Power System Planning Research Articles

Optimal voltage set-point of automatic tap changer transformers and generators and reactive power compensation to increase power system predictability

In recent years, renewable energy sources (RESs) such as wind turbines (WTs) have received much attention in both the operating and planning of power systems. These sources have caused many uncertainties in the operation of power systems due to their unpredictable nature. Mentioned uncertainties besides load demands uncertainty, and possible failures in power system components, make the system state unpredictable. The operational decisions are completely challenging in these situations. Any efforts to increase the network operator awareness from the system state will be completely valuable for making accurate decisions.This paper tries to increase the predictability of the power system state through optimal regulating of the voltage setpoint of automatic tap changer transformers and generators besides the reactive power of shunt capacitors. However, conventional objectives such as decreasing losses are considered, simultaneously. This paper uses the K-medoids data clustering method to evaluate the effect of uncertainties. Also, the correlation between input uncertain variables is considered and handled by the Cholesky decomposition technique combined with the Nataf transformation technique. The multi-objective optimization problem is tackled using an improved version of the non-dominated sorting genetic algorithm (NSGA-II). To ensure for validity of the results, studies are repeated using the multi-objective particle swarm optimization (MOPSO) algorithm as another evolutionary-based method.The proposed approach is applied to the IEEE 14 and 30 bus standard system. The results showed that the predictability of the power system can be considerably increased while keeping the losses approximately at its minimum amount.

Integrating Hydrological and Machine Learning Models for Enhanced Streamflow Forecasting via Bayesian Model Averaging in a Hydro-Dominant Power System

Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations.

Navigating Power Paths: Assessing Line Losses in the IEEE 14-Bus System amidst Electric Vehicle and Renewable Energy Integration

Purpose: This study thoroughly investigates the impacts of Electric Vehicles (EVs) and Renewable Energy Sources (RES) on line losses within the IEEE 14-Bus System, with a particular emphasis on addressing challenges and optimizing power system planning. The primary purpose of this study is to assess and understand the intricate relationships between EVs, RES, and line losses within the IEEE 14-Bus System. The research aims to provide insights that contribute to the development of tailored strategies in power system planning, addressing challenges posed by EV adoption and RES integration. By exploring the impact on grid efficiency, sustainability, and reliability, the study aims to guide future power system planners in optimizing the integration of EVs and RES.
 Materials and Methods: The study utilizes load flow analysis, specifically the Newton-Raphson method, to model and simulate scenarios reflecting EV charging and discharging dynamics alongside intermittent RES integration within the IEEE 14-Bus System. A 24-hour dynamic load flow analysis is conducted to capture the diverse and dynamic impacts under varying load conditions. This comprehensive approach allows for a detailed assessment of line losses in the presence of EVs and RES.
 Findings: The study reveals nuanced impacts, indicating higher EV adoption and increased RES integration result in notable escalations in line losses. This highlights challenges associated with grid efficiency, emphasizing the need for tailored strategies in power system planning.
 Implications to Theory, Practice and Policy: The study advances theoretical understanding of dynamic interactions between EVs, RES, and power systems. In practice, it underscores the importance of adaptive control mechanisms and optimized strategies for accommodating EVs and RES while minimizing line losses. On a policy level, the findings suggest the need for regulatory frameworks incentivizing sustainable energy practices and technological research to mitigate challenges posed by EV adoption and RES integration.

Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.

Stochastic programming based coordinated expansion planning of generation, transmission, demand side resources, and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies, the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems. A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations. Multiple types of system components, including demand response service entities, converter stations, DC transmission systems, cascade hydropower stations, and other traditional components, have been extensively modeled. Moreover, energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence. Demand-response service entities from the load side are used to reduce and move the demand during peak load periods. The uncertainties in wind, solar energy, and loads were simulated using stochastic programming. Finally, the effectiveness of the proposed model is verified through numerical simulations.

Development of a long-term solar PV power forecasting model for power system planning

Purpose This paper aims to Solar photovoltaic (PV) power can significantly impact the power system because of its intermittent nature. Hence, an accurate solar PV power forecasting model is required for appropriate power system planning. Design/methodology/approach In this paper, a long short-term memory (LSTM)-based double deep Q-learning (DDQL) neural network (NN) is proposed for forecasting solar PV power indirectly over the long-term horizon. The past solar irradiance, temperature and wind speed are used for forecasting the solar PV power for a place using the proposed forecasting model. Findings The LSTM-based DDQL NN reduces over- and underestimation and avoids gradient vanishing. Thus, the proposed model improves the forecasting accuracy of solar PV power using deep learning techniques (DLTs). In addition, the proposed model requires less training time and forecasts solar PV power with improved stability. Originality/value The proposed model is trained and validated for several places with different climatic patterns and seasons. The proposed model is also tested for a place with a temperate climatic pattern by constructing an experimental solar PV system. The training, validation and testing results have confirmed the practicality of the proposed solar PV power forecasting model using LSTM-based DDQL NN.

Fluent Restoration Modelling Applied to a Real Power System Using Colored Petri Nets

In this article the Colored Petri Nets (CPN) and the Critical Path Method along with the Program Evaluation Review Technique (CPM/PERT) are described and their characteristics compared for application on the real-time fluent restoration of power systems. A CPN based method is proposed and has been found more suitable since it is able to represent the temporal sequencing and the logical dependency between restoration activities. The restoration planning of electrical power systems are based on a worksheet drawn up by experts, which contains a list of procedures for restoration. The restoration process has been improved and a real case study is presented using the CPN modeling applied with success to an operative fluent restoration post-outage in the Brazilian interconnected system

Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network

In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.

Innovative Method for Reliability Assessment of Power Systems: From Components Modeling to Key Indicators Evaluation

Power systems comprise different electrical, electronic, electromechanical and electrochemical components. Adequacy, security, resilience and reliability represent essential requirements for grids functioning mode. The evaluation of such aspects can constitute a delicate task in the presence of heterogeneous components. Focusing on reliability assessment, several Reliability Prediction Models are available. They are suitably applied according to the type of component under evaluation. The lack of homogeneity of these models forbids the comparison of performance and identification of unreliable systems and grid section. This paper aims to face the mentioned issue proposing a unique reliability assessment methodology able to characterize different equipment connected to radial/meshed/ring grids and subjected to different stressing and ageing factors. It is customized for electrical lines, transformers, circuit breakers, converters and renewables plants. Component and systemic key indices are calculated. Furthermore, a novel “load feeding reliability“ indicator is evaluated for providing information about the supply reliability of a specific load. This index is meaningful for the identification of unreliable grids, microgrids and systems. Such an approach can contribute to improve power systems design, planning and control. The proposed method is integrated in a software application implemented for grid reliability assessment. The obtained results are reported for an urban grid including an underground transportation area.

China’s Zero-Coal Power System Future

Facing the challenges of climate deterioration, environmental pollution, and energy shortages, China has put forward the goals of “carbon dioxide peaking and carbon neutrality”, and the electricity sector will be the key area and the main direction of carbon emissions reduction. To prospect China’s power system and explore the potential for establishing China’s zero-coal power system in the future, this paper proposes a forecasting-test framework for long-term power system planning. We predict, evaluate, and analyze China’s power system from 2025 to 2060 using the proposed forecasting-test framework. During this process, the power capacity mix and the electricity generation mix of China’s power system will gradually transition from coal-fired generators dominated to wind and solar generators dominated. Moreover, the roles of different generators and energy storages are illustrated through the seasonal comparison and sensitivity analysis of the installed capacity of generators. The results indicate that coal-fired generators can be completely withdrawn from the power system, confirming the feasibility and possibility of achieving China’s zero-coal power system by 2060. Although the timing may be advanced or delayed in different scenarios, achieving China’s zero-coal power system will require exercising nationwide planning and making joint efforts of various sectors from now on.

New Power System Planning and Evolution Path with Multi-Flexibility Resource Coordination

With the continuous development of large-scale wind and photovoltaic power worldwide, the net load fluctuation of systems is increasing, thereby imposing higher demands for power supply assurance and new energy consumption capacity within emerging power systems. It is imperative to establish a quantifiable and efficient model for planning new power systems, to propose an analytical approach for determining optimal evolutionary paths, and to advance research on flexible resource planning across wide areas. In this paper, based on the simplified operating characteristics of multi-type flexible resources, a source-grid-load-storage collaborative planning and evolution analysis framework is established. Secondly, the lowest total cost of the whole life cycle of the system is taken as the optimization goal, the multiple flexible resource investment decisions and production operation constraints of various flexible resources on all sides of the system are considered, and the source-grid-load-storage planning model is established. Then, through the investment decision-making strategy setting of the system in different planning level years, the evolutionary path analysis method of the whole life cycle economy and weighted environmental protection benefit of the system is given. Finally, by taking the sending-end power grid in Gansu Province as an example, a case study is carried out. Calculations of new energy, key channels within the province, energy storage capacity, and load-side response capacity requirements for 2025, 2030, and 2060 are optimized. Based on the above analysis, the optimal evolution path of the power grid is proposed. When considering the weighted benefits of economy and environmental protection, the greater the weight of environmental protection benefits, the greater the possibility of choosing a radical scheme. The model and method proposed in this paper can provide technical reference for the future development planning and evolution analysis of new power systems.

Research on the double-layer clustering method of residential energy use characteristics under the background of energy system energy savings and carbon reduction

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

Research on a new power system development planning model based on two-tier planning

With the rapid development of the power industry, the safe and stable operation of the power system has become an important basis for safeguarding economic and social development. In order to adapt to the trend of rapid growth of new energy sources and the demand for transformation and upgrading of the power industry, the capacity allocation scheme of the power system needs to be constantly optimized and adjusted. To address the development planning of new power systems, a new power system development planning model based on two-layer planning is constructed, taking into account the operational output results of various types of units and the interaction between the configuration schemes. Through the analysis of the evolution of the power system in a region in the next 15 years, the development pattern of the new power system in different stages under the high, medium, and low scenarios of new energy development is optimally calculated to guide the development of the power system. The results show that the model can provide a quantitative reference for decision-making on energy policies and power strategies in the medium and long term.

