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Electricity price forecasting (EPF) is essential to the bidding strategy formulation and market operation. Since EPF is important in the electricity market, lots of forecasting approaches are proposed. However, the new scene caused by the volatility of renewable power generation and other volatility factors has made previous methods inaccurate and inapplicable. To address this problem, we propose an online self-adaptive forecasting method based on random forest, which is different from the traditional batch learning. Our approach takes possible fluctuations of the market into consideration, and adapts to them by maintaining training sets of different sizes. A case study using actual electricity market data has shown that our proposed approach obtains higher accuracy than ordinary approaches, as well as sheds light on possible concept drift in the market. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of hydrogen powered vehicles, the demand for hydrogen refueling stations (HRSs) in the transportation field is growing. The renewable energy, e.g., wind energy, is used to produce and store hydrogen on site for HRSs, which may be a relatively cheap and clean solution. Considering the wind farm (WF) and HRSs belong to different entities, a new cooperative operation model of the WF-HRSs combined system is proposed. Specifically, the robust optimization methods are implemented to characterize the uncertainties of wind power and market electricity price, respectively, to alleviate risk. To ensure the fairness of profit allocation, the bargaining power is measured by various contribution levels of each stakeholder based on the Nash–Harsanyi bargaining game theory. Furthermore, the model is transformed into two sequential subproblems: energy trading problem (SP1) and the payment bargaining problem (SP2). Accordingly, a solving technique adopting column and constraint generation algorithm is provided to solve the SP1. In addition, for the sake of privacy protection, a distributed approach based on data-centric mode is developed to solve SP2. Finally, the numerical results can validate the effectiveness and scalability for the proposed scheme and algorithms. [ABSTRACT FROM AUTHOR]

Periodical validation and calibration to microgrid models are necessary for investigating the behaviors of microgrids and evaluating the stability of connected power grids. However, instead of calibrating the problematic parameters that vary from their actual values, the commonly used methods normally adjust the selected sensitive parameters. The performance of the calibrated models is still in doubt. Therefore, this article proposes a generic approach for microgrid model calibration, which aims to accurately locate the indeed “ill-conditioned” problematic parameters, as well as address the multiple solutions issue. With the application of sensitivity and correlation analysis, the potentially problematic parameters (PPPs), which are sensitive and independent, are selected to ensure the accuracy and uniqueness of parameter calibration results. Then, the problematic parameters are further screened out by comparing the energy distribution of PPPs and model errors in the frequency domain. Also, hybrid dynamic simulation based parameter estimation is employed to adjust the problematic parameters, by which each device in microgrids can be calibrated independently. Finally, the simulation results demonstrate the outstanding performance in parameter calibration of a real microgrid. [ABSTRACT FROM AUTHOR]

In recent years, a large variety of short-term load forecasting methodologies have been proposed, but a common drawback of them is that require a large amount of historical data to train the model. However, in reality, the cases in limited historical data would be more frequently occurred, for example, a new house or substation is built. To tackle this issue, a combining forecasting method of deep residual neural network (ResNet)-based transfer learning for residential loads is proposed in this article. To improve the accuracy and reliability of transfer learning, a novel deep ResNet with dual skip connections (ResNet-DSC) is proposed as the base model. Then with sparse training data, a Bayesian probability weighted averaging (approach is proposed, to address the model combination parameters estimation problem. In addition, probability density function forecasting is also delivered by the method. For demonstration, a series of experiments, with actual residential load data, was performed. With the comparison with other transfer learning and nontransfer learning approaches, the effectiveness and improvement of the proposed method have been validated. [ABSTRACT FROM AUTHOR]

To enhance the resilience of power systems toward the temporal and spatial impacts caused by extreme weather events, e.g., hurricanes, the flexibility of cloud data centers (CDCs) is treated as a kind of efficient demand response. Since the workloads of CDCs have the shifting capacity between different locations and time slots, a day-ahead unit commitment problem including data centers is proposed to explore the integrated spatial and temporal flexibility of the workloads to its full extent through task migration. Considering the uncertainty of probability density functions, the line failure rates, workload arrival rates, power loads are integrated into an ambiguity set. The scheduling process of generators and CDCs is modeled as a two-stage distributionally robust optimization problem, which is reformulated as a large-scale deterministic mixed-integer linear programming problem and solved by the multicuts Benders decomposition method. The performance of the proposed scheduling strategy is tested in both the IEEE 24-bus RTS system and the three-area RTS-96 system. The results reveal that the method could mitigate the adverse impacts of hurricanes by enhancing the resilience of power systems and decreasing the dropping workloads of CDCs. [ABSTRACT FROM AUTHOR]

