A comprehensive reviewing of existing interharmonic analysis and estimation methodologies irrespective of application is carried out. This study is enlisting the characteristics of an appropriate method to analyse and estimate interharmonics in photovoltaic (PV) systems, and linking these characteristics with the features of the reviewed methodologies. The distinctive characteristics of interharmonic emissions related to PV systems are, therefore, presented. The various methodologies are classified, summarised, and a checklist is prepared to emphasise the areas to be paid attention to while establishing an apt method for interharmonic analysis in PV systems. The priorities for selection of a method by a practising engineer vary case by case. This study will serve as a guideline for selection and further development of a suitable method for interharmonic analysis in a PV-included power system.

The installation of photovoltaic (PV) systems is continuously increasing in both standalone and grid-connected applications. The energy conversion from solar PV modules is not very efficient, but it is clean and green, which makes it valuable. The energy output from the PV modules is highly affected by the operating conditions. Varying operating conditions may lead to faults in PV modules, e.g. the mismatch faults, which may occur due to shadows over the modules. Consequently, the entire PV system performance in terms of energy production and lifetime is degraded. To address this issue, mismatch mitigation techniques have been developed in the literature. In this context, this study provides a review of the state-of-the-art mismatch mitigation techniques, and operational principles of both passive and active techniques are briefed for better understanding. A comparison is presented among all the techniques in terms of component count, complexity, efficiency, cost, control, functional reliability, and appearance of local maximums. Selected techniques are also benchmarked through simulations. This review serves as a guide to select suitable techniques according to the corresponding requirements and applications. More importantly, it is expected to spark new ideas to develop advanced mismatch mitigation techniques.

Due to the inherent uncertainties of wind power, its large-scale integration strongly impacts the planning and operation of power systems. To investigate these impacts, a stochastic model is required to more accurately capture the wind power's characteristics. This study proposes an improved Markov chain (MC)-based time series (TS) modelling method for the stochastic generation of synthetic wind power TS. First, a self-adaptive state division strategy is proposed to objectively classify historical data into several typical states. This strategy combines a state optimisation clustering model with a random-variable-modelling-oriented filter parameter optimisation method. Then, a three-dimensional state transition probability matrix (STPM) is proposed and constructed to generate synthetic wind power state TS. In contrast to the previous STPMs, the proposed STPM can capture the changing pattern of the transition probability against the state duration. Finally, the fluctuation quantity and noise are separately and sequentially added to the generated state TS, as an improvement over previous fluctuation characteristic addition methods, to obtain the final synthetic wind power TS. The results show that the proposed method outperforms previous MC-based TS modelling methods in reproducing historical characteristics, such as the transition and fluctuation characteristics, and does not increase the STPM construction algorithm's time complexity.

Wind speed forecasting is important for high-efficiency utilisation of wind energy and management of grid-connected power systems. Due to the noise, instability and irregularity of atmosphere system, the current models based on raw historical data have encountered many problems. In this study, a deep novel feature extraction approach is developed based on stacked denoising autoencoders and batch normalisation. Then the deep features extracted from raw historical data are fed to long short-term memory (LSTM) neural networks for prediction. Meanwhile, density-based spatial clustering of applications with noise is employed to process the numerical weather prediction data. By picking out the abnormal samples, the representative training samples are selected to improve the efficiency of the model. For illustration and verification purposes, the proposed model is used to predict the wind speed of Wind Atlas for South Africa (WASA). Empirical results show that deep feature extraction can improve the forecasting accuracy of LSTM 49% than feature selection, indicating that proper feature extraction is crucial to wind speed forecasting. And the proposed model outperforms other benchmark methods at least 17%. Hence, the proposed model is promising for wind speed forecasting.

In this study, multi-objective particle swarm optimisation (MOPSO) and preference order (PO) ranking-based multi-objective planning model is presented for placement and sizing of wind and solar-based distributed generators (DGs), and capacitors in the multi-phase distribution network, under uncertainty considering network reconfiguration. Uncertainty in solar irradiance, wind speed, and load are considered using Monte Carlo simulation (MCS) with suitable probabilistic models. The planning problem is formulated considering different scenarios generated using MCS. For objective function formulation with varying demand and generation conditions, a dynamic load generation model is developed. A priority vector is proposed for DG and capacitor placement using the analytic hierarchy process to reduce the search space and computational time. The key benefits of the proposed DG placement algorithm are that it gives a single solution that is nearly optimal for all the possible network topologies and it works well for both unbalanced and balanced conditions. The proposed technique has been applied to IEEE 34-bus and IEEE 123-bus systems. The result shows a significant reduction in power losses, current unbalancing and improvement in reliability after the placement of DGs and capacitors.

