Search

This paper proposes a comprehensive method to estimate non-synchronous inertia in an AC microgrid system by analyzing measured frequency transients. Non-synchronous inertia includes inertial support from various inverter-interfaced renewable energy sources and energy storage devices in the form of virtual or emulated inertia. In the proposed method, a multi-step approach is used to identify a matrix of parameters that estimates the overall non-synchronous inertial response. Firstly, motivations are drawn from the fundamental system requirements of adequacy in RoCoF and maximum deviation in frequency, for any credible contingency. A systematic classification of various contributors to the non-synchronous inertial response is also carried out. System-identification studies are then performed to identify various component-models that contribute to the non-synchronous inertia. The component models include a faster RoCoF-based response model, a relatively slower frequency deviation based model, and in some cases, a slow, integral-of-frequency based model. Lastly, the concept of releasable kinetic energy and non-linear estimation is used to identify the capability of the virtual inertia sources and improve the accuracy of the overall estimate. The developed methodology is tested in a microgrid network hosting a mix of renewable energy generations and battery energy storage systems. The estimate of non-synchronous inertia will help system operators to accurately estimate the maximum frequency deviation and the time to reach the maximum deviation for any credible contingency. The estimate can also help in deciding the type of virtual inertia that will best maintain frequency stability in the microgrid system.

This paper proposes a novel multi- objective planning framework to determine optimal capacity of battery energy storage system (BESS) for coordinated operation of large scale offshore wind farm (OWF) and BESS. Multiple objectives such as battery cost and lifetime, availability of wind turbines (WTs), expected energy not supplied (EENS), loss of load hour (LOLH) and curtailment of wind energy have been considered for determining optimal capacity of BESS for OWF. Wake effect loss in OWF and uncertainty of wind speed have been also incorporated the optimal BESS framework for determining aforementioned objectives. Moreover, as WTs are susceptible to random failure, availability of WTs is an important issue which has been also considered in the planning stage for determining optimal size of BESS in this study. One of the main features of the proposed approach is that it provides flexibility to decision makers in selecting best available alternative for BESS (among different types) to minimize mismatch between forecasted generation schedule and actual generation of OWF, while taking into account economical and operational reliability issues. The effectivene ss of the proposed planning strategy has been validated through different case studies conducted on Horns Rev- 1 OWF situated in Denmark.

Large-scale renewable energy integration and consequent displacement of synchronous machines result in low system inertia. Low system inertia leads to high rate of change of frequency, higher frequency nadir, and potentially require higher primary frequency reserve to stabilise system frequency following a sudden generation shortfall. To address such frequency stability issue in wind energy integrated system, this paper has proposed a new and realistic control strategy for inertial and primary frequency support from HVDC connected Offshore Wind Farms (OWF). The proposed control strategy, which is based on offshore AC voltage control is not only addressing limitations of high rate of frequency change in the offshore grid, but also does not result in any frequency deviation in the offshore grid while providing frequency support to the main grid. The proposed control strategy is robust for any frequency deviation in the main grid, which utilises HVDC converters to map onshore frequency variation in to voltage variation in the offshore grid, thus enabling adequate inertial and primary frequency support from the OWF. The proposed control strategy has been tested on two test systems and the results are compared with conventional method to demonstrate the robustness, effectiveness and reliability of the proposed frequency support strategy.

This paper presents an improved method to estimate the available inertia in an islanded AC microgrid. Inertia estimation is carried out based on measured frequency response for any arbitrary disturbance that occurs in the system. Modifications are made to the conventional swing equation-based curve-fitting method to obtain an accurate estimate for a system with high penetration of renewable generations. A polynomial curve fit over the total power generation is introduced to estimate the size of the disturbance accurately. Additionally, a variable order polynomial fit is carried out over the measured frequency, which not only improves the estimate of inertia but also helps to refrain the influence of network topology and size/location of the disturbance. The test microgrid system considered is a modified Standard IEEE distribution network, which consists of radial feeders and distributed generations. Firstly, the proposed method is tested on a system with only synchronous generations to assess the accuracy of the estimate. This is followed by the integration of Type 3 and Type 4 wind turbines, and a PV array within the microgrid system. Virtual inertia control is then implemented in the wind turbines to obtain inertial support. Estimation study of the microgrid system with virtual inertia is then carried out. The developed estimation method can accurately estimate the inertia provided by the synchronous sources within the generation mix. Finally, from all the results and observations, the inertia estimation process in a microgrid system is segregated into synchronous and nonsynchronous inertia estimation.

In this paper, a novel wind farm control strategy is proposed to mitigate voltage dip induced frequency excursion in wind integrated power systems. In the proposed control strategy, conventional fixed reactive/active current injection priority is replaced by an optimum adaptive priority based reactive and active current injection. The control problem is formulated as a binary integer optimization problem, which simultaneously minimizes active power shortfall from wind generation during fault and complies with grid code requirements. Detailed wind farm model and wake effect are taken into consideration to achieve realistic response from the wind farm, while IEC 61400-27-1 wind turbine generator model is adopted for wind turbine modeling. Dynamic model of the test system has been developed in DIgSILENT PowerFactory, while the static model is also developed in MATLAB, and cosimulation platform has been used to test and validate the proposed control strategy. The proposed control strategy is applicable to variable speed wind turbine-based wind farm.

In conventional power systems, synchronous generators have been the main source of dynamic reactive power. However, displacement of conventional power plants by non-synchronous generation resources, such as wind power plants (WPPs) and other renewable energy sources, result in diminished dynamic reactive power reserve at system level. System operators in various countries are planning to tackle this issue by introducing dynamic reactive power reserve as a new ancillary service product. In this paper, reactive power capability of Type-4 wind turbine based WPP considering practical constraints is explored. A new and effective centralized WPP capability based control strategy is proposed to supply dynamic reactive power from a WPP, particularly, beyond grid code requirement. An adaptive method to dispatch reactive power from wind turbine generators (WTGs) in a WPP, is also proposed. In the presented study, wake effect and detailed WPP model are taken into consideration. The proposed strategy is implemented on three wind integrated benchmark power systems developed in DIgSILENT PowerFactory platform using IEC 61400–27–1 standard model of WTG.

This paper presents a new approach to select a suitable wind turbine (WT) for a wind farm (WF) site. In this approach, economy, reliability, resilience, and environmental aspects have been taken into account, as all the four aspects have a great deal of impact on the selection of a WT for a given site. Six commercially available WTs have been considered for this study. The energy output from a WF is significantly affected by wake, hence, in the proposed approach, placement of WTs in a WF is optimized using particle swarm optimization to reduce wake effect while increasing the energy production. A new algorithm has been proposed to find the reliability indices of the WF considering forced outage of WTs. In this context, two important reliability indices, such as expected energy not supplied and availability have been adopted to study the reliable viability of the optimized WF with all the six types of WTs. Power system resilience study has been gaining great deal of importance in recent times, but individual WF resilience study has not been addressed till now. This paper incorporates resilience as one of the criteria to select a suitable WT for any site. A new approach has been described in this regard to quantify the WF operational resilience by proposing an ideal resilience pentagon. The impact of all the six WTs on environment in terms of reduction of greenhouse gas emission has also been considered. Two different shaped WFs (same area) with same wind profile have been considered to study the proposed approach. A priority table has been formulated according to the ranking of WTs for different indices. The results show that the choice of WTs for WFs of different geometrical shape, however, with the same wind profile and the area may be different and largely depend on the choice of decision maker.