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Topology identification and parameter estimation form the basis for the operation and control of the distribution network with little or no observability. This paper proposes a method to estimate the admittance matrix for open-loop distribution networks based only on active/reactive power injection and voltage magnitude data. We derive the linear relationship between the admittance matrix and the measurements based on decoupled linear power flow (DLPF) equations. A total least squares regression method is proposed based on analysis of the characteristics of the open-loop grid admittance matrix. Case studies on the IEEE 33-bus system demonstrate the proposed method's effectiveness, accuracy, and efficiency. [ABSTRACT FROM AUTHOR]

Due to the geographic features and fruitful water resources, the distributed generation of Small Hydropower Generator Stack (SHGS) are vigorously developed in southwest China. As the more concerns about security and stability of power grids with the large amount of SHGS feed into the grid, the accurate models are critical to analyze those potential impacts. With the development of online monitoring technologies in smart grid, PMU based dynamic equivalent modeling for SHGS drawn many attentions. However, field applications reveal that traditional SHGS equivalent model may not be robustness enough to adapt the different disturbances. To overcome this deficiency, method to improve the robustness of SHGS dynamic equivalent modeling is proposed. In the paper, a coherency identification based multi-machine modeling method is introduced to enhance the accuracy of equivalent model. Sensitivity and correlation analysis based key parameters selection scheme is implemented to select the critical parameters, meanwhile avoiding the multiple solutions problem. Moreover, multi-objective optimization algorithm is designed for key parameters identification of the equivalent model. Additionally, hybrid dynamic simulation has adopted into the equivalent process to improve the computational efficiency. The effectiveness of the proposed method has been validated with an actual case from China Southwest grids. [ABSTRACT FROM AUTHOR]

Energy systems, e.g., power systems and natural gas systems, are increasingly interdependent, which brings benefits under normal conditions and introduces risks under extreme weather events, e.g., hurricanes. A comprehensive method is proposed to reflect the spatial-temporal hurricane impacts on interdependent power and natural gas systems. Mechanism analysis based and data based component failure models are used to map hurricane effects on exposed component failure probabilities based on which system states are generated using Monte Carlo simulation. For system states with failed components, the integrated energy flow is developed to determine minimum joint load shedding considering inherent properties of each system and characteristics of interdependence coordinately. Operational metrics and infrastructure metrics are employed to quantify the service adequacy and network resistance of the interdependent energy systems, respectively. The modified IEEE 24-reliability test system and the 12-node natural gas system under a simulated hurricane are used to validate the effectiveness of the proposed method. Simulation results show the method can identify weak parts and quantify the performance of interdependent power and natural gas systems under hurricanes to provide information for effective and coordinative preparedness. [ABSTRACT FROM AUTHOR]

Nowadays, with the highly penetrated wind generations, the accurate wind farm (WF) model is required for power system stability analysis. Due to the complexity on detailed WF model, the aggregated model, with a reasonable reduction of the detailed model meanwhile retaining the required level of accuracy is essential to be developed. In this paper, an improved WF aggregated modeling method for large-scale power system stability studies is proposed. To overcome the limitations of the traditional methods, a geometric template matching based time series wind turbines clustering method is developed. Moreover, a multiobjective optimization algorithm, which fully considered the wind speed disturbance and system fault together, is designed to identify both the generator and control parameters. Additionally, to shrink the size of the identified parameters and increase the modeling accuracy, a sensitivity and correlation analysis based key parameters selection scheme is also adopted. To verify the effectiveness of the proposed method, dynamic responses of the proposed aggregated model are compared against the responses of the traditional equivalent model for various wind scenarios through an actual case. [ABSTRACT FROM AUTHOR]

With high renewable power penetration, uncertainty and intermittence of renewable sources become major concerns of power system planning and operation. Large and fast wind speed change may cause great variation of active power generation, which may lead to system stability and reliability problems. The response speeds of the committed conventional generators (CGs) for frequency regulations are critical for system reliable and stable operation. The slow response of the committed CGs may result in power shortage or surplus, which may affect system frequency. This paper proposes a technique to evaluate operational reliability and efficiency problems of power systems with high wind power penetration from frequency aspect. Energy unnecessarily consumed and less supplied during system frequency control processes are modeled in detail. The reliability and economic indexes are formulated with considering system dynamic frequency control processes. The IEEE-RTS79 is used to verify the proposed models and method. [ABSTRACT FROM AUTHOR]

