Energy Power System Research Articles

Comprehensive Analysis on Topological Structure of Modular Multilevel Converters

This paper discusses the importance and application of modular multilevel converter (MMC) in the field of power conversion. Firstly, the article starts with the basic concept of MMC and introduces its key role in high-power electrical energy conversion. Subsequently, to address the complexity of MMC, the article describes in detail the common topologies of MMC, including diode-clamped multilevel topology, flyover capacitor topology, H-bridge cascade topology, and switched-capacitor multilevel topology, and also emphasizes the different scenarios in which they are applied to power systems. The last section describes the capacitor voltage balancing strategy in MMC. Several common capacitor voltage balancing strategies, including hardware clamping method, capacitor voltage closed-loop method, and capacitor voltage sequencing method, are comprehensively discussed in this paper, and the principles, advantages, disadvantages, and applicability of each strategy are thoroughly analyzed. Ultimately, this article offers academics and engineers a thorough review of MMC and offers valuable references for further study and real-world applications in the areas of power system conversion and energy management.

Vehicle-to-Grid Power in Danish Electric Power Systems

The integration of renewable energy systems is often constrained by the variable nature of their output. This demands for the services of storing the electricity generated from most of the renewable energy sources. Vehicle-to-grid (V2G) power could use the inherent energy storage of electric vehicles and its quick response time to balance and stabilize a power system with fluctuating power. This paper outlines the use of battery electric vehicles in supporting large-scale integration of renewable energy in the Danish electric power systems. The reserve power requirements for a high renewable energy penetration could be met by an amount of V2G based electric vehicles less than 10% of the total vehicle need in Denmark. The participation of electric vehicle in ancillary services would earn significant revenues to the vehicle owner. The power balancing services of electric vehicles in an electricity network with a large variable renewable generation in Denmark seems feasible and realistic.

Economy-environment-energy performance evaluation of power to hydrogen and power to methane considering renewable energy generation and carbon capture

Abstract The integration of multienergy complementary systems has become increasingly prevalent to mitigate the waste of renewable energy. As power-to-gas (P2G) technology advances, converting electricity into gas presents a viable solution for bolstering the reliability of power supplies within integrated energy frameworks. P2G plays a pivotal role in harnessing unused renewable resources, such as wind and solar energy, to produce sustainable power. While numerous studies have explored the advancement of P2G, there remains a lack of comprehensive evaluation of its various component subsystems. This research aims to design a renewable energy power system, augmented by gas-fired generation, assessing the collective advantages of both the power-to-hydrogen (P-H) and power-to-methane (P-M) conversion processes within the P2G infrastructure. It is important to note that the economic potential of P-H conversion is currently constrained by cost factors within existing P2G systems. This study takes into account the dynamics of carbon trading, as well as the hydrogen and methane markets, to compare the economic, environmental and energy-specific benefits of P-H and P-M conversions under different power demand scenarios. The goal is to formulate enhanced strategies to optimize P2G deployment in these varied contexts.

Wind energy systems and power quality: matrix versus two-level converters

This paper presents data of the actual installed electric power capacity for the renewable energy power systems in Portugal. Also, this paper presents a contribution consisting in a modulation strategy for simulation of a wind energy system with different topologies for power converters, consisting on a matrix and a two-level converter. Although more complex, this modulation strategy is justified for more accurate results.

A proposed PMU-based voltage stability and critical bus detection method using artificial neural network

Voltage stability detection is currently still becoming the main issue in the modern integrated renewable energy power systems. To assess the voltage stability, the classical methods based on continuation power flow (CPF) technique were used to show nose curve. However, the classical methods require complete model of power system and long computation time. Data driven analysis and synchronized real time measurement technologies currently are developing in power systems monitoring, including the stability detection. The detection method is built based on the historical event model and uses the real time measurement as an input. For that reason, the algorithm to detect the voltage instability and critical bus is proposed using the artificial neural network (ANN) technique to represent the historical event model using the PMU measurement data. The ANN model architecture for this application is developed by creating seven hidden layers consisting of one normalization, four rectifier linear unit, one softmax and one sigmoid layer. To warrant the accuracy, the k-fold cross-validation is used. The algorithm is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and Photovoltaic (PV) integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16 and 18 s per detection which makes the scalability to the real time detection is reasonable.