This study proposes a novel resilience-directional robust dispatch (RRD) model for an islanded AC/DC hybrid microgrid (HMG). The inherent uncertainties on the source–load power and the occurrence of meteorological disasters are considered in this model. When a meteorological disaster strikes, the wind turbine (WT), photovoltaic (PV), and bidirectional converter of the HMG should be offline to ensure the stability of the HMG and the safety of these sensitive units. When affected by such double uncertainties, the output constraints of the WT, PV and load are bilinear but are linearised via big-M approach. The proposed RRD model manifests as a min–max–min tri-layer problem with mixed-integer recourse variables, which is difficult to solve directly. Therefore, a nested column-and-constraint generation algorithm is adopted to convert the tri-layer problem to a two-stage mixed-integer linear programming (MILP) model. The MILP problem is addressed by the commercial solver, thereby obtaining the minimal operating cost and establishing robust scheduling plans with the worst disaster scenario. The effectiveness and rationality of the proposed RRD model and its solution methodology are verified in numerical tests.

The widely distributed large-scale photovoltaic (PV) modules suffer from a large probability of grounding fault due to their own characteristics and natural weather. This problem has been well explored in this study. Firstly, it is analysed that the grounding fault in PV modules will cause the third-harmonic voltage, DC bias voltage and common ground circulating current in the PV inverter system. Secondly, a 0-axis based control strategy is proposed to solve the above problems, which means that the zero-sequence voltage is controlled through a proportional-resonant controller in the outer loop, and the zero-sequence current is controlled in the inner loop through a proportional controller. Thirdly, the square of DC side voltage is controlled to address the non-linear relationship between capacitor energy and its voltage. Finally, the experimental results based on RTLAB platform are presented to verify the effectiveness of the proposed control strategy. In summary, this study proposes a control thought to suppress the undesirable phenomenon caused by the grounding fault in PV modules, and the method needs no status switching and hardware cost.

In order to maximise the power from a harvester including a great number of piezoelectric transducers (PZTs), the outputs of these transducers should be connected in a suitable way. Since each PZT can be thought of as a non-ideal source, it is also clear that direct serial connection or parallel connection of these PZTs will not be a very good strategy. In this study, a new circuit topology is proposed for the electric connection between PZTs in a harvester including a great number of PZTs. This proposed circuit topology at the same time presents an efficient rectification and regulation strategy for each PZT used. The process of rectification takes place with minimum voltage loss due to the structure of the proposed circuit topology. In addition, the output of the proposed circuit topology can be used directly to charge an energy storage unit in addition to being connected to the input of any interface circuit. An experimental setup was designed to compare the performance of circuit topology proposed in this study in the form of connection used commonly in the literature. With this experimental setup used, various connection forms and the proposed circuit topology were compared under the same conditions.

This study presents a model to analyse the effect of an increasing level of residential and commercial photovoltaic and storage devices, intended for self-consumption, on a power system. A whole year is focused and modelled through a set of selected representative days. A 24-h two-stage stochastic unit commitment is solved for each representative day and expected results are weighted to achieve annual expected results. This optimisation problem includes appropriate representation of the day-ahead and on-line operations and an adequate representation of the uncertainty associated with renewable sources and demand. The main contributions are: (i) the definition of a single model in which customers are price-makers and large-scale and customers’ installations are appropriately modelled, therefore bridging the current macroscopic and microscopic modelling approaches, and (ii) the division of customers into four non-overlapping groups, which allows a sensitivity analysis of the effect of each group. Five main results are focused: net demand, CO₂ emissions, load factors, system operation cost, and customer savings. The model is applied to a case study based on the Spanish power system to show the model capability to quantify the effect of an increasing level of these promising technologies on a power system.

Distributed energy resources such as stand-alone photovoltaic (PV) systems have been widely used in electric systems in the last decades due to the increase in global energy demand, and the concern about the environmental problems caused by fossil fuels power generation. Owing to the fast growing of direct current (DC) microgrids and electronic devices, such as their current compatibility nature in relation to primary sources, a small-scale stand-alone PV system with battery storage for DC applications is proposed in this work. The system consists of three DC–DC converters which operate according to the PV panel power and the main bus load demand. The control of these converters considers the quality in power supply ensuring safety to the loads and batteries of the system. Thus, this study proposes the implementation of the control units through the use of one-cycle control (OCC) which allows fast response to transient conditions, great accuracy of convergence and has low-cost analogue implementation. In this study, one-cycle controllers’ waveforms are analysed in steady-state conditions and its duty cycles are compared with the expected in relation to the system operation. Simulations of transition conditions were performed through real situations such as cloud-edge effects, slow passing clouds and absence of sunlight.

