Line Overloads Research Articles

A Scenario-Based Simulation Study for Economic Viability and Widespread Impact Analysis of Consumption-Side Energy Storage Systems

This study investigates energy storage within the contexts of production-side and consumption-side energy storage concepts. The theoretical advantages of consumption-side energy storage over production-side systems are initially explored. The analysis is supported by a scenario-based simulation, with results presented to assess the feasibility and applicability of consumption-side energy storage under varying conditions. The simulation examines multiple scenarios, incorporating economic assessments to evaluate the viability of such systems. Additionally, the study explores the broader impact of consumption-side energy storage when adopted by 5 million, 10 million, 20 million, and 40 million residential consumers across separate scenarios. The analysis emphasizes the potential for shifting peak-period energy consumption to nighttime usage and assesses the corresponding reduction in energy generation requirements and transmission line loads, alongside the economic benefits derived from postponing energy infrastructure investments. The study focuses exclusively on residential consumers, with the energy storage systems referred to as residential energy storage systems (RESS). These systems are assumed to be organized and managed by energy provider companies rather than individual consumers. The research also considers the potential costs associated with implementing RESS. The simulation-based findings reveal significant benefits, including reduced reliance on new power plants, decreased risk of transmission line overload, increased utilization of renewable energy resources, financial advantages for both energy providers and consumers, and positive environmental impacts. These results provide valuable insights with implications for shaping future energy policies, particularly in the United States.

Opinions on dynamic topology reconfiguration of distribution networks for PV hosting capacity enhancement

This paper investigates on dynamic topology reconfiguration of distribution networks to enhance PV hosting capacity. Firstly, a dynamic topology reconfiguration model of distribution networks considering N-1 security constraints is proposed to accommodate intermittent PV generation, and thus high penetration of renewable resources can be ensured to satisfy operational requirements under both normal and fault conditions with load transfer of feeders. Then, a hosting capacity enhancement strategy is presented to decrease risks of voltage and line overloading with reactive power flexibility of energy storage inverters, and is implemented to enhance the maximum hosting capacity of distributed renewable energy sources. Finally, simulation results have validated the effectiveness of the proposed method for PV hosting capacity enhancement of distribution networks.

Active sensitivity coefficient-guided reinforcement learning for power grid real-time dispatching

The integration of renewable energy sources into power systems and the expansion of grid scale have introduced a higher degree of operational unpredictability, thereby increasing the risk of safety hazards such as transmission line overloads. Existing reinforcement learning algorithms that incorporate expert knowledge aim to facilitate secure dispatching of the power grid. However, these algorithms, which adopt strategies akin to imitation learning during the learning process, do not effectively address systemic constraints such as line limits and power balance. In response to this issue, this study introduces a novel active sensitivity coefficient-guided reinforcement learning method for real-time power grid dispatch (SRL-Dispatching). Firstly, a sensitivity-based reinforcement learning framework is proposed using the Soft Actor-Critic (SAC) algorithm. Secondly, a sensitivity coefficient-based method for compressing the action space boundary is proposed to address the security implications associated with transmission line overloads. Subsequently, a feasible domain projection approach is introduced to ensure that dispatch operations adhere to safety constraints. By employing sensitivity factors to guide the learning process of the agent in managing line overloads and ensuring safe operation, the precision and robustness of dispatch strategies in the power system environment are significantly enhanced. Simulation results using the SG-126 power grid simulator indicate that SRL-Dispatching accelerates training by a factor of 9.8 compared to state-of-the-art RL methods, with comparable decision-making times. Across various load levels, SRL-Dispatching demonstrates superior performance in terms of renewable energy integration, line overload management, and power balance control.

Static Security Assessment: A Case Study of the Saudi National Grid

The static security assessment of the Saudi National Network helps to identify the weaknesses and potential threats and provides a comprehensive understanding of the network’s ability to address such incidents due to probable outages. This paper presents the static security assessment of the Saudi national grid with reference to the transmission line overloads and the bus voltage deviations in the system following the initiation of the specified contingencies. Critical contingencies are identified, and these are ranked on the basis of the limit violations of the line overloads and the bus voltage deviations obtained through the Newton-Raphson load flow analysis. The system loading conditions considered correspond to that of a typical day of the Hajj Pilgrimage period, during which millions of pilgrims from various parts of the country and also the world visit the Holy Mosque at Makkah. The preliminary investigations reveal that there are some transmission lines and certain substations requiring special attention to enhance the system security.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods

This document presents a solution for an assignment related to the Energy Markets and Regulation curricular unit of PDEEC. The assignment consists of assessing optimal dispatch and pool market (symmetric and asymmetric) results for different periods. To check the technical feasibility of implementing the dispatch recommended by the pool market, a DC power flow is analyzed, by accounting for a network with six busbars. Results show that in some periods of higher demand, there could be an overload in some transmission lines of the considered network for certain results of market dispatch.

Two-Area Automatic Generation Control for Power Systems with Highly Penetrating Renewable Energy Sources

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system’s frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events.

Optimal Network Reconfiguration and Power Curtailment of Renewable Energy Sources to Eliminate Overloads of Power Lines

The increasing number of renewable energy sources in power systems contributes to overloads of power lines in emergency situations. Lines made with relatively small cross-section cables, which in the past were designed for an operating temperature of 40 °C, are particularly exposed to overloads. Currently, they constitute the so-called “bottlenecks” in network capacity. This is manifested in the fact that when carrying out expert opinions aimed at examining the impact of a source on the network, computational analyses show overloads of its elements. This article proposes a methodology for eliminating these overloads. It involves the use of two methods at the same time, namely optimal network reconfiguration combined with minimisation of the total power curtailment in RE sources. The search for the optimal network configuration will also allow for minimising power curtailment in renewable energy sources, and thus reduce the costs of this type of operation. With such a tool, network operators will be able to achieve the effect of relieving the line load with the lowest possible cost of redistribution. Based on the IEEE 118 bus test network, calculations were performed that confirmed the effectiveness of the proposed approach. The operation of the proposed methodology is presented with the example of two selected network failure states. The novelty of the proposed solution lies in the simultaneous use of two methods of eliminating line overloads. This streamlines the entire process and improves its effectiveness.

