IET Renewable Power Generation
Volume 13, Issue 7, 20 May 2019
Volumes & issues:
Volume 13, Issue 7
20 May 2019
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- Author(s): Muhammad Naveed Akhter ; Saad Mekhilef ; Hazlie Mokhlis ; Noraisyah Mohamed Shah
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1009 –1023
- DOI: 10.1049/iet-rpg.2018.5649
- Type: Article
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The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
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- Author(s): Iram Akhtar ; Sheeraz Kirmani ; Majid Jamil
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1024 –1036
- DOI: 10.1049/iet-rpg.2018.5117
- Type: Article
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Sustainable microgrid primarily powered by renewable energy sources is a recent concept to fulfill the pledge of delivering reliable power supply for upcoming power systems. This study presents a microgrid system primarily powered by wind and solar energy sources and identifies the issues related to the design, operation, and control of the system. The system is designed and simulated to detect the practical issues involved in the control and operation of the sustainable microgrid system based on wind and solar sources. The technical challenges and a brief plan of conceptual methods to detect some of the technical issues are presented for further analysis. To achieve power quality improvement, effective control architecture is described here. Furthermore, an advanced random pulse position modulation technique for the voltage source inverter is proposed to influence the DC-link inductor which further reduces the harmonic distortion. A current injected control loop (CICL) is also proposed to improve the dynamic behavior of microgrid, due to change in solar radiation and wind speed, which causes DC bus voltage oscillation and, hence, affects in proper system operation. The simulation results report that the microgrid system powered by renewable energy sources have a good performance.
- Author(s): Farzad Arasteh and Gholam H. Riahy
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1037 –1049
- DOI: 10.1049/iet-rpg.2018.5295
- Type: Article
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The non-preventable ever-increasing rate of wind power generation in market-based power systems faces the operators with challenging situations for making optimal decisions. So, it is essential to equip the operators with applicable control strategies and further corresponding control facilities. Moreover, the high-priority of cheap wind power utilisation increases the probability of transmission lines congestion. Therefore, different solutions such as transmission switching (TS) and demand response (DR) programs have been recently introduced to manage the intermittent wind power generations. Accordingly, this study addresses the social welfare maximisation problem with coordinated control of TS and DR facilities to handle the regarding uncertainties using yet another linear matrix inequality parser (YALMIP). In fact, rapid algorithm and powerful employed solvers as well as simplicity of use, make YALMIP a practical modelling and optimisation toolbox. In this respect, the MOSEK solver is preferred by YALMIP to solve the proposed mixed integer linear programming problem. In addition, wind power uncertainty is modelled using the discrete-time Markov chain approach and optimisations are performed on the 8-bus and the large-scale IEEE 118-bus test systems. Results show that the proposed control strategy is highly capable of maximising social welfare by determining the optimal control commands in a real-time manner.
- Author(s): Sina Ghaemi ; Javad Salehi ; Farid Hamzeh Aghdam
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1050 –1061
- DOI: 10.1049/iet-rpg.2018.5573
- Type: Article
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Energy management problem is regarded as an important subject in the networked microgrids (MGs) based distribution system, where each entity possesses individual objectives. In this study, risk aversion energy management has been proposed for each MG and the distribution network (DN) in order to assess risks associated with uncertain sources. Conditional value at risk technique is utilised for including variability of profit to the objective function. In addition, this study investigates usefulness of possibility of power trading among MGs. For this aim, separate power line is considered between two different MGs besides of their power lines with DN. Power flow constraints are implemented to energy management formulation of each MG and DN as well. The proposed energy management problem is expressed based on stochastic linear programming. In this study, auxiliary problem principle decentralised approach is utilised to solve the optimisation problem in order to consider computer hardware limitations and privacy constraints. The recommended energy management approach has been applied to the IEEE 33-bus DN which is modified by MGs. Then, the effectiveness of providing power link between each two different MGs on the obtained profit has been evaluated under the both islanded and grid-connected modes of operation and various risk aversion parameter of entities.
- Author(s): Dayse Pereira Nascimento ; Valeska L. Menezes ; Monica Carvalho ; Ricardo Chacartegui
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1063 –1075
- DOI: 10.1049/iet-rpg.2018.5777
- Type: Article
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The potential energy uses of landfill gas (LFG) are investigated herein, focusing on electricity generation and leachate evaporation. LFG generation is estimated with the application of the GasSim method, using real data on the municipal solid waste disposal at the Metropolitan Sanitary Landfill of João Pessoa (Northeast Brazil). The results show significant LFG generation, with an estimated peak production of 11,277.28 t in 2028, and LFG generation until 2042. Commercially available biogas-operated equipment were analysed for electricity production in situ, considering production and operation restrictions at the landfill. It was verified that after 2018, a cogeneration facility can generate electricity and the available heat is sufficient to evaporate leachate. Positive net economic results are obtained, with an internal rate of return over 30% after 26 years, demonstrating that the cogeneration facility is profitable and self-sustainable.
