Energy Power System Research Articles

Improving Distributed Renewable Energy Power Planning Through Particle Swarm Algorithm

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.

Joint Scheduling Method for Pumped Storage Units and Renewable Energy Sources

With the integration of renewable energy sources, how we can improve the stability of the new energy power system has become an urgent issue pursued by scholars. In this paper, a joint scheduling method for pumped storage units (PSUs) and renewable energy sources (RESs) considering frequency deviation and voltage stiffness constraints is proposed. First, the analytical expression of the frequency deviation constraint is derived based on the primary frequency regulation model. Then, the voltage stiffness constraint is constructed to describe the system’s capability of maintaining the voltage magnitude. Finally, to minimize the renewable energy abandonment caused by stability requirements, the joint scheduling model for PSUs and RESs is established. The application of the method ensures that the frequency deviation of the modified IEEE 39 bus system is less than 0.5 Hz, while the voltage stiffness remains above 0.9.

Review and prospect on the construction method of security region for integrated natural gas and power systems

Natural gas and power systems are both important components of modern energy systems. As an efficient and clean peak-shaving power source, natural gas power generation is crucial for ensuring the safe operation of a high-penetration renewable energy power system —supporting China's strategic goal of "carbon peaking and carbon neutrality." However, in recent years, there have been frequent incidents of cascading faults between the gas and power grids. Studying the security region of the integrated electricity-gas system (IEGS) and identifying the system's security margin online is the key to actively preventing such accidents. In view of this, this paper first introduced the typical coupling forms of electricity-gas networks and summarizes the security analysis and evaluation methods from the perspectives of power systems and natural gas systems, respectively. Then, the basic model of the IEGS security region considering the interconnection of gas and electricity networks was extracted, and various methods for solving IEGS security boundaries and dealing with uncertain disturbance factors were compared. Finally, the possible development directions of the construction method of the security region for IEGS were proposed.

Research on the application of renewable energy in power system based on adaptive hierarchical fuzzy logic maintenance

Condition-based maintenance is very desirable for minimizing the maintenance and failure costs of power systems without sacrificing reliability. A systematic approach including an adaptive maintenance advisor and a system maintenance optimizer is proposed here for effectively handling the operational variations and uncertainties for condition-based maintenance. First, the maintenance advisor receives and implements the maintenance plans for its key components from the system maintenance optimizer, which optimizes the maintenance schedules with multi-objective evolutionary algorithm, considering only major system variables and the overall system performance. During operation, the offshore substation will experience continuing ageing and shifts in control, weather and load factors, measurement and human judgment detected from the connected grid and all other equipments with uncertainties. Then, the advisor estimates the changes of reliability indices due to operational variations and uncertainties of its key components by hierarchical fuzzy logic and sends the changes back to the maintenance optimizer. The maintenance optimizer will upgrade the load-point reliability and report any drastic deterioration of reliability within each substation, which may lead to re-optimization of the substation's maintenance activities for meeting its desired reliability. The offshore substation connected to a medium-sized onshore grid will be studied here to demonstrate the ability of this proposed approach in dealing with uncertainties in the implementation of maintenance with significant reduction of computational complexity and rule base.

Research on Electricity Balance and Measurement Optimization of New Energy Power System Considering Renewable Energy Consumption Mechanism

With the rapid development of renewable energy, the new energy power system is facing the challenge of large-scale grid connection and consumption of renewable energy. In order to achieve efficient utilization and stable power supply of renewable energy, this study proposes a renewable energy consumption mechanism based on optimization methods. By establishing a power and electricity balance model, consider the relationship between different types of renewable energy generation and electricity demand. Various optimization strategies have been proposed for energy consumption issues in different scenarios, including power generation scheduling, energy storage optimization, and flexible load management. Validate the effectiveness of the proposed mechanism in terms of electricity balance and metering optimization through a model. The experimental results indicate that this mechanism can effectively enhance the renewable energy consumption capacity of the new energy power system, reduce energy waste, promote energy cleanliness and sustainable development, and has certain theoretical and practical significance for promoting the sustainable development of the new energy power system and responding to energy transformation.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

Task Offloading in Real-Time Distributed Energy Power Systems

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms.

A reinforcement learning-based online learning strategy for real-time short-term load forecasting

Real-time Short-Term Load Forecasting (STLF) is crucial for energy management and power system operations. Conventional Machine Learning (ML) methodologies for STLF are often challenged by the inherent variability in energy demand. To tackle the challenge associated with inherent variability, this paper presents a novel Reinforcement Learning (RL)-enhanced STLF method. Different from conventional methods, our method dynamically improves the STLF model by selecting optimal training data to capture recent power usage trends and possible variations in demand patterns. By doing so, our method can significantly reduce the impact of unforeseen fluctuations in real-time forecasting. In addition to the novel RL-enhanced STLF method, we propose a comprehensive evaluation framework, encompassing three key dimensions: accuracy, runtime efficiency, and robustness. Tested on three distinct real-world energy datasets, our RL-enhanced method demonstrates superior forecasting performance across three evaluation metrics by achieving accurate and robust predictions in real-time under varying scenarios. Furthermore, our approach provides uncertainty bounds for practical predictions applications. These results underscore the significant advancements made by our RL-based method in forecasting precision, efficiency, and robustness. We have made our algorithm openly accessible online to promote continued development and advancement of STLF methods.

Solar-Powered Combined Cooling, Heating, and Power Energy System with Phase-Change Material and Water Electrolysis: Thermo-Economic Assessment and Optimization

Solar-Powered Combined Cooling, Heating, and Power Energy System with Phase-Change Material and Water Electrolysis: Thermo-Economic Assessment and Optimization

Frequency stability of new energy power systems based on VSG adaptive energy storage coordinated control strategy

A self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.

Universal SSO Stabilization of Renewable Energy Power Systems

Universal SSO Stabilization of Renewable Energy Power Systems

Design and control studies of six-phase interleaved boost converter for integrated energy efficiency improvement of green ship

The operation of offshore vessels is characterized by stable loads during normal voyages and significant load variations when leaving port or docking. Conventional ships emit large amounts of greenhouse gases during navigation. In the development of the modern shipbuilding industry, how to effectively reduce gas emissions during ship sailing is a challenging task that still remains under-explored. Green ships equipped with new energy power systems have a greater potential for application. They are equipped with fuel cells and lithium batteries as energy sources, and such a hybrid system requires a comprehensive energy management system to achieve the power distribution between the two energy sources. In this paper, a powerful 10kW six-phase interleaved boost converter (IBC) for the hybrid power generation system of ships is designed to manage the output power of the fuel cell. Firstly, the new circuit topology design principle and construction process are introduced. Later, a step-by-step current adjustment controller is designed for the converter to progressively enforce output current and a maximum limitation of the fuel cell. Afterwards, LTspice simulation is performed to verify the ripple suppression effect and stable anti-interference performance. Finally, the designed IBC has been experimentally constructed, including the input/output current and voltage sampling, temperature control and sampling, and other functions, significantly benefiting real-world marine high-power fuel cells. The stability, reasonableness and effectiveness of the designed IBC are further evaluated through the extensive experimental analyses.

A hybrid approach based economic assessment EV charging station powered by integrating solar PV and biomass

In today's world, electric vehicles (EVs) are becoming increasingly popular. A significant power outage occurs in the system when EVs are charged from grid-connected charging stations. This manuscript proposes a hybrid approach for charging station electricity generation by integrating solar PV and biomass. The proposed hybrid technique is a combination of both the Dandelion Optimizer (DO) and Deep Attention Dilated Residual Convolutional Neural Network (DADRCNN). It is hence called DO-DADRCNN. The proposed approach DO is used to optimize the control parameter of the converter. And DADRCNN model used to predict the control parameter of the converter. This paper's main goal is the minimization of levelized cost of energy (LCOE), initial cost and net present cost (NPC). The most ecologically friendly system is the Photo Voltaic (PV)/battery hybrid since it emits no pollutants. The new uses for hybrid renewable energy power systems that combine solar photovoltaics and biogas, as well as an examination of the system's economic and environmental performance. The DAO-DADRCNN approach and the environmental economic evaluation of hybrid sustainable energy systems are intended to be investigated. Biogas and solar PV-based EV charging solutions provide techno economic and environmental feasibility. The proposed strategy is implemented in the MATLAB platform and the emission of carbon-di-oxide (CO2) emission is compared with various existing techniques like Wild horse optimizer, Salp Swarm Algorithm, and Particle Swarm Optimization. The proposed approach DO-DADRCNN obtains lower carbon dioxide (CO2) emission than the existing techniques. The CO2 emissions of the proposed technique is 5 kg/h which is lower and the cost for energy is 0.8$ which is lower than the existing methods.

Research on defense strategies for power system frequency stability under false data injection attacks

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF, and improve the stability of LREPS.

Sustainable freshwater/energy supply through geothermal-centered layout tailored with humidification-dehumidification desalination unit; Optimized by regression machine learning techniques

The application of regression machine learning techniques is crucial for the analysis and optimization of energy systems based on geothermal energy to produce freshwater, power, and heating. This study applied regression machine learning techniques to investigate and optimize a geothermal tri-generation energy system that combines double-flash geothermal energy, humidification-dehumidification desalination, and transcritical carbon dioxide Rankine cycle. The goal was to produce freshwater, power, and heating. The algorithms performed remarkably well, with R-squared values surpassing 96 %. It is worth mentioning that for specific parameters like freshwater production, heating capacity, and efficiency, the R-squared values exceeded an impressive 99 %. The optimum conditions include maintaining a Rankine pressure ratio of 3, a mass flow rate of 30 kg/s, a geothermal temperature of 220 °C, and a turbine pressure ratio of 2.2. By following these ideal parameters, it is possible to achieve the highest level of system performance. This will lead to the production of 7.88 kg/s of freshwater, the generation of 1283 kW of power, a heating capacity of 30.8 kg/s, and an impressive system efficiency of 24.98 %. The findings reveal that the integration of regression machine learning algorithms in geothermal energy systems for freshwater, power, and heating production holds great promise for a sustainable and efficient future.

