IET Renewable Power Generation
Volume 12, Issue 6, 30 April 2018
Volumes & issues:
Volume 12, Issue 6
30 April 2018
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- Author(s): Dongxiao Wang ; Xiaodan Gao ; Ke Meng ; Jing Qiu ; Loi Lei Lai ; Sen Gao
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 615 –624
- DOI: 10.1049/iet-rpg.2017.0590
- Type: Article
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p.
615
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Variable-speed operation of wind turbines (WTs) achieved through electronic converters can decouple wind generator dynamics from power systems. Besides, the wide rotor operation range makes it possible for the WT to tap the potential of kinetic energy (KE) in the rotating mass. This study provides an overview of utilising KE from WTs for grid connections. The availability of KE in WTs is acquired by the rotor speed alternation, both in static and dynamic processes. For the static state, the rotors of WTs operate at a pre-defined power curve which leaves the generation margin for KE release; while for the dynamic process, the KE is passively changed by the imbalance between mechanical and electrical power. Some emerging technologies in frequency regulation and low-voltage ride through capability with the participation of KE stored in WTs are investigated. The applications of KE in terms of wind smoothing are summarised as well. Finally, this literature review also discusses some potential future applications of KE management.
Utilisation of kinetic energy from wind turbine for grid connections: a review paper
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- Author(s): Fengji Luo ; Gianluca Ranzi ; Weicong Kong ; Zhao Yang Dong ; Fan Wang
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 625 –632
- DOI: 10.1049/iet-rpg.2017.0485
- Type: Article
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p.
625
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Home energy management system (HEMS) provides an effective solution to assist residential users in dealing with the complexity of dynamic electricity prices. This study proposes a new HEMS in contexts of real-time electricity tariff and high residential photovoltaic penetrations. First, the HEMS accepts user-specified residential energy resource operation restrictions as inputs. Then, based on the forecasted solar power outputs and electricity prices, an optimal scheduling model is proposed to support the decision making of the residential energy resource (RES) operations. For the scheduling of heating, ventilating, and air conditioning system, an advanced adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. For the controllable appliances, the ‘user disturbance value’ metric is proposed to estimate the psychological disturbances of an appliance schedule on the user's preference. The proposed scheduling model aims to minimise the future 1 day energy costs and disturbances to the user. A new biological self-aggregation intelligence inspired metaheuristic algorithm recently proposed by the authors (a natural aggregation algorithm) is applied to solve the model. Extensive simulations are conducted to validate the proposed method.
- Author(s): Dipesh Lamsal ; Victor Sreeram ; Yateendra Mishra ; Deepak Kumar
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 633 –638
- DOI: 10.1049/iet-rpg.2017.0346
- Type: Article
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633
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In this study, a discrete Kalman filter-based approach is presented for minimising the output power fluctuations of wind and photovoltaic systems. The control strategy is based on the change in power fluctuation which is determined by the weighted average of the highest and lowest values of the power fluctuation for each interval of time. A genetic algorithm optimisation approach is utilised to determine the optimal value of weighted average such that the power fluctuation rate is minimum. This study also gives the optimum battery power and its state of charge to achieve smoothing determined by the optimal weighted average. On the basis of this optimum battery power, the specification and configuration of the battery energy storage system are also determined.
- Author(s): Junjie Yang ; Juan Liu ; Zilu Fang ; Weiting Liu
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 639 –648
- DOI: 10.1049/iet-rpg.2017.0330
- Type: Article
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p.
639
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With the development of smart grid, energy consumption on residence will play an important role in the electricity market, while the Home Energy Management System (HEMS) has huge potential to help energy conservation. In this study, a practical HEMS model with renewable energy, storage devices and plug-in electric vehicles, considering the battery sustainability and the full utilisation of the renewable energy, is first established. Then, according to the combinations of the genetic algorithm (GA) and the multi-constrained integer programming method, an improved GA is proposed, which goal is to minimise the electricity purchase and maximise the renewable energy utilisation. Finally, it is demonstrated by an example that the proposed method is significant in cost saving and reducing energy wastes. To verify the performances of the proposed algorithm, the numerical results indicate that the proposed algorithm has high computational efficiency and good robustness. In addition, it can avoid the disadvantages easy to trap at a local optimal point, and are insensitive to initial solutions. The effect of the storage device on system property and the sensitivity of cost savings versus demand response, size of the battery, and the electricity price sell to the grid are also analysed.