Probabilistic Forecasting-Based Reserve Determination Considering Multi-Temporal Uncertainty of Renewable Energy Generation

Operating reserve, among the most important ancillary services, is a powerful prescription for mitigating increasing uncertainty of renewable generation. Traditional reserve determination neglects uncertainty of renewable generation variations within the dispatch interval, which cannot gurantee sufficient reserve deliverability in real-time operation. This paper proposes a probabilistic forecasting-based reserve determination method to efficiently deal with multi-temporal uncertainty of renewable energy in power systems. A novel probabilistic forecasting approach is proposed by integrating Dirichlet process Gaussian mixture model into bootstrap-based extreme learning machine. A unified uncertainty model is constructed for depicting both interval-averaged uncertainty and intra-interval uncertainty by combing the probabilistic forecasts and linearized Itô-process model. In current business practices of many independent system operators, the coordinated determination of two well-designed reserve products, including regulating reserve and ramp capability reserve, is formulated in a two-stage robust optimization framework. A Bernstein polynomial-based model reformulation approach is then employed to handle the existing heterogeneity in the sub-models of each stage. Consequently, the integrated reserve determination is embedded in a generic two-stage robust optimization problem, which can be efficiently solved by the adopted modified column-and-constraint generation method. Finally, numerical simulations are implemented to validate the effectiveness and profitability of the proposed approach.

Carbon-Energy Synergetic Energy Power System Economic Dispatch Method

Low carbon is a key driver for the future development of power systems. This paper proposes a carbon-energy synergetic approach for economic dispatch in energy power systems. Firstly, by integrating multi-energy conversion models and carbon emission flow theories, a coupled interactive mechanism model is constructed. Building upon this foundation, an economic optimization dispatch model for comprehensive energy power systems is introduced, leveraging the synergistic value of carbon energy. This method supports economic dispatch in comprehensive energy power systems, considering both security and economic requirements while reducing carbon emissions. This integrated approach offers a promising solution for achieving low-carbon operational objectives and supporting the transition to sustainable energy infrastructure. Typical case studies in this paper validate the effectiveness of the proposed method.

Fast-tracking method of inertial constant based on system identification

Aiming at the problem of quantitative inertia evaluation of a new energy electric power system, the system inertia constant tracking method based on system identification is studied. The method is divided into two categories: non-recursive algorithm and recursive algorithm. The non-recursive algorithm uses a batch of data for batch processing to obtain the estimated value of the identification model parameters. The recursive algorithm is based on the estimated value of the model parameter at the previous moment and corrects the estimated value based on the new data currently obtained. From the perspective of the identification principle, the difference and internal relationship between the two in terms of calculation storage and identification speed are analyzed. The IEEE typical system is used to compare and verify the experimental examples. Theoretical analysis and experimental results show that the recursive algorithm has high identification accuracy, stable identification results and fast identification speed. It is suitable for the identification of objects with large numbers of nodes and complex structures, which is conducive to real-time monitoring and fast perception of the inertia constant of the new energy power system.

Multi-Stage Decision Rules for Power Generation & Storage Investments With Performance Guarantees

We develop multi-stage linear decision rules (LDRs) for dynamic power system generation and energy storage investment planning under uncertainty and propose their chance-constrained optimization with performance guarantees. First, the optimized LDRs guarantee operational and carbon policy feasibility of the resulting dynamic investment plan even when the planning uncertainty distribution is ambiguous. Second, the optimized LDRs internalize the tolerance of the system planner towards the stochasticity (variance) of uncertain investment outcomes. They can eventually produce a quasi-deterministic investment plan, which is insensitive to uncertainty (as in deterministic planning) but robust to its realizations (as in stochastic planning). Last, we certify the performance of the optimized LDRs with the bound on their sub-optimality due to their linear functional form. Using this bound, we guarantee that the preference of LDRs over less restrictive – yet poorly scalable – scenario-based optimization does not lead to financial losses exceeding this bound. We use a testbed of the U.S. Southeast power system to reveal the trade-offs between the cost, stochasticity, and feasibility of LDR-based investments. We also conclude that the LDR sub-optimality depends on the amount of uncertainty and the tightness of chance constraints on operational, investment and policy variables.

