NEW APPROACHES FOR VERY SHORT-TERM STEADY-STATE ANALYSIS OF AN ELECTRICAL DISTRIBUTION SYSTEM WITH WIND FARMS

Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation. Dispersed generation systems require particular attention due to their incorporation of uncertain energy sources, such as wind farms, and due to the impacts that such sources have on the planning and operation of distribution networks. In particular, the foreseeable, extensive use of wind turbine generator units in the future requires that distribution system engineers properly account for their impacts on the system. Many new technical considerations must be addressed, including protection coordination, steady-state analysis, and power quality issues. This paper deals with the very short-term, steady-state analysis of a distribution system with wind farms, for which the time horizon of interest ranges from one hour to a few hours ahead. Several wind-forecasting methods are presented in order to obtain reliable input data for the steady-state analysis. Both deterministic and probabilistic methods were considered and used in performing deterministic and probabilistic load-flow analyses. Numerical applications on a 17-bus, medium-voltage, electrical distribution system with various wind farms connected at different busbars are presented and discusse

Machine Learning-Incorporated Transient Stability Prediction and Preventive Dispatch for Power Systems with High Wind Power Penetration

Historically, transient instability has been the most severe stability challenge for most systems. Transient stability prediction and preventive dispatch are two important measures against instability. The former measure refers to the rapid prediction of impending system stability issues in case of a contingency using real-time measurements, and the latter enhances the system stability against preconceived contingencies leveraging power dispatch. Over the last decade, large-scale renewable energy generation has been integrated into power systems, with wind power being the largest single source of increased renewable energy globally. The continuous evolution of the power system poses more challenges to transient stability. Specifically, the integration of wind power can decrease system inertia, affect system dynamics, and change the dispatch and power flow pattern frequently. As a result, the effectiveness of conventional stability prediction and preventive dispatch approaches is challenged. In response, a novel transient stability prediction method is proposed. First, a stability index (SI) that calculates the stability margin of a wind power-integrated power system is developed. In this method, wind power plants (WPPs) are represented as variable admittances to be integrated into an equivalent network during transients, whereby all WPP nodes are eliminated from the system, while their transient effects on each synchronous generator are retained. Next, the calculation of the kinetic and potential energies of a system is derived, and accordingly, a novel SI is put forward. The novel approach is then proposed taking advantage of the machine learning (ML) technique and the newly defined SI. In case of a contingency, the developed SI is calculated in parallel for all possible instability modes (IMs). The SIs are then formed as a vector and applied to an ensemble learning-trained model for transient stability prediction. Compared with the features used in other studies, the SI vector is more informative and discriminative, thus lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to the existing methods, followed by a discussion of results. In addition, to address the issues existing in preventive dispatch for high wind power-integrated electrical systems, an hour-ahead probabilistic transient stability-constrained power dispatching method is proposed. First, to avoid massive transient stability simulations in each dispatching operation, an ML-based model is trained to predict the critical clearing time (CCT) and IM for all preconceived fault scenarios. Next, a set of IM-categorized probabilistic transient stability constraints (PTSCs) are constructed. Based on the predictions, the system operation plan is assessed with respect to the PTSCs. Then, the sensitivity of the probabilistic level of CCT is calculated with respect to the active power generated from the critical generators for each IM category. Accordingly, the implicit PTSCs are converted into explicit dispatching constraints, and the dispatch is rescheduled to ensure the probabilistic stability requirements of the system are met at an economical operating cost. The proposed approach is validated on modified IEEE 68- and 300-bus test systems, wherein the wind power installed capacity accounts for 40% and 50% of the total load, respectively, reporting high computational efficiency and high-quality solutions. The ML-incorporated transient stability prediction and preventive dispatch methods proposed in this research work can help to maintain the transient stability of the system and avoid the widespread blackouts

Unscented Transformation-based Probabilistic Optimal Power Flow

Renewable energy-based generation causes uncertainties in power system operation and planning due to its stochastic nature. The load uncertainties combined with the increasing penetration of renewable energy-based generation lead to more complicated power system operations. In power system operation, optimal power flow (OPF) is a widely-used tool in Energy Management System (EMS), for scheduling power generation of power plants, to operate the power system with least cost of generation and to ensure the security and reliability of power transmission grids. On the other hand, in order to deal with the stochastic variables (e.g., renewable energy-based generation and load uncertainties), probabilistic optimal power flow (POPF) has been instituted. This thesis introduces a new Unscented Transformation (UT)-based POPF algorithm. UT-based OPF has a key advantage in handling the correlated random variables, and has become an open research area. Integrated wind power and independent or correlated loads are represented using a Gaussian probability distribution function (PDF). The UT is utilized to generate the sigma points that represent the PDF with a limited number of points. The generated sigma points are then used in the deterministic OPF algorithm. The statistical characteristics (i.e. means and variances) of the UT-based POPF solutions are calculated according to the inputs and their corresponding weights. Different UT methods with their corresponding sigma point selection processes are evaluated and compared with Monte Carlo Simulation (MCS) as the solution benchmark. In the thesis, Locational Marginal Price (LMP) in the transmission network is evaluated as the output of the UT-based POPF. The proposed algorithm is successfully verified on the standard IEEE 30- and 118-bus power transmission systems with wind power generation and unspecified loads. These two test cases represent a portion of American Electric Power (AEP) transmission grid

Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration:A comprehensive review

Abstract The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single‐objective and multi‐objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system

Load flow calculation for droop-controlled islanded microgrids based on direct Newton-Raphson method with step size optimisation

Load flow calculation for droop-controlled islanded microgrids (IMGs) is different from that of transmission or distribution systems due to the absence of slack bus and the variation of frequency. Meanwhile considering the common three-phase imbalance condition in low-voltage systems, a load flow algorithm based on the direct Newton-Raphson (NR) method with step size optimisation for both three-phase balanced and unbalanced droop-controlled IMGs is proposed in this study. First, the steady-state models for balanced and unbalanced droop-controlled IMGs are established based on their operational mechanisms. Then taking frequency as one of the unknowns, the non-linear load flow equations are solved iteratively by the NR method. Generally, iterative load flow algorithms are faced with challenges of convergence performance, especially for unbalanced systems. To tackle this problem, a step-size-optimisation scheme is employed to improve the convergence performance for three-phase unbalanced IMGs. In each iteration, a multiplier is deduced from the sum of higher-order terms of Taylor expansion of the load flow equations. Then the step size is optimised by the multiplier, which can help smooth the iterative process and obtain the solutions. The proposed method is performed on several balanced and unbalanced IMGs. Numerical results demonstrate the correctness and effectiveness of the proposed algorithm

Planning and control of electric distribution networks with integration of wind turbines

2011 - 2012In this thesis, deterministic and probabilistic methods are developed for optimal planning of distribution networks with integration of WTs within a market environment. With regards to the deterministic methods, hybrid optimization methods for optimal allocation of WTs from viewpoints of DG-owning DNOs and WTs’ developers respectively for jointly minimizing annual energy losses and maximizing SW as well as maximizing NPV and SW are proposed: (i) The method jointly minimizes the annual energy losses and maximizes the SW considering different combination of wind generations and load demands to determine the optimal locations, sizes and numbers of WTs to be allocated at candidate buses. The GA is used to select the optimal locations and sizes among different sizes of WTs while the market-based OPF is used to determine the optimal number of WTs. DNO acts as the market operator of the DNO acquisition market that estimates the market clearing price and the optimization process for the active power hourly acquisition. The stochastic nature of both load and wind is modeled by hourly time series analysis. The method is also able to model the correlation among wind resources, i.e. for each range of generation capacity of the first wind profile, a layer with the coincident hours of demand/generation can be created for the second wind power profile. (ii) The method combines the PSO and the market-based OPF to jointly maximize the NPV associated to investment made by WTs’ developers and the SW in DNO acquisition market environment. The PSO is used to select the optimal sizes among different sizes of WTs while the market-based OPF to determine the optimal number of WTs in order to maximize the SW considering network constraints. The presented case study highlighted that WTs’ developers by optimally allocating WTs at buses with the highest LMPs can both improve their profits and increase consumers’ benefits by energy cost reduction, power losses decrease and network constraint alleviation. With regards to probabilistic methods, a probabilistic method to evaluate the effect of WTs integration into distribution networks within market environment was proposed. Combined MCS and market-based OPF are used to maximize the SW considering different combinations of wind generation and load demand. The method can be utilized as a simulation tool to study the probabilistic SW and the impact of wind power penetration on LMPs throughout the network. Furthermore, it characterizes how LMP changes by increasing wind power penetration. It also can be used as a tool for DNOs to approximate the amount of wind power that can be injected into the network taking into account cost reduction and consumers’ benefits. Regarding the control of distribution networks, a fuzzy controller for improving FRT capability of WTs is designed to compensate the voltage sags and swells at the PCC by controlling both the reactive and active power generated by WFs. The FRT capability improvement is investigated considering Danish grid code. The proposed method is able to simultaneously regulate active and reactive power during voltage variations. During voltage sag only the reactive power is injected by using the controller in order to improve the voltage sag effects while during a voltage swell, when the absorbed reactive power is not adequate, the active power generated by WFs is decreased by using the active power modulator that is sent by fuzzy controller to the RSC to increase the absorbed reactive power. In this case, according to both the WTs’ power curve and capability curve, the WFs will not generate the maximum active power but it has positive effects on voltage regulation at the PCC, i.e. within the limited size of the DFIG converters, the reduction of active power increases the maximum reactive power absorbed by WTs. [edited by author]XI n.s

Wind Power Integration into Power Systems: Stability and Control Aspects

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting