To mitigate the uncertainty of wind power integration and enhance the techno-economic competitiveness of wind power operators in the spot electricity market, the optimal joint operation of wind farms and flexible loads is crucial. To this end, this paper proposes a performance evaluation system for the wind farm-flexible load joint operation system (WF-FLJOS) to quantify the optimality of different operational schemes. The proposed multidimensional evaluation framework encompasses technical, economic, and environmental indices to assess the benefits brought by WF-FLJOS for the wind farm, flexible loads, and the main grid. To render this, first, the weights derived from the order relation analysis (G1) and the criteria importance through intercriteria correlation methods are coupled using the principle of minimum discrimination information to generate integrated weight values. Then, a self-decision mechanism for wind farm output deviation rate tiers is introduced by developing an operational model for the WF-FLJOS that participates in the day-ahead energy and frequency regulation markets. Finally, multiple alternative operating schemes for the WF-FLJOS are quantified and ranked based on the technique for order of preference by similarity to ideal solution method. Case studies demonstrate that the joint operation mode not only improves the benefits for the wind farm and flexible loads but also lessens the regulation burden of the main grid. Furthermore, the proposed evaluation system effectively measures the comprehensive benefits of the WF-FLJOS and facilitates the selection of the optimal operational scheme.

In constructing power system load flow mapping relationships using machine learning algorithms, the fundamental prerequisite for ensuring the computational accuracy and generalization performance of the mapping model is the availability of a suitably sized, well-distributed, and high-quality labeled sample set that can be supplied in a precursory and efficient manner. Here, the quality of a load flow sample set is defined concretely by three concurrent properties, as follows: physical consistency (satisfaction of Kirchhoff’s and Ohm’s laws), representative coverage of the operational state space, and low inter-sample redundancy. Currently, both online and offline techniques for power flow samples are incapable of efficiently providing large-scale, high-quality power flow sample sets in this sense. To address this, the paper proposes a method for generating power flow sample sets that integrates a physical model of the power grid. This method encompasses the following: non-iterative, high-speed generation techniques for massive load flow samples; partitioned generation and multi-region splicing techniques for large power grid load flow samples; and the design of capacity requirements and quality technical indicators for load flow sample set production. Analytical results demonstrate that the proposed method can efficiently produce high-quality power flow sample sets of appropriate capacity based on actual needs. Case studies on the IEEE 9-bus and 39-bus systems show that sample generation is about 19 and 32 times faster than the whole-network Newton–Raphson method, respectively, for 100,000 samples, and the voltage-band capacity design requires only about 12.6% of the samples needed by uniform sampling for equal boundary-condition coverage, at a comparable learning error.

With the growing penetration of wind energy in China, the main grid operator imposes stringent requirements on the accuracy of wind farm output, prompting wind power producers to explore new business models to enhance profitability. To mitigate wind power uncertainty on the source side while saving energy bills on the load-side, this paper proposes a framework for the wind farm-flexible load joint operation system (W-FJOS). Based on the operational requirements of the main grid, the types of services that can be developed by the W-FJOS are analyzed, and the applicable business models are designed. Taking the participation of W-FJOS in electricity trading and valley filling ancillary services as an example, this paper compares the operating strategies of W-FJOS under different business models and presents the bases for business model selection. Numerical results indicate that, compared with traditional dispatch model, the two business models proposed in this paper not only improve the benefits for participants but also promote the nearby consumption of wind power. Moreover, the diverse services organized by the W-FJOS can further increase the profit potential of both the wind farm and flexible loads while supporting the integration of wind farms to the grid.

The uncertainty of wind power output affects the efficient operation of the electricity spot market and has become a key factor restricting the participation of wind farms in the market. To this end, this paper proposes a day-ahead joint market operation strategy that considers allowable deviation rates of wind power output. Unlike traditional electricity markets which impose uniform deviation requirements on all wind farms, the main grid side provides a more diverse range of selectable deviation rates. The bidding strategy for wind farms in the joint day-ahead and balancing markets is explored, allowing them to independently select deviation rates and submit schedule curves and offer prices. A joint clearing model for the day-ahead energy-reserve and balancing market is established, incorporating the carbon emission trading costs of thermal power units, with the aim of minimizing the system operating cost. Numerical results indicate that compared with the traditional market participation method, the proposed strategy not only encourages wind farms to improve output accuracy, but also reflects the market economic principle of high quality and high price. Meanwhile, integrating carbon emission trading costs into the model helps to reduce carbon emissions while ensuring the economic operation of the system.

The decreasing rotational inertia in new Power Systems exacerbates the issue of frequency stability. When the system experiences a large active power deficit, it is necessary to quickly tune the support power demand of the nodes involved in frequency emergency control to ensure that the transient frequency drop trajectory remains above the target. To address this, this paper proposes a method for tuning the emergency support power demand of nodes based on the control of the transient frequency target trajectory. First, under the premise of meeting inertia indicators of the system frequency stability criteria, an optimal target state is established where the initial transient frequency drop trajectory in any power deficit scenario should remain relatively stable. This target state serves as a benchmark to evaluate the performance of the actual frequency drop at each monitoring node. Second, data is collected based on the short-term frequency drop trajectory at the initial stage of disturbance. The trajectory data is used to assess the difference in equivalent inertia time constants according to its deviation from the target. This difference is then converted into the emergency support power demand for each node, thereby providing significant reference for further allocation of frequency precise control.

The dynamic frequency control processes and economic operations of the large-scale power grids are separately applied. However, for the small inertia microgrids (MGs), the operating conditions tend to be more volatile due to relatively more uncontrollable entities being integrated. Hence, the frequency control solution of MGs should take the economic operation of MGs at a time-scale that is much shorter than the traditional economic dispatch of the large power grids. To this end, this paper proposes a two-layer coordinated frequency control strategy for MGs with enhanced economic operation consideration. For the upper optimal power-sharing layer, the distributed bisection algorithm is applied to obtain the optimal power sharing among heterogeneous resources. For the lower control layer, an autonomous control strategy that integrates both primary control and secondary control reference is applied by adopting the event-trigger mechanism. The proposed control approach can realize integrated active power control of MGs by simultaneously taking primary control, secondary control, and economic operation into consideration. Simulation studies with a heterogeneous resources-powered MG demonstrate its effectiveness.

The increasing penetration of wind power poses challenges to the power grid operation and scheduling. Yet, if the uncertainty of wind power can be economically and effectively managed on the source side, it can drive the power grids towards renewable-dominant future. In this paper, an enhanced scheduling strategy for wind farm-flexible load joint operation system (WF-FLJOS) is proposed. The proposed strategy is designed to manage the uncertainty of wind power on the generation side when integrated into a large-scale power grid. Moreover, it can contribute to saving energy costs on the load side. Compared with the current wind farm operation rules, more stringent assessment requirements are put forward for wind power output accuracy, and the internal organization framework of WF-FLJOS is designed. For potential power violations of wind farms and flexible loads, the violation penalty mechanisms are developed to regulate the behavior of the participants. The joint operation model of the WF-FLJOS is proposed and the submission and tracking approach of the generation schedule for the wind farm is investigated. Numerical results indicate that the proposed strategy can not only improve the ability of the wind farm to track the generation schedule, but also consider the benefits of both the farm side and the load side. Meanwhile, the proposed strategy effectively reduces the schedule adjustment pressure on the main grid caused by the rolling correction mode of the intraday schedule for wind farms.

To improve the economic efficiency of urban integrated energy systems (UIESs) and mitigate day-ahead dispatch uncertainty, this paper presents an interconnected UIES and transmission system (TS) model based on distributed robust optimization. First, interconnections are established between a TS and multiple UIESs, as well as among different UIESs, each incorporating multiple energy forms. The Bregman alternating direction method with multipliers (BADMM) is then applied to multi-block problems, ensuring the privacy of each energy system operator (ESO). Second, robust optimization based on wind probability distribution information is implemented for each ESO to address dispatch uncertainty. The column and constraint generation (C&CG) algorithm is then employed to solve the robust model. Third, to tackle the convergence and practicability issues overlooked in the existing studies, an external C&CG with an internal BADMM and corresponding acceleration strategy is devised. Finally, numerical results demonstrate that the adoption of the proposed model and method for absorbing wind power and managing its uncertainty results in economic benefits.

By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station.© 2017 Elsevier Inc. All rights reserved.

Due to the effects of windless and sunless weather,new power systems dominated by renewable energy sources ex-perience power supply shortages,which lead to severe electrici-ty shortages.Because of the insufficient proportion of controlla-ble thermal power in these systems,this problem must be ad-dressed from the load side.This study proposes an orderly pow-er utilization(OPU)method with load as the primary dispatch-ing object to address the problem of severe electricity shortag-es.The principles and architecture of the new urban power grid(NUPG)OPU are proposed to complete the load curtail-ment task and minimize the effects on social production and daily life.A flexible load baseline division method is proposed that considers the effects of factors such as gross domestic prod-uct,pollutant emission,and carbon emission to increase the flex-ibility and applicability of the proposed method.In addition,an NUPG OPU model based on the load baseline is proposed,in which the electric quantity balance aggregator(EQBA)serves as a regular participant in the OPU and eliminates the need for other user involvement within its capacity range.The electric quantity reserve aggregator(EQRA)functions as a supplemen-tary participant in the OPU and primarily performs the remain-ing tasks of the EQBA.The electric power balance aggregator primarily offsets the power fluctuations of the OPU.Case stud-ies demonstrate the effectiveness and superiority of the pro-posed model in ensuring the completion of the load curtailment task,enhancing the flexibility and fairness of OPUs,and im-proving the applicability of the proposed method.

With the increasing proportion of renewable energy sources (RESs) in power grid, the reserve resource (RR) scarcity for correcting power deviation of RESs has become a potential issue. Consequently, the power curve of RES needs to be more rigorously assessed. The RR scarcity varies during different time periods, so the values of assessment indicators should be dynamically adjusted. The assessment indicators in this paper include two aspects, i. e., deviation exemption ratio and penalty price. Firstly, this paper proposes a method for dynamically calculating the supply capacity and RR cost, primarily taking into account the operating status of thermal units, forecast information of RES, and load curve. Secondly, after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators, this paper establishes a mapping function between them. Based on this mapping function, a dynamic setting method for assessment indicators is proposed. In the future, RES will generally be equipped with battery energy storage systems (BESSs). Reasonably utilizing BESSs to reduce the power deviation of RESs can increase the expected income of RESs. Therefore, this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs. The case study demonstrates that the dynamic setting method of assessment indicators can increase the revenue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs. Additionally, the power curve optimization strategy can help RESs further increase income and fully utilize BESSs to reduce power deviation.

Under the dual-carbon goal, the operation environment and form of the urban power grid have new characteristics, and the urban power grid will be transformed into a new urban power grid (NUPG), which puts forward higher requirements for flexibility. It is urgent to establish a flexible dispatching system to meet the operation of the NUPG. This paper first introduces the eight major new operation characteristics and challenges of the NUPG. Secondly, a flexible dispatching architecture with the load as the main body is constructed, and the types of dispatching objects, dispatching means and media, and internal and external dispatching tasks are introduced. On this basis, four basic dispatching key technologies of the NUPG are pointed out: load response capacity assessment and distribution technology, load reserve configuration and power plan formulation technology, load participation in power grid frequency regulation/peak shaving/voltage regulation technology, and internal and external fault handling technology based on load resources. Finally, the effectiveness of the NUPG dispatching architecture and key technologies is verified by simulation analysis of the IEEE39-bus system and the actual power grid in East China.

The carbon flow distribution information in the power grid link is generated due to the electric energy allocation process of the carbon-bearing grid-connected source flow and is highly coupled with the electric energy allocation relationship of all connected source flows. To this end, this paper proposes a method for calculating carbon flow distribution information based on the electric energy allocation results of power grid source flow. Firstly, based on the electric energy flow information of the source-stream-related path chain set, calculate the carbon flow distribution information between any specified power source and load pair; secondly, based on the electric energy flow information of the source-related path chain set, the carbon flow direction information of carbon-bearing (including thermal power generation natural emission carbon and carbon market trading purchase carbon, etc.) power sources is calculated; thirdly, based on the electric energy flow information of the load-flow related path chain set, calculate the carbon traceability information of any specified load. The case study demonstrates that the method can accurately calculate the carbon flow distribution information reflecting the real-time operational status of the power grid, providing technical support for further optimizing the electric-carbon operation mode of the power grid.

With the development of low-carbon energy, the use of thermal and hydropower units for regulation in the power system dominated by new energy (PSDNE) is decreasing. Electric buses (EBs) are expected to become the primary dispatching objects in this context. Accurately evaluation EB's response capability (RC) is vital for the scheduling operation of the PSDNE. However, current evaluation methods for EB's RC are relatively crude, lacking detailed and quantifiable measures. This paper proposes a novel multi-dimensional evaluation method to provide the aforementioned RCs. The EB operational schedule revise model considering traffic and temperature is proposed to obtain more accurate EB charging load by revise arrival charging time and remaining battery energy. The proposed EB charging model considering carbon emissions contributes to revealing the impact of various charging modes, such as low-carbon and electricity price, on the Eb's RC. The EB multi-dimensional evaluation model is established to obtain quantified and detailed RCs to support the operation of NEDPS. These capabilities include fast response within various periods, sustained response over extended durations, and remaining RC after executing scheduling instructions. Finally, using the actual data from an EB station of a city in China, the effectiveness of the proposed evaluation model is verified.

