Existing Power Grid Research Articles

Offshore wind farms interfacing using HVAC-HVDC schemes: A review

Offshore wind farms (OWF) have emerged as a pivotal component in the transition towards renewable energy, offering substantial potential for reducing carbon emissions and enhancing energy security. Integration these offshore installations into the existing power grid present opportunities and challenges, particularly in terms of efficiency, stability, and cost-effectiveness. This review explores the interfacing of OWF with the power grid through High Voltage Alternating Current (HVAC) and High Voltage Direct Current (HVDC) schemes. This research offers a thorough examination of the latest technologies, and comparing the benefits and drawbacks of HVAC and HVDC systems for offshore wind applications. The review delves into various aspects, including technological advancements, economic considerations, and environmental impacts of HVAC and HVDC technologies. The review also addresses recent developments and contrasting viewpoints in the field, highlighting innovations and ongoing debates related to integrating OWF. By synthesizing recent studies and industry reports, this paper offers a balanced overview of the strengths and weaknesses associated with HVAC and HVDC schemes. It identifies critical areas for future research and provides recommendations for optimizing offshore wind farm integration. This review seeks to enhance understanding of current technologies and support decision-making in the development and deployment of offshore wind energy infrastructure.

A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Accurate wind power forecasting is pivotal in facilitating the efficient integration of renewable energy sources into existing power grids. This study proposes a novel hybrid framework, termed BWO-BiLSTM-ATT, which synergistically combines the strengths of a bidirectional long short-term memory (BiLSTM) network, a self-attention mechanism, and the Beluga Whale Optimization (BWO) algorithm. The BiLSTM architecture, with its unique ability to capture bidirectional temporal dependencies, effectively models the intricate dynamics present in wind power time series data. Integrating the self-attention mechanism further enhances the model's performance by discerning and emphasizing the most salient features within the input data. Furthermore, employing the BWO algorithm optimizes the model's hyperparameters, ensuring optimal configuration and enhancing its predictive accuracy and generalization capabilities. The proposed BWO-BiLSTM-ATT framework was rigorously evaluated using real-world data from an offshore wind farm in Yangjiang City, China. Comparative analyses were conducted against several baseline algorithms, including persistence, ARIMA, SVR, and vanilla LSTM models. The results demonstrate the superior performance of the BWO-BiLSTM-ATT framework, achieving higher R-squared and explained variance scores, coupled with lower error metrics such as MSE, RMSE, MedAE, and MAE. Statistical significance tests further corroborated the substantial performance improvements offered by the proposed framework. The combination of advanced deep learning techniques and metaheuristic optimization algorithms embedded in the BWO-BiLSTM-ATT framework provides a robust and accurate solution for wind power forecasting, enabling efficient management and seamless integration of renewable energy resources into existing power grids.

Optimal utilization of frequency ancillary services in modern power systems

The widespread global adoption of wind energy sources has established a significant presence in the existing power grid. However, the massive integration of intermittent wind energy poses forecasting errors, prompting the need for supplementary reserves from conventional energy sources with increased operational expenses and carbon emissions. Hence, to facilitate the seamless operation of large-scale wind-integrated power grids, it is imperative to harness the potential of renewable energy sources and leverage flexible loads to deliver power-balancing services. In this research, dynamic real-time power dispatch strategies have been developed for the Automatic Generation Control (AGC) system to integrate the reserve capacities of conventional generation units and wind power plants and utilize the demand response capabilities of flexible loads for power balancing services. A comprehensive power system grid model was developed in DigSilent PowerFactory software, consisting of coal-based energy systems, wind energy systems, gas turbines, and cold storage units as flexible loads. The study is divided into different case studies to assess the impact of each scenario on system operation in mitigating the forecasting errors of wind power plants. Further, a comparative analysis was performed to illustrate the effectiveness of each case study. The analysis showed that Case Study III, where reserves are provided by coal energy systems and cold storage units, yielded the highest reduction in Positive Regulation (PR) and Negative Regulation (NR) errors, at 89.0% and 94.15%, respectively. Conversely, Case Study IV demonstrated the least reduction in errors, with 67.82% in PR and 78.41% in NR. However, it indicates that reserves can be supplied from wind energy systems and flexible loads without the support of conventional power plants.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Enhancing Renewable Energy-Grid Integration by Optimally Placed FACTS Devices: The Nigeria Case Study