Solar energy is the base for both photovoltaic (PV) power generation and plant growth. Inspired by this biological phenomenon, a novel crowded plant height optimisation (CPHO) algorithm was developed for solar PV maximum power point tracking (MPPT). This CPHO-tuned MPPT algorithm was developed with the aim of obtaining the optimal duty cycle (d) for DC-DC boost converter for maximum solar power extraction from PV panels with the help of a proportional-integral controller. Crowded plants regulate the growth of their stem height in relation to neighbouring plants, also known as height convergence. Using this CPHO-algorithm, the stable height of the plant found in a numerical value is taken as the optimal height of the plant. This optimal numerical value was converted into (d) for the converter. Under dynamic weather conditions, the (d) was optimally adjusted by the proposed algorithm to regulate the DC output of the converter. On the utility side, d–q vector control-based voltage source inverter was used for PV power integration into the grid. The performance of the converter control strategy of the proposed CPHO algorithm was compared with perturb and observe algorithm-based MPPT control, which was analysed on MATLAB/Simulink platform.

The combustion of a biomass with coal may be one of the most effective methods to improve energy utilisation efficiency. This study aims to investigate the combustion characteristics of distillers’ grains with bituminous coal using a thermo-gravimetric analyser. The results demonstrate that increasing the proportion of distillers’ grains in coal results in a reduced ignition temperature (T _i) and burnout temperature (T _b), and activation energy (E) in the combustion stage. Therefore, the comprehensive combustion index (CCI) improves. In an O₂/CO₂ atmosphere, T _i increased by ∼15°C, while T _b did not change much. However, E and CCI declined relative to an O₂/N₂ atmosphere. Under the O₂/CO₂ atmosphere, with an increase in O₂ concentration from 10 to 40%, T _i changed over a small range, but T _b declined from 745.8 to 641.4°C, while E and CCI improved. As the heating rate rose from 10 to 30°C/min, the values of T _i decreased (from 281.4 to 244.9°C), but the values of T _b increased significantly (from 662.8 to 745.8°C); the value of E reduced, and CCI also improved. This research could provide useful information as a practical reference for heat and power production.

This study presents a two-stage stochastic linear programming (LP) model to calculate the water value in an isolated hybrid diesel/wind/pumped-storage power system with high wind power penetration and with a closed-loop pumped-storage power plant (i.e. there is no natural inflows in the upper pond), that takes into account in an approximate manner that the start-up cost of the diesel units depends on the time passed since the previous shut-down. The proposed model is applied in the power system of El Hierro island in the Canary Archipelago. The water values provided by the proposed model have been used as input for the day-ahead generation scheduling. The results obtained in the study indicate that the day-ahead generation schedule obtained when using the water values provided by the proposed model is similar to the one obtained when using the water values provided by a detailed mixed integer LP (MILP) model. The computational time required to obtain the water value with the proposed model is several orders of magnitude lower than the one needed to solve the MILP model.

The rapidly increasing penetration of renewable energies has introduced severe challenges to power system frequency controls due to the highly intermittent and uncertain power output of renewable energies. This study proposes a proactive frequency control method to control traditional synchronous generators in advance in anticipation of sudden power fluctuation. Therefore, the frequency deviations can be well mitigated by such early-acted control signal. In order to obtain this new control reference, an ensemble-forecasting model based on the extreme learning machine algorithm is designed to predict ultra-short-term power fluctuations, which serves as an extra signal for automatic generation control. The proposed method was verified on an equivalent model of the Singapore power system with various types of generations and loads. The simulation results clearly demonstrate the accuracy of the forecasting model and the advantages of the proposed control method. The proposed method can also reduce the frequency charging/discharging of energy storage systems, which can effectively extend their lifetime.