Accurate equivalent model can be efficient to analyze the dynamic properties of active distribution network (ADN) as well as assess their impacts on stabilities of interconnected power system. However, due to the stochastic nature of renewable resources and time-varying configurations of load conditions, traditional ADN equivalent model may not be robust enough to different operation conditions. To overcome the limitations, this article proposed a robustness-improved method for dynamic equivalent modeling of ADN. To sketch out the most representative operation conditions of ADN, two-step clustering method with fisher discriminant analysis are used for grouping of operation conditions featured characteristic data sets. With the key parameter based identification technique applied, the multiple solution issue in parameter identification process could be effectively avoided. To further enhance the robustness of equivalent model, long short-term memory neural network is adopted to generalize the identified parameters. The performance of proposed modeling method is comprehensively evaluated by an actual ADN based verification cases. [ABSTRACT FROM AUTHOR]

In this article, a fixed and smooth-switch-sequence modulation strategy is proposed to equalize the dc-link voltages among the cascaded rectifier. The structure of a single-phase three-level neutral-point-clamped cascaded rectifier is studied. Three core steps of this strategy are analyzed respectively, including carrier wave disposition calculation, fixed pulse generator, and voltage rank function. In addition, a criterion of the degree of the load unbalance is established in order to measure the voltage balancing boundary of the presented approach. The superiorities of the proposed control strategy embody the smooth switch sequence, the strong voltage balance capability, as well as the fixed switch sequence. Owe to these advantages, the cascaded rectifier could effectively reduce the switching frequency, achieve the goals of voltage balancing, and reduce the calculation burden of the controller when the number of cascaded modules is large. The effectiveness of the proposed strategy is verified by simulation and experiment results. [ABSTRACT FROM AUTHOR]

The uncertainty of power system is intensified by the integration of large-scale renewable energy resources such as wind farm (WF). Considering the impact of system uncertainties, the probabilistic stability analysis methods have been used in the stability assessments of power system with the WF integration. However, in traditional probabilistic analysis methods, WF is normally considered as PQ bus or aggregated as one single-machine equivalent model regardless of wake effect, which might reduce the accuracy of probabilistic stability studies. In this article, a dynamic equivalent modeling method of WF for probabilistic stability assessments is proposed. The wake effect is considered in the modeling process and a practical four-machine clustering method is used in wind turbines clustering. Besides, the fisher discriminant analysis (FDA) is adopt to combine the similar WTs clustering results reasonably. Then, the WF is aggregated to a multi-machine equivalent model by capacity weighted method. Also, the established WF probabilistic model can be directly used in time-domain simulation based on Monte Carlo simulation and FDA. Finally, the efficiency of the proposed method is verified in an actual WF in China. [ABSTRACT FROM AUTHOR]

Due to the maturity of wide area measurement technology, phasor measurement unit (PMU)-based dynamic equivalent modeling method (PDM) has been wildly used in large-scale power system modeling. However, traditional “event-based” PDM equivalent model may face serious robustness issues, such as although derived equivalent models show the good ability on reproduction of the system behaviors under recorded events where they derived from, they may not work well for other possible disturbances. In this article, to improve the robustness of equivalent model, a novel method on dynamic equivalent modeling of power system is proposed. Based on the result of coherency identification, generators inside the equivalent system can be aggregated into different coherent subgroups and represented by multimachine equivalent model. Key parameter selection, which can shrink the scale of identified parameters, is designed to avoid multiple solutions of the equivalent model. Moreover, a multiobjective parameter identification algorithm is developed to fully consider the PMU measurements in different system events. In addition, hybrid dynamic simulation is performed so that each equivalent submodel can be identified separately. Finally, the effectiveness of the proposed method is verified in an actual power grids in China. [ABSTRACT FROM AUTHOR]

With more distributed energy resources penetrated into the residential community, nanogrid based peer-to-peer (P2P) energy market has rapidly emerged over recent years. Due to the complexities on the decision-making process of each market participant, an efficient, fair and beneficial oriented bidding strategy is thus necessary. In this article, a two-stage bidding strategy for P2P trading of nanogrid is proposed. To overcome the limitations of traditional methods, in the first stage, a supply-demand relationship considered two-step price predictor, which aims to promote the usage of local renewable energy, is formulated to provide the guidance on transaction adjustment. In the second stage, trading preference based simultaneous game-theoretic approach is fully introduced, which can optimize the market equilibrium and then increase the social welfare of the P2P market. Additionally, to mitigate the possible failure of price matching, value-at-risk is implemented through the trading process as a risk hedging tool. To verify the effectiveness of the proposed strategy, usages of local renewable energy, economic benefits and success rates of transaction is compared against the traditional method for various cases. [ABSTRACT FROM AUTHOR]