Theoretical research on carbon emission factors of distributed smart distribution network under high proportion of renewable energy access

In the context of the “dual carbon” goal, a high proportion of renewable energy connected to the grid to replace traditional high-carbon units has become a general trend. Taking the high proportion of renewable energy power system as the research object, the medium and long term planning of the installed capacity of source, grid, load and storage of power system is carried out considering the two dimensions of investment and operation maintenance, and the optimization model of source, grid, load and storage of power system is established. A low-carbon dispatching model considering carbon emission and demand response of distribution network is also proposed. Firstly, considering the characteristics of distribution network receiving power from the main network, the carbon emission model of distribution network based on carbon emission flow theory is constructed, and the low-carbon index of distribution network is proposed. Secondly, the improved dynamic carbon emission factor of distribution network is proposed, which is used as a guide signal to provide reference for low-carbon electricity consumption of distribution network users. According to the characteristics of users in the distribution network, the industrial demand response model aiming at low dispatch cost and low carbon and the residential demand response model aiming at electricity consumption satisfaction and low carbon satisfaction are constructed respectively.

Enhancing envrionmental sustainability via interval optimization for low-carbon economic dispatch in renewable energy power systems: Leveraging the flexible cooperation of wind energy and carbon capture power plants

In response to pressing environmental challenges, the power system is gradually developing towards a more efficient, clean, and sustainable direction. Carbon capture power plants (CCPPs) and wind power generation will play a significant role in this eco-friendly transformation. However, the high energy consumption of CCPPs and the anti-peak regulation characteristics of wind power lead to contradictions in their coordinated operation during peak shaving, restricting the system's low-carbon economic development. Aiming at the above challenges, an interval optimization method is proposed to coordinate wind power and CCPPs. Firstly, a flexible operation model of CCPPs is established, and the coordinated scheduling mechanism of wind power joint carbon capture operation and load-side demand response is introduced to optimize the system carbon emission through low carbon scheduling on both sides of the source and load. Secondly, interval numbers are used to manage the uncertainty of wind power output, and the interval-optimized scheduling model of wind power combined CCPPs is established. To solve the proposed model, the improved interval possibility method is adopted to transform the uncertain model into a deterministic one, which is solved by the CPLEX solver. Finally, the simulation outcomes using the IEEE 30-bus system framework reveal that the proposed approach significantly enhances the coordination between CCPPs and wind power. Notably, when compared to scenarios that solely focus on CCPPs, the proposed method achieves a substantial reduction in total costs by 16.67 % and decreases carbon emissions by 26.53 %. This sets a significant reference for low-carbon economic dispatch within power systems, providing key insights and foundational guidelines for future implementations, as well as novel strategies and technological support for the sustainable transformation of the power system towards lower carbon emissions.

Stability Analysis and Optimization of Energy Power System Based on Advanced Control Strategy

Stability Analysis and Optimization of Energy Power System Based on Advanced Control Strategy

Research on optimal allocation scheme of power system energy storage under large-scale new energy access

Abstract Renewable distributed power sources such as wind power and photovoltaic power generation are connected to the power grid, which will bring problems such as increased network loss and reduced power quality. In this paper, a two-layer optimization model for energy storage systems is proposed under large-scale new energy access, and the coupling effects of energy storage planning and operation are considered comprehensively. In the upper layer model, energy storage planning is considered, with the location and capacity of energy storage configuration being the decision variables and the planning cost of the energy storage system being the objective function. The lower layer considers the economy and stability of the grid when the energy storage system is operating, with the output of the energy storage system at each moment as the decision variable and the grid vulnerability, active network loss, and power purchase cost as the objective function. The improved IEEE33 node system has been selected for simulation verification. The results show that the proposed model calculates the optimal capacity configurations of wind power combined energy storage as 0.919 and 0.820 MWh, respectively, and the model can obtain a reasonable energy storage configuration scheme and, in comparing the economic indicators under different scenarios, can obtain the dual storage configuration as the best scheme for the simulation system.

