Power Planning Research Articles

Optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response

AbstractTo reduce phenomenon of abandoning wind and photovoltaic power, improve the limitations of traditional methods in dealing with uncertainty of wind and photovoltaic power and system planning, and improve the optimal configuration of resources, an optimal capacity configuration of joint system considering uncertainty of wind and photovoltaic power and demand response is proposed. Firstly, using probability distribution information of wind and photovoltaic power output, the distance between actual probability distribution and forecast probability distribution is constrained based on the 1‐norm and ∞‐norm. A fuzzy set considering uncertainty probability distribution is constructed, and a two‐stage distributed robust planning model is established. The first stage involves optimizing joint system capacity for scenarios with the lowest probability of wind and photovoltaic power; the second stage builds on capacity optimization scheme from the first stage and aims to minimize operating costs through simulation optimization. Secondly, column and constraint generation is used to solve the model. Finally, constructing an example based on actual data from a power grid in Northeast China for simulation and analysis, the results show that the method achieves a balanced optimization of robustness and economy, effectively reduces carbon emissions and improves ability of the system to consume wind and photovoltaic power.

Laser power planning in directed energy deposition by deep reinforcement learning

Laser power planning in directed energy deposition by deep reinforcement learning

The influence of grid-forming energy storage and distributed synchronous condenser on the multiple renewable energy stations short circuit ratio

Abstract With the improvement of technical performance and reduction of cost, the scale of grid-forming energy storage and distributed synchronous condenser centralized access is constantly expanding, and their impact on the Multiple Renewable Energy Stations Short Circuit Ratio (MRSCR) is highly concerned by the power planning and operation departments. In this paper, the MRSCR calculation model considering distributed synchronous condenser and grid-forming energy storage is constructed, and the influence of distributed synchronous condenser and grid-forming energy storage on MRSCR is analyzed, which are very helpful for improving the MRSCR and the short-circuit capacity.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Optimal planning method of distributed generation in AC/DC microgrid considering power balance

Abstract To optimize AC/DC hybrid microgrid power planning, improve operational efficiency, and balance power distribution, we constructed an optimization model for AC/DC microgrid power generation, covering power, economy, reliability, and environmental protection, and set constraints to solve optimal planning. The experiment shows that this method enhances voltage stability, improves power supply quality, enhances distributed power generation efficiency, and reduces carbon emissions.

A Study on Energy Production and Consumption Based on Seasonal ARIMA and REAO

In the face of the global energy challenge, this study delves into the intricate landscape of energy dynamics within the rapidly evolving global economy and technological sphere. Recognizing energy as a pivotal driver of societal progress, we present a comprehensive evaluation and optimization model for China's energy sources, emphasizing economic, cost, electric energy, and environmental considerations. Employing time series models, specifically ARIMA and gray scale forecasting, we meticulously analyze wind and solar power generation, as well as overall power consumption. The results showcase nuclear power and hydropower as significant contributors to the energy landscape. Leveraging a linear programming model, we simulate and optimize the future energy development structure, considering constraints such as cost, energy, capacity, and carbon emissions. Our findings reveal the optimal quota contributions of thermal power, hydro power, nuclear power, wind power, and solar power as 13.7%, 14.2%, 17.6%, 54.5%, and 0%. Building upon these insights, we propose strategic recommendations for sustainable electric energy development. Emphasizing wind and solar energy's potential, we advocate for continued interest and increased support for technological advancements. Additionally, we call for the establishment of comprehensive power and energy development plans, integrating economic and environmental goals. To address imperfections in China's electricity tariff system, we recommend ongoing reforms aligned with market dynamics and environmental considerations.

