Abstract:In power systems, there are many uncertainty factors such as power outputs of distributed generations and fluctuations of loads. It is very beneficial to power system analysis to acquire an explicit function describing the relationship between these factors (namely parameters) and power system states (or performances). This problem, termed as parametric problem (PP) in this paper, can be solved by Galerkin method, which is a powerful and mathematically rigorous method aiming to seek an accurate explicit approximate function by projection techniques. This paper provides a review of the applications of polynomial approximation based on Galerkin method in power system PPs as well as stochastic problems. First, the fundamentals of polynomial approximation and Galerkin method are introduced. Then, the process of solving three types of typical PPs by polynomial approximation based on Galerkin method is elaborated. Finally, some application examples as well as several potential applications of power system PPs solved by Galerkin method are presented, namely the probabilistic power flow, approximation of static voltage stability region boundary, and parametric time-domain dynamic simulation.

Abstract:Emissions from the internal combustion engine (ICE) vehicles are one of the primary cause of air pollution and climate change. In recent years, electric vehicles (EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large-scale production, EVs are becoming cheaper. In the near future, mass deployment of EVs will put severe stress on the existing electrical power system (EPS). Optimal scheduling of EVs can reduce the stress on the existing network while accommodating large-scale integration of EVs. The integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with EPS are classified based on their relevance to different players in the electricity market. This classification refers to four players: generation company (GENCO), distribution system operator (DSO), EV aggregator, and end user. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in the grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.

Abstract:Transient stability assessment (TSA) is of great importance in power systems. For a given contingency, one of the most widely-used transient stability indices is the critical clearing time (CCT), which is a function of the pre-fault power flow. TSA can be regarded as the fitting of this function with the pre-fault power flow as the input and the CCT as the output. In this paper, a data-driven TSA model is proposed to estimate the CCT. The model is based on Mahalanobis-kernel regression, which employs the Mahalanobis distance in the kernel regression method to formulate a better regressor. A distance metric learning approach is developed to determine the problem-specific distance for TSA, which describes the dissimilarity between two power flow scenarios. The proposed model is more accurate compared to other data-driven methods, and its accuracy can be further improved by supplementing more training samples. Moreover, the model provides the probability density function of the CCT, and different estimations of CCT at different conservativeness levels. Test results verify the validity and the merits of the method.

Abstract:One of the most critical issues in the evaluation of power systems is the identification of critical buses. For this purpose, this paper proposes a new methodology that evaluates the substitution of the power flow technique by the geodesic vulnerability index to identify critical nodes in power systems. Both methods are applied comparatively to demonstrate the scope of the proposed approach. The applicability of the methodology is illustrated using the IEEE 118-bus test system as a case study. To identify the critical components, a node is initially disconnected, and the performance of the resulting topology is evaluated in the face of simulations for multiple cascading faults. Cascading events are simulated by randomly removing assets on a system that continually changes its structure with the elimination of each component. Thus, the classification of the critical nodes is determined by evaluating the resulting performance of 118 different topologies and calculating the damaged area for each of the disintegration curves of cascading failures. In summary, the feasibility and suitability of complex network theory are justified to identify critical nodes in power systems.

Abstract:In this paper, an economic emission dispatch (EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network (RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization (PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

Abstract:Wind turbine generators can be operated in various types of system configurations. Once the configurations change, the system oscillation mode shapes change accordingly. The modelling of the full system is necessary for studying this issue, yet it is quite hard. In this paper, a simple approach is developed to study the mode shapes of wind power systems without the necessity of adopting the complex full-system models. The key is that the q-d axis model of electric power system is transformed into the single-axis model, so that it could integrate with the equivalent circuit model of drive train mechanism. After analyzing some of the system configurations organized by the well-known MOD-2 wind turbine generator unit, the proposed approach is found to be effective for analyzing various oscillation modes such as the local torsional oscillations, as well as the inter-unit and inter-area oscillations.