Establishing the Influence of Methanol Fuelled Power Propulsion and Energy Systems on Ship Design

The adoption of alternative energy carriers is one of the key ways to meet the increasingly stricter emission regulations faced by shipping vessels from the international maritime organisation (IMO) and European Commission. To support this objective, this study examines the challenges and uncertainties associated with implementing a methanol power propulsion and energy (PPE) system on the design of a vessel. This paper argues that new fuels, such as methanol, should be treatedas disruptive innovations due, in part, to the uncertainties surrounding their implementation. Their integration causes challenges regarding systems selection, layout design, and maintaining strict safety measures. In the case of methanol, current research treats the fuel as a system conversion based on diesel fuel. This paper provides a review of the state-of-the-art on the design of methanol fuelled vessels, and identifies a research gap related to the need for a new suitable design method for the design of ships integrating future alternatively fuelled PPE systems. A design approach inspired by model-based systems engineering integrating uncertainty modelling is proposed to examine the influence of uncertainty on the design of the vessels. The impact of uncertainty on the design is investigated through a case study of a simplified engine room layout utilizing a genetic algorithm to produce layouts for variable PPE systems dimensions within a MonteCarlo simulation.

Research on Large-scale Energy Storage of Chinese Power System Based on Demand Analysis

With the construction and development of a low carbon and environmental protection society, China is promoting the construction of a clean, low carbon, safe and efficient energy supply system, the most critical of which is to promote the rapid construction of new energy installed capacity. However, with the continuous expansion of the new energy installed capacity, the random volatility of the power supply has become an important factor that puzzles the power balance of the current power system, not only formed a larger peak pressure, but also became one of the important factors restricting the development of new energy. At the same time, the new energy power electronic equipment has weak supporting characteristics, which also makes the proportion of new energy power system continues to increase, and has a high impact on security. In this context, this paper carries out a demand analysis, firstly discussing the demand for large-scale energy storage in the development of new energy for power system, and secondly analyzing the demand for large-scale energy storage in the safe operation of large power grid, so as to promote the construction of GW-level electrochemical energy storage power station and effectively deal with the power imbalance and safety problems.

Remaining Useful Life Prediction of a Planetary Gearbox Based on Meta Representation Learning and Adaptive Fractional Generalized Pareto Motion

The remaining useful life (RUL) prediction of wind turbine planetary gearboxes is crucial for the reliable operation of new energy power systems. However, the interpretability of the current RUL prediction models is not satisfactory. To this end, a multi-stage RUL prediction model is proposed in this work, with an interpretable metric-based feature selection algorithm. In the proposed model, the advantages of neural networks and long-range-dependent stochastic processes are combined. In the offline training stage, a general representation of the degradation trend is learned with the meta-long short-term memory neural network (meta-LSTM) model. The inevitable measurement error in the sensor reading is modelled by white Gaussian noise. During the online RUL prediction stage, fractional generalized Pareto motion (fGPm) with an adaptive diffusion is employed to model the stochasticity of the planetary gearbox degradation. In the case study, real planetary gearbox degradation data are used for the model validation.

Water-Energy Nexus in Power Systems: A Review

As the world continues to transition towards cleaner and more efficient energy sources, the intricate interplay between water and energy in power systems has emerged as an essential and multifaceted relationship with profound implications for sustainable energy planning. This comprehensive exploration considers a diverse range of academic databases and synthesizes relevant research to systematically investigate the current state of knowledge on the water-energy nexus. By distilling key findings and concepts related to the water-energy nexus in power systems, this work underscores the pivotal role of water in power generation and the energy required for water treatment and distribution. Additionally, this exploration brings into focus the challenges that the water-energy nexus faces, including the far-reaching impacts of climate change and the potential of renewable energy solutions. The complex policy and regulatory frameworks that govern the water-energy nexus in power systems are also examined, highlighting the crucial need for integrated approaches in energy and water management. By identifying key areas for further research and emphasizing the urgency for innovative solutions, this exploration stresses the need to prioritize sustainable management of water and energy resources in an effective, efficient, and resilient manner.

