Analysis and planning of power grids: A network perspective

Performance and reliability of electrical power grids under cascading failures

As for the complex integration of power grid planning data and difficulty in meeting the power grid development needs by the current power grid planning simulation and calculation capability, this paper conducted the study on power grid planning data structure system, power grid consistency planning decision-making system by using big data and cloud computing technology. The key technologies, functional architecture and network architecture for the implementation of the power grid consistency planning decision-making system are proposed. The decision-making system for power grid consistency planning is developed to realize the distributed maintenance, structured consistency management and intelligent cloud storage of power grid planning data resources, and realize the dynamic allocation and overall coordination of calculation and verification, which can meet the needs of power grid consistency planning simulation calculations at different management levels and voltage levels, and provide technical support for large-scale power grid planning decision-making. The field application in more than 120 power grid companies illustrates the economic benefits of the developed decision-making system for power grid consistency planning.

It is crucial for the integral future development of the power grid to measure grid planning rationality during the rapid growth period of it. Through the analysis of the evaluation characteristics and influencing factors of China's power grid planning, this paper determined the value orientation of the grid planning assessment and evaluation content based on a quantitative evaluation of the existing power grid planning model, and established comprehensive assessment index system of grid planning covering security, economic, excellence and coordination based on the safe and stable operation of the power grid, and proposed optimize weight assessment methods applicable to the assessment of the overall level of grid planning based on improved particle swarm optimization. Finally this paper provides the rationality and feasibility of the comprehensive evaluation index system and optimized weight assessment method through engineering empirical analysis.

Urban grid planning needs to focus on sound investment plans to improve the safety and reliability of power system power supply to meet the growing needs of users. Does not adversely affect the environment. The contradiction in urban power grid and urban planning is an important reason that plagues the rapid development of power grid enterprises. Therefore, under the trend of urbanized dogs, urban power grid construction planning and urban overall planning are required to match. Urban planning needs to consider urban power grid planning as an important component, and match urban development to achieve simultaneous development. The integrated management and distribution system is the platform that contains all the information processing. It can share information and improve the reliability and security of the grid operation. This paper first introduces the integrated system of urban problems and its concept and architecture, focusing on the overall design form and process information management model, system function structure and several aspects of the main functions of the integrated system.

Urban power grid planning is a crucial part of urban infrastructure, which is essential for ensuring stable power supply and promoting economic and social development. In this process, multi-objective optimization algorithms play a crucial role, providing scientific decision support for power grid planning by balancing cost-effectiveness, environmental protection, and power supply reliability. Meanwhile, the rapid development of big data technology has made large-scale data model analysis an important tool for revealing the complex operating rules of the power grid and predicting future electricity consumption trends. This article proposes a novel power grid data analysis platform that cleverly combines particle swarm optimization algorithm (PSO) and machine learning (ML) technology. This platform leverages the global search advantage of PSO and the complex pattern recognition capability of ML to conduct in-depth mining and analysis of power grid operation data, aiming to optimize power grid layout, improve fault prediction precision, and optimize energy allocation. The experimental results show that the platform can not only significantly shorten the power grid planning cycle and reduce operating costs, but also effectively enhance the stability and safety of the power supply system.

In the new power system with renewable energy as the main body, wind power and photovoltaic have the characteristics of volatility, uncertainty, and poor predictability. To improve the penetration rate of renewable energy, sufficient flexible and adjustable power is vital to absorb large-scale renewable energy sources, especially wind power. Therefore, it is necessary to consider a reasonable proportion of wind power, flexible and adjustable power sources and ordinary conventional coal-fired power sources in the power grid coordination planning. In the paper, the K-Means clustering method is proposed to sample the load and wind speed values at different times of a typical day from the actual data. A mathematical model for coordinated planning of dynamic power grids with coal-fired power generation is then built up. The goal of this model is to make optimal investment in candidate generating units and candidate lines under the premise of satisfying the constraints of safe operation of the power grid, so as to ensure that the total present value of investment cost and operating cost is the smallest during the planning period. At the same time, the simplified unit combination model is used to simulate the dispatching operation of various types of units in the typical intraday power system, so as to illustrate the flexible adjustment effect of gas-fired generating units on wind power and load fluctuations. The established mathematical model is a mixed integer linear programming model, and the model is solved by using GAMS software to realize programming. The IEEE-30 node case verifies the validity of the proposed model.