Abstract:Conventional transmission line distance protection approaches are subject to malfunction under reverse fault-induced current transformer (CT) saturation for the typically employed breaker-and-a-half configuration. This paper addresses this issue by proposing a new distance protection approach that combines the blocking and unblocking criteria of distance protection based on the values of incomplete differential current, operation voltage, and current harmonic content. The proposed approach is verified by theoretical analysis, dynamic simulation testing, and field operation to ensure that the obtained distance protection is reliable and refrains from operating unnecessarily under reverse fault-induced CT saturation in the breaker-and-a-half configuration. Meanwhile, the proposed approach is demonstrated can operate reliably when forward faults occur or various reverse faults are converted to forward faults.

Abstract:Connecting the voltage source converters (VSCs) to various types of AC systems results in different operation characteristics and core problems associated with traditional control strategies. Therefore, it is necessary to optimize the control strategies of the VSCs according to the types of AC systems. For the VSCs connected to islanded renewable power plants, a voltage/frequency droop control strategy is proposed to damp fluctuations of AC voltage and frequency in the island, which is vital for bipolar VSC control. In addition, a multi-branch impedance equivalent method for renewable power plants is proposed, with which large-scale renewable power plants can be modeled accurately in the frequency domain to prevent wide-band oscillation. For the VSCs connected to strong AC systems, smart AC voltage and coordinated frequency transient control strategies are proposed, which can improve AC system transient stability. For the VSCs connected to weak AC systems, the relationship between the system stability and strength is analyzed, and then the control strategy of inner-loop control parameter optimization and outer-loop power limiting (if necessary) is proposed to improve the stability of the allied system. The proposed strategies are verified by both software simulation and field commissioning.

Abstract:The nonlinear operation of metal oxide varistor (MOV)-protected series compensator in transmission lines introduces complications into fault detection approaches. The accuracy of a conventional fault detection schemes is adversely affected by continuous change of the system impedance and load current at the point of a series compensation unit. Thus, this study suggests a method for detecting the faulted phase in MOV-protected series-compensated transmission lines. Primarily, the fault feature is identified using the covariance coefficients of the current samples during the fault period and the current samples during the pre-fault period. Furthermore, a convenience fault detection index is established by applying the cumulative sum technique. Extensive validation through different fault circumstances is accomplished, including different fault positions, resistances, and inception times. The experimental results show that the proposed method performs well with high resistance or impedance faults, faults in noisy conditions, and close-in and far-end faults. The proposed method is simple and efficient for faulty phase detection in MOV-protected series-compensated transmission lines.

Abstract:In this study, a novel approach for dynamic modeling and closed-loop control of hybrid grid-connected renewable energy system with multi-input multi-output (MIMO) controller is proposed. The studied converter includes two parallel DC-DC boost converters, which are connected into the power grid through a single-phase H-bridge inverter. The proposed MIMO controller is developed for maximum power point tracking of photovoltaic (PV)/fuel-cell (FC) input power sources and output power control of the grid-connected DC-AC inverter. Considering circuit topology of the system, a unique MIMO model is proposed for the analysis of the entire system. A unique model of the system includes all of the circuit state variables in DC-DC and DC-AC converters. In fact, from the viewpoint of closed-loop controller design, the hybrid grid-connected energy system is an MIMO system. The control inputs of the system are duty cycles of the DC-DC boost converters and the amplitude modulation index of DC-AC inverters. Furthermore, the control outputs are the output power of the PV/FC input power sources as well as AC power injected into the power grid. After the development of the unique model for the entire system, a decoupling network is introduced for system input-output linearization due to inherent connection of the control outputs with all of the system inputs. Considering the decoupled model and small signal linearization, the required linear controllers are designed to adjust the outputs. Finally, to evaluate the accuracy and effectiveness of the designed controllers, the PV/FC based grid-connected system is simulated using the MATLAB/Simulink toolbox.

Abstract:Increasing electric vehicle (EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator (DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints, and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers (ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.