In arc flash hazards calculations, protection boundary can be used to gain insight in personnel protection against thermal injuries. Although IEEE 1584-2002 has provided the calculation guide based on tests with the arcing electrodes in a vertical plane, applications where the traditional vertical arrangement is not directly applicable have been drawing more attention over the past few years. Due to the varied thermal behaviors of arc under different equipment configuration and the varied bus connection mode, for the purpose to achieve the adequate protection on arc flash thermal injury, it is necessary to analyze the effect of different electrode geometries on arc flash protection boundary (AFPB). According to the prior work on the development of IEEE 1584-2018, this article fully discusses the impact of electrode geometry on incident energy exposure level and AFPB. With the multiple electrode geometry designed tests, thermal behaviors of arc under different electrode orientation, gap distance, and equipment configurations have been analyzed. According to test results, correction equations were derived under each typical electrode geometry to adjust the AFPB calculation model of IEEE 1584-2002. Through the comparisons, the corrected model can better reflect the actual effects of electrode geometry, meanwhile showing a good ability to provide reasonable AFPB for practical uses. [ABSTRACT FROM AUTHOR]

This paper introduces a compensation strategy to deal with both current and voltage measurement errors in the three-phase pulsewidth modulated rectifier system. The dc offset and scaling errors in the voltage and current measurements cause the injection of undesired dc and unbalanced currents into the three-phase input current and subsequently lead to voltage ripple at the dc output voltage. This paper proposes a compensation scheme for current measurement error where the dc offset and scaling errors in the current measurement are estimated from the characteristic of the dc output voltage ripple combining with simple band-pass and low-pass filters. Meanwhile, an advanced current controller designed with a proportional integral plus two resonant controllers tuned at the fundamental grid frequency $({{\omega _s}})$ and $2{\omega _s}$ in the synchronous (d–q) reference frame is suggested to reject the impact of the dc offset and scaling errors in the voltage measurement. The proposed compensation method is developed without the need of extra hardware circuit, sensor, or precise information of system parameters so that it can be considered as more cost-effective and robust solution. The effectiveness of the proposed solution is verified by experimental results. [ABSTRACT FROM AUTHOR]

The rapidly developing data center industry results in a large amount of energy consumption. Considering its unique demand characteristics, it becomes desirable to improve its energy efficiency by reusing its waste heat. In this paper, an integrated energy management scheme is proposed to optimize the operation cost for a data center microgrid. The waste heat recovered from data center operation is optimally scheduled with other resources in the integrated energy management model to minimize the operation cost of data center microgrid. Taking the uncertain nature of renewable supply, power and heat demand into consideration, a two-stage stochastic programming model is formulated and analyzed under different test cases. The numerical study results demonstrate the effectiveness of the proposed integrated energy management model. [ABSTRACT FROM AUTHOR]

Internet data centers are booming in the last few years, leading to gigantic amount of energy consumption and greenhouse gas emission. In this paper, a data center microgrid including conventional units, energy storage, and renewable generation is modeled to optimize its electricity bill and carbon footprint. Considering the randomness of electricity price, renewable output and workload distribution, the volatility risk is incorporated to measure the associated operation risk. Then, a day-ahead emission-aware stochastic resource planning scheme is formulated to decide the strategy on power procurement, energy storage operation, batch workload allocation, and unit commitment of conventional units. Simulation results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Due to voltage mismatch between phase legs and the dc bus in modular multilevel converters (MMCs), the differential current in MMCs is inherently subjected to circulating even-order harmonics. Repetitive control based active harmonic suppression methods can be adopted to eliminate such harmonics. Nevertheless, conventional repetitive controllers have a relatively slow dynamic response, because all the sampled errors in the past one cycle have to be stored, which causes a response delay for one fundamental period. This paper proposes an improved repetitive control scheme that exclusively copes with even-order harmonics based on the circulating current characteristics of MMC systems. The design details of the even harmonic repetitive control scheme according to the harmonics characteristics are provided. The proposed even-harmonic repetitive control scheme requires halved data memory to store error samplings and the delay introduced by the repetitive controller is also reduced. According to the frequency domain analysis, the even-harmonic repetitive control features faster convergence rate, greater low-frequency gains, higher crossover frequency, and higher tolerance against system frequency deviation, while possessing the same even-order harmonics suppression capability and stability as conventional ones. Simulation and experimental results are presented to show the steady-state harmonics suppression capability, dynamic response, and disturbance tolerance of the proposed even-harmonic repetitive control scheme. [ABSTRACT FROM AUTHOR]

The increasing demand for high power density requires the power converter to operate in high switching frequency. Silicon carbide (SiC) power module is regarded as one of the most promising candidates for high-frequency applications due to the superior switching speed and low switching loss. With the increase of switching frequency, the switching loss will be the limiting factor of efficiency. Hence, it should be minimized during each switching transition. The optimization of switching loss is normally achieved by the repetitive double pulse test experiments. It is time-consuming to find an optimum gate resistance to achieve the tradeoff between switching loss and electromagnetic interface. In this paper, an accurate subcircuit model for SiC power module is proposed to assist optimization of switching loss in converter design. By considering the device physics and structure, an accurate Miller capacitance model is obtained. Moreover, a parameter extraction procedure is presented, which is based on the datasheet. Good agreements are achieved between the PSpice simulation and experiment. [ABSTRACT FROM AUTHOR]

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising. [ABSTRACT FROM PUBLISHER]