterally. Results show the interdependence of electricity and heat prices as well as the economic impact of energy hub performance.

Management of renewable-based multi-energy microgrids in the presence of electric vehicles

Abstract: This study proposes a stochastic optimisation programming for scheduling a microgrid (MG) considering multiple energy devices and the uncertain nature of renewable energy resources and parking lot-based electric vehicles (EVs). Both thermal and electrical features of the multi-energy system are modelled by considering combined heat and power generation, thermal energy storage, and auxiliary boilers. Also, price-based and incentive-based demand response (DR) programs are modelled in the proposed multi-energy MG to manage a commercial complex including hospital, supermarket, strip mall, hotel and offices. Moreover, a linearised AC power flow is utilised to model the distribution system, including EVs. The feasibility of the proposed model is studied on a system based on real data of a commercial complex, and the integration of DR and EVs with multiple energy devices in an MG is investigated. The numerical studies show the high impact of EVs on the operation of the multi-energy MGs.

Planning, operation and flexibility contribution of multi-carrier energy storage systems in integrated energy systems

Abstract: Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day-ahead scheduling for multi-carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi-carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi-period optimal energy flow problem in multi-carrier energy networks, this study utilises the well-known teaching-learning based optimisation algorithm. The investigated multi-carrier energy system consists of electrical, natural gas and district heating sub-networks in which an ESS is included in the electrical sub-network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively.

Small signal stability improvement of a microgrid by the optimised dynamic droop control method

Abstract: Renewable energy-based energy conversion technologies have become more relevant due to environmental considerations even though they are intermittent in nature. As a result, the concept of microgrid and microgrid control techniques have been evolved as major areas of power system research. Among different inverter control methods, the droop-based control method is more popular in microgrid systems due to its simplicity and non-requirement of expensive communication systems. The transient performance, power-sharing accuracy and decoupling between real and reactive power are improved by modifying the natural droop control method. In this study, the selected microgrid system consists of two inverters operating in parallel, two interconnecting lines and three loads. A state-space model of the microgrid is created based on the small-signal stability and the transient response is improved by introducing virtual impedance and dynamic droop gains. The different controller parameters are optimised using particle swarm optimisation ensuring stability. Eigenvalue analysis is done to analyse stability. The analysis of the response of the system for various disturbances validates the effectiveness of the proposed controller. The strategy developed ensures improved power-sharing capability with high values of natural droop gains without compromising stability by using optimised dynamic droop gains.