- Author(s): Shama Naz Islam ; Md Apel Mahmud ; Sajeeb Saha ; Md Enamul Haque
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1076 –1086
- DOI: 10.1049/iet-rpg.2018.5624
- Type: Article
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In this study, the energy data transfer problem in a DC microgrid with multiple renewable powered base stations (BSs) is considered. These BSs can share the renewable generation among each other. The energy cooperation is optimised by the control unit. For effective energy cooperation, energy data needs to be transferred from BSs to the control unit with low latency and high reliability. For cellular enabled microgrid communication, the energy data exchange and cellular communication both use the same communication resources. Thus, there will be interference at the control unit and cellular user (CU), which degrades the reliability of energy data transfer. To solve this problem, a linear precoding technique is designed to minimise the mean square error of the desired messages at the control unit, BSs, and CU while the interferences are kept at a predefined level. For the designed precoders, the expressions of signal-to-interference-plus-noise ratio are formulated and the error performance is analysed. Numerical simulation has been performed to compare the considered precoding technique with other precoding techniques. The simulation results demonstrate that optimum precoding can improve the error performance at the control unit, BSs, and CU by 1, 5, and 3 dB, respectively.
- Author(s): Juan Ospina ; Alvi Newaz ; M. Omar Faruque
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1087 –1095
- DOI: 10.1049/iet-rpg.2018.5779
- Type: Article
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This paper proposes a novel forecasting model designed to accurately forecast the PV power output for both large-scale and small-scale PV systems. The proposed model uses available temperature data, approximate and detailed coefficients obtained from the decomposed PV power time series using the stationary wavelet transform (SWT), and statistical features extracted from the historical PV data. The model is comprised of four long–short–term memory (LSTM) recurrent neural networks (RNN) designed to perform multi-step forecasting on the individual approximate and detailed coefficients decomposed by the SWT and a final deep neural network (DNN) designed to perform the next time step PV power forecast. The DNN makes use of the reconstructed values estimated by the four LSTM networks together with temperature data and statistical features to predict the final forecasted value of the next time step PV power. 30-min resolution data from a 12.6 MW PV system located in the state of Florida are used for testing and evaluating the proposed method against several models found in the literature. The results obtained suggest that the proposed model improved the forecasting accuracy significantly in the metrics used to compare with other models while reducing the number of features needed to perform the forecasting operation.
- Author(s): Chen Duan ; Zhang Minglu ; Zhang Changbing ; Yang Mengjiao ; Mao Cheng ; Shen Chunhe
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1096 –1104
- DOI: 10.1049/iet-rpg.2018.5663
- Type: Article
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Aiming at the complex hydraulic and electrical interference, as well as the stability of the hydropower plant that one diversion tunnel supplies water to multiple turbines, a high-order coupling model based on the dual control mode of the speed and excitation regulation is developed. Some scenarios, involving the hydraulic interference law, the effect of partial load rejection on the stability and dynamic characteristics of the units, and the optimisation of the power system stabiliser (PSS), are investigated. It is found that the change of the guide vane opening (or power) of one of the units causes the water hammer in its spiral case and penstock, and the water level fluctuation in the surge chamber. Besides, hydraulic disturbances suffered by other units are mainly caused by water level fluctuation in the surge chamber, rather than directly from the water hammer. The results demonstrate that the unit wiring of the generator and transformer is better than the expansion unit wiring in terms of the dynamic characteristics. In addition, PSS not only can suppress low-frequency oscillations, but also affect the hydraulic transition process, and the improved PSS2B-proportional–integral–derivative can increase the positive damping, and more effective in suppressing system oscillations.
- Author(s): Shenglin Li ; Junjie Yang ; Jicheng Fang ; Ziqi Liu ; Helong Zhang
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1105 –1114
- DOI: 10.1049/iet-rpg.2018.5715
- Type: Article
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Nowadays with the development of smart residential microgrid (RMG), the distributed energy storage system (DESS) can help consumers to not only balance generation and consumption but also participate in demand respond. However, the unadjustable capacity of DESS and the lack of energy sharing among users have become the major challenges to the further development of RMG. This paper proposes a novel electricity scheduling architecture based on energy cloud (EC) for RMGs and designs an electricity scheduling optimisation. The EC is used in order to link different end-users and promote coordination. In the proposed EC-based electricity scheduling architecture, the mathematical model for the RMG is provided. Moreover, considering the depreciation cost of battery, the optimisation model is established with the objective of minimising the electricity cost. Compared with the traditional RMG, simulation results show that the proposed strategy can not only allow consumers to adjust their optimal energy storage capacity but also further reduce electricity payment costs. The designed strategy provides a new and effective research perspective for electricity scheduling of RMGs.
- Author(s): Zhen-Long Li ; Jing Xia ; An Liu ; Peng Li
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1115 –1122
- DOI: 10.1049/iet-rpg.2018.5673
- Type: Article
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For solar power and wind speed prediction, the uncertainty and randomness of prediction model or parameters make it a challenging task to optimise the accurate output. This study presents a novel backward bat algorithm (BBA) for the parameter tuning of the support vector machine (SVM). Then, the BBA-SVM approach is used to predict the solar power and wind speed output in different situations. The salient feather of the novel BBA-SVM is that an improved flying principle is developed by adopting the backward flying mechanism, which enhances the randomly searching ability and thus avoids the local optimum effectively. Compared to traditional SVM methods, the BBA-SVM gains higher training accuracy, shorter training time, and better prediction performance. Take the solar power output in a sunny day as the validation case, the real data sets from Australia are adopted for comparative simulations, demonstrating the priority of the BBA-SVM against some other SVMs like the grid searching SVM, bat algorithm SVM, and generic algorithm aided BBA-SVM.
- Author(s): Mehran Heidari ; Taher Niknam ; Mohsen Zare ; Solmaz Niknam
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1123 –1131
- DOI: 10.1049/iet-rpg.2018.5842
- Type: Article
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This paper presents a comprehensive model of dynamic optimal operation management of the smart nano-grids (NGs) including the micro wind turbines (WTs) and micro photovoltaics (PVs) as renewable energy sources (RESs) while micro turbines (MTs) and fuel cell (FC) are considered as non-RESs. Furthermore, two types of lead-acid and lithium-ion batteries are considered besides the three types of controllable, curtailment-able and must run loads to increase the flexibility of the proposed formulation. The different objective functions of NG operation problem to be minimised include operating cost, environmental damage cost of pollution gases and exchanged power cost with the main grid. Also the power losses of batteries are modelled using the quadratic functions based on types and output powers of considered batteries, while these losses impose additional cost to operation cost functions of batteries. A modified teaching-learning-based optimisation (MTLBO) algorithm is used to cope with the multiobjective problem considering the constraints.
- Author(s): Yu Cheng ; Wang Wang ; Zhaohao Ding ; Zhiyao He
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1132 –1141
- DOI: 10.1049/iet-rpg.2018.5863
- Type: Article
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As the progress of electrification for the public transportation sector is accelerated, it becomes more and more important to integrated planning charging infrastructure for public transportation, while emerging technologies such as distributed renewable generation and energy storage system should be accounted. Here, an integrated resource planning framework is proposed where both planning investment cost and operational cost are considered. An aggregation strategy is also proposed to optimise the charging decisions for electric bus on different routes, which could effectively improve the planning and operation efficiency. To address the uncertainties involved in this process, a scenario-based chance-constrained programming approach is adopted. A numerical case study is also included to demonstrate the effectiveness of this proposed resource planning model.
- Author(s): Seghir Benhalima ; Ambrish Chandra ; Miloud Rezkallah
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1142 –1152
- DOI: 10.1049/iet-rpg.2018.5245
- Type: Article
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This paper presents the experimental implementation of a LMS-Adaline-based ANFIS controller of an improved power-quality photovoltaic (PV) generating system connected to the grid. The proposed system applies an adaptive neuro-fuzzy inference system (ANFIS) to control the DC–DC boost converter integrated with PV to achieve the maximum power point tracking (MPPT) operating condition. For power-quality improvement at the point of common coupling (PCC), Adaline (adaptive linear element)-based control algorithm is used to estimate the reference grid currents. To achieve high performance with fast dynamic response during transition and to regulate constant the DC and the AC voltages without saturation phenomena, ANFIS controller is employed. The real-time benchmark realised in the laboratory, to implement the setup, uses a dSPACE controller. To demonstrate the performance of the proposed configuration, the system is first simulated offline under numerous critical scenarios. The experimental results are then presented to validate the concept.
