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Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power's high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output's high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper's model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

Since the power generation structures and load characteristics in each province in China are quite different, the distribution of peak regulating resources and demands are extremely imbalance. Restricted by a low power marketization degree, peak regulating resource shortages, and transmission channel blocks, the efficient utilization of new energy is facing greater pressures. In order to improve the mutual aid in regional power grids and to obtain more precise simulation results, this paper proposes a two-layer optimization dispatching model, considering the mutual aid of peak regulation resources between each province. It determines the optimal startup mode and the units' power output in each province and obtains the power output arrangements for all the units and the technical and economic indicators. The model and the solution method are original and innovative. And it effectively solved the unequal distribution problem between the peak regulating demands and resources of each provincial power grid. Finally, taking an actual regional power grid in China as an example, the simulation results show that the proposed model can significantly improve the utilization rate of new energy, which verifies the effectiveness and feasibility of the proposed model and methods presented in this paper. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

In order to solve the problem of randomness and volatility caused by the rapid growth of renewable energy (RE), energy storage systems (ESSs)—as an important means of regulation—can effectively improve the flexible regulation capacity of power systems utilizing a high proportion of RE. Most of the current ESS capacity configuration procedures are carried out based on the typical scenario method or time series production simulation. This method tends to determine the size of the ESS configuration through multiple trial simulations. Uncertainty of simulation prediction data can result in the existence of an excess capacity or lack of configured capacity. In addition, this method reflects the ESS demand under specific targets, but it fails to fully utilize RE generation characteristics. The configuration process lacks the mathematical mechanism of RE consumption, and the calculation process is too complicated. In view of the shortcomings of traditional ESS optimal configuration methods, this paper examines the mathematical mechanism of RE consumption and proposes the ESS optimal configuration analysis method based on "equal area criterion". First, the principle of RE consumption is analyzed and the "RE consumption characteristic curve" is proposed according to RE characteristics. In addition, a working principle diagram of RE consumption, including ESS, is constructed to visually show the consumption capacity of RE and the working position of ESS. Then, the ESS optimal configuration process, based on the "equal area criterion", is proposed to achieve an accurate match between ESS capacity demand and RE consumption targets. Finally, the power grid of a region in China is taken as an example. We prove that the proposed method can save 1.41 × 103 MWh of ESS capacity and provide a more "mathematical" and "convenient" systematic solution for RE consumption and ESS optimization compared to the production simulation method. [ABSTRACT FROM AUTHOR]

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China's power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid. [ABSTRACT FROM AUTHOR]

With the continuous development of intelligent transportation technologies, new ways of energy usage in transportation continue to emerge, which puts forward new requirements for the planning and design of energy systems. However, comprehensive analyses on the characteristics of transportation energy systems and the development trend of energy usage patterns brought by intelligent technologies have rarely been carried out so far. This paper explores this subject by reviewing the recent development and utilization of intelligent technologies in the transportation system and its impacts on energy usage. This review is carried out from three aspects, covering the representative intelligent transportation and energy technologies on vehicles, infrastructures and systems. The scope is limited within road, railway and water transport domains, with a focus on the recent developments in China as a representative. In terms of vehicles, the development trend of the power systems for new energy vehicles, the characteristics of energy usages in electric vehicles and the effects on energy saving and emission reduction are summarized. In terms of infrastructures, new technologies on smart road, smart port, intelligent railway energy system and the usage of clear energy on electric grid for transportation are reviewed, with the consideration of their potential influences on energy usages and the energy consumption characteristics of typical facilities also being analyzed. As for the transportation system, this review has focused on intelligent and connected transportation systems, train control and autonomous systems, and intelligent shipping system, with the emphasis on the energy saving and emission reduction effects of applying these intelligent technologies. The overall development trend of the transportation energy system is then analyzed based on the above materials, in particular, the future energy usage patterns in transportation system are given and the major challenges and obstacles approaching the future scenarios are also identified. [ABSTRACT FROM AUTHOR]