On the issue of applying the dynamic thermal rating in electric power distribution system

The article considers separate studies and review articles on dynamic thermal rating system in power transmission lines and electrical equipment. Available researches of foreign authors are mainly devoted to forecasting algorithms, model-ing methodologies and implementation of dynamic thermal rating in order to increase the capacity of power transmis-sion lines. It is shown that for the tasks of planning and management of the electric power system in Russia, the need to study and coordinate domestic and foreign developments on the assessment of the capacity of power transmission lines and power distribution is currently relevant. The expansion of residential areas increases the load on electric distribution network due to the shift of maximum power consumption. The currently dynamically changing conditions of electricity consumption direct attention to the development of new technological concepts and the applicability of the existing approaches to assessing the capacity of electricity transmission lines to distribution networks. Available research on dynamic thermal rating in electric distribution networks has focused much attention on both the potential problems and benefits of its implementation in the integration of distributed generation based on the renewable energy sources, in particular wind generation. The ambiguous impact of dynamic thermal rating systems on the reliability of the electric distribution network and the need for their reasonable use, as well as a significant lack of research in relation to cable distribution networks and consistency in assessing their capacity depending on the cable insulation material are being not-ed. Few studies have verified the adaptation of the state-of-the-art approach of dynamic thermal rating of overhead transmission lines to cable lines for predicting parameters corresponding to operational objectives. The research results can be useful in the development and modernization of metering systems, in which new functions of power loss moni-toring and automated determination of distribution network reliability indicators are implemented, which will allow, within the framework of “smart grids”, to ensure the required level of reliability of power supply to consumers.

A Lightning Search Algorithm for Optimal Planning of Power Systems With Short/Long-Term Storage

A Lightning Search Algorithm for Optimal Planning of Power Systems With Short/Long-Term Storage

Enhancing Resilience and Efficiency in Power System: A Five‐Level Dynamic Planning Model

Researchers consider the planning of power systems to be of utmost importance, and due to its significant budget, further studies on the resilience of energy systems are necessary. Therefore, a five‐level dynamic model is developed to improve resilience to natural disaster events. The initial level focuses on expansion planning and system uncertainties, specifically examining the utilization of wind turbines in the proposed energy hub to incorporate the thermal energy market together with demand response program. Its purposes are to improve resilience, minimize input transmission costs, and minimize operating costs. The second level is related to the corrective actions that the system operator should take after the accident for network reconfiguration. The earthquake‐induced and attack events, considering unknown events, are modeled and assessed in the third level. Generation expansion planning is performed at the fourth level. As with the whole model, this level tries to improve resilience. The fifth level deals with transmission expansion planning to boost resilience, and the capability of transmission switching is regarded as a low‐cost correction factor. The first level optimizes energy hub according to the differential evolutionary algorithm, but the next four levels employ a robust symphony orchestra search algorithm. Finally, the dynamic planning model proposed here is employed on a Garvers 6‐bus system and then on a 400 kV 52‐bus Iranian power grid. According to the findings, the proposed planning model demonstrates a reduction in capital costs of transmission switches by up to 26%. Also, the expected profit of the generation units increases by as much as 3.75%. Accordingly, the results show that the model is both feasible and effective.

Prospective power systems models development algorithms

This research aims to automate the creation of prospective models essential for long-term power system planning. Several algorithms and accompanying tools have been developed to transform an existing power system simulation model (SM) into a series of prospective simulation models (PSMs). This article outlines a general approach to SM conversion using a command system, enabling the binding of SM variants to specific planning periods and operating conditions (OC). Several tools to ensure PSM quality and adequacy, including forecasted power consumption balancing and security-constrained optimal power flow (SCOPF), are presented. Advanced techniques for assessing transient stability model quality and implementing hierarchical power system modeling are also integral parts of this development process.

Machine Learning Application for Renewable Energy Forecasting

Renewable energy is a clean source known as green energy. Its benefits are enough established. However, its effective use and increasing its share have become a major challenge for system operators. Due to its direct dependence on environmental and meteorological factors, there are often uncertainties and unexpected consequences for integrated energy system planning. Thus, the prediction of the production of renewable sources is a very relevant issue. This paper considers the application of ensemble machine learning models for renewable energy forecasting. As input data for the machine learning modem, historical data on power generation was used for the 2019–2021 period of renewable energy including meteorological data from the power plants operating in the central power system of Mongolia. The ensemble machine learning model allows us to determine the non-linear and non-stationary dependence of the time series and can be implemented in the task of forecasting the daily generation schedule. The proposed model creates a day-ahead forecast of the hourly generation curve of the photo-voltaic power plants under consideration with a normalized absolute percentage error of 6.5 – 8.4%, and for wind farms, 12.3-13.3%. Increasing the accuracy of renewable energy forecasting can positively affect the operation and planning of the central power system of Mongolia.

Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting

Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.