Integrating renewable energy sources into existing power grids presents considerable challenges, especially with the intermittency of wind and solar power. This issue is particularly acute in developing countries like Nigeria, where grid infrastructure is often weak, significantly limiting the potential for RE penetration. This study explores strategies to enhance RE integration in Nigeria by employing Flexible Alternating Current Transmission System (FACTS) devices. By leveraging the reactive-power sensitivity index through modal analysis, the optimal location for the FACTS device can be determined. Analysis of the Nigerian power grid demonstrates that the deployment of FACTS devices, specifically Static Synchronous Compensators (STATCOMs), can increase the penetration limit of RE by 40%. This enhancement allows for the integration of an additional 152 MW of wind energy without compromising system stability. The findings underscore the potential of FACTS devices to improve voltage profiles and overall grid stability, thereby facilitating a higher integration of renewable energy sources into weak grids without necessitating substantial changes to the existing power system architecture. This solution can help Nigeria and other countries with similar infrastructure challenges to overcome their renewable energy integration hurdles and transition towards a more sustainable, reliable, and resilient energy mix, paving the way for a cleaner and greener future.

A multi-agent deep reinforcement learning paradigm to improve the robustness and resilience of grid connected electric vehicle charging stations against the destructive effects of cyber-attacks

The rising deployment of electric vehicle charging stations (EVCS) in existing power grids and their integration with electric vehicles through communication systems are increasingly vulnerable to cyber-attacks, such as false data injection (FDI) and uncertainties of communication system. This research introduces an EVCS evaluation from a techno-economic standpoint, utilizing the developed and data-oriented TSKFS&MADRL technique to identify and rectify inaccuracies stemming from cyber-attacks like FDI and communication system delays. This method aims to enhance the resilience of EVCSs against sabotage attacks by ensuring fast dynamic responses. Initially, the study models the potential targets of hackers, such as communication systems and transducer sensors, and employs the Euclidean norm theory, weighted least square error method, and residual error technique to identify cyber-attacks resulting from FDI through the comparison of measured data with reference values based on probability distribution functions. Subsequently, the network control and recovery requirements are facilitated by the proposed controller. The proposed approach application has been demonstrated in the IEEE 33 bus, showcasing a 7.33 % lower experimental operation cost compared to the RL method and 12.15 % lower than the CNN method. Additionally, the FDI detection time is observed to be 40 % quicker than alternative methods based on the experimental outcomes.

Optimal Passive Power Line Communication Filter for NB-PLC Applications

Narrowband Power Line Communication (NB-PLC) involves transmitting data by overlaying a high frequency low amplitude signal (ranging from 9 kHz to 500 kHz) onto the low-frequency high amplitude signal (50 Hz to 60 Hz) of the power grid. While using the existing power grid for communication is convenient, it was not originally designed for this purpose, leading to challenges such as conducted emissions and infrastructure constraints. To overcome these technical obstacles, power line filters (PLFs) are a viable solution. The results of our research work, focusing on the optimization of PLFs for NB-PLC to ensure their design fits the needed use case while avoiding over-engineering, are presented in this article. Our study concentrates specifically on the filtering of PLC signal and conducted emissions up to 500 kHz. Building upon a PLC PLF extensively discussed in our previous work—which blocks the PLC signal in the CENELEC-A frequency band regardless of its placement within the electrical installation, sometimes leading to over-engineering—this research aims to adapt the filter order and components for a variety of real scenarios in CENELEC-A, FCC, and ARIB frequency bands. By characterizing different filters, our work provides tailored solutions for these scenarios and serves as a framework for future filter designs in PLC applications.