A simulation model based on Dymola modelling was developed to investigate the dynamic characteristics of automatic generation control (AGC) for variable-load thermal power units in this study. Specifically, a 300 MW unit from a power plant in northern China was used to verify the model's validity in steady-state processes and to analyze the behavior of the main thermal parameters under different rates of load changes. The economic performance of the unit under different rates of load changes is also analyzed by combining the economic indexes of "two regulations" in the power grid. Results indicate that as the rate of load changes increases, boiler output, main steam temperature, reheat steam temperature, main steam pressure, and working temperatures of various equipment fluctuate more intensely. Specifically, at a rate of load reduction of 2.0% Pe MW/min, the maximum deviation of the main steam temperature can reach 7.6 °C, with the screen-type superheater experiencing the largest heat exchange. To achieve a balance between safety and economics for the unit, the rate of load raising should not exceed 1.2% Pe MW/min, and the rate of load reduction should not exceed 0.8% Pe MW/min. This paper applies the covariance index and AGC assessment index of the thermal power unit load control system to the established dynamic simulation model to supplement the AGC assessment index in the "two regulations", and to provide a flexible and reasonable system evaluation result for field operators to refer to, so as to improve the economics of the system on the basis of safety. [ABSTRACT FROM AUTHOR]

Frequency stability is an important factor for the safety and stability of the power system operation. In a traditional power system, the operation stability is ensured by the inertia response, primary frequency modulation, and secondary frequency modulation. In recent years, in order to achieve the goal of carbon neutralization and carbon peaking, China has made great efforts in new energy development. With large-scale new energy connected to the power grid, the proportion of traditional conventional synchronous units has gradually declined. At the same time, a large number of power electronic devices have been used in the power grid, which led to the capability decline of the inertia response and primary frequency modulation. For example, the East China Power Grid has experienced a sharp frequency drop in such an environment. In order to solve the above problems, the operation principle and control mode of various new energy resources are analyzed in this paper. Moreover, the process and principle of power grid frequency response are studied and the evaluation index of frequency response capability is proposed. The research results can quantitatively evaluate the system inertia response and primary frequency modulation level and provides a judgment tool for dispatching operators and system planners. [ABSTRACT FROM AUTHOR]

Overhead line ice-shedding causes line breakage and results in electrical faults occurring between transmission lines, which seriously affects the safe operation of the power grid. In this paper, a risk assessment method for ice-shedding on 500 kV compact overhead lines is proposed. Two ratios were used to assess the discharge risk and mechanical failure risk of a line. A 500 kV compact overhead line in southern China was taken as an example, the proposed risk assessment method was used to carry out a risk assessment of the ice-shedding line with an ice thickness of 15 mm, and the suppression effects of different spacer arrangement schemes on the ice-shedding line were analyzed. The results showed that the impact coefficient of the conductor reached 1.88, the maximum jump height of the lower phase conductor reached 22.596 m, and the interphase clearance of the conductor decreased sharply when the lower phase conductor of 500 kV case line underwent full-span ice-shedding synchronously. The ice-shedding line displayed both a discharge risk and a mechanical failure risk. When interphase spacers were installed at L/3 from both ends of the span for the upper-left phase and the lower phase, the upper-right phase, and the lower phase, the impact coefficient of the line became 1.76, which was lower than the recommended value of 1.8; there was no mechanical failure risk; and the maximum jump height became 10.266 m. This study verified the effectiveness of this spacer arrangement scheme. [ABSTRACT FROM AUTHOR]

As one of the power auxiliary services, peak shaving is the key problem to be solved in the power grid. With the rapid development of DGs, the traditional peak shaving scheduling method for centralized adjustable energy is no longer applicable. Thus, this paper proposes two-layer optimization methods of allocating the peak shaving task for DGs. Layer 1 mainly proposes four evaluation indexes and the peak shaving priority sequence can be obtained with modified TOPSIS, then the DG cluster's task is allocated to the corresponding DGs. On the basis of dynamic evaluation and the self-renewal mechanism, layer 2 proposes a peak shaving optimization model with dynamic constraints which assigns peak shaving instructions to each cluster. Finally, the effectiveness of the method is verified by using the real DGs data of a regional power grid in China based on the MATLAB simulation platform. The results demonstrate that the proposed methods can simply the calculation complexity by ranking the DGs in the peak shaving task and update the reliable aggregate adjustable power of each cluster in time to allocate more reasonably. [ABSTRACT FROM AUTHOR]