- Author(s): Fernando M. Camilo ; Rui Castro ; Maria Eduarda Almeida ; Victor Fernão Pires
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 649 –656
- DOI: 10.1049/iet-rpg.2017.0482
- Type: Article
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649
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Nowadays, a strong concern to decrease greenhouse gas emissions is encouraging the implementation of renewable energy sources closer to end-users, in low-voltage (LV) distribution networks. Due to the expected high microgeneration (µG) penetration level, several problems are likely to arise, such as overvoltages and reverse power flow. This study presents a review of the several techniques used to deal with these problems. These are compared in terms of their capacity to smooth the voltage profile and avoid reverse power flow. An unbalanced three-phase power flow algorithm, based on current summation method for radial distribution networks, is proposed. A study based on a highly unbalanced test radial LV distribution network for a typical summer day, with a high µG penetration, is performed. The voltage profile, active power flow in the service transformer, and power losses on the network are the monitored electrical quantities. The obtained results indicate that self-consumption with storage is the recommended solution to eliminate overvoltages, to avoid reverse power flow and allow for a decreasing in the power losses. Nevertheless, the economic viability of this solution must be carefully assessed, because the profitability of the project is not straightforward at the current time.
- Author(s): Mostafa Vahedipour-Dahraie ; Amjad Anvari-Moghaddam ; Josep M. Guerrero
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 657 –667
- DOI: 10.1049/iet-rpg.2017.0720
- Type: Article
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657
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Uncertainties in renewable energy resources and electricity demand have introduced new challenges to energy and reserve scheduling of microgrids, particularly in autonomous mode. In this study, a risk-constrained stochastic framework is presented to maximise the expected profit of a microgrid operator under uncertainties of renewable resources, demand load and electricity price. In the proposed model, the trade-off between maximising the operator's expected profit and the risk of getting low profits in undesired scenarios is modelled by using the conditional value-at-risk (CVaR) method. The influence of consumers’ participation in demand response (DR) programs and their emergency load shedding for different values of lost load (VOLL) are then investigated on the expected profit of the operator, CVaR, expected energy not served and scheduled reserves of the microgrid. Moreover, the impacts of different VOLL and risk aversion parameters are illustrated on the system reliability. Extensive simulation results are also presented to illustrate the impact of risk aversion on system security issues with and without DR. Numerical results demonstrate the advantages of customers’ participation in the DR program on the expected profit of the microgrid operator and the reliability indices.
- Author(s): Mohsen Tajdinian ; Ali Reza Seifi ; Mehdi Allahbakhshi
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 668 –679
- DOI: 10.1049/iet-rpg.2017.0668
- Type: Article
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668
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The growth of utilisation of wind turbines in the contemporary power systems, the transient stability (TS) evaluation has turned to a challenging issue in the modern power grids. An innovative and high-precision real-time method based on the transient energy function (TEF) is proposed which aims to evaluate the TS of power system in the presence of wind turbines. The proposed method is designed so that the initial point of TEF is calculated precisely by performing offline calculations. Structure preserving is used to guarantee the accuracy of the proposed method in order to consider the details of power system modelling. Moreover, the proposed method takes into account the impacts of the wind turbines controllers utilising new formulation based on a sensitivity-based approach. The new formulation is utilised to calculate the effects of the controllers reflected in the potential terms of TEF during the fault occurrence. As a result, in addition of considering fault-on trajectory, the latter technique causes reducing the computational burden and also guarantees accurate real-time assessment of TS. The simulation results demonstrate the efficiency and also comprehensiveness of the proposed method in calculation of critical clearing time and TS assessment.
