Power System Planning Research Articles

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network. The objective function of the problem aims to minimize operational costs, energy losses, and anticipated energy not supplied, and the voltage security index. The constraints of this problem include AC optimal power flow model, security, and reliability limits. Scheme extracts a convex-linear model for the formulation using the conventional linearization technique. Pareto optimization driven by the aggregation of weighted functions results in a single-objective framework for the lower-level problem, and the Karush-Kuhn-Tucker technique presents a single-level model for the scheme. The approach utilizes stochastic optimization has been adopted to model load, wind farms, and network equipment availability uncertainties. Finally, the study cases yielded numerical findings that showcase the efficacy of the proposed scheme in achieving the desired economic, operational, security, and reliability status within the transmission network. This is particularly true in situations where there is appropriate generation and transmission expansion.

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.

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options..

OCAE-based feature extraction and cluster analysis of high-energy-consuming plant loads

Currently, in power systems and energy management, the extraction and clustering of load features play a critical role. It can support the planning, scheduling and equipment design of power systems, holding significant theoretical importance and practical benefits. Addressing the complexity of high-dimensional daily load data poses challenges in effectively capturing the intricate features of load sequence data and achieving optimal clustering outcomes. This paper proposes an optimal convolutional autoencoder (OCAE) model designed for good feature extraction. First, a large number of test comparisons demonstrate that the OCAE effectively mines deep feature data from load sequences and exhibits strong data reconstruction capabilities. Second, the OCAE model is compared with other autoencoders to validate its superior feature extraction performance. Finally, the deep feature information extracted by the OCAE encoder is utilized in the k-means clustering algorithm for cluster analysis and is quantitatively evaluated against other methods using Davies–Bouldin index (DBI) and silhouette coefficient (SC). The experimental results confirm the superiority of OCAE joint k-means clustering over improved 1D-CAE joint k-means clustering, with a reduction of approximately 12.3 % in DBI and an improvement of approximately 11.1 % in SC. The outstanding clustering outcomes achieved through OCAE joint k-means clustering have significant scientific implications for power system dispatch planning.

Multi-stage planning and calculation method of power system considering energy storage selection

Abstract Under new power system flexibility, this text proposes a multi-stage random generation-transmission-energy storage, integrated programming method that considers the short-period flexibility demand. The model can accurately reflect the relationship between short-period operation and long-period uncertainty within an extraordinary method and can also correctly evaluate the value of investment flexibility. Based on this, this text proposes a parallel processing solution, which can be separated into subproblems through column generation and shared decomposition and enables simulation calculation using a distributed measuring platform in long-period programming. The calculation proves the optimal solution speed of the proposed method and can effectively solve problems that cannot be dealt with by the known methods.

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

Abstract The proposed article focuses on developing a methodology for constructing typical operational scenarios in power systems considering the spatiotemporal characteristics of new energy output. The integration of new energy sources on a large scale into power systems can lead to issues such as line congestion and supply-demand imbalances. To support the planning and scheduling of future high-capacity power systems, this paper introduces a method that accounts for the spatiotemporal characteristics of new energy outputs. The method begins by proposing a spatiotemporal fusion-based multi-head attention model for generating new energy output scenarios. This model employs multi-head attention mechanisms to extract the spatiotemporal characteristics of new energy outputs, eliminating redundancy in the feature data. The processed spatiotemporal feature information is then transferred to a gated feature fusion model for integration, using an encoder structure based on Long Short-Term Memory (LSTM) to generate new energy output scenarios. The paper, by means of an advanced Fuzzy C-Means (FCM) clustering algorithm, identifies typical scenarios of net energy output. Subsequently, the proposed method’s efficacy and precision are confirmed through simulation analysis with real data from a certain area.

Development of low-carbon technologies in China's integrated hydrogen supply and power system

Hydrogen and electricity are crucial and interdependent energy carriers in China's pursuit of carbon neutrality, suggesting the necessity of utilizing cost-effective low-carbon technologies that facilitate their integrated development. The cost-optimal, provincial level, deployment of low-carbon technologies under this long-term goal remains to be determined. This study employs the REPO model to identify the cost-optimal, low-carbon hydrogen production mixes and the evolution of the integrated power system of China from 2020 to 2050. The integrated planning and operation of hydrogen supply and power systems are explored at the provincial level. The role of high-temperature gas-cooled reactors in this integrated energy system is also analyzed. The results reveal that electrolytic hydrogen would dominate China's hydrogen supply after 2040, with alkaline, proton exchange membrane, and solid oxide electrolyzers produce over 1 Mt of hydrogen in the short term, by 2035, and in 2050. Leveraging the low-carbon heat production of high-temperature gas-cooled reactors in addition to its electricity generation to meet the thermal requirements of solid oxide electrolyzers could boost the output to 4.2 Mt in 2050 and reducing the total system CO2 emissions and costs by 2.28% and 0.05%, respectively. By 2050, the integration of hydrogen supply and power systems also generates up to 2194 TW h of flexible electricity demand by electrolyzers, which raised the renewable energy penetration by 4 percentage points while decreasing the need of flexible natural gas power generations and energy storages. This study is valuable for proposing the analytical framework and performing the provincial-level study of decarbonization of China's integrated hydrogen supply and power system.