Electricity consumption is often the hotspot of life cycle assessment (LCA) of products, industrial activities, or services. The objective of this paper is to provide a consistent, scientific, region-specific electricity-supply-based inventory of electricity generation technology for national and regional power grids. Marginal electricity generation technology is pivotal in assessing impacts related to additional consumption of electricity. China covers a large geographical area with regional supply grids; these are arguably equally or less integrated. Meanwhile, it is also a country with internal imbalances in regional energy supply and demand. Therefore, we suggest an approach to achieve a geographical subdivision of the Chinese electricity grid, corresponding to the interprovincial regional power grids, namely the North, the Northeast, the East, the Central, the Northwest, and the Southwest China Grids, and the China Southern Power Grid. The approach combines information from the Chinese national plans on for capacity changes in both production and distribution grids, and knowledge of resource availability. The results show that nationally, marginal technology is coal-fired electricity generation, which is the same scenario in the North and Northwest China Grid. In the Northeast, East, and Central China Grid, nuclear power gradually replaces coal-fired electricity and becomes the marginal technology. In the Southwest China Grid and the China Southern Power Grid, the marginal electricity is hydropower towards 2030. [ABSTRACT FROM AUTHOR]

Since the development of large scale power grid interconnections and power markets, research on available transfer capability (ATC) has attracted great attention. The challenges for accurate assessment of ATC originate from the numerous uncertainties in electricity generation, transmission, distribution and utilization sectors. Power system uncertainties can be mainly described as two types: randomness and fuzziness. However, the traditional transmission reliability margin (TRM) approach only considers randomness. Based on credibility theory, this paper firstly built models of generators, transmission lines and loads according to their features of both randomness and fuzziness. Then a random fuzzy simulation is applied, along with a novel method proposed for ATC assessment, in which both randomness and fuzziness are considered. The bootstrap method and multi-core parallel computing technique are introduced to enhance the processing speed. By implementing simulation for the IEEE-30-bus system and a real-life system located in Northwest China, the viability of the models and the proposed method is verified. [ABSTRACT FROM AUTHOR]

Power grid investment behavior (PGIB) is a necessary prerequisite to ensure safe and reliable power supply. The supervision of PGIB is an important measure to reduce electricity price. It helps to release the dividends of the new power system reform and promote the sustainable development of the power industry. However, for a long time, China's regulation of PGIB was relatively weak, lacking clear supervision awareness and effective supervision methods. On the basis of clarifying the contents of PGIB supervision, this paper establishes an evaluation index system for the compliance of PGIB from the government supervision perspective, involving four dimensions and 14 secondary indicators. Furthermore, a hybrid evaluation model based on the combined decision-making trial and evaluation laboratory method (DEMATEL-ANP), analytical network process method, and vague set is proposed. The results reveal that the control over cost and efficiency of the PGIB in China is not enough. According to the results, this paper puts forward the indicators that close attention should be paid to and the work priorities in China's supervision of PGIB, which has certain practical value for the implementation of new power system reform and the sustainable development of Chinese power industry. [ABSTRACT FROM AUTHOR]