The study proposes efficient controllers to regulate frequency in highly penetrated power systems by renewable energy sources. Frequency controllers for variable speed wind turbines are designed to extract the stored energy in rotating masses, and efficiently regulate the pitch angle for frequency support. A temporary fast participation of storage battery incorporated with photovoltaic sources is provided via auxiliary controller. Controllers' fine tuning is realised by using stochastic fractal optimiser (SFO). The integral time absolute error in area frequency and tie line power represents the adapted objective function subjects to set of constraints. The performance assessments are carried out in three phases: (i) at initial stage, the secondary control is disabled and impact of wind penetration level on frequency nadir and frequency deviations are investigated, (ii) coordinated inertia/energy storage control is demonstrated, and (iii) finally, robustness analysis is made considering system parametric variations and real weather data. The proposed control strategy is verified by simulation in MATLAB/SIMULINK environment. The drawn numerical results by the SFO are compared with those achieved by genetic algorithm and the built-in controller tuner in SIMULINK. Performance assessments, comparative study along with robustness analysis of the SFO results confirm its viability and effectiveness.

In this study, the performance of a shade resilient smart module is studied under a dynamic shading pattern. A smart module architecture is developed to mitigate the non-linear shading effect on the module performance. Partial shading decreases the output current of the shaded cells and affects the unshaded cells’ output power. After distributing the module cells into small groups, based on a least square support vector machine optimisation method, DC–DC buck converters compensate the decreased current levels, by adjusting the output current and voltage level from any individual group of cells. The system is simulated in the MATLAB Simulink environment, and the output results are presented. Results show that the module performs efficiently and output power of the unshaded groups of cells never decreased because of the effect of shading on the other groups. Additionally, the maximum output power is harvested from all groups simultaneously. Prototype hardware is designed and built to implement the proof of concept. The real-time results of hardware testing show that the smart module performs as expected and mitigates partially shaded conditions by extracting maximum power from each group, regardless of other groups shading condition.

This article presents a new decision-making framework for strategic trading of demand response aggregator (DRA). The proposed framework consists of three layers. At the first layer, the wholesale energy market operated by independent system operator (ISO), which seeks to maximise the social welfare in day-ahead (DA) market and to minimise the cost of minimise the cost for maintaining energy balance in real-time (RT) markets. At the second layer, the DRA act as an intermediate operator between ISO and flexible customers. Profit-maximising DRA affects the clearing results of market prices by strategic trading in DA energy market, providing reserve capacity in DA reserve market and offsetting energy deviation in RT. At the third layer, customers seek to optimise the trading strategy between earnings incentive reward from the DRA and considering uncomfortable cost of customers. The tri-layer framework is modelled as two bilevel optimisation models, which capture interaction between different entities. The bilevel model can be transformed to single level linear model through Karush-Kuhn-Tucker conditions and dual theory to solve the couple non-linear problem. The numerical studies using the IEEE 9-bus and 118-bus test systems are presented to illustrate the application of the proposed tri-layer decision-making framework.

Accurate and reliable fault detecting plays a key role in application of grid-connected wave power generation systems. This study presents a novel IoT-based approach to condition monitoring of the wave power generation system, which has faster operating rate and lower hardware requirement. The compressed sensing (CS) method is adopted to compress the data, which aims to reduce the data uploaded to cloud platform; and then, the extreme learning machine (ELM) algorithm is used to achieve the condition monitoring of wave power generation system in cloud platform. In order to validate the effectiveness of the proposed method, the IoT-based wave power generation condition monitoring system test platform is established. The experiment results illustrate the high efficiency and reliability of proposed method. The proposed method has a potential of practical applications.

Microalgae using non-arable land were considered as promising biomass for biocrude production through hydrothermal liquefaction (HTL) and can also be used for environmentally-friendly wastewater treatment. In this study, we illuminated the effect of reaction temperature on the products distribution and biocrude properties from HTL of the Chlorella sp. grown in wastewater from anaerobic digestion (AD) of chicken manure via HTL. The highest biocrude yield (30.35%, daf) and energy recovery rate (49.6%) were both achieved at 330 °C, while the highest HHV (37.17 MJ/kg) of biocrude oil was achieved at 290 °C. The biocrude oil yield increased from 25.46 to 30.35% as the temperature increased from 270 to 330 °C. The gases yield showed a similar trend under 310 °C. On the contrary, the solid products yield decreased as the temperature approached 350 °C. The lowest nitrogen content (4.6%) and highest H/C (1.50) of biocrude oil were obtained at 290 and 270 °C, respectively. Results imply that deoxygenation was enhanced with temperature due to the enhanced decarboxylation at higher temperatures. Specially, the nitrogen content decreased by 39.4–47.4% in the temperature range of 270–350 °C. The integrated pathways for energy production and nutrient recycling from animal manure coupling AD/HTL, and algae technologies were further proposed.

