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Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

Robust look-ahead dispatch (RLAD) is essential to manage uncertainties in power systems. As its key step, the worst-case scenario identification (WCSI) problem is cast to a max-min program. This computationally intensive procedure has to be performed repeatedly, impairing the computational efficiency of RLAD. To address this issue, an efficient RLAD scheme incorporating critical region preparation in gap time is proposed. The computation burden is mostly transferred from the online decision stage to the gap time via a customized technique based on multi-parametric programming. The accuracy of computing the critical regions is validated by the test in a six-bus system, and the proposed method is shown to cut down 21% of total iterations and save computational time dramatically by the test in a practical-scale system in Northeastern China. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

Regional wind power prediction (WPP) is conducive to achieving scientific dispatch of the power system as well as improving its reliability. Data mining based on a large number of samples is an effective way to improve the accuracy of WPP. A new regional power forecasting method in the context of data mining is proposed in the article. First, a previously described regional WPP method, single feature similarity matching (SFSM) method, is analyzed and a new multifeature similarity matching (MFSM) method is proposed. Second, the optimization and analysis processes of four key parameters in the MFSM method are proposed and the impact of each key parameter on prediction error and the applicability of the method under different regional scales is analyzed. Finally, the new method and the optimization and analysis processes are verified by wind farm clusters, composed of 52 wind farms in northeast China, and the new method is compared with the conventional method, such as additive method, statistical up-scaling method, and the SFSM method. The results show that the improved MFSM method is superior to the conventional prediction method, whereby the root mean square error (RMSE) of 0 to 2 h is reduced by 3.65% and the RMSE of 0 to 4 h is reduced by 1.56%. The new method has been proved to be an effective WPP method for wind farm clusters and has potential industrial application prospects. [ABSTRACT FROM AUTHOR]

Currently, the increasing wind power penetration, with consequent randomness and variability, presents great challenges to power system planning and operation. Probabilistic power flow (PPF) has been developed to calculate the power flow under uncertain circumstances. However, the current wind power models are subject to specific probability distributions, limiting their accuracies in wider applications. Additionally, the cumulant method (CM)-based PPF, if nonlinear relationship is considered in, would face an impractically high computational complexity. To address these problems in modeling and cumulant calculation, this article proposes a novel generalized density/distribution fitting method (GDFM) combining with the Copula function to establish a joint probability model for wind power generation. A special impulse- mixed probability density (IMPD) integration method is also introduced to derive the input cumulants from the model. Finally, a fast cumulant method (FCM) is proposed to reduce the computational burden of output cumulant calculation while retaining a high accuracy in a nonlinear context. Case study on the IEEE-118 test system validates the effectiveness of the proposed methods, and a real application to a provincial power grid in China provides some useful power flow risk information for decision making. The whole FCM-based PPF scheme can be helpful for future power flow examination in power system planning and operation. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

With the largest installed capacity in the world, wind power in China is experiencing a ∼20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet unclear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and nonconvex energy generation constraints. The model is applied to simulate a regional energy system (Jing–Jin–Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data, and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly. [ABSTRACT FROM AUTHOR]

The matching between random wind supply and electric vehicle (EV) charging demand can reduce the requirement of traditional power sources and the emission of CO2. This problem is of great practical interest but involves system dynamics in multiple timescales. We consider this an important problem in this paper. In order to capture the randomness in the wind supply and EV charging demand, we formulate the problem as a bilevel Markov decision process. At the upper level, the charging demand of EVs in different locations is aggregated into multiple aggregators. The system operator dispatches power among the aggregators in a coarse timescale to maximize the wind power utilization. At the lower level, the aggregator schedules the charging process of individual EVs at a finer timescale to minimize the charging cost. In order to solve this large-scale problem, a bilevel simulation-based policy improvement (SBPI) method is developed. It is mathematically proved that the SBPI can improve from base policies in both levels. The performance of this multi-timescale and bilevel coordination approach is demonstrated through case studies in the city of Beijing. [ABSTRACT FROM AUTHOR]

With the largest installed capacity in the world, wind power in China is experiencing a \sim 20\% curtailment during operation. The large portion of the generation capacity from inflexible combined heat and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions. [ABSTRACT FROM PUBLISHER]

Due to the uneven geographical distribution between load and wind power, currently it is difficult to integrate the large-scale wind power into the State Grid in China. There are significant wind curtailments for wind farms due to system restrictions. This paper proposes a new way of utilizing wind power, i.e., in-situ consumption, as an alternative to the costly development of long-distance transmission of large-scale wind power. Electrolytic aluminum load, which is one of the most typical high energy consuming loads, is employed to absorb the excess wind power in western China. An actual isolated industrial power system for aluminum production driven by wind power and coal-fired power is described. The penetration level of wind power in the isolated power system is up to 48.8%. The power imbalance between generation and load demand caused by wind power fluctuation or the tripping of coal-fired generators can dramatically impact the frequency stability of the isolated power system, which is of small inertia. An online identification method of power imbalance based on Wide Area Measurement System (WAMS) is presented. According to the characteristics of the electrolytic aluminum load, a system frequency control method by regulating the bus voltages of aluminum loads to eliminate the power imbalance is introduced. The simulation is done in Real Time Digital Simulator (RTDS) and the results verify the validity of the proposed frequency control method. [ABSTRACT FROM PUBLISHER]

In view of the uncertainty and intermittency of wind power, this paper proposes an optimal economical dispatch (ED) model and develops a method to estimate risk and manage hybrid power systems (traditional + wind~power~systems) for the short-term (24 h) operations. The model and the method have taken into account the large wind power penetration and the wind variability. The particle swarm optimization (PSO) refid="ref1"/ algorithm with constraints is applied to solve the ED problem. Value at risk (VaR) refid="ref2"/ and integrated risk management (IRM) refid="ref3"/ are used separately to assess the risk, so that an optimal tradeoff between the profit and risk is made for the system operations. The model and the method are tested on the standard IEEE 30-bus power system and network in Shanghai. The validity of the model and the method has been approved. [ABSTRACT FROM PUBLISHER]