Renewable energy generation has different static and transient characteristics than traditional synchronous power plants. The connection of renewable energy to the grid will have varying degrees of impact on the voltage stability of the local power grid. In a power grid with large renewable energy penetration, in addition to the voltage stability problem, renewable energy integration changes the original power flow distribution, line transmission power and inertia of the entire system, the power system transient stability has undergone tremendous changes. Renewable energy power generation technology and grid-connected scale are developing rapidly. The technical requirements for renewable energy and the research on grid-connected technology need to be continuously deepened and improved. However, some large-scale renewable energy off-grid accidents in recent years have brought serious threats to the security and stability of power systems, and have also raised higher research requirements for the stability of large-scale renewable energy grid-connected. This paper provides an introduction about the control and stability of renewable energy grid integration on both technologies and standardization aspects, and tries to explore potential the trend in active power and frequency control, reactive power and voltage control, security and stability control respectively caused by the integration of RE generation into power systems.

Invulnerability of power grids based on maximum flow theory

As the basis for the construction and development of the power grid industry, power grid planning not only affects the development direction of power grid enterprises, but also determines the economic level, social stability and quality of life of urban residents. In the construction and development of the new era, the digital transformation of power grid planning business is an important part of the construction and improvement of the digital power grid system, which will guide the power grid planning business to develop stably in the direction of intelligence, automation and information, actively create a safe and reliable smart grid, and lay a solid foundation for improving the development level of power grid enterprises. Therefore, after understanding the current digital transformation goals and development status of power grid planning business, this paper mainly explores the specific plan for the transformation of power grid planning business of a regional power enterprise according to the structure and transformation requirements of the new power system, and finally explores the practical significance of digital transformation according to the construction effect and development goals, providing a reference for the reform and development of the power industry.

High-risk cascading failure (CF) screening is the basis for making targeted defense measures to prevent large-scale power outages. With the integration of renewable energy, the uncertainty in operating conditions (OCs) has led to an increased uncertainty in the CF propagation paths. Moreover, the defense measures implemented by the power grid can further influence the direction of CF propagation. To address the above issues, a high-risk CF screening method considering OC uncertainty and defense measures is proposed in this paper. Firstly, a stochastic response surface method integrated with distribution factor and error evaluation is proposed to obtain probabilistic load flow in the CF screening process. Then, a decision-making model of CF defense measures considering the severity of faults at both current and following levels within CFs is constructed. Finally, by decoupling the screening of high-probability CFs from high-risk CFs, a two-stage screening method considering OC uncertainty and defense measures is proposed to obtain high-risk CFs. The effectiveness of the proposed screening method is verified in both practical and standard power grids.

This paper presents a concept for optimal dimensioning of power-to-gas units within the context of integrated power and gas distribution grid planning, considering different business models and stakeholder perspectives. For the dimensioning, annual simulations that consider the business models provision of local power grid services, spot market optimization, ancillary services and direct hydrogen sale are performed. The results show that economic viability can be achieved with the multi-use deployment of Power-to-Gas units and that integrated planning can lead to better economic results.