Chronological DC transmission expansion planning considering new energy and load uncertainties

Abstract The integration of renewable energy into the grid and the expansion of new loads have significantly increased the uncertainty of the power system, which has brought many new challenges to power system planning. Hence, it is necessary to establish a transmission expansion planning model that adapts to future development to improve the economic benefits of power grid planning. This paper proposes a chronological DC transmission expansion planning method for new energy sources, such as wind and solar energy, considering the time characteristics of source loads. First, the method establishes a chronological DC transmission expansion planning model by simulating long-term grid operations. Among them, the objective function aims to minimize the sum of investment costs and power generation operating costs related costs. Secondly, to simulate unit operation, a loop optimization modeling method is used to optimize the model structure. Finally, simulation analysis is conducted using the improved Garver’s 6 bus system under random planning scenarios as a basis for comparison. By comparing various scenarios, it is concluded that the proposed chronological DC transmission expansion planning can provide a more economical solution than non-chronological planning. This helps ensure that the system operates cost-effectively under uncertain source and load scenarios while enhancing the integration capabilities of wind and solar power generation.

Multi-temporal assessment of wind, solar, and hydropower resources for off-grid microgrid

For a proposed multi-source all-renewable microgrid in Nigeria’s Middle-belt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multi-source all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location’s climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study.

Representing Carbon Dioxide Transport and Storage Network Investments within Power System Planning Models

Carbon dioxide (CO2) capture and storage (CCS) is frequently identified as a potential component to achieving a decarbonized power system at least cost; however, power system models frequently lack detailed representation of CO2 transportation, injection, and storage (CTS) infrastructure. In this paper, we present a novel approach to explicitly represent CO2 storage potential and CTS infrastructure costs and constraints within a continental-scale power system capacity expansion model. In addition, we evaluate the sensitivity of the results to assumptions about the future costs and performance of CTS components and carbon capture technologies. We find that the quantity of CO2 captured within the power sector is relatively insensitive to the range of CTS costs explored, suggesting that the cost of CO2 capture retrofits is a more important driver of CCS implementation than the costs of transportation and storage. Finally, we demonstrate that storage and injection costs account for the predominant share of total costs associated with CTS investment and operation, suggesting that pipeline infrastructure costs have limited influence on the competitiveness of CCS.

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.

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.

Stochastic weather simulation based on gate recurrent unit and generative adversarial networks

AbstractThe weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem of missing or insufficient. Meteorological data are characterized by changeable, rapid change, and high dimensions. Therefore, it is a challenging task to accurately grasp the law of weather data. This article presents a random weather simulation model based on gate recurrent unit (GRU) and generative adversarial networks (GAN). GRU selectively learns or forgets what was in the previous moment during training; it can learn the previous and current data of the time series data. When combined with the GAN, it will produce data with the same distribution as the original weather data. The proposed method was evaluated on a real weather dataset, and the results show that the proposed method outperforms the other contrast algorithms.

Coordinated energy storage and network expansion planning considering the trustworthiness of demand-side response

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems.

Optimal planning of hybrid power systems under economic variables and different climatic regions: A case study of Türkiye

Renewable energy-based hybrid power systems (HPS), proposed based on demand in the transition to clean energy-indexed societies, are high-potential investments. However, cost-based optimal sizing and feasibility analyses are complicated due to unforeseen variables and do not guarantee a reliable and robust optimization framework. This study optimizes minimum-cost HPS planning for diverse loads across varied climates and economic conditions, providing holistic comparisons of technical, financial, and environmental viability. According to the analysis, fluctuating economic structure emphasizes that unsubsidized hybrid power system installations are inefficient, the severe imbalance between inflation and interest rates limits the benefits of hybrid power systems and creates an uncertain investment environment for stakeholders. The gradual increase in interest rates to limit inflation has created a more viable renewable energy investment environment, while payback periods are reduced to 7.21 years. Even under identical economic conditions, the renewable fraction of energy in regions with high solar potential can be up to 25 % higher than in regions with less potential. Moreover, payback periods can vary up to 6 years, depending on the variability in solar generation and load profiles. Considering the significant impact of economic uncertainty on HPS investments, optimization plans that reduce investment risks will be helpful to stakeholders.

Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority.

