Northeast Power Grid Research Articles

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network

With the continuous expansion of power grids and the gradual increase in operational uncertainty, it is progressively challenging to meet the capacity requirements for power grid development based on manual experience. In order to further improve the efficiency of the operation mode calculation, reduce the consumption of manpower and material resources, and consider the sustainability of energy development, this paper proposes a typical power grid operation mode generation method based on Q-learning and the deep belief network (DBN) for the first time. Firstly, the operation modes of different generator combinations located in different regions are obtained through Q-learning intelligent generation. Subsequently, the generated operation modes are clustered as different operation mode sets according to the data characteristics. Furthermore, comprehensive evaluation indexes are proposed from the perspectives of the steady state, transient state, and the economy. These multi-dimensional indexes are integrated via the analytical hierarchy process–entropy weight method (AHP-EWM) to enhance the comprehensibility of the evaluation system. Finally, DBN is introduced to construct a rapid operation mode evaluation model to realize the evaluation of operation mode sets, and typical operation mode sets are obtained accordingly. In this way, the system calculator only needs to compare the composite values to obtain the typical operation modes. The proposed method is validated by the Northeast Power Grid in China. The experimental results show that the proposed method can quickly generate typical power grid operation modes according to actual demand and greatly improve the efficiency of operation mode calculation.

Optimal Strategy for Low-Carbon Development of Power Industry in Northeast China Considering the ‘Dual Carbon’ Goal

With China’s carbon peak and target proposed and the power system as an important source of carbon emissions, its carbon reduction issues are of great concern. However, the mismatch between power demand, resources, and generation facilities in different regions of China creates enormous carbon reduction pressures, especially in the region covered by China’s Northeast Power Grid (NE Grid). Therefore, in this study, a two-stage power optimization model considering Carbon Capture, Utilization, and Storage (CCUS) was established to optimize the national power generation and transmission in China from 2020 to 2060 first and then to optimize the regional road map of energy structure adjustment for China’s NE grid and its CO2 storage strategy. The first stage optimization results show that the national power interaction in China can make full use of the energy resource advantages of the NE grid, especially Jilin and Mengdong, delivering clean electricity to the North China grid as it reaches its “dual carbon” goal. Additionally, the second-stage optimization results show that the NE grid can make good use of CCUS for its thermal power transition. Annual CO2 storage will peak at 204 Mt in 2050 as thermal power withdraws in the NE grid region, which provides a reference for other regions to realize carbon neutrality of their power system by using CCUS.

Structural and Operating Features of the Creation of an Interstate Electric Power Interconnection in North-East Asia with Large-Scale Penetration of Renewables

Transition to green energy is the dominant process in the electricity sector globally, including in North-East Asia (NEA). The interstate power grid expansion in the NEA will facilitate the large-scale development of intermittent and uncertain green generation. This paper is aimed at considering the structural and operating features and effectiveness of a potential NEA power grid with large-scale penetration of renewables. A computing and geo-information system provides collection, processing, storage, and geo-visualization of technical and economic data. It incorporates a mathematical model for the optimization of the expansion and operation of power systems. Benefits (including saving the capacity, investment, fuel cost, and total cost) of power interconnection have been estimated in the study. Transfer capability required for the interstate electric ties was calculated and proved quite significant. A tax on greenhouse gases emission from thermal power plants, including carbon dioxide (CO2), has been used in the study as an economic incentive to facilitate the penetration of renewable energy sources in NEA power interconnection. An installed capacity, power generation mix, power exchange among countries, and operating modes (dispatching) have been calculated for different levels of CO2 emission tax. This study has shown the economic viability of the interconnection, defined major indices of interstate transmission grid infrastructure, revealed the changes in the mix of generating capacities and their operation under conditions of large-scale expansion of renewables, and found out the roles of various countries with different levels of CO2 tax, detailed the impact of CO2 emission tax in encouraging capacity additions and power generation growth from renewables. These capacities altogether suppress the expansion of coal-fired power plants in the potential North-East Asia power grid and contribute to achieving Sustainable Development Goals (SDG), particularly SDG 7, to ensure access to affordable, reliable, sustainable, and modern energy for all.

