• Energy Research

Abstract An intractable but common problem in photovoltaic systems is that the power generated by photovoltaic will reduce seriously due to partial shading. In order to solve this problem, the photovoltaic array reconfiguration methods are developed to mitigate the impact of partial shading and increase output power. This work aims to undertake a comprehensive review on state-of-the-art photovoltaic array reconfiguration methods through a thoroughly investigation of 125 recently published papers. Compared with prior reviews, this work makes a more exhaustive classification, in which sixty-four methods are thoroughly categorized into nine groups. In addition, nine evaluation criteria are summarized for researchers to choose according to their specific requirements. Furthermore, a comprehensive comparison is provided based on ten specific indicators, such as monitor variables, complexity, response speed, rate of shadow dispersion, merits-demerits and application range, etc. Among these methods, the dynamic methods represented by meta-heuristic algorithms show more desirable performance than the static methods due to their faster response speed and prominent adaptability (e.g. the water cycle algorithm has the best performance with a power enhancement of 28%–37%, the TomTom algorithm has inferior performance with power enhancement of only 5%–25%). Finally, this review proposes six constructive suggestions and perspectives to offer technical inspirations for future research in the related fields.

Abstract The generation efficiency of thermoelectric generation system is relatively low, thus how maximize its power production is of great importance. This paper designs a novel greedy search based data-driven method for centralized thermoelectric generation system to achieve maximum power point tracking under non-uniform temperature distribution. In order to effectively distinguish the local maximum power points and the global maximum power point under non-uniform temperature distribution, greedy search based data-driven employs a two-layer feed-forward neural network to accurately fit the curve between the power output and the controllable variable based on the real-time updated operation data. Based on the approximation curve, a greedy search is designed to efficiently approach the global maximum power point from a shrinking search space. Cases studies such as start-up test, step variation of temperature, stochastic temperature change, and analyse of sensitivity, are implemented to prove the effectiveness and superiority of the proposed algorithm. Simulation results verify that the proposed method can generate the highest energy under non-uniform temperature distribution condition, e.g., 391.34%, 115.71%, 110.92%, and 109.43% to that of perturb and observe, particle swarm optimization, whale optimization algorithm, and grey wolf optimizer in the stochastic temperature change. Lastly, the implementation feasibility of the proposed method is demonstrated by the hardware-in-the-loop experiment based on dSpace platform.

Abstract Global interests in solid oxide fuel cells (SOFCs) has been dramatically increased over the last few decades thanks to their high energy conversion efficiency, prominent stability, and powerful sustainability. In particular, fault diagnosis of SOFC systems plays a crucial role in their overall optimal control and reliable operation. Thus far, a large number of fault diagnosis methods have been developed to improve the durability and promote the commercialization of SOFC systems. Until now, several reviews have been proposed to summarize these methods but all exist distinct drawbacks, such as incomplete classification and discussion, lack of practical recommendations, etc. Hence, this paper attempts to offer a systematic summarization and classification of various SOFC systems fault diagnosis approaches. Note that eighteen specific faults are addressed detailedly, along with fifteen fault detection and isolation (FDI) methods which are categorized into four groups, i.e., electrochemical impedance spectroscopy (EIS)-based, model-based, data-driven, and hybrid FDI. Besides, practical applications, diagnosis accuracy, diagnostic diversity, complexity, advantages, and disadvantages of all methods are thoroughly compared and evaluated. Lastly, eleven perspectives are proposed for future in-depth researches.

Abstract Precise and reliable modelling of solid oxide fuel cells (SOFC) is critical for simulation analysis and optimal control of SOFC systems, which typically relies on an accurate identification of its unknown parameters. However, such problem is characterized by high non-linearity, multi-variable, and strong nonconvexity, thus conventional strategies cannot always achieve satisfactory results. With the rapid advancement of computer science, numerous meta-heuristic algorithms have been developed to solve such obstacle. However, there is no prior review to systematically summarize these approaches, thus a thorough survey study is absolutely needed. Hence, this paper undertakes a comprehensive survey on state-of-the-art meta-heuristic algorithms and related variants utilized in SOFC parameter identification. Besides, various specific experimental performance of each algorithm, and other latest estimation techniques are also elaborated. Moreover, a thorough summary is carried out to systematically compare and summarize their basic features, upon which readers can effectively grasp and utilize them. Lastly, some constructive perspectives and recommendations are proposed in conclusion for future researches. Note that hybrid variants can often achieve more satisfactory results than individual algorithms. Hence, the combination of various effective methods is crucial for novel parameter identification techniques development, upon which more reliable and efficient approaches can be devised for better simulation analysis and optimal control of SOFC systems. Besides, normalized models on such problem are also needed to be established for more accurate performance evaluation and prediction. In general, this paper can be regarded as a one-stop handbook for future in-depth studies in the related field.