Energy saving and emission reduction have become common concerns in countries around the world. In China, with the implementation of the new strategy of "carbon peak and neutrality" and the rapid development of the new smart grid infrastructure, the amount of data of actual power grid dispatching and fault analysis show exponential growth, which has led to phenomena such as poor supervision effectiveness and difficulty in handling faults in the process of grid operation and maintenance. Existing research on retrieval recommendation methods has had a lower accuracy rate at cold-start due to a small sample of user interactions. In addition, the cumulative learning of user personalization during general retrieval results in a poor perception of potential interest. By constructing a power knowledge graph, this paper presents a power fault retrieval and recommendation model (PF2RM) based on user-polymorphic perception. This model includes two methods: the power fault retrieval method (PFR) and the user-polymorphic retrieval recommendation method (UPRR). First, we take the power grid fault dispatching business as the core and reconstruct the ontology layer of the power knowledge graph. The PFR method is used to design the graph-neighbor fault entity cluster to enhance the polymerization degree of a fault implementation scenario. This method can solve the search cold-start recommendation problem. At the same time, the UPRR method aims to form user retrieval subgraphs of the past-state and current-state and make a feature matching for the graph-neighbor fault entity cluster, and then realize the accurate prediction of the user's general search intention. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–8% improvement in the cold-start recommendation effect and 2–10% improvement in regular retrieval. The model has the best average recommendation performance in multiple metrics and has good results in fault analysis and retrieval recommendation. It plays a helpful role in intelligent operation and maintenance of the power grid and auxiliary decision-making, and effectively improves the reliability of the power grid. [ABSTRACT FROM AUTHOR]

In China, with the increasing permeability of wind power, the power supply capacity is enough overall, but has shortage in partial-time. During peak hours, the capability of wind power consumption is poor and the power balance becomes more difficult. In order to maximize the utilization of wind power, the net loads are chosen as the response objectives, which contain significant uncertainties and have no probabilistic distribution characteristics. Under the traditional day-ahead power dispatch mode with fixed length time intervals and in the regions with insufficient hydroelectricity, the thermal generators take charge of the peak-load shaving. The frequent adjustments of thermal power output affect the system operation safety, economic benefits, and environmental benefits. Thus, a time-interval-varying optimal power dispatch strategy based on net load time-series characteristics is proposed in this paper. The net loads respond differently to intervals. The length of each time interval is determined based on the net load time-series characteristics analyzed by random matrix theory. The dispatch mode in each time interval is determined according to the characteristic quantification index calculated by the empirical modal decomposition and sample entropy. The proposed strategy and method can extend the continuous and stable operation time of the thermal generators, reduce the coal consumption caused by the ramping operation, and improve the safety, stability, and economy of the system. Furthermore, the proposed dispatch mode is environmentally friendly with reduced environmental cost and increased carbon credits. An actual provincial power grid in northeast China is taken as the example to verify the rationality and effectiveness of the proposed method and strategy. [ABSTRACT FROM AUTHOR]

With the reform of the power system in China, investments in power grid projects across the whole power system are increasing. However, there are various objectives to achieve in the investment decision processes of power grid projects, so the rational investments of a grid project can be seen as a multi-objective optimization problem. Meanwhile, these issues have rarely been studied at home and abroad, and this paper will fill this gap. As a result, this study critically analyzed the application of a multi-objective optimization model to power grid investment. Firstly, the objective factors of grid investments were explored, which were quantified through quantitative methods. Secondly, based on the characteristics of power grid investment, a multi-objective optimization model was established, and the assumptions and constraints of the model were presented. Finally, NSGA-II was used for solving the multi-objective optimization model. The results show that: (1) Multi-objective optimization models are suitable for the study of and deriving solutions for power grid investment by establishing suitable objective functions, assumptions and constraints, (2) According to the conventional steps of NSGA-II, suitable steps can be established to search for an optimal solution to the objective set of a power grid investment and (3) Due to the different concerns of different project scenarios, Pareto frontier solutions can be selected as the practical references of power grid projects. Therefore, the solution set makes the implementation scheme more flexible. [ABSTRACT FROM AUTHOR]

For avoiding the occurrence of large-scale blackouts due to disconnected nodes in the power grid, a modified PageRank algorithm is proposed to identify key nodes by integrating the topological information and node type. The node betweenness index is first introduced based on complex network theory, which is modified to reflect the node topological information in the power grid. Then, according to the characteristics of different node types in the power grid, a modified PageRank algorithm is proposed to rapidly identify key nodes, which takes the generator nodes, load nodes, and contact nodes into account. IEEE 39-Bus system and IEEE 118-Bus system are used for the simulations. Simulation results showed that the network transmission efficiencies of the power grid are reduced from 64.23% to 5.62% and from 45.4% to 5.12% in the two simulation systems compared with other methods. The proposed identification algorithm improved the accuracy, and a provincial power grid simulation system in China is used to verify the feasibility and validity. The identified nodes are removed, which split the power grid according to importance index values. The proposed method in this paper is helpful to prevent the occurrence of cascading failure in the power system, and it can also be used to power systems with renewable energy sources and an AC/DC hybrid power grid. [ABSTRACT FROM AUTHOR]

Inter-provincial power transmission in China solved the problem of electricity production and consumption spatial mismatch, which also facilitated Chinese green energy transition and sustainable development. Aiming to understand the spatial patterns and main driving factors, this paper accounted the aggregate carbon intensity per electricity produced by each province, built the inter-provincial Out–Destination matrixes of carbon emission embodied in power transmission among 27 provincial areas of 2006 and 2019, and quantitatively analyzed the change in spatial patterns of carbon flow and carbon intensity difference between power inward and outward provinces. We found that the inter-provincial power transmission has led to the reduction in the national average carbon intensity per electricity produced from 0.855 kg/kWh in 2006 to 0.628 kg/kWh in 2019 and the green energy transition of all provinces. The spatial morphology characteristics of carbon flow show the 'three corridors of West-East Power Transmission (WEPT)' pattern, in which power is transmitted from some main thermal power-dominated provinces in central and north-west China to the eastern coastal provinces. Further, it resulted in the carbon leakage from electricity consumption of the coastal provinces and carbon overload of the hinterland provinces. Despite natural resource endowment, the location of the provinces in the national power grid, the national low-carbon energy transition strategy, as well as advances in long-distance ultra-high-voltage power transmission technologies were the main factors to the formation and evolution of Chinese electricity green transition. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays an important role in the economic dispatch of power systems. Obtaining accurate short-term load can greatly improve the safety and economy of a power grid operation. In recent years, a large number of short-term load forecasting methods have been proposed. However, how to select the optimal feature set and accurately predict multi-step ahead short-term load still faces huge challenges. In this paper, a hybrid feature selection method is proposed, an Improved Long Short-Term Memory network (ILSTM) is applied to predict multi-step ahead load. This method firstly takes the influence of temperature, humidity, dew point, and date type on the load into consideration. Furthermore, the maximum information coefficient is used for the preliminary screening of historical load, and Max-Relevance and Min-Redundancy (mRMR) is employed for further feature selection. Finally, the selected feature set is considered as input of the model to perform multi-step ahead short-term load prediction by the Improved Long Short-Term Memory network. In order to verify the performance of the proposed model, two categories of contrast methods are applied: (1) comparing the model with hybrid feature selection and the model which does not adopt hybrid feature selection; (2) comparing different models including Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) using hybrid feature selection. The result of the experiments, which were developed during four periods in the Hubei Province, China, show that hybrid feature selection can improve the prediction accuracy of the model, and the proposed model can accurately predict the multi-step ahead load. [ABSTRACT FROM AUTHOR]

According to the International Energy Agency (IEA)'s forecast, China will fully electrify its railway system by 2050. However, the development of electrified railways is limited in the weak areas of China's power grid. To surpass these limitations, we turn our attention to new railway energy sources, among which the most suitable is photovoltaic power generation. To evaluate the feasibility of integrating railway systems and photovoltaic power generation in China, this paper analyzes the geographical conditions and railway layout of China, gives a potential method for evaluating railway asset energy transformation measures and asset energy transformations that are suitable for China's national conditions, and proposes an application of railway system energy transformation on this basis. Then, this method is used to evaluate the potential of a substation section of the Lanxin high-speed railway in China for reference. The results show that the green energy potential and scheduling potential of China's railway assets are great and can effectively alleviate the energy anxiety of China's railway system. [ABSTRACT FROM AUTHOR]