Power Balance Partition Control Based on Topology Characteristics of Multi-Source Energy Storage Nodes

Aiming at the power balance control of multi-source energy storage grid in the case of a high proportion of new energy grid connection. In this article, a power grid dynamic partition method based on the Markov energy field principle and a priori knowledge model is proposed. Combined with the coordinated dispatching of power grid source-load, a two-layer power balance partition control model based on the topological characteristics of multi-source energy storage nodes is established. First, in the upper-layer model, the energy homogenization method of multi-source energy storage nodes is studied, and the Markov energy field model of power grid node partition based on energy interaction constraints between nodes is established to partition the power grid initially. Combined with the prior model of node dynamic partition, the initial partition is dynamically optimized to realize the dynamic partition of the multi-source energy storage grid. Then, in the lower-layer model, the source-load coordinated dispatching model in the power grid partition area is established to realize the dynamic partition control of the power grid. Finally, based on the real operation data of a northeast power grid and IEEE39 node system, a dynamic partition power control simulation model of a multi-source energy storage power grid is established. The simulation results and analysis show that the dynamic partition power control strategy proposed in this article can effectively improve the regulation ability and economy of the power grid.

Optimal Control Measures For Dynamic Stability Level Of Northeast Power Grid

Due to the rapid development of new energy such as nuclear power at the transmission end of Northeast power grid, the improvement of transmission capacity of Lugu UHVDC and the large-scale development of power base, the problem of dynamic stability of Northeast Power Grid has surfaced. Based on the reality of Northeast power grid, this paper deeply and carefully carries out the dynamic stability level analysis of Northeast power grid from three aspects of source, network and load, studies the effective methods and measures to improve the damping ratio and robustness of main vibration modes between power grid regions, takes into account the scientificity, systematicness and scientificity of the measures, and provides reference for domestic and foreign related grid operation.

Research on quick judgment of power system stability using grid hierarchy net

Although deep learning has been introduced in the stability simulation analysis of power system, the structure of model needs to be further studied. A good structure can reflect the essence and simplify the solving process, like convolutional neural network (CNN) for image recognition. In this paper, a novel neural network model is proposed based on the power grid connection called grid hierarchy net (GHNet). The model can significantly reduce the number of trainable parameters while using more input variables to improve the accuracy of the model. Firstly, the construction method of GHNet is introduced based on electrical distance of stations. Then, some key issues are discussed including input and output selection. Finally, the actual data of the Northeast Power Grid of China was used to verify the feasibility and effectiveness of GHNet which meets the requirements of online security and stability analysis.

The multiagent cooperative AGC strategy for multiregional energy interconnection

<p indent=0mm>Traditional reinforcement learning cannot effectively solve the strong random disturbance problem caused by large-scale new energy access; therefore, the response speed of AGC (automatic generation control) becomes slower and the performance becomes poorer. This study considers the double Q learning that can solve the problem of overestimation of the value of state-action pairs in the reinforcement learning algorithm based on the Q framework as the fulcrum. It integrates a grey wolf optimization algorithm that can quickly search for the optimal solution in the unknown search space. Additionally, it proposes a multiagent cooperative AGC strategy for multiregional energy interconnection, namely, the GWDQ strategy, to quickly obtain the multiregional cooperative optimal solution in the AGC process. The two-area integrated energy system model, such as the hybrid-electric gas turbine system, CCHP and other forms of energy, and the northeast power grid model with a multiregional energy interconnection, are simulated. The results showed that the proposed strategy has a strong learning ability. Besides, the convergence speed and control performance are superior to that of the traditional reinforcement learning algorithm. The proposed strategy can quickly obtain the multiregional collaborative optimal solution in the AGC process.

Bidding strategy of thermal power compound differential evolution game under the market mechanism of peak regulation auxiliary service

As the main provider of peak regulation auxiliary services, thermal power units are particularly important to ensure their revenue in an uncertain environment. To obtain the optimal bidding strategy for thermal power units, a thermal power peak regulation bidding model based on the Northeast Power Grid's auxiliary service market bidding mechanism is established. Secondly, the evolutionary game theory is introduced into the bidding strategy of thermal power units. Meanwhile, in order to solve the multi-party game problem, a compound differential evolution game algorithm is constructed to solve the two-tiers bidding. Finally, based on the actual operating data of a typical day, the efficiency of the compound differential evolution game algorithm bidding strategy is verified, and the bidding strategy of thermal power units in different situations is discussed. The results show that when all units participate in peak regulation, the first-tier quotation is an optimal strategy at 0.19 yuan kWh−1 and below, and the second-tier quotation is distributed between 0.6–0.8 yuan kWh−1. When only some units are involved in peak regulation, the first-tier quotation of thermal power units adopts a high price strategy, mostly at 0.3 yuan kWh−1, and the second-tier quotation is still based on a high price strategy.