This article demonstrates the automatic generation control of a multi-area system incorporating various sources. Area-1 and area-2 consist of thermal and parabolic trough solar thermal plant (PTSTP) of fixed and random solar insolation, respectively, and area-3 comprises of thermal and realistic dish-stirling solar thermal system units. A maiden effort has been made to use a fractional order (FO) proportional-integral minus FO derivative with filter coefficient (FOPI-FODF) controller as a secondary controller. The controller gains are optimised using a novel algorithm called crow search algorithm. Comparisons of system dynamic response with and without renewable energy sources for FOPI-FODF, and for some commonly used integer order and FO controller's reveals the better performance of FOPI-FODF controller. A study comparing both fixed and random solar insolation in PTSTP of different areas have been tested. An accurate high-voltage direct current (AHVDC) line model is designed using inertia emulation strategy and is placed instead of AC tie line, and with parallel AC tie lines resulting in better system dynamic performances than AC tie line alone. The selection of the optimum location of AHVDC line in parallel AC tie line exposes the AHVDC line connected with the area where the system is disturbed.

This study forms and optimises the renewable energy hub to supply load demand in the autonomous building disconnected from electrical grid (i.e. net-zero energy building). The proposed energy hub is made of hybrid wind-solar-hydro generation systems and it is also strengthened by pumped-storage hydroelectricity and hydrogen storage systems. The hydro system includes two water reservoirs in cascade connection and one pumped-storage hydroelectricity. The introduced optimisation programming designs proper capacity for cascade water reservoirs and energy storage system as well as optimises their operation. The uncertainties of parameters are included to make the stochastic programming. It is demonstrated that increasing the flow-in of reservoir 1 by 25% decreases the planning cost by ∼2.5% and decreasing the flow-in of reservoir 2 by 50% increases the planning cost by ∼16%. When the hydro system does not operate, the wind power must be increased to 10 kW in order to supply the load. When the wind and solar powers are not integrated, the hydro energy must be increased by 50%.

This study presents a practical mathematical model for the determination of the optimal point of connection of large wind farms to the transmission network. A comparative analysis of costs of connecting wind farms to the transmission network is given, about their distance from the point of common coupling (PCC). Calculations have been made for different installed capacity of the wind farm and for different voltage levels of PCC. Based on these calculations, there have been defined competitive distances of a wind farm with a certain power from the network of different voltage levels (e.g. 110 and 220 kV) for which the connection costs are equal. Such calculations are widely applicable and can be used by transmission system operators and wind power plant developers to optimise and plan the connection of a wind farm to the transmission network.

In Iran, the feed-in tariff (FIT) has been used for the promotion of photovoltaic (PV) systems, as an incentive, FIT cannot adjust power consumption. On the other hand, the PV generation has not reached grid parity; therefore, a tariff-based method for self-consumption is not attractive. This study presents a novel efficient incentive for the deployment of residential PV systems, which is based upon the levellised value of energy. The avoided cost-based method is proposed to evaluate the true value of PV (VOPV), including three main parameters: avoided energy cost, avoided capacity cost and avoided carbon emission cost. Numerical results show that in the first steps of the PV penetration, VOPV is more than the current FIT that makes self-consumption beneficial. The proposed model can both increase or decrease customer loads based on PV output and grid conditions. By applying this method, the domestic consumers will be willing and able to provide demand adjustment and it may increase the share of investment on residential solar systems with a reasonable profit. It can be shown that the proposed self-consumption platform can reduce losses and capacity requirements and consequently increase efficiency.

Although solar chimney power plant (SCPP) has a promising potential to convert the solar energy to electric power, greater efforts are needed to ensure its successful commercialisation on a large scale. The main obstacles with regard to the large-scale applications of SCPP are its efficiency, bulk size and dependency on solar irradiation. A comprehensive study on the integration of the Internet-of-Things (IoT) as a novel technique to monitor and hence improve its performance is presented. A small-scale SCPP is designed and constructed specifically for this purpose in Aswan, Egypt. The instantaneous performance parameters (temperature, solar intensity, wind speed, open circuit voltage, power etc.) were measured and further processed using IoT. The results show that IoT has a promising potential to enhance the performance of the system by providing a complete picture of the whole measurements. This is considered as a step forward for enhancing the technologies which will ensure the success of large-scale SCPP in the real world.