Large-scale adoption of moving electric vehicle (EV) loads significantly accelerates the integration of power and traffic systems. This paper proposes an approach to assess and alleviate the impacts of stochastic EV charging and movement in an integrated power and traffic system. Level of Congestion (LoC), Nodal Voltage Deviation (NVD) and Energy Loss Rate (ELR) are proposed as nodal indices to spatially and temporally assess the impacts due to the inter-node movement of EVs on power system operation. Nodal Time-of-Use (NTOU) price and Road Traffic Congestion (RTC) price are developed to shift the charging and movement of EV loads. EV driver's charging and driving decisions are modeled by fuzzy logic inference system in response to NTOU and RTC prices. Two transportation systems have been developed and integrated with the Roy Billinton test power system (RBTS) to illustrate the proposed technique and model. [ABSTRACT FROM PUBLISHER]

In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg–Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Uncertainty and geographical diversity of wind speeds create risk for a wind power generation company (WGenco) to make the maximum profit from power market trading. A probabilistic approach is proposed to estimate the profit of a WGenco considering geographical diversity and uncertainty of wind speeds and market prices. The objective of the optimization problem is to assist a WGenco to do the optimal bidding in market trading in order to make the maximum profit under various market and wind speed uncertainties. The point estimate method (PEM) has been improved to facilitate stochastic modeling of the aggregated power output of wind turbine generators in different locations in the problem formulation. The principal component analysis (PCA) is used to manage correlated wind speeds. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic approach in the modeling of stand-alone microgrids to predict their operational adequacy performance considering uncertainty of energy storage system (ESS), photovoltaic system (PVS) and conventional generator (CG). Instead of using the daily or hourly time step, operating period a minutely time step is considered to incorporate the effect of fast ramp up/down of system components on microgrid operating adequacy through expected energy not supplied (EENS) and expected energy not used (EENU), due to load and resource variations. A time varying state of charge (SOC) model is proposed to determine power output of an ESS in reliability modeling. The reliability of a PVS is modeled in detail based on the total cross-tied configuration (TCTC) of photovoltaic (PV) cells and arrays. The proposed technique and indices will be useful for system planners to select the type and size of microgrid systems that contain alternative energy sources and storage. [ABSTRACT FROM PUBLISHER]

With the rapid urbanization, electrical infrastructure spreads to raw areas without existing loads. How to achieve accurate long-term load forecasts based on land use plans is a realistic problem. On the other hand, load forecasting (LF) should be extended to high spatial resolutions to guide middle- or low-voltage planning and time domain to consider the impacts of distribution generations and diversified users on multi-period system demands. A data-driven bottom-up spatial and temporal LF approach is proposed in this paper to solve these challenges. Land plots are treated as basic LF resolution to describe available multi-attribute data in smart grids and modern cities. Kernel density estimation and adaptive k-means are adopted to aggregate typical load densities and profiles of different land use types. Stacked auto-encoders are utilized to forecast the unknown plot load quantities. The neighbor plot loads are summed up to obtain the estimated loads of larger areas based on clustered load profiles. Case studies demonstrate that the proposed LF is more applicable than benchmark methods both in accuracy and application potential. The estimated hierarchical spatial and temporal results are of great significance to guide load balancing, power system planning, and user integration in different voltage levels. [ABSTRACT FROM AUTHOR]

High renewable energy penetration increases the electricity seasonal imbalance in the long-term timescale. Power system planning needs to consider the optimal configuration of various flexibility resources and electricity balance in different timescales. The coupling of multiple timescales largely increases the computation complexity of the power system planning problem. Thus, this paper presents an efficient source-grid-storage co-planning model which incorporates a year-round hourly operation simulation. To improve the computation efficiency of the planning model, from the temporal scale, a self-adaptive compact panorama time series (CPTS) model is applied, which greatly reduces the number of variables related to short-term decisions. From the spatial scale, a network-constrained relaxed clustered unit commitment (NC-RCUC) model is introduced, which significantly reduces the number of variables related to unit commitment decisions. Case studies on the modified Garver’s 6-node system and HRP-38 system prove the validation and efficiency of the proposed model (“HRP” stands for high renewable penetration). The studies on the China power grid in 2035 demonstrate the future planning results of generation, transmission and storage in China power systems based on the proposed model. [ABSTRACT FROM AUTHOR]