Abstract This paper develops a novel passive fractional-order sliding-mode control (PFOSMC) of a supercapacitor energy storage (SCES) system in microgrid with distributed generators. Firstly, a storage function is constructed and thoroughly analysed to investigate the inherent physical characteristics of SCES systems. Then, the beneficial terms are carefully retained for the sake of transient responses improvement, while the other detrimental terms are fully removed to achieve a globally control consistency. In order to further enhance the robustness of the closed-loop system, a fractional-order sliding-mode control (FOSMC) framework is synthesized as an additional input, which employs the fractional-order P D α sliding surface as well as an energy reshaping mechanism to realize a more flexible control performance. Four case studies, including (a) Active power and reactive power supply, (b) System restoration under power grid fault, (c) Power support under stochastic solar energy and wind energy integration, and (d) Robustness with system parameter uncertainties, are carried out to study the control performance of PFOSMC compared to that of PID control, interconnection and damping assignment passivity-based control (IDA-PBC), and FOSMC, Finally, a hardware-in-the-loop (HIL) experiment using dSpace platform is undertaken to validate its implementation feasibility.

Abstract Wind energy has the inherent nature of intermittence and randomness, such that its accurate prediction is extremely critical to ensure safe and stable operation of power system with increased wind power integration. This paper aims to present a thorough state-of-the-art one-stop handbook on various approaches applied in wind forecasting based on three perspectives: wind speed and power forecasting, uncertainty forecasting, and ramp events forecasting. Firstly, four classifications of wind data according to data source, along with twenty-seven data pre-processing technologies which can efficiently improve prediction precision are carefully addressed. Then, a systematic literature review of model principle based wind speed and power forecasting strategies is investigated, which is categorized into three groups: physical approaches, statistical approaches, and combined approaches. Peculiarly, it has reported that combined approaches can realize more than 70% accuracy improvement compared with single model. Besides, three novel technologies developed in recent years are also discussed, e.g., spatial correlation forecasting, regional forecasting, and offshore forecasting. Moreover, thirty-seven evaluation criteria of wind speed and power forecasting are thoroughly summarized for performance verification. Additionally, uncertainty forecasting and ramp events forecasting which can provide more risk information for operators to handle decision-making issues in power system are also elaborated. Lastly, eight recommendations for further development in wind forecasting are also proposed. The prominent merit of this work is that a total of one hundred and seven wind forecasting methods from three perspectives are comprehensively summarized and compared based on inputs, time-scale, space-scale, forecasting variables, metrics, and features, which aims to help readers more effectively utilize these approaches for future in-depth researches.

Abstract This paper designs a novel maximum power point tracking (MPPT) technique for centralized thermoelectric generation (TEG) system under heterogeneous temperature difference (HeTD). Since the HeTD can result in multiple local maximum power points (LMPPs) for centralized TEG system, a fast atom search optimization (FASO) is designed to approximate the global maximum power point (GMPP) from multiple LMPPs. In order to accelerate the convergence for a high quality solution, the Euclidian distance ratio of the original atom search optimization (ASO) is employed to adaptively update according to the dynamic optimization results, as well as for the number of neighbours for each atom. Through case studies such as start-up test, step variation of temperature, stochastic temperature change, and analyse of sensitivity are carried out, in which the practicability and superiority of FASO are compared with that of MPPT method based on a single LMPP and four meta-heuristic algorithms. Simulation results show that the energy output produced by FASO ranges from 101.64% to 451.49% to the alternatives. Finally, through hardware-in-the-loop (HIL) experiment based on a dSpace, the feasibility of the hardware realization is confirmed, in which the difference between simulation results and HIL experiment results is less than 2.3%.

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

Abstract A novel interacted collective intelligence (ICI) algorithm was developed in this work for maximum power point tracking (MPPT) of centralized thermoelectric generation (TEG) systems at non-uniform temperature gradients (NTG), upon which waste heat recovery ability can be considerably enhanced to enhance resources utilization efficiency. As centralized TEG system often exists numerous local maximum power points (LMPPs) under NTG, ICI is adopted to effectively seek global maximum power point (GMPP), upon which energy exploitation and utilization can be efficiently improved. To achieve a higher searching efficiency, a sub-optimizer with the best solution from all sub-optimizers is dynamically selected during each iteration to guide others. Although multiple sub-optimizers based ICI might lead to a higher computation complexity, a wider global search and more stable convergence can be achieved compared with single meta-heuristic algorithm. Four case studies, e.g., start-up test, step variation of temperature, stochastic temperature variation, and sensitivity analysis, validate the effectiveness and superiority of ICI. Simulation results indicate that ICI based MPPT can produce the largest energy with minimum power fluctuation under NTG compared against other five sub-optimizers, e.g., 109.24%, 112.54%, 108.61%, 107.10% and 116.75% to that of dragonfly algorithm (DA), firefly algorithm (FA), salp swarm algorithm (SSA), moth flame optimization (MFO) and multi-verse optimization (MVO) respectively in the step variation of temperature.