Study on stability feature extraction of power system using deep learning

Dynamic security assessment (DSA) of power grids is widely used in dispatching operation systems, and calculation speed is one of its most important performance indicators. In this paper, a stability feature extraction method is proposed, which is useful for quick judgment of stability and assisted decision-making. Firstly, a simulation sample database is constructed based on historical online data and a deep learning model with least absolute shrinkage and selection operator (LASSO) is trained to pick both the high level and low level stability features. While a new operation mode needs to be evaluated, a fast search is implemented to obtain the most similar samples in the database using the chosen high level features; the final result will be determined comprehensively by the familiar samples. If the power grid is in critical condition, a decision-making will be done by using the low level features. The validity of proposed method is verified by the simulation using online data of Northeast Power Grid of China. It is proved that the method meets the requirements for speed and accuracy of online analysis system.

Research for Participation of Electric Vehicles in the Power System Optimal Dispatching

The rapid development of renewable clean energy and low-carbon transportation has brought new opportunities and challenges to the safety and economic operation of power system. This paper studies the economic dispatching operation of the power system that contains clean energy and large-scale electric vehicles, and establishes the objective function of minimizing the amount of discarded wind power and the lowest cost of thermal power generating units. In order to improve the optimization efficiency, the original system is decoupled into the wind-vehicle-hydro subsystem and the thermal power subsystem according to the characteristics of multi-source output. The two subsystems are connected by the load balance and the reserve constraints of the system, and their respective scheduling models are given respectively. In this paper, an improved random black hole particle swarm optimization (IRBHPSO) algorithm with fast convergence and effective avoidance of local optimization is proposed by combining the black hole theory and particle swarm optimization. The effectiveness and superiority of the improved algorithm were verified on a 10-unit test system. Finally, this paper modifies the IEEE-RTS system according to the proportion of installed capacity of the Northeast Power Grid. The simulation results verified the feasibility of the proposed optimal scheduling model and the effectiveness of the algorithm, which provides strategies and feasible suggestions for the large-scale electric vehicles to participate in the economic dispatching operation of the power system.

Engineering structural robustness in power grid networks susceptible to community desynchronization

Networked power grid systems are susceptible to a phenomenon known as Coherent Swing Instability (CSI), in which a subset of machines in the grid lose synchrony with the rest of the network. We develop network level evaluation metrics to (i) identify community substructures in the power grid network, (ii) determine weak points in the network that are particularly sensitive to CSI, and (iii) produce an engineering approach for the addition of transmission lines to reduce the incidences of CSI in existing networks, or design new power grid networks that are robust to CSI by their network design. For simulations on a reduced model for the American Northeast power grid, where a block of buses representing the New England region exhibit a strong propensity for CSI, we show that modifying the network’s connectivity structure can markedly improve the grid’s resilience to CSI. Our analysis provides a versatile diagnostic tool for evaluating the efficacy of adding lines to a power grid which is known to be prone to CSI. This is a particularly relevant problem in large-scale power systems, where improving stability and robustness to interruptions by increasing overall network connectivity is not feasible due to financial and infrastructural constraints.

Research on Coordinated Development Strategy of Northeast Power Grid and National Energy Grid

In the new era, the Northeast power grid is facing a series of opportunities and challenges. In this paper, the strategy of “complementary”, “transmission” and “mutual benefit” coordinated development of Northeast China Power Grid and national energy grid is formulated based on the relevant national policy planning; Considering the difference of unique geographical location, natural resources, human resources, technology and other factors of the northeast and other areas; taking into account the development situation and future development needs of all regions, formed a coordinated development strategy layout, covering the unified planning, new energy development, peaking capacity construction, power substitution and talent support. It is beneficial to improve the efficiency of the national energy allocation and utilization, and to solve the practical problems faced by the power grid, so as to realize the coordinated development of the Northeast Power Grid and the national energy grid.