Operational reliability evaluation integrating prediction models for enhanced situational awareness in wind-integrated system

The extensive implementation of wind power generation (WPG) challenges power systems' flexibility and reliability during operations. Fluctuating wind speeds lead to variability in generation within wind energy systems, directly impacting operational reliability. Therefore, evaluating wind energy systems' situational awareness (SA) is imperative to forecast two-day-ahead operational reliability, facilitating effective power system planning. The foundation of SA for wind energy systems lies in monitoring the wind profile. This involves gaining insights into the system's state through perceiving wind speed and WPG, comprehending the dynamics of its state, and subsequently predicting operational reliability. To achieve this, a Simulink model of the wind system has been developed and integrated with dSPACE hardware through a real-time interface for real-time validation and implementation of a cloud-based IoT platform. This IoT platform aids in acquiring real-time data on wind profiles, enhancing the assessment of SA. The representation of the wind energy system utilizes a multi-state wind speed model, incorporating the inherent randomness of wind energy generation. A time-series-based non-linear autoregressive with exogenous input (NARX) artificial neural network (ANN) model is employed to predict wind speed and WPG for SA-based operational reliability. This model proves instrumental in forecasting the dynamic nature of wind speed and WPG. The proposed approach for operational planning is validated by implementing the wind energy system in real-time on the dSPACE platform and integrating it with IoT. This approach considers the uncertainties associated with wind generation, providing a comprehensive framework for assessing SA and optimizing power system operations.

تأثير مولدات التوزيع (DG) على شبكة التوزيع الكهربائية الزهراء-ليبيا 30 كيلو فولت

Libyan electric distribution networks are suffering many problems of voltage limits violation, high system losses and low system performance. In this paper, a load flow study will be performed for a large power distribution of Alzahra area in Libya that include Alzahra power station. Considering distribution generation (DG) penetration method instead of conventional replanning methods, such as the expanding and adding methods that needs efforts and time. Alzahra 30KV electric system is taken as the case study including Alzahra power plant, different operation cases are considered includes the operation of the considering DG penetration during peak loads. The availability of high-performance software's such as NIPLAN helps in redesigning and replanning of electric power system for enhancing performance and solving system problems. The loss in the general phase was 8MW, and after increasing the loads by 8%, the loss ratio increased to 10MW. When adding the distribution generation in the right place, we note that the loss has been saved to approximately 4.5MW. KEYWORDS: DG, Penetration, Load flow, NIPLAN software, Voltage drop, Power system planning.

Hybrid machine learning model combining of CNN-LSTM-RF for time series forecasting of Solar Power Generation

Forecasting solar power generation (SPG) is vital for the development and planning of power systems, offering significant benefits in terms of technical performance and financial efficiency. It enhances system reliability, safety, stability and it reduces the operational costs. This paper's primary goal is to develop models that can precisely forecast solar power generation by analyzing real first-hand dataset of solar power. The value of these forecasting models lies in their ability to anticipate future solar power generation, thus optimizing resource use and minimizing expenses. To achieve this, the study utilizes various classical machine learning, deep learning, and hybrid machine learning techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Recurrent Neural Networks (RNN), Random Forest (RF), Support Vector Regression (SVR), Bi-directional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN). Among these, the hybrid model combining CNN-LSTM-RF demonstrated superior accuracy with R-squared of 92 %, a Root Mean Square Error (RMSE) of 0.07 kW, and a Mean Absolute Error (MAE) of 0.05 kW. This indicates that the hybrid machine learning model combining of CNN-LSTM-RF is effective in forecasting solar power generation.

An integrated development environment based situational awareness for operational reliability evaluation in wind energy systems incorporating uncertainties

Wind power fluctuations impact power system flexibility and reliability, emphasizing situational awareness (SA) in wind power integration. Monitoring wind speed and power generation forms the core of SA, enabling insight into wind system conditions. An Integrated Development Environment (IDE) becomes pivotal in assessing the SA of wind-integrated systems, which is vital for forecasting operational reliability in power system planning. The IDE facilitates Simulink-based wind model development and is linked to dSPACE hardware through a real-time interface (RTI) for validation. This IDE-driven integration utilizes cloud-based Internet of Things (IoT) capabilities, acquiring wind speed and power generation data for precise SA evaluations. A multi-state wind speed model is employed to incorporate energy generation fluctuations. To improve this, the artificial neural network (ANN) model, i.e., a time series-based non-linear autoregressive with exogenous input (NARX) ANN, is utilized to predict future wind speed and power generation, strengthening SA-based operational reliability. The study investigates the effectiveness of a suggested operational planning strategy under wind generation uncertainties. This assessment is carried out within a real-time deployment of wind energy systems seamlessly integrated with IoT on the dSPACE platform. The primary objective is to evaluate the system's ability to maintain SA effectively.