Integrated resource strategic planning considering inter-regional flexibility supply–demand balance: A case study for the Northwest and Central Grid in China

China’s rapid advancement in renewable energy and regional power grid interconnection has highlighted the need for enhanced system flexibility to ensure the security and stability of regional power systems. To address this issue, this study presents a new framework for integrated resource strategic planning for inter-regional flexibility (IRSP-IF). This framework evaluates inter-regional flexibility demand and optimizes power resource allocation across regions. The northwest and central grids in China are used as case studies to evaluate inter-regional power resource allocation from 2022 to 2035. This analysis considers key factors including flexibility supply–demand balance constraints, regional power grid interconnections, and time scales for assessing flexibility demand. The results show that incorporating these constraints and interconnections into the strategic power plan enables greater integration of renewable energy while reducing both installed capacity and the overall cost of regional power resources. In the S3 scenario, which includes inter-regional flexibility supply–demand balance, the total integration of wind and solar power increases by 1.54%. Additionally, the combined average annual fixed and operating costs decrease by 1.44 billion yuan compared to the S2 scenario, which only considers single-region flexibility supply–demand balance. Furthermore, evaluating flexibility demand at a shorter time scale results in a higher proportion of flexible resources, with an average annual growth of 24.43%.

Reward and penalty mechanism considering uncertain carbon emission's green certificates–carbon trading power planning

Reward and penalty mechanism considering uncertain carbon emission's green certificates–carbon trading power planning

Coordinated planning of thermal power, wind power, and photovoltaic generator units considering capacity electricity price

AbstractWith the implementation of China's carbon reduction policies, the role of thermal power units will transition to a regulating power source. Hence, the electricity market fails to accurately reflect the capacity value of thermal power units, resulting in potential future losses for these units. Therefore, it is imperative to establish a rational capacity compensation mechanism that guarantees the revenue of thermal power units and offers effective investment and construction signals. Therefore, this paper proposes a capacity compensation mechanism for thermal power units based on effective capacity. To achieve this, a two‐layer power source planning model is established. At the upper level of the model, the installed capacity and capacity price of various types of power sources are optimized, while minimizing the operating costs of the planning horizon year under constraints such as annual net profit of units. The lower level focuses on the operation of typical days, optimizing the output of various types of units. Through case analysis, it can be concluded that the proposed model can achieve coordinated planning of capacity and capacity prices for various types of units, effectively ensuring the economic efficiency of the system while safeguarding the revenue of each unit.

Operation of coupled power-gas networks with hydrogen refueling stations, responsive demands and storage systems: A novel stochastic framework

Because of factors such as low natural gas prices, low greenhouse gas emission and high efficiency has increased the penetration of natural gas-fired generators in electric power systems. Large gas consumption of gas-fired generators has caused intense interdependence of electricity and gas systems. Most often, the operational planning of power and gas systems is conducted through a co-optimisation model in which the total cost of electricity and gas systems is minimised and the constraints of both systems are considered. On the other hand, due to environmental concerns, the usage of fuel cell vehicles (FCVs) is increasing. Hydrogen refueling stations (HRSs) are needed for refueling FCVs. This paper puts forward a stochastic model for co-optimisation of wind-integrated power and gas systems, hosting HRSs, electric storage systems (ESS's) and responsive electricity and gas demands. The effect of ESS's, responsive demands, contingencies, wind uncertainties and demand response on operation of power-gas nexus is scrutinized. An incentive-based demand response program has been used for both electricity and gas demands. The case study is the Belgian gas network connected to the a 24-bus power system. The findings indicate that responsive electric demands reduce electric system cost by 9.4 %, while responsive gas demands reduce the gas network cost and total operation cost by 1 %. The contingency analysis on the power-gas nexus shows that single line outage contingencies in power system does not result in any demand shed and does not increase the operation cost of either electricity network or gas network.

How to choose mobile energy storage or fixed energy storage in high proportion renewable energy scenarios: Evidence in China

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system.

Improving land-use efficiency of solar power in China and policy implications

Improving the power output of solar photovoltaic (PV) farms is critical to maximize the potential of PV power and reduce extensive land use in the context of large-scale deployment of renewable energy. In this paper we developed an integrated solar power potential assessment framework to quantify the gap between technical potential and actual generation of solar PV farms on national, provincial, and plant scales, and identify the key factors that cause the underperformance of PV farms. Specifically, to assess technical potential, hourly meteorological reanalysis data is adopted and the power generation-related parameters are calculated (i.e. optimal panel tilt, array spacing, packing factor etc.) with consideration of their spatial variability. For actual power generation, a detailed plant-level dataset is first established by this study which integrates technical, operational, and geospatial information from 145 solar farms across seven provinces in China. Our results show that the actual PV power generation per square meter is only 1/3 of the estimated technical potential. Technological factor is the primary factor, accounting for 48.43% of the underperformance, followed by engineering and management factors, accounting for 38.55% and 13.02%, respectively. The novelty of our study is revealing the underperformance level of solar farms in reality, identifying the causing factors, and highlighting the importance of integrating land-use efficiency indicators into solar power planning and development.