In order to satisfy the strategic needs of energy sustainable development, renewable energy has developed rapidly and the power systems have been transformed to a new generation of power systems. In the renewable energy power generation technologies, the fastest developing wind power generation are highly intermittent and fluctuating. When high penetration of renewable power connects to the power grid and participates in the system dispatch, there will be more difficulties and challenges in the energy balance control. In this paper, a wind-thermal-nuclear-storage combined time division power dispatch strategy based on numerical characteristics of net load is proposed, where a specific thermal generating mode and an unconventional nuclear generating mode are discussed. In the strategy, the dispatch time division method is introduced in detail and the sample entropy theory is used to calculate the net load complexity. An adaptive thermal generating mode is determined according to the numerical characteristics of the net load. The nuclear generating modes of constant power operation, time division operation, and net load tracking time division operation are compared and analyzed, respectively. Finally, the wind-thermal-nuclear-storage combined time division power dispatch strategy aiming at decreasing the ramping power of thermal generators is achieved, and the increasing of the participation of pumped storage and improving of the continuous and steady operation time of thermal generators are realized. The experiment simulation is developed on an actual provincial power system in the northeast of China. The results verify that the thermal generator ramping power in the case based on SampEn are reduced, and the participation of pumped storage is improved. When both of the thermal generating mode and nuclear generating mode are according to the changing of net loads, the ramping powers of thermal generators are further decreased. [ABSTRACT FROM AUTHOR]

Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation. [ABSTRACT FROM AUTHOR]

Low frequency oscillations (LFOs) in power systems usually fall into two types, i.e., forced oscillations and natural oscillations. Waveforms of the two are similar, but the suppression methods are different. Therefore, it is important to accurately identify LFO type. In this paper, a method for discriminating LFO type based on multi-dimensional features and a feature selection algorithm combining ReliefF and minimum redundancy maximum relevance algorithm (mRMR) is proposed. Firstly, 53 features are constructed from six aspects—time domain, frequency domain, energy, correlation, complexity, and modal analysis—which comprehensively characterize the multidimensional features of LFO. Then, the optimal feature subset with greater relevance and less redundancy is extracted by ReliefF-mRMR. In order to improve the classification performance, a modified Support Vector Machine (SVM) with Genetic Algorithm (GA) optimizing the key parameters is adopted, which is conducted in MATLAB. Finally, in 179-bus system, the samples of LFOs are generated by the Power System Analysis Toolbox (PSAT) and the accuracy of the LFO type identification model is verified. In ISO New England and East China power grid, it is proven that the proposed method can accurately identify LFO type considering the influences of noise, oscillation mode, and data incompletion. Hence, it has good robustness, noise immunity, and practicability. [ABSTRACT FROM AUTHOR]

Due to booming economic development over the past decades, energy demands in most of China's provincial power grids have increased sharply, and it has become challenging to guarantee the energy balance at peak periods. In many provincial electric systems of China, gas-fired generators are one of the most important peaking power sources to respond the load change at peak periods. To meet this practical necessity, a novel mixed integer linear programming model is proposed in this paper for the peak operation of gas-fired generating units with disjoint-prohibited operating zones. In the developed model, the objective function is chosen to minimize the peak-valley difference of the remaining load series that is obtained by subtracting the total generation of all the gas-fired units from the original load curve. The real-world simulations in several cases show that the developed model is able to generate satisfying scheduling results by reasonably allocating the power outputs of all the gas-fired generators in the scheduling horizon. Then, the management implications obtained lie in the fact that it is necessary to increase the share of peak power sources in the mid- to long-term planning of an electrical power system; and in the daily operation of the power grid, greater flexibility should be given to the gas-fired units to reduce peak pressure. [ABSTRACT FROM AUTHOR]