Preface

The International Conference on Power Electronics and Energy System (ICPEES 2024) has been held in Kuala Lumpur, Malaysia during March 1-3, 2024 in hybrid format, including both on-site and cloud meeting. This conference aimed to provide an attractive platform for academics, scientists, researchers, experts, and students to express and discuss their interests in power electronics and energy system. The participants were from almost every part of the world, with various background, including China, Malaysia, Morocco, Indonesia, India, Turkey, Brunei, South Sudan and more. There were two renowned speakers who illustrated their latest research, to include Prof. Ismail Musirin from Universiti Teknologi MARA and Prof. M A Hannan from Sunway University. The conference also had 2 technical sessions. ICPEES 2024 became an effective communication platform for all the participants who took the opportunity to share their research results and discuss potential scientific and engineering developments from their work. This contributed to the success of the conference. The conference proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. All papers in the proceedings have passed the vigorous review process involving reviewers by the technical program committee. The variety of research topics presented in the conference and novelty exhibited in the papers published in the proceedings demonstrated the impact of ICPEES 2024. We would like to acknowledge all of those who supported ICPEES 2024. Each individual and institutional help were very important for the success of this conference. Especially we would like to thank the committee chairs, committee members and reviewers, for their tremendous contribution in conference organization and peer review of the papers. We sincerely hope that ICPEES 2024 was a forum for excellent discussions that put forward new ideas, promote collaborative research, and support researchers as they take their work forward. We are sure that the proceedings will serve as an important research source of references and knowledge, which will lead to not only scientific and engineering progress but also other new products and processes. List of Committees are available in this pdf.

Integrated production simulation of renewable energy and transportation consumption

Abstract In order to improve the reserve adequacy of high proportion new energy power systems and realize the efficient consumption of large-scale new energy, the source of reserve resources is expanded to the source network and load storage, and the operating reserve level that can be provided by the source network and load storage flexible regulating resources is quantified in terms of the direction of regulation and the mode of participation. Then, according to the regulation idea of “synchronous units bear the responsibility during regular hours and multiple types of resources share the responsibility during special hours”, a coordinated and participatory operating reserve capacity allocation strategy of source, network, load, and storage is proposed, and a reserve distribution optimization model incorporating multiple types of heterogeneous resources is constructed to achieve the optimization of reserve distribution of each type of reserve resource in the whole network. The optimization model of standby distribution is constructed to incorporate multiple types of heterogeneous resources and realize the step-by-step refinement of standby resources distribution among the whole grid - resources of the same type - units/stations. A case study is carried out based on a provincial power grid, and the effectiveness of the proposed strategy is verified by comparing it with the existing operational reserve allocation guidelines.

Current ripple optimization design of DC/DC converter

Abstract Renewable energy has the advantages of renewability and high temperature and has great potential for development, but its instability limits its large-scale promotion. Aiming at the stable operation of the new energy power system and ensuring the safe and reliable operation of the power grid, this project defines the basic topology of the variable multiple DC/DC converter according to the idea of switching circuit parallel factor or reducing ripple under each duty cycle. Secondly, the system structure under the boost mode is modeled, and the system is optimized by the combination of a single closed-loop current balance and duty cycle feedforward control. Finally, the model is simulated, and the results show that the method is correct and reasonable.

Frequency stability study of energy storage participation in new energy power system using virtual synchronous machine control

Aiming at the frequency stability of the power system under the increasing proportion of new energy sources, the study adopts the virtual synchronous machine-based energy storage adaptive control strategy and the frequency response model of the new power system. The energy storage adaptive control strategy coordinates the control of the battery’s charging state and the grid operation state by monitoring the grid operation parameters and the battery operation state in real time, so as to calculate the commanded power of the grid-connected converter. The results show that the system stability of adaptive virtual synchronization control improves when the electric transmission power increases from 205 to 510, while the system stability of virtual synchronization control decreases. The extended SFR model possesses excellent frequency matching ability when the new energy penetration rate is 8.497% and 15.66%. Virtual synchronous energy storage control strategy and the power system frequency response model can effectively predict and control the system frequency change to improve system stability under the increasing penetration rate of new energy.

Distributed Energy Resources Aggregation and Optimization Scheduling Model and Algorithm for Virtual Power Plants

With the ongoing energy transition and power system reform, Distributed Energy Resources (DERs) are playing an increasingly important role in modern power systems. DERs mainly include distributed photovoltaics, distributed wind power, small hydropower, biomass energy, fuel cells, and controllable loads, characterized by decentralized deployment and local consumption. The large-scale integration of these resources presents new opportunities and challenges for power systems. Effectively coordinating and managing these distributed energy resources has become a hot research topic.