In this paper, the model of EVs batteries with lifespan factors is established.As one part of power grid, it is proved to be effective to the primary regulation.According to a power law equation in which the capacity loss of batteries follows the current, temperature and DOC/DOD with time,the model is established.In this model, current and DOC/DOD are the main factors.By using the data from the simulation of power grid, the capacity loss of different strategies can be analysed.When charging/discharging integrated, in one cycle, the capacity loss of EVs batteries can be obtained at room temperature.

Generation expansion planning (GEP) is a crucial tool in guiding the growth of electric power systems towards achieving goals of carbon peaking and neutrality. Accurately capturing the high-density variations of renewable energy sources (RES) necessitates integrating the unit commitment (UC) decision process into the planning investment stage. However, the UC-integrated GEP model typically comprises millions of variables and constraints, rendering it unsolvable by commercial solvers directly. To tackle this large-scale optimization problem, this paper presents a customized Benders Decomposition approach, partitioning the UC-integrated GEP model into a master problem optimizing investment variables and several sub-problems representing annual operation. The optimality cuts and feasibility cuts, fundamental to the decomposition algorithm, are explicitly formulated, and the solving procedure is provided. Case studies on a simplified dataset representing the Electric Reliability Council of Texas demonstrate efficiency of customized Benders decomposition approach. This approach efficiently addresses the large-scale GEP problem over a 20-year planning horizon, entailing 820,200 general integer variables, 1,189,141 continuous variables, and 2,595,441 constraints, with a solution time of under 30 minutes.

This article presents a series of quality analysis methods for power flow samples to support artificial intelligence learning algorithms. These mechanisms extract adequate physical quantities from a large amount of flow physical information, simplifying the complexity of sample quality analysis. The proposed method can effectively evaluate the redundancy of power flow samples and suggests a practical design for generating representative flow samples. The complete mechanism includes quality analysis, physical information selection, and power flow sample dataset re-integration, which helps data-dependent AI algorithms to be more convenient and efficient in the application to power systems.

Predicting wind power ramp events directly based on the historical ramp event time series has drawn increasing attention recently. But the class imbalance problem of the ramp event time series significantly affects the prediction accuracy of ramp events. In the present study, a layer oversampling (LOS) method is proposed considering the relation characteristics of wind power amplitudes and the occurrence frequency of wind power ramp events. Meanwhile, a hybrid sampling method of error bootstrap‐LOS (EB‐LOS) is proposed by combining LOS with the EB oversampling method. After balancing the samples of the ramp and nonramp events by using different sampling methods, the backpropagation neural network (BPNN), and the long short‐term memory (LSTM) methods are employed to directly predict ramp events based on historical data collected from eight wind farms. Comparison results proved that the proposed EB‐LOS method achieves the best prediction performance with an average recall of 0.8196 when using the BPNN model to directly predict ramp events. The best prediction performance of the EB‐LOS method is also proved by using the LSTM model to directly predict ramp events.

In a net-zero carbon power grid (NZCPG) with a relatively high proportion of renewable energy sources, significant power fluctuations occur, severely constraining the development of both the NZCPG and the New Urban Power Grid (NUPG). In the NZCPG, the proportion of traditional hydro and thermal power units used to mitigate power fluctuations has decreased. It is necessary to explore the load resources in the NUPG to stabilize the power fluctuation between the NUPG and the NZCPG, and to reduce the operating pressure of the NZCPG. Firstly, this paper proposes a method for developing external tie line schedules based on the load operation band (LOB) to relax the constraints on the schedule for the NZCPG. Secondly, A revised load response aggregation model is proposed to improve the aggregation accuracy under uncertainty factors by online correcting their impact on the load response characteristics. Thirdly, considering the flexible loads in NUPG can only compensate for a part of the power fluctuation, this paper proposes a method to judge the power fluctuation that these loads can offset. Two PFS optimal strategies are proposed among NUPG partitions considering deviation penalty and within each NUPG partition reducing the number of participated load-users. This paper focuses on the PFS in bulk NZCPGs other than in the distribution grids and microgrids. Our work can effectively smooth the power fluctuation within NUPG regulation capability meanwhile reducing the number of participated load-users. Fourthly, a method is proposed to judge the power fluctuation beyond NUPG regulation capacity in NUPG. Using the actual load response capacity, the over-amplitude/time layered smoothing strategy with optimal load is proposed, which can offset the power fluctuation of the bulk NZCPG to the largest extent. Finally, the IEEE-39 system is used to verify the effectiveness of the proposed methods. & COPY; 2017 Elsevier Inc. All rights reserved.