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Highlights • A novel model based on online sequential outlier robust extreme learning (OSORELM) is proposed. • The hybrid mode decomposition (HMD) method is used to deeply decompose the original wind speed. • The OSORELM with HMD decomposition is used for short-term multi-step wind speed prediction. • The crisscross optimization (CSO) is applied to optimize the parameters of the model. Abstract As the wind energy developing, wind speed prediction is important for the reliability of wind power system and the integration of wind energy into the power network. This paper proposed a novel model based on hybrid mode decomposition (HMD) method and online sequential outlier robust extreme learning machine (OSORELM) for short-term wind speed prediction. In data pre-processing period, wind speed is deeply decomposed by HMD, which is comprised of variational mode decomposition (VMD), sample entropy (SE) and wavelet packet decomposition (WPD). The crisscross algorithm (CSO) is applied to optimize the input-weights and hidden layer biases for OSORELM, which have impact on the forecasting performance. The experiment results show that: (a) HMD is an effective way of wind speed decomposition, which can capture the characteristics of wind speed time series accurately and thus promote the prediction performance; (b) the OSORELM performs better than offline models in practical forecasting; (c) the proposed forecasting model has greatly improved the accuracy in multi-step wind speed forecasting. [ABSTRACT FROM AUTHOR]

As cogeneration plays an increasingly important role in energy utilization, the combined heat and power economic dispatch (CHPED) becomes an important task in power system operation. In this paper, a novel crisscross optimization (CSO) algorithm is implemented to solve the large scale CHPED problem, which is a challenging non-convex optimization problem with a large number of local minima. The feature of applying CSO to address the CHPED problem lies in two interacting operators, namely horizontal crossover and vertical crossover. The horizontal crossover searches for the new solutions within a half population of hyper-cubes with a large probability while in their respective peripheries with a decreasing probability. The vertical crossover provides a effective mechanism for those stagnant dimensions of a population to escape from premature convergence. The combination of both gifts CSO with a powerful global search ability. The effectiveness of the proposed method is validated on six cogeneration systems with different characteristics. The numeric results demonstrates that the proposed CSO method achieves much better results than other methods reported in the literature. To investigate the robustness and applicability of CSO in large power system, two new systems with 96 and 192 units by duplicating the system of case 4 two times and four times are also studied. The results obtained substantiates the suitability of CSO for large-scale constrained CHPED problem. [ABSTRACT FROM AUTHOR]

Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily. [ABSTRACT FROM AUTHOR]

• An isolated hybrid power system is simulated and analysed. • Optimal sizing and electricity demand with cost improvement. • Photovoltaic panels, biomass gasifier, and batteries units. • A Quantum model of a RUNge Kutta optimization algorithm. In this paper, an isolated hybrid power system (HPS) is simulated and analyzed to obtain the optimal sizing and meet the electricity demand with cost improvement for servicing Alrashda village, which is a small village of the Dakhla Oasis villages in the New Valley, Egypt. The major configuration of this HPS is Photovoltaic panels (PV), Biomass gasifier, and Batteries units. A Quantum model of a RUNge Kutta optimization algorithm called QRUN algorithm is applied to guarantee that all load needs are covered with high reliability with the minimum cost of energy (COE) of the proposed system. To ensure the superiority of the QRUN technique, its results are compared with those obtained by the original optimization algorithm RUNge Kutta optimization algorithm (RUN), and a well-known optimization algorithm, Aquila optimizer algorithm (AO) and Gray Wolf algorithm (GWO). The simulation results show that the modification QRUN algorithm gives the best Fitness Function with 0.080076 $/kWh of the COE, net present cost (NPC) with 3,352,062 $, and loss of power supply probability (LPSP) with 0.490866. [ABSTRACT FROM AUTHOR]

• A novel enhanced MPA algorithm has been proposed for effective PV modelling. • Static and dynamic PV model parameters have been estimated. • The estimated parameters have been validated via experimental data-sets. • The statistical analysis has been performed for effectiveness of the EMPA method. • The results confirm the EMPA efficiency comparing with state-of-the-art algorithms. Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301 e - 04 and 5.9135 e - 07 . Similarly for double diode are 7.4396 e - 04 and 3.1849 e - 05 , respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505 ± 1.0971 e - 17 , which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505 ± 9.6235 e - 14 and 0.0084505 ± 2.5235 e - 9). The results show the proposed model's superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems play an important role in today's power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching–learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners' ranking probability is presented to adaptively choose the teacher or learner phase of TLBO; ii) based on the learners' ranking probability, an enhanced teaching manner with an adaptive teaching factor T F is proposed to make use of the exploitation abilities of better learners in the teacher phase; iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems. • A hybrid adaptive TLBO with DE algorithm (ATLDE) is proposed. • Learners ranking probability is used to adaptively select the teacher or learner phase. • DE is embedded in TLBO to improve population diversity and exploration ability. • The superiority of ATLDE is verified by solving PV parameters identification problems. [ABSTRACT FROM AUTHOR]

Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 × 9 , 6 × 20 , 16 × 16, and 25 × 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization–reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization–photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs. • A novel fitness function is proposed for photovoltaic reconfiguration approach. • Artificial ecosystem optimizer is applied to different patterns of shadow. • The proposed approach is implemented for 9x9, 6x20, 16x16, and 25x25 photovoltaic arrays. • The results of the proposed technique are compared with recent counterparts. • Several metrics are computed to confirm the superiority of the proposed approach. • Artificial ecosystem optimizer based on the novel objective boosts the produced power. [ABSTRACT FROM AUTHOR]

• An enhanced adaptive differential evolution is proposed to extract photovoltaic parameters. • The accuracy, reliability, and convergence of the proposed method are confirmed. • The proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models. Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models. [ABSTRACT FROM AUTHOR]