The Influence of New Environment on the Development of Northeast Electric Power Industry

Electricity is the foundation of the national economy, and the influence on the social and economic development is self-evident. At the same time, the power industry is also deeply affected by the economy and society. Firstly, this paper introduces the new environment faced by Northeast power industry from four aspects: power system reform, bilateral stochastic system, technological innovation and related policies; then It deeply analyzed the impact of the new environment on the development of the Northeast Power Industry from the above four angle; finally, the opportunities and challenges of the new environment for the future development of the Northeast power industry are summarized. To pave the way for the formulation of Northeast power grid development planning, and it is also helpful to improve and scientificity and accuracy of the Northeast Power Grid Planning.

A Multi-Timescale Quasi-Dynamic Model for Simulation of Cascading Outages

Many blackouts in electric power grids throughout the world are caused by cascading outages, which often involve complex processes in various timescales. The multi-timescale nature of cascading outages makes conventional quasi-static simulation methods inaccurate in characterizing actual evolution of outages. This paper proposes a multi-timescale cascading outage model using a quasi-dynamic simulation method. The model establishes a framework for simulating interactions among dynamics in quite different timescales. It realizes simulation of cascading outages with representation of time evolution, so it overcomes ambiguity of time in conventional cascading outage models and hence has better practicality. Moreover, the model considers dynamics, e.g., load variation and generator excitation protection which affect voltage and reactive power profiles. Also, an improved re-dispatch model based on sensitivity is proposed. These improvements facilitate better simulation for a realistic power system. Also, dynamic simulation can be flexibly incorporated into the simulation of short-term processes in this model as needed. Case studies with the proposed multi-timescale model on the IEEE 30-bus system discuss the role of generator protection in cascading outage evolution, and analyze stage characteristics in outages. The multi-timescale model is also demonstrated on a reduced 410-bus US-Canada northeast power grid. Moreover, impacts from dispatchers' involvements are analyzed.

Blackout Model Considering Slow Process

In this paper a blackout model that considers the slow process at the beginning of blackouts is proposed based on the improved ORNL-PSerc-Alaska (OPA) model. It contains two layers of iteration. The inner iteration, which describes the fast dynamics of the system, simulates the power system cascading failure, including the tree contact and failure of lines caused by heating. The simulation of protective relays and the dispatching center is also improved to make it closer to practical conditions. The outer iteration, which describes the long-term slow dynamics of the system, adds the simulation of tree growth and utility vegetation management (UVM). Typical blackout process with slow process is simulated on the Northeast Power Grid of China and self-organized criticality (SOC) characteristic of this system is analyzed with the proposed model. The effectiveness of the proposed model is verified by the simulation results.

Towards Estimating the Statistics of Simulated Cascades of Outages With Branching Processes

Branching processes can be applied to simulated cascading data to describe the statistics of the cascades and quickly predict the distribution of blackout sizes. We improve the procedures for discretizing load shed data so that a Galton-Watson branching process may be applied. The branching process parameters such as average propagation are estimated from simulated cascades and the branching process is then used to estimate the distribution of blackout size. We test the estimated distributions with line outage and load shed data generated by the improved OPA and AC OPA cascading simulations on the IEEE 118-bus system and the Northeast Power Grid of China.

An Improved OPA Model and Blackout Risk Assessment

The ORNL-PSerc-Alaska (OPA) model is a blackout model proposed by researchers at Oak Ridge National Laboratory (ORNL), Power System Engineering Research Center of Wisconsin University (PSerc), and Alaska University (Alaska). Although the OPA model is a landmark study, it has two limitations. First, there is a significant difference between simulation and practice in transmission line outage and update; and second, the simulation of cascading failure and the probability distribution of blackout size are in general not accurate enough. Hence, an improved OPA model is proposed in this paper to address these limitations. The proposed model contains two layers of iteration. The inner iteration describes the fast dynamics of the system and considers the influence of power flow, dispatching, automation, relay protection, and so on. The outer iteration describes the slow overall system evolution and is concerned with the update of the power grid, operation modes and planning. Such a model can be applied to practical large-scale systems. Furthermore, based on the Value at Risk (VaR) and Conditional Value at Risk (CVaR), two new complementary blackout risk indices are defined, which reveal critical characteristics of blackouts and are used to evaluate security levels of power systems. The effectiveness of the improved OPA model is verified by the simulations concerning the Northeast Power Grid of China.