- Author(s): Rinalini Lahon and Chandra P. Gupta
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 680 –690
- DOI: 10.1049/iet-rpg.2017.0578
- Type: Article
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680
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In view of the tremendous benefits induced by cooperative operation of microgrids, such as reduced power loss, lower operational cost and load peak reduction, this study presents a new energy management strategy for coalition forming microgrids under an electricity market environment. The proposed framework models the time-variant and intermittent attribute of renewable energy sources using a worst-case transaction mechanism. The energy management strategy minimises the net operating cost of the microgrids forming the coalition, which not only includes the cost of distributed generation but a worst-case net transaction cost to account for the intermittency in renewable energy-based sources. Extensive numerical results are shown to corroborate the efficacy of the proposed framework.
- Author(s): Ahmad Nikoobakht ; Jamshid Aghaei ; Taher Niknam ; Vahid Vahidinasab ; Hossein Farahmand ; Magnus Korpås
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 691 –701
- DOI: 10.1049/iet-rpg.2017.0575
- Type: Article
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691
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This study jointly addresses two major challenges in power system operations: (i) sustained growth of intermittent offshore wind farms (OWFs) connected to AC grid via multi-terminal voltage source converter (VSC)-based high-voltage DC (HVDC) grid that brings new challenges to the power system operation, and (ii) dealing with non-linearity of the AC power flow equations with the multi-terminal VSC-based HVDC grid model. To overcome these challenges, firstly, to deal with the uncertainties caused by the high penetration of the intermittent OWFs, this study introduces a robust optimisation approach. The proposed framework is computationally efficient and does not require the probability density function of the wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the set of uncertainties. Secondly, the mathematical representation of the full AC optimal power flow (OPF) problem, with the added modelling of multi-terminal VSC-based HVDC grid in a day-ahead scheduling problem, is a mixed-integer non-linear programming (MINLP) optimisation problem, which is computationally burdensome for large-scale systems. Accordingly, this paper proposes a computationally efficient method for adjustment of solutions set points, which is also compatible with existing customary solvers with minimal modification efforts.
- Author(s): Prachitara Satapathy ; Snehamoy Dhar ; Pradipta Kishore Dash
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 702 –717
- DOI: 10.1049/iet-rpg.2017.0286
- Type: Article
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An effective reduction in power prediction error profile and an improved battery management system design for photovoltaic (PV) based microgrid application are presented in this study, where battery life and power loss are considered to be effectiveness measures. For local energy management the prediction error has a direct influence on distributed generator (DG) control reference calculation and thus in system stability. The silent effect of prediction error in battery energy storage life deterioration is highlighted in terms of battery temperature and power losses. The PV power prediction challenge (null versus positive volatility nature) is addressed with effective error reduction by kernel-based feature mapping function. To obtain fast prediction (operational references to DG primary control) in an online manner, a new fast reduced Morlet kernel-based online sequential extreme learning machine is proposed in this study. The battery (lithium-ion) temperature effect is addressed by introducing a new secondary controller, which comprises battery temperature reference model (model reference) along with rule-based temperature tolerance switching of stacks. The effectiveness of the proposed design is presented by rigorous case studies (MATLAB and TMS320 C6713), where extreme performance is achieved by simultaneous prediction error and local uncertainty.
- Author(s): Niina Helistö ; Juha Kiviluoma ; Hannele Holttinen
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 718 –726
- DOI: 10.1049/iet-rpg.2017.0107
- Type: Article
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This study presents the potential role of thermal power generation in a future power system with high shares of variable generation while considering different sources of demand side flexibility such as heat pumps and heat storages in district heating, demand response from industries and electric vehicles. The study was carried out using a generation planning model combined with a unit commitment and economic dispatch model. The results from the planning model show a strong shift away from combined cycle gas turbines to open cycle gas turbines and gas engines as the share of wind power and solar photovoltaic increases. Demand side flexibility measures pushed this trend further. The results from the unit commitment and economic dispatch model demonstrate that the flexibility measures decrease the ramping frequency of thermal units, while the ramp rates of thermal units remain largely unchanged or increased. This indicates that the flexibility measures can cover smaller ramps in the net load more cost-effectively but that thermal power plants are still valuable for larger ramps. Impacts on emissions and electricity prices are also explored.