In the face of extreme events, e.g., hurricanes, the transmission systems, especially the transmission lines, are affected across time and space. To mitigate these impacts on the day-ahead market from a probabilistic perspective, a resilient unit commitment (UC) problem is formulated as a two-stage distributionally robust and robust optimization (DR&RO) problem. In the first stage, the commitment, energy, and reserves of generators are pre-scheduled to minimize the operational cost, responding to the worst load forecasting and line failure scenario in the operating day. The operating status of transmission lines are depicted by a novel uncertainty set with a distributionally chance constraint considering the repair of failed lines. This chance constraint is reformulated to its deterministic equivalence. Using both load shedding and generation curtailment, recourse problems are formulated in the second stage considering the time-varying operating status of transmission lines. The formulated DR&RO problem is solved using a hybrid Benders decomposition and column-and-constraint generation scheme. Simulations are conducted on the modified IEEE reliability test system (RTS) and two-area IEEE RTS-96 under hurricanes. Results verify the effectiveness of the proposed method, in comparison with prevalent two-stage stochastic and robust optimization methods. [ABSTRACT FROM AUTHOR]

The Newton-Raphson (NR) method is a powerful tool in nonlinear equations. However, a well-known disadvantage of this method is that the initial iteration value must be chosen sufficiently close to a true solution in order to guarantee its convergence. The Kantorovich theorem is virtually the only known sufficiency condition for convergence of NR method, and gives very conservative bounds. This letter analyzes the convergence of the NR method in feasible power-flow for direct-current (DC) network, and an explicit convergence condition is obtained. Comparing with the existing results, the proposed convergence condition is more concise and less conservative. Moreover, according to the proposed condition, one can easily set the initial iteration value to guarantee the convergence of the NR method. Case studies verify the correctness of the presented analysis. [ABSTRACT FROM AUTHOR]

The utilization of voltage-source converter-based multi-terminal HVDC (VSC-MTDC) technology has been envisaged as a cost-effective solution to transmit bulk renewables from multiple sending ends to receiving-end AC systems. However, with complex configurations and various types of components, the reliability evaluation of a VSC-MTDC system is more complicated than that of a conventional HVDC system. In addition, due to stochastic failures and complex operating modes, the application of a VSC-MTDC system can have a significant influence on the reliability of hybrid AC/DC power systems. This paper presents a new technique for evaluating the nodal reliability of a hybrid AC/DC power system based on the VSC-MTDC system. The proposed technique is incorporated within a multi-state model for the VSC-MTDC system. Then, an optimization model for contingency management of a hybrid AC/DC power system is formulated. The model is established based on the optimal power flow technique for a VSC-MTDC based hybrid AC/DC power system. The objective of the model is to minimize the total system cost when determining the load curtailment and generation re-dispatch results for each contingency state. Nodal reliability indices for hybrid AC/DC power systems are proposed to evaluate the reliability performance of customers at each node. The effectiveness of the proposed methods is validated using a modified IEEE reliability test system. [ABSTRACT FROM AUTHOR]

Modern aircrafts are evolving toward more electric aircraft (MEA), resulting in greater reliance on the electrical system for safe flight. On-board power system of MEA integrates a large number of power electronic converters, and it is reported that semiconductor devices and electrolytic capacitors in power converters are the most vulnerable links impacted by loading conditions; thus, reliability becomes a critical concern in an MEA power system. This paper proposes a hierarchical approach for systematic reliability modeling and evaluation for the on-board power system of MEAs. It consists of three hierarchical levels (HLs): component level (HL1), subsystem level (HL2), and system level (HL3). In HL1, failure rates of power electronic components are modeled considering relevant inner structure and loading conditions; in HL2, the reliability of individual subsystems such as converters are constructed; in HL3, the system reliability is quantified based on the network architecture and reliability of the subsystems. The impacts of different parts (components/subsystems) on the overall system are assessed effectively with the identification of the vulnerable parts. This also provides a guideline for reliability enhancement by using thermal control techniques, adding redundancies or performing maintenance on the vulnerable parts to ensure the satisfactory of system reliability requirements. The proposed method is demonstrated on the future MEA power system architectures (hybrid ac–dc architecture and HVdc architecture). [ABSTRACT FROM AUTHOR]