2246 Mind the HAP – an initiative to improve the diagnosis, management and prevention of hospital acquired pneumonia on elderly care

Abstract Introduction The commonest nosocomial infection in the UK is Hospital Acquired Pneumonia (HAP), associated with prolonged length of stay and mortality. The HAP incidence on Elderly care wards was >5% of admissions, exceeding the national average. An initiative ‘Mind the HAP’ was launched which included doctors, nurses, pharmacists, SLTs, physiotherapists and coders to improve HAP diagnosis, management and prevention. Methods To monitor the effectiveness of the interventions 3 audit cycles were performed between 2019 and 2023. Several interventions were implemented between 2019–2023.A multidisciplinary steering committee was formed with 3 work streams (diagnosis, management and prevention). To improve the accuracy of diagnosis and management of HAP, focused educational sessions were conducted for junior doctors with monthly meetings with coders. Nurses championed implementing the HAP prevention strategies i.e. hand hygiene, mouth care and positioning at 30–45 degrees. Regular comprehensive training sessions were held. HAP awareness and education campaign was launched. Daily nursing huddles helped to identify high risk patients. Physiotherapists provide chest physiotherapy to yield sputum sample collection among pneumonia patients. An electronic dashboard of incidence of HAP against the preventative measures and sputum culture reports has been launched with help from informatics. Information leaflets on HAP were created for patient awareness. An electronic HAP power plan to facilitate diagnosis and management of HAP will be launched from February 2024. Results HAP incidence has dropped to <2%, diagnostic accuracy improved from 35% to 81%, and sputum collection has increased from 9% to 24%. The HAP Quality Improvement Project received first prize for the most impactful Quality Improvement initiative at the Trust-wide conference in 2023. The results have been shared with the regional Microbiologists. Conclusion The collaborative efforts coupled with effective leadership and guidance, have been pivotal to the success of ‘Mind the HAP’ project.

Development of a DSP based collaborative computing platform for power system

Abstract With the advancement of information technology, there has been tremendous progress in data production, processing, and storage technology, especially in data processing technology. With the rapid development of the power grid, its operation has become more intelligent and efficient. Traditional power system simulation and analysis tools have disadvantages, such as slow message transmission and the inability to share data, which cannot meet the requirements of modern power system simulation calculations. DSP was introduced and developed by the Southern Power Grid Research Institute and can perform various simulation calculations such as transient stability calculation, power flow calculation, and short circuit calculation. It is widely used in departments such as power planning and design. However, the data calculated by DSP is input through a data card, which is cumbersome and difficult to manage. In order to meet the new requirements of power system simulation analysis, a power system collaborative computing platform has been developed based on DSP, which makes DSP data maintenance and management more efficient and intuitive. It comprehensively improves the efficiency of power grid planning work. This article provides a detailed analysis of the architecture design and application problems of the power system collaborative computing platform, clarifies the requirements of large-scale interconnected power grids for simulation computing data management, and proposes solutions that can meet the needs of simulation computing data management in China’s power grid from two aspects: key data architecture issues and software architecture technology.

Risk of Tropical Cyclones and Floods to Power Grids in Southeast and East Asia

Power grids play a critical role in modern society, serving as the lifeline of a well-functioning economy. This article presents a first large-scale study on the risk estimation of tropical cyclone (TC)-induced winds and coastal floods, which can widely impact power grids in Southeast and East Asia. Our comprehensive risk model incorporates detailed infrastructure data from OpenStreetMap (OSM) and government power grid maps, along with global hazard maps and vulnerability curves. The results reveal that the estimated expected annual damages from TCs and coastal floods to OSM-mapped assets account for approximately 0.07% (0.00–0.38%) and 0.02% (0.00–0.02%) of the total GDP of the study area, respectively. We analyzed the main sources of uncertainty in the risk model and emphasized the importance of understanding asset vulnerability. These results highlight the urgent need to strengthen power infrastructure to withstand the impacts of natural hazards, and the significance of reliable risk information for improving power grid design and planning. Focusing on developing more region-specific infrastructure data and vulnerability curves will improve the accuracy of risk estimation and provide valuable insights not only for the electricity sector but also for customers of other infrastructure systems that heavily rely on a stable supply of electricity.