Research on Optimal Dispatching of Renewable Energy and Traditional Power System

This research seeks to determine the most efficient method of incorporating renewable energy into the traditional power system. By analyzing the challenges and obstacles faced in merging renewable energy with the traditional power system, this study outlines the major variations in energy sources, the volatility of renewable energy, the uncertainty caused by climate change and weather patterns, the impact of renewable energy on grid stability. The power system's capacity and dependability were also taken into account. This study presents recommended scheduling suggestions to tackle these issues, encompassing intelligent energy prediction and management, incorporation of different energy storage technologies, enhancements to the distributed energy system, and utilization of smart grid technology.

Thermo-economic assessment and optimization of an innovative tri-generation system using supercritical CO2 power system and LNG cold energy recovery

The present study introduces a novel tri-generation system that is highly efficient and cost-effective, employing a supercritical CO2 (sCO2) Brayton cycle as the primary mover and harnessing the cooling potential from liquefied natural gas (LNG) as a heat sink. This innovative system simultaneously provides electricity, cooling, and hydrogen. By capitalizing on the substantial temperature difference between the high-temperature sCO2 power cycle and the low-temperature LNG cold source, an integrated combination of the Kalina cycle (KC) and organic Rankine cycle (ORC) is utilized. Additionally, the system incorporates a proton exchange membrane electrolyzer (PEME) and an absorption refrigeration cycle (ARC) to create a highly efficient trigeneration system. The system is rigorously evaluated through energy, exergy, and exergoeconomic analyses, which are critical for assessing performance. Additionally, this study conducts a parametric investigation to elucidate the impact of key parameters on system performance. Furthermore, optimization is performed using a genetic algorithm (GA), with the objective of maximizing energy and exergy efficiencies or minimizing the overall product cost. Results from the baseline scenario demonstrate impressive energy and exergy efficiencies of 54.38 % and 59.46 % respectively, along with a low total product unit cost of 11.642 ($/GJ), outperforming existing benchmarks in the literature. The system also provides substantial net power output, cooling capacity, and hydrogen production rates of 276.71 MW, 60.19 MW, and 176.25 kg/h, respectively. This combination of high performance and cost-effectiveness makes the proposed system a versatile choice, underscoring its significance in sustainable and environmentally friendly energy solutions.

Characteristics of cavitating flows in cryogenic pump units in a combined cycle power system based on liquefied natural gas and solar energy

The cavitation of cryogenic pump units is an important cause for the low cold energy recovery efficiency and low utilization efficiency of solar energy in combined cycle power systems based on liquefied natural gas (LNG) and solar energy. Through numerical simulation of cavitation in cryogenic pump units, the research established the correlation mechanism between cavitation evolution and energy characteristics. By using the transport-based cavitation model considering the thermomechanical effect of cryogenic media, a novel numerical simulation model for two-phase flows during cryogenic cavitation was developed. In addition, the numerical model of cavitation was verified to be reliable. The cavitation performance of the two-stage LNG submerged pump in cryogenic pump units was studied under multiple conditions. In addition, the evolution of cavitating flow regimes under conditions of different flow rates and its influences on flow patterns of various flow passage components were investigated by combining analysis on temperature changes and flow fields. The entropy production diagnostic model (EPDM) was also adopted to discuss the distribution of the irreversible entropy production rate (EPR) in the evolution process of cavitation. Results show that the interfacial EPR that characterizes the phase-transition intensity after inception of cavitation exerts significant influences. Analysis of influencing mechanisms of changes in temperature, changes in flow fields, and distribution characteristics of energy consumption can provide a theoretical basis for further optimization.