- Author(s): Yun Zeng ; Jing Qian ; Yakun Guo ; Shige Yu
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1153 –1159
- DOI: 10.1049/iet-rpg.2018.6123
- Type: Article
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Calculation of the hydraulic coupling in the single penstock multi-machine (SPMM) is one of the main obstacles for control design of the hydro turbine governor. In order to resolve this problem, a multi-machine differential equation model suitable for control design and stability analysis is established in this study. First, the multi-machine differential equation with the hydraulic coupling model is then established to facilitate building the joint model of the hydro turbine with the hydraulic system and the generator considering the hydraulic-mechanic-electric coupling. Second, the dynamic head in the common penstock satisfying the superposition principle is revealed. This means that the dynamic head in the common penstock can be calculated by using the state variables of the branch pipe. Third, simulation reveals that the differential item in the hydraulic coupling is the main factor affecting the computational convergence. As such, the approximate method ignoring the differential item in the hydraulic coupling is proposed to solve the problem of computational convergence of the SPMM. Finally, the classical method of characteristic (MOC) is employed to verify the proposed model. The results show that the proposed model has higher accuracy and is easy to connect with the non-linear model of the generator.
- Author(s): Jie Wei ; Yue Zhang ; Farshid Sahriatzadeh ; Anurag K. Srivastava
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1160 –1167
- DOI: 10.1049/iet-rpg.2018.6019
- Type: Article
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With increasing penetration of demand response and distributed renewable generation in the distribution system, distribution locational marginal price (DLMP) helps to provide a right price signal for participants in an active distribution system. Unlike the transmission system, the distribution system is unbalanced, which may result in different phase price at each bus. In this study, a three-phase current injection based optimal power flow (OPF) with robust convergence is proposed to compute DLMP at each bus and phase. Several scenarios have been modelled to validate the application of the proposed DLMP to manage distributed generation, demand response and line congestion for a modified IEEE test system.
- Author(s): Alberto Lorenzo-Bonache ; Andres Honrubia-Escribano ; Jens Fortmann ; Emilio Gómez-Lázaro
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1168 –1178
- DOI: 10.1049/iet-rpg.2018.6098
- Type: Article
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The widespread use of renewable energies around the world has generated the need for new tools and resources to allow them to be properly integrated into current power systems. Power system operators need new dynamic generic models of wind turbines and wind farms adaptable to any vendor topology and which permit transient stability analysis of their networks with the required accuracy. Under this framework, the International Electrotechnical Commission (IEC) and the Western Electricity Coordinating Council (WECC) have developed their own generic dynamic models of wind turbines for stability analysis. Although these entities work in conjunction, the focus of each is slightly different. The WECC models attempt to minimise the complexity and number of parameters needed, while the IEC approach aims to optimise comparison with real turbine measurements. This study presents a detailed comparison between these two different approaches for modeling a Type 3 (i.e., DFIG) wind turbine in MATLAB/Simulink. Finally, several simulations are conducted, with which the consequences of the different approaches are evaluated. The results of this paper are of interest to power system operators as well as wind turbine manufacturers who require further assistance in adapting their specific models to the simplified versions provided by the International Committees.
- Author(s): Jussi Ikäheimo ; Esa Pursiheimo ; Juha Kiviluoma ; Hannele Holttinen
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1179 –1189
- DOI: 10.1049/iet-rpg.2018.5007
- Type: Article
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In order to achieve significant greenhouse gas emission reductions, decarbonisation of all economic sectors must be considered. Here, the authors study the provision of renewable energy for the power, district heating, transport and industrial sectors in nine North European countries by integrating a large amount of wind and solar power into the system with power-to-gas and power-to-fuel plants enabling balancing and sector coupling. Simultaneous optimisation of plant capacities and operation was performed. Two different synthetic liquid fuel production pathways were compared. The cost of synthetic liquid fuel remained, depending on the production pathway and amount, 30–120% higher than estimated fossil alternative cost. Biomass potential emerged as a limiting factor with high shares of biomass-based synthetic liquid fuel production. The need for energy storage system was estimated. The total optimal capacity of synthetic natural gas, hydrogen, synthetic liquid fuel, and heat storages varied between 37 and 54 TWh (1.7–2.5% of energy demand) depending on the scenario, when emergency stocks were not included. The cost of energy storages remained small compared to the total system cost, with heat storages exhibiting the highest cost.