Innovative Smart Inverter Design for Enhanced Integration of DistributedEnergy

This comprehensive exploration delves into the innovative field of smart inverter design, with a particular focus on the role of artificial intelligence (AI) in enhancing the integration of Distributed Energy Resources (DERs) into power grids. The rapid evolution of energy systems and the increasing reliance on renewable energy sources have necessitated the development of more sophisticated technologies for power system management. Smart inverters, at the heart of this transformation, are crucial for integrating DERs like solar panels and wind turbines into existing power grids. The integration of AI into smart inverters represents a significant advancement in managing and optimizing these complex systems. This study demonstrates how these advanced technologies can lead to substantial improvements in inverter reliability, power distribution efficiency, and overall management of renewable energy sources. Realistic models highlight the resilience of smart inverters to fluctuating grid conditions and their ability to maintain system reliability and efficiency. Furthermore, the study critically assesses the current challenges facing this field, such as the need for standardized communication protocols, robust cybersecurity measures against digital threats, and the ongoing requirement for innovation to keep pace with rapid technological advancements. Finally, the exploration forecasts future developments and areas that will further enhance smart inverter technology, emphasizing the essential role of continued research and development in fully realizing AI's potential to transform power grids. The examination concludes by highlighting the promising future of AI in smart inverter design, particularly in contributing to the development of more efficient, reliable, and sustainable energy systems.

Analysis of Power Grid User Behavior Based on Data Mining Algorithms – System Design and Implementation

A data mining based power grid user behavior analysis system has been designed to address the issues of insufficient stability and accuracy in existing power grid user behavior analysis systems. Design the overall structure of the power grid user behavior analysis system; In terms of system hardware design, select a core controller, build and install a server as the foundation for system information transmission and logical operation; Based on ZigBee wireless communication technology, a ZigBee wireless communication protocol stack and communication expansion board were designed; In terms of system software design, Python is used to crawl user behavior data in the system data collection layer, and Python language is used to maintain the crawling program; Use the K-means algorithm to perform secondary mining and clustering on power grid user behavior data, obtain the analysis results of power grid user behavior, and transmit them to the system visualization display layer. The weight and Rand coefficient of data analysis were used as indicators to test the application effect of the method in this paper. The experimental results showed that the system can stably and accurately analyze the behavior of power grid users, and has good application effect. This research achievement has important reference significance for the research in the field of power grid user behavior analysis in the world scientific community.

Coupling methanol oxidation with CO2 reduction: A feasible pathway to achieve carbon neutralization

The energy consumption of up to 90 % of the total power input in the anodic oxygen evolution reaction (OER) slows down the implementation of electrochemical CO2 reduction reaction (CO2RR) to generate valuable chemicals. Herein, we present an alternative strategy that utilizes methanol oxidation reaction (MOR) to replace OER. The iron single atom anchored on nitrogen-doped carbon support (Fe-N-C) use as the cathode catalyst (CO2RR), low-loading platinum supported on the composites of tungsten phosphide and multiwalled carbon nanotube (Pt-WP/MWCNT) use as the anode catalyst (MOR). Our results show that the Fe-N-C exhibits a Faradaic selectivity as high as 94.93 % towards CO2RR to CO, and Pt-WP/MWCNT exhibits a peak mass activity of 544.24 mA mg−1Pt, which is 5.58 times greater than that of PtC (97.50 mA mg−1Pt). The well-established MOR||CO2RR reduces the electricity consumption up to 52.4 % compared to conventional OER||CO2RR. Moreover, a CO2 emission analysis shows that this strategy not only saves energy but also achieves carbon neutrality without changing the existing power grid structure. Our findings have crucial implications for advancing CO2 utilization and lay the foundation for developing more efficient and sustainable technologies to address the rising atmospheric CO2 levels.