Abstract:Demand response, the reactive power output of distributed generation (DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG. With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

Abstract:The region-based method has been applied in transmission systems and traditional passive distribution systems without power sources. This paper proposes the model of total quadrant security region (TQSR) for active distribution networks (ADN) with high penetration of distributed generation (DG). Firstly, TQSR is defined as a closed set of all the N-1 secure operation points in the state space of ADN. Then, the TQSR is modeled considering the constraints of state space, normal operation and N-1 security criterion. Then, the characteristics of TQSR are observed and analyzed on the test systems with different DG penetrations. TQSR can be located in any quadrant of the state space. For different DG penetrations, the shape and security features of TQSR are also different. Finally, the region map is discovered, which summarizes the features of different types of distribution networks.

Abstract:Large-scale voltage collapse incidences, which result in power outages over large regions and extensive economic losses, are presently common occurrences worldwide. To avoid voltage collapse and operate more safely and reliably, it is necessary to analyze the voltage security operation region (VSOR) of power systems, which has become a topic of increasing interest lately. In this paper, a novel improved particle swarm optimization and recursive least square (IPSO-RLS) hybrid algorithm is proposed to determine the VSOR of a power system. Also, stability analysis on the proposed algorithm is carried out by analyzing the errors and convergence accuracy of the obtained results. Firstly, the voltage stability and VSOR-surface of a power system are analyzed in this paper. Secondly, the two algorithms, namely IPSO and RLS algorithms, are studied individually. Based on this understanding, a novel IPSO-RLS hybrid algorithm is proposed to optimize the active and reactive power, and the voltage allowed to identify the VSOR-surface accurately. Finally, the proposed algorithm is validated by using a simulation case study on three wind farm regions of actual Hami Power Grid of China in DIgSILENT/PowerFactory software. The error and accuracy of the obtained simulation results are analyzed and compared with those of the particle swarm optimization (PSO), IPSO and IPSO-RLS hybrid algorithms.

Abstract:With the deteriorating effects resulting from global warming in many areas, geographically distributed data centers contribute greatly to carbon emissions, because the major energy supply is fossil fuels. Considering this issue, many geographically distributed data centers are attempting to use clean energy as their energy supply, such as fuel cells and renewable energy sources. However, not all workloads can be powered by a single power sources, since different workloads exhibit different characteristics. In this paper, we propose a fine-grained heterogeneous power distribution model with an objective of minimizing the total energy costs and the sum of the energy gap generated by the geographically distributed data centers powered by multiple types of energy resources. In order to achieve these two goals, we design a two-stage online algorithm to leverage the power supply of each energy source. In each time slot, we also consider a chance-constraint problem and use the Bernstein approximation to solve the problem. Finally, simulation results based on real-world traces illustrate that the proposed algorithm can achieve satisfactory performance.

Abstract:The energy consumption of buildings has risen steadily in recent years. It is vital for the managers and owners of the building to manage the electric energy demand of the buildings. Forecasting electric energy consumption of the buildings will bring great profits, which is influenced by many factors that make it very difficult to provide an advanced forecasting. Recently, deep learning techniques are widely adopted to solve this problem. Deep neural network offers an excellent capability in handling complex non-linear relationships and competence in exploring regular patterns and uncertainties of consumption behaviors at the building level. In this paper, we propose a deep convolutional neural network based on ResNet for hour-ahead building load forecasting. In addition, we design a branch that integrates the temperature per hour into the forecasting branch. To enhance the learning capability of the model, an innovative feature fusion is presented. At last, sufficient ablation studies are conducted on the point forecasting, probabilistic forecasting, fusion method, and computation efficiency. The results show that the proposed model has the state-of-the-art performance, which reflects a promising prospect in application of the electricity market.