The effect assessment of wind turbine capacity on offshore wind power generation potential in guangdong

Abstract The installed capacity of offshore wind power directly impacts the number of turbines and the total electricity generation. This study uses wind speed and offshore wind power planning data, and Guangdong Province is a case study. Three scenarios are set (7.5 MW, 9.5 MW, and 15 MW) to explore the influence of turbine installed capacity on electricity generation. The results indicate that when the turbine capacity is 9.5 MW, it generates the highest electricity, which accounts for approximately 162718.9 GWh per year.

Study on the optimization of power planning and design based on the integration of source-grid-load-storage

Abstract In order to improve the level of clean energy utilization and the operation efficiency of the power system, the power supply scale and energy storage capacity configuration of the integrated park project are solved. Based on the historical data on the electricity load in the park, this paper analyzes the characteristics of the monthly and daily electricity load. An integrated mathematical model with operating cost and new energy consumption as the optimization goal and multiple energy operation characteristics as the constraints is proposed to realize the optimization of the comprehensive planning of source-grid-load-storage in the region. Taking an electrolytic aluminium project as a case study, the overall curtailment rate of new energy in the optimized scheme is 3.56%, and the proportion of electricity consumed by new energy is 51.19% of the overall electricity consumption of the project.

Performance analysis of deep learning-based electric load forecasting model with particle swarm optimization

With the widespread application of deep learning technology in various fields, power load forecasting, as an important link in power system operation and planning, has also ushered in new opportunities and challenges. Traditional forecasting methods perform poorly when faced with the high uncertainty and complexity of power loads. In view of this, this paper proposes a power load forecasting model PSO-BiTC based on deep learning and particle swarm optimization. This model combines a temporal convolutional network (TCN) and a bidirectional long short-term memory network (BiLSTM), using TCN to process long sequence data and capture features and patterns in time series, while using BiLSTM to capture long-term and short-term dependencies. In addition, the particle swarm optimization algorithm (PSO) is used to optimize model parameters to improve the model's predictive performance and generalization ability. Experimental results show that the PSO-BiTC model performs well in power load forecasting. Compared with traditional methods, this model reduces the MAE (Mean Absolute Error) to 20.18, 17.57, 18.61 and 16.7 on four extensive data sets, respectively. It has been proven that it achieves the best performance in various indicators, with a low number of parameters and training time. This research is of great significance for improving the operating efficiency of the power system, optimizing resource allocation, and promoting carbon emission reduction goals in the urban building sector.

Enhancing Economic Operation and Planning of Power Systems through Artificial Intelligence: Implementation of Optimal Power Flow in Chennai Utility Bus System

The aim of this paper is to integrate Artificial Intelligence (AI) into Economic Operation and Planning (EOP) methodologies of power systems, specifically by implementing an Optimal Power Flow (OPF) optimization method in the Chennai Utility Bus System. Many traditional approaches to optimize power generation and planning are inherently limited and many of these limitations can be overcome by the use of Artificial Intelligence (AI) techniques. Herein, we have used the AI powered optimization algorithms such as Multi Objective Particle Swarm Optimization (MOPSO) and Multi Objective Genetic Algorithm (MOGA) to increase the efficiency in economical ranking and also grid planning. Implementation of AI techniques in Chennai utility bus system and also evaluating it in real time using Multi Objective Particle Swarm Optimization, Multi Objective Genetic Algorithm, Newton Raphson method and their results are compared. These AI-based methods aim to reduce operation costs, minimize power loss and improve voltage stability as well as minimizing deviation of voltages in order to increase the efficiency. Future work will expand the use of these techniques to more intricate systems, such as the Indian utility 146-bus system to validate their effectiveness, in real world applications.

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.