Integrating Floating Photovoltaics with Hydroelectricity

The transition process from fossil fuels to environmentally friendly renewable energy sources carries the risk of creating new environmental damages. Photovoltaic technology represents one of the alternatives with the least risk of harmful environmental impact. However, this technology has two important drawbacks: the significant land occupation for the installation of PV systems and the uncontrollability of production. By constructing floating photovoltaic plants on hydroelectric reservoirs, both of these problems can be reduced to an acceptable level. Some artificial reservoirs, originally built for hydroelectric power plants, have acquired a significant secondary function as recreational areas and fish breeding sites. Therefore, there is justified resistance from the local community to change the existing appearance and purpose of such reservoirs. This paper proposes a completely new concept of integrating the interests of the local community into such objects. In addition to preserving existing uses, the concept also offers new features. This can make the entire system environmentally friendly and sustainable. This paper details the technology behind the construction of floating photovoltaic power plants on artificial reservoirs and emphasizes their various advantages. These benefits include the non-utilization of cultivable land, the ease of assembly and construction, integration into existing power grids, and the potential to address electricity storage issues. For instance, Buško Lake, covering an area of 55.8 km2, may host 2.93 km2 of installed floating photovoltaic (FPV) facilities, enabling a total installed capacity of 240 MW. With an average of 5.5 h of daily sunshine, this totals 2007 annual hours, equivalent to a 55 MW thermal power plant. An analysis showed that, with losses of 18.2%, the average annual production stands at 302 GWh, translating to an annual production value of 18 million € at 60 €/MWh. The integration of this production into an existing hydroelectric power plant featuring an artificial reservoir might boost its output by 91%. The available transmission line capacity of 237 MW is shared between the hydroelectric power plant (HPP) and FPV; hence during the FPV maximum power generation time, the HPP halts its production. HPP Orlovac operates a small number of hours annually at full capacity (1489 h); therefore in combination with the FPV, this number can be increased to 2852 h. This integration maintains the lake’s functions in tourism and fishing while expanding its capabilities without environmental harm.

State-of-Art Review of Renewable Energy-Based Systems for Electrical Machine Applications: Prospects and Challenges

Abstract Renewable energy sources (RES) are a game-changer since they produce no greenhouse gases (GHG). There is an increasing amount of interest in it across the world. The intermittent nature of RES makes it difficult to incorporate them into the existing power grid infrastructure and for use in electrical machines for industrial and domestic applications. As the number of RES grows along with different machine variants, innovative methods of interconnecting them are being explored. This study gives a thorough analysis of net zero RES (wind, solar and hydro) for electric machine applications considering their efficiency, feasibility, and reliability coupled with the trend of connection topologies. The Paradigm shift in material technology for RES application is also examined from the 1950s till the present paving the way for innovative design of novel electrical machines with optimal output performances and cost minimization. Modelling and optimization approaches were captured with the benefits and drawbacks highlighted. For electric machines to function at peak efficiency over time, energy harvesting from these sources becomes significant and must be controlled and maintained risk-free. In addition, the challenges, and prospects, associated with the operation of an Electric Machine technology in a clean environment have all been highlighted.

Optimizing renewable energy systems through artificial intelligence: Review and future prospects

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

The Storage Process of Electric Energy Produced from Renewable Sources from Hydrogen to Domestic Hot Water Heating

The expansion of renewable electricity storage technologies, including green hydrogen storage, is spurred by the need to address the high costs associated with hydrogen storage and the imperative to increase storage capacity. The initial section of the paper examines the intricacies of storing electricity generated from renewable sources, particularly during peak periods, through green hydrogen. Two primary challenges arise: firstly, the complexity inherent in the storage technology and its adaptation for electricity reproduction; and secondly, the cost implications throughout the technological chain, resulting in a significant increase in the price of the reproduced energy. Electric energy storage emerges as a pivotal solution to accommodate the growing proportion of renewable energy within contemporary energy systems, which were previously characterized by high stability. During the transition to renewable-based energy systems, optimizing energy storage technology to manage power fluctuations is crucial, considering both initial capital investment and ongoing operational expenses. The economic analysis primarily focuses on scenarios where electricity generated from renewable sources is integrated into existing power grids. The subsequent part of this paper explores the possibility of localizing excess electricity storage within a specific system, illustrated by domestic hot water.

Mathematical Models for the Single-Channel and Multi-Channel PMU Allocation Problem and Their Solution Algorithms

Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the 0/1 integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids.

Complementary operation based sizing and scheduling strategy for hybrid hydro-PV-wind generation systems connected to long-distance transmission lines

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ-constraint method is employed and decision makers’ attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations.

Comparative study of time-series forecasting models for wind power generation in Gujarat, India

The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of less than 5.5 on the prediction dataset.