- Author(s): Min Xie ; Wenhao Luo ; Peijun Cheng ; Shaojia Ke ; Xiang Ji ; Mingbo Liu
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 727 –734
- DOI: 10.1049/iet-rpg.2017.0513
- Type: Article
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Multidisciplinary collaborative optimisation (MCO) is an effective theory to solve the design optimisation problems of complex systems. Here, power-system day-ahead dynamic economic dispatch with integrated wind power is studied. The MCO method is introduced to decouple the scenarios and collaboratively optimise the multiple scenarios to deal with the uncertainty of wind-power generation. Based on this, the dynamic economic-dispatch problem is divided into a system-level model and multiple subdisciplinary optimisation models for the forecasting and error scenarios. A dynamic relaxation algorithm is then introduced to solve the system-level optimisation model. The decoupled subdisciplinary models for the error scenarios are solved by a grid-computation tool in parallel, which greatly improves computational efficiency. Finally, the established model and its corresponding solution method are applied to a 10-machine, 39-bus test system. It is shown that the proposed MCO-based dynamic economic dispatching method performs much better in high-dimensional scenarios, which are the inherent limitations of the traditional centralised multi-scenario method.
- Author(s): Harikrishna Muda and Premalata Jena
- Source: IET Renewable Power Generation, Volume 12, Issue 6, p. 735 –746
- DOI: 10.1049/iet-rpg.2017.0089
- Type: Article
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The phase angle-based principal component (PC) technique for islanding detection of distributed generations (DGs) is proposed. The phase angle between the positive-sequence components of voltage and current is derived at the DG terminal and used as an input feature vector for the PC technique to identify the islanding situation (IS). The change in phase angle is prominent for both ISs and non-ISs (NISs). By exploiting this change in phase angle, PCs are computed to discriminate between ISs and NISs. The proposed technique is evaluated using data simulated with a real-time digital simulator for IEEE 13-bus microgrids. Critical issues such as a perfect power match IS, different scenarios of microgrids having a synchronous generator, doubly fed induction wind generator, photovoltaic with various control strategies and battery energy storage systems are addressed during performance evaluation of the proposed technique. It is found that the technique identifies the IS under low active and reactive power mismatches and hence overcomes the non-detection zone problem. All NISs such as the fault type, DG tripping and feeder disconnection in the presence of multiple DGs are considered. It is noteworthy to mention that the proposed technique discriminates ISs and NISs within a cycle from the inception point.
Coordinated residential energy resource scheduling with vehicle-to-home and high photovoltaic penetrations
Achieving a minimum power fluctuation rate in wind and photovoltaic output power using discrete Kalman filter based on weighted average approach
Electricity scheduling strategy for home energy management system with renewable energy and battery storage: a case study
Assessment of overvoltage mitigation techniques in low-voltage distribution networks with high penetration of photovoltaic microgeneration
Evaluation of reliability in risk-constrained scheduling of autonomous microgrids with demand response and renewable resources
Sensitivity-based approach for real-time evaluation of transient stability of wind turbines interconnected to power grids
Energy management of cooperative microgrids with high-penetration renewables
Towards robust OPF solution strategy for the future AC/DC grids: case of VSC-HVDC-connected offshore wind farms
Performance validation of battery management system under prediction error for photovoltaic based distribution system
Long-term impact of variable generation and demand side flexibility on thermal power generation
Multidisciplinary collaborative optimisation-based scenarios decoupling dynamic economic dispatch with wind power
Phase angle-based PC technique for islanding detection of distributed generations
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