- Author(s): Pedro M.S. Carvalho and Luís A.F.M. Ferreira
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1190 –1196
- DOI: 10.1049/iet-rpg.2018.5838
- Type: Article
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In this study, end-use storage loads with elasticity constrained by a time window are modelled by a particle hopping cellular automaton. The automaton model is introduced and parameterised to obtain results on the ideal load-shifting response. Simulation is used to analyse the intrinsic limitations of load-shifting ramping capabilities. New concepts are introduced: load particle velocity and particle density. These concepts are used to advance formal hypotheses on the ramping limitations. Hypotheses are tested against experimental results to conclude about the underlying potential of load-shifting demand response.
- Author(s): Hadi Bisheh and Bahador Fani
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1197 –1204
- DOI: 10.1049/iet-rpg.2018.6083
- Type: Article
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Despite the increasing attention paid to multi-agent systems (MASs), their applications in protection schemes of distribution networks would face serious challenges when the generation level of photovoltaic (PV) systems is significantly increased, i.e. the inherent variable generation of PV systems would not only increase the communication burden, but it can also cause protection miscoordination. To solve this problem, this study first classifies the protection tasks into two hierarchical categories as the ‘first-control-level’ and ‘second-control-level’ functions, where the first group is responsible for the urgent task of fault clearing, and the second group updates protection settings in the event of network/generation changes. Given clearing the fault should be accomplished as soon as possible, the first-control-level functions are designed to require the least possible data communication. Presenting a penetration-free approach, the study next describes the mechanism of managing the various generation-change events through a first-control-level function. Therefore, communication failure, as well as protection miscoordination risks, would be mitigated. Finally, the effectiveness of the proposed method is demonstrated using a practical PV-integrated distribution network. This study tackles an important challenge in the protection of distribution systems with a high level of distributed generation penetration where MAS-based schemes are applied.
- Author(s): Zhiyong Ma ; Dameng Wang ; Wei Teng ; Yibing Liu
- Source: IET Renewable Power Generation, Volume 13, Issue 7, p. 1205 –1213
- DOI: 10.1049/iet-rpg.2018.5399
- Type: Article
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High failure rate and maintenance cost of wind turbines are one of the key issues in wind energy. In view of this, reliability centred maintenance (RCM) has been introduced to distinctively treat equipment failures according to their criticality. Criticality analysis (CA) is a pivotal step of RCM, derived from the failure frequency and failure modes effects of equipment, which determines when and how to maintain the wind turbines. However, in traditional CA, failure data is roughly considered, e.g. failure frequency is categorised as different ranks instead of accurate numerical value. In addition, the method of calculating criticality by multiplying variables is sensitive to variable ranges and values, which is unable to distinctly distinguish the contributions of different variables. Aimed at these deficiencies, an improved CA method with expansibility is proposed to assess the criticality of equipment failures in wind turbines based on Euclidean distance of failure vectors. To assess disparities among criticality ranks from diverse CA methods, an inverse number based method is presented. Several existing CA methods and the proposed CA in this study are compared using the data of wind turbines in literature, and the results verify the effectiveness of the proposed method.
Analysis and design of a sustainable microgrid primarily powered by renewable energy sources with dynamic performance improvement
Social welfare maximisation of market based wind integrated power systems by simultaneous coordination of transmission switching and demand response programs
Risk aversion energy management in the networked microgrids with presence of renewable generation using decentralised optimisation approach
Energy analysis of products and processes in a sanitary landfill
Linear precoder design for base station energy cooperation in DC microgrids
Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model
Research on hydraulic–electric interference and optimisation of multi-turbine hydropower system based on the dual control mode
Electricity scheduling optimisation based on energy cloud for residential microgrids
States prediction for solar power and wind speed using BBA-SVM
Integrated battery model in cost-effective operation and load management of grid-connected smart nano-grid
Electric bus fast charging station resource planning considering load aggregation and renewable integration
Real-time experimental implementation of an LMS-adaline-based ANFIS controller to drive PV interfacing power system
Differential equation model of single penstock multi-machine system with hydraulic coupling
DLMP using three-phase current injection OPF with renewables and demand response
Generic Type 3 WT models: comparison between IEC and WECC approaches
Role of power to liquids and biomass to liquids in a nearly renewable energy system
Intrinsic limitations of load-shifting response dynamics: preliminary results from particle hopping models of homogeneous density incompressible loads
Local penetration-free control approach against numerous changes in PV generation level in MAS-based protection schemes
Improved criticality analysis method of equipment failures in wind turbines
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