Abstract:We aim to provide a tool for independent system operators to detect the collusion and identify the colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented for collusion detection in electricity markets. The possible scenarios of the collusion among generation firms are firstly identified. Then, for each scenario and possible load demand, market equilibrium is computed. Market equilibrium points under different collusions and their peripheral points are used to train the collusion detection machine using supervised learning approaches such as classification and regression tree (CART) and support vector machine (SVM) algorithms. By applying the proposed approach to a four-firm and ten-generator test system, the accuracy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are compared with other supervised learning and statistical techniques.

Abstract:We propose a two-stage stochastic model for optimizing the operation of energy storage. The model captures two important features: uncertain real-time prices when day-ahead operational commitments are made; and the price impact of charging and discharging energy storage. We demonstrate that if energy storage has full flexibility to make real-time adjustments to its day-ahead commitment and market prices do not respond to charging and discharging decisions, there is no value in using a stochastic modeling framework, i.e., the value of stochastic solution is always zero. This is because in such a case the energy storage behaves purely as a financial arbitrageur day ahead, which can be captured using a deterministic model. We show also that prices responding to its operation can make it profitable for energy storage to “waste” energy, for instance by charging and discharging simultaneously, which is normally sub-optimal. We demonstrate our model and how to calibrate the price-response functions from historical data with a practical case study.

Abstract:The variability of wind power generation requires the allocation of a flexible energy reserve which is capable of compensating for possible imbalances between the load and generation. To reduce the variability of wind power generation and loss of load in generation deficit, we propose operation strategies for coordinating battery energy storage with wind power generation. The effects of the operation strategies on system reliability are evaluated by the developed computation model that represents the main aspects and operation limitations of the batteries. The performance evaluation of the power system is based on the composite reliability indices of loss of load probability (LOLP) and expected energy not supplied (EENS), which is calculated through sequential Monte Carlo simulation. Tests are performed by the developed model with a tutorial system consisting of five busbars and the IEEE RTS system. The results show that the use of large-scale batteries is an alternative to physically guarantee the wind power plants and to act as an operation reserve to reduce the risk of loss of load.

Abstract:In this paper, a perturb and observe (P&O) based voltage regulator (POVR) and a capacitor compensator (CC) circuit are proposed for the implementation on 31-level asymmetrical switch-diode based multi-level DC-link (MLDCL) inverter. Since the application of MLDCL in a standalone photovoltaic (PV) system requires constant DC voltages from PV panels, the POVR strategy is deployed to regulate the voltage along with the capability to deliver the maximum power at full load. Boost DC-DC converters are used as the interface between the panels and the inverter for the POVR operation. The results show that POVR is capable of achieving the desired fixed DC voltages even under varying environmental and load conditions, with a steady 230 V at the output. At full load, the standalone system successfully delivers 97.21% of the theoretical maximum power. Additionally, CC is incorporated to mitigate voltage spikes at the output when supplying power to inductive loads. It successfully eliminates the spikes and also reduces the total harmonic distortion (THD) of output current and voltage from more than 10% to less than 5%, as recommended in IEEE 519 standard.

Abstract:Recent development trends in wind power generation have increased the importance of the safe operation of wind-turbine blades (WTBs). To realize this objective, it is essential to inspect WTBs for any defects before they are placed into operation. However, conventional methods of fault inspection in WTBs can be rather difficult to implement, since complex curvatures that characterize the WTB structures must ensure accurate and reliable inspection. Moreover, it is considered useful if inspection results can be objectively and consistently classified and analyzed by an automated system and not by the subjective judgment of an inspector. To address this concern, the construction of a pressure- and shape-adaptive phased-array ultrasonic testing platform, which is controlled by a nanoengine operation system to inspect WTBs for internal defects, has been presented in this paper. An automatic classifier has been designed to detect discontinuities in WTBs by using an A-scan-imaging-based convolutional neural network (CNN). The proposed CNN classifier design demonstrates a classification accuracy of nearly 99%. Results of the study demonstrate that the proposed CNN classifier is capable of automatically classifying the discontinuities of WTB with high accuracy, all of which could be considered as defect candidates.

Abstract:Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control (MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.