Reviewing the impact of AI on renewable energy efficiency and management

In recent years, the intersection of artificial intelligence (AI) and renewable energy has emerged as a pivotal domain with transformative potential. This review delves into a comprehensive review of the impact that AI technologies have had on enhancing efficiency and management in the renewable energy sector. The integration of AI into renewable energy systems has ushered in a new era of optimization, addressing challenges and unlocking opportunities for sustainable energy production. AI's role in renewable energy begins with its ability to analyze vast datasets generated by energy systems, weather patterns, and consumption trends. Machine learning algorithms have been employed to predict energy demand, optimize grid operations, and enhance forecasting accuracy, thereby contributing to the increased efficiency of renewable energy sources. This application is crucial for overcoming the intermittent nature of renewable sources such as solar and wind power. Moreover, AI-driven technologies facilitate the intelligent management of energy storage systems, enabling better utilization of excess energy during peak production periods. Advanced control mechanisms, empowered by AI, have significantly improved the coordination of diverse energy sources, ensuring a seamless integration of renewable energy into existing power grids. Additionally, AI-driven predictive maintenance has proven instrumental in reducing downtime and optimizing the performance of renewable energy infrastructure. The review also explores the potential environmental and economic benefits of AI in renewable energy, emphasizing the role of smart grids, demand response systems, and decentralized energy production. As the world strives towards a sustainable energy future, this review offers valuable insights into the ongoing evolution of AI technologies and their impact on revolutionizing the efficiency and management of renewable energy systems. The findings presented here contribute to the growing body of knowledge aimed at accelerating the global transition to cleaner and more sustainable energy solutions.

Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems

The share of renewable energy resources in modern electrical power networks is increasing in order to meet environmental and technical targets. Consequently, energy researchers and power providers have been focusing on optimizing the integration of renewable energy into existing power grids. One of the most significant growing applications of renewable energy resources is residential photovoltaic (PV) systems; therefore, this paper discusses a new methodology to enhance the utilization of small-scale and medium-scale PV systems. For this purpose, this study proposes a user-friendly interface to help novice users optimally design their own PV projects with the highest possible utilization of the installed panels. Unlike the commercially available design tools, the proposed interface in this paper provides a higher degree-of-freedom computational process, as well as the option of improving the generated power quality, while maintaining the simplicity of the required tools and inputs. The proposed methodology mainly relies on a deep mathematical analysis considering different generation and consumption aspects, such as the load profile, time of usage, ambient temperature, PV system specifications and location. Furthermore, the mechanism of integrating a small portion of Energy Storage Systems (ESSs), to improve the quality of the extracted power, is also discussed in this study. The user interface provides the ability to estimate optimal ESS usage versus the estimated price when energy is urgently required. The case study was conducted in Riyadh, Saudi Arabia, and the results showed an essential improvement in the efficiency, solar fraction and power quality of the studied PV project, which can be extended to other home and distributed generation (DG) scales.

A risk-aware and second-order cone programming model for wind expansion planning considering correlated uncertainties using the copula method

Integration of large-scale renewable energy sources (RESs) into existing power grids is challenging for power system operators and planners due to the intermittent and uncertain characteristics of RESs and the limitation of transmission capacity. This paper presents a new risk-based framework to obtain the optimal placement and sizing of wind farms in the bulk power system considering the impact of mutual correlation between wind farms’ power productions in different candidate buses. In this regard, a second-order cone programming (SOCP) optimization problem is developed to minimize the total expected social cost including the operation cost of conventional generators, wind curtailment cost, and the investment cost of newly installed wind farms. In the proposed model, the correlated uncertainties of wind power production are incorporated using the copula method. Also, the conditional value-at-risk (CVaR) measure is utilized to manage the risk of the model associated with uncertainties. The proposed planning model is then implemented into the Garver 6-bus and IEEE 118-bus test systems. Given the results, employing SOCP-based AC OPF in the presented planning model results in a precise solution, demonstrating an 87% reduction in running time. Moreover, the numerical results illustrate that by considering the correlation between wind farms’ power production and reducing the risk parameter, there is a significant increase in the installed capacity of wind farms.