When large-scale power grids are connected to the power grid, it is conceivable that adverse effects on the operation and planning of power grids can be imagined. Therefore, wind power has characteristics such as intermittency, volatility, anti-peaking characteristics, and large prediction errors. At the same time, the ability to absorb wind power is also weakened by the contradiction between the peak load and the heating load. In addition, due to the thermo-electric coupling characteristics of the cogeneration unit, ie, “heating with electricity,” this characteristic directly determines that its heat output severely limits the regulation of the electric output. The results of the study indicate that the co-production unit’s electric power and thermal power are coupled. Strong nature, the peak load capacity of the unit is greatly affected by the heat load, and the electric power and thermal power distribution lacks a unified calculation and analysis method, which restricts the means of improving the peaking capacity of the unit by controlling the thermal power output of the unit, and through implementation, establishes the main power equipment, The steady-state power model of the thermal equipment, the steady-state power analysis and calculation method of the regional energy system of the power thermocouple, and the realization of adjusting the thermal load related parameters to improve the peak capacity of the thermal power unit.

Due to the rapid advances of integrated circuit technology, the size of power distribution network (power grid) is becoming larger and larger. There are usually multi-million nodes on a power grid. Analyzing these huge power grids has become very expensive in terms of both time and memory. This paper presents an efficient parallel implementation of the Additive Schwarz Method (ASM) for IR-drop analysis of large-scale power grid. Based on distributed memory system, a new data storage method is proposed to overcome memory bottleneck of traditional methods. Techniques including overlapping in multiple layer and irregular power grid, via detection and grouping are utilized to accelerate the simulation. Moreover, a new communication strategy exhibiting minimum communication overhead is proposed. The proposed method is very accurate in the final solution, with the maximum error less than 0.1mv. Experimental results on industrial medium size benchmarks show that the proposed method achieves more than 110X speedup over a state-of-the-art direct LU solver. The proposed approach can easily solve very large-scale benchmarks, while LU solver fails to obtain the solution because of system memory limitation. It is the first time reported in literature that IR-drop analysis of power grid with over 190M nodes is successfully solved within 5 minutes.

Over the past 10 years, in most countries of Europe, North America and China, renewable energy grid integration technical standards are mostly developed by some electricity association or power grid companies. However, with the increasing scale of renewable energy generation and gradually intensified impact on the power grid, more countries confirm the legitimacy of renewable energy grid integration provisions through the legislation in national level. Due to the difference in the power source structure, load characteristics, power grid situation, and other specific circumstances in different countries or regions, grid code of renewable energy integration in different countries are not identical. However, these technical standards have some common focuses. They all stressed that the renewable energy power plants must have a certain active power control, reactive power / voltage control and fault ride through capability as well as the ability to withstand the power system voltage and frequency deviation. Some of the standards require the renewable energy power plants to provide model information, operating parameters and grid integration performance testing reports. This paper provides an introduction about common focus on technical standards of renewable energy grid integration, and tries to explore potential challenge caused by RE grid integration.

Power grid planning and operation decisions are made based on simulation of the dynamic behavior of the system. Enabling substantial energy savings while increasing the reliability of the aging North American power grid through improved utilization of existing transmission assets hinges on the adoption of wide-area measurement systems (WAMS) for power system stabilization. However, adoption of WAMS alone will not suffice if the power system is to reach its full entitlement in stability and reliability. It is necessary to enhance predictability with "faster than real-time" dynamic simulations that will enable the dynamic stability margins, proactive real-time control, and improve grid resiliency to fast time-scale phenomena such as cascading network failures. Present-day dynamic simulations are performed only during offline planning studies, considering only worst case conditions such as summer peak, winter peak days, etc. With widespread deployment of renewable generation, controllable loads, energy storage devices and plug-in hybrid electric vehicles expected in the near future and greater integration of cyber infrastructure (communications, computation and control), monitoring and controlling the dynamic performance of the grid in real-time would become increasingly important. The state-of-the-art dynamic simulation tools have limited computational speed and are not suitable for real-time applications, given the large set of contingency conditions to be evaluated. These tools are optimized for best performance of single-processor computers, but the simulation is still several times slower than real-time due to its computational complexity. With recent significant advances in numerical methods and computational hardware, the expectations have been rising towards more efficient and faster techniques to be implemented in power system simulators. This is a natural expectation, given that the core solution algorithms of most commercial simulators were developed decades ago, when High Performance Computing (HPC) resources were not commonly available.