Multi-resource dynamic coordinated planning of flexible distribution network

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

The impact of renewable energy sources on the overload of high voltage lines – power flow tracking versus direct current method

Studying the impact of renewable energy sources planned to be connected to the grid, requires the preparation of expert opinions. The task of this opinion is to verify that there are possibilities enabling the connection of the considered source to the network. Each opinion is required to take into account other facilities and those sources which were previously connected to the grid or connection agreement were signed with them. The need to take into account such a large number of sources contributes to potential thermal overloads of high-voltage lines. Sometimes these overloads are insignificant, but in certain situations it turns out that their occurrence may be a reason for refusing to sign connection agreements for new sources. According to network operators, their presence may constitute a threat to the operational security of the grid. The article presents the use of the method of tracking active power flows and the DC method of determining power flows to estimate the impact of these sources on thermal overloading of lines. Using of the IEEE-118 test network, selected nodes were analysed where connecting sources might significantly worsen overloads previously observed or would cause new overloads. The proposed approach will enable potential investors to make proper decisions regarding selection of source connection points. Combining the results obtained by both methods at the same time will allow for the indication of appropriate connection nodes for sources from the point of view of minimising the number of overloaded lines and prospective costs of their uprating.

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.

Key technologies and applications of cloud-edge collaborative integrated low and medium voltage distributed photovoltaic coordination control

The large-scale distributed photovoltaic access leads to overvoltage, equipment overload, and difficulty in on-site consumption, which causes power to be sent back to the high voltage level, posing hidden dangers to the safe operation of the distribution network. This article proposes optimizing the medium-voltage distribution network at the cloud side with the goal of maximum consumption and operating cost, forming a rolling strategy adjustment based on load forecasting within the day. At the edge side, the distributed photovoltaic control in the low-voltage distribution network within the jurisdiction is adjusted based on the daily adjustment strategy issued by the cloud side, completing the optimization and regulation of distributed photovoltaics on a distribution substation unit basis to achieve the control and integration of distributed power sources and consumption, coordination between operation and side, and coordinated control in the integrated medium and low-voltage zoning, achieving autonomous distribution substations and feeder lines. Field applications have proven that this solution solves problems such as line and transformer overload, and voltage quality caused by distributed photovoltaic power generation.

On the operation and implications of grid-interactive renewable energy communities

The increasing installations of photovoltaic systems, electric vehicle chargers, heat pumps, and battery storage systems challenge distribution system operators to prevent critical grid operations. Nonetheless, flexibility potential from customer-owned assets remains untapped due to lacking federated coordination and incentives. In Europe, emerging renewable energy communities can address this challenge by facilitating collective optimal operation of flexible assets and providing grid services. This paper presents an approach for renewable energy communities to offer participants’ flexibility through grid-interactive operations. Distribution system operators then based on optimal power flow calculations request flexibility use to prevent voltage violations, transformer overloads, and line overloads. A case study with simulations for a future rural low-voltage grid is conducted. The results show that, compared to business-as-usual operations or renewable energy communities with fixed import and export limits, the grid-interactive approach ensures non-critical grid operations cost-efficiently and reduces curtailment by 60% to 90%, down to 1.1% of total generation. By providing both upward and downward flexibility and aligning with distribution system operators’ requirements, grid-interactive renewable energy communities can be key in enhancing the efficient use and stability of distribution grids.

Optimal scheduling of blockchain-based virtual power plants considering distribution network congestion

The development of renewable energy has brought new challenges to the power system. Virtual power plant based on blockchain is an effective means to solve the problem of new energy consumption. Solving the internal risk of line overload or voltage over-limit of a virtual power plant can make it play a better role. This paper proposes a scheduling method for a virtual power plant based on blockchain, which can solve the congestion problem. Firstly, the scheduling process of a blockchain-based virtual power plant is designed. Secondly, it is discretized as the basis of the congestion management scheme. Thirdly, the price penalty is introduced into the scheduling process to solve the congestion problem. Finally, an example is designed for simulation. The results show that the method can solve the congestion problem while realizing the virtual power plant scheduling.

Renewable Energy Real-Time Carrying Capacity Assessment Method and Response Strategy under Typhoon Weather

Currently, the integration of distributed power supply into the power grid is steadily increasing. The grid’s carrying capacity serves as a crucial metric for evaluating the grid’s resilience following the widespread integration of distributed power supply. During typhoon conditions, if the power grid experiences line breakage and load loss faults, the grid’s framework is altered, rendering the conventional carrying capacity assessment method obsolete. This study introduces a method for assessing the risk of line carrying capacity and an index for line overload probability under typhoon conditions, integrating line and transformer capacity constraints to evaluate the grid’s carrying capacity risk. The probability of line failure is modeled during typhoon events, and a modified IEEE39 node example is employed to simulate a high-penetration grid in a typhoon scenario. Addressing the issue of inadequate intraday dispatch capability under insufficient carrying capacity, we propose a multi-timescale dispatch method and derive the optimal grid dispatch strategy using the viscous bacteria algorithm. The efficacy of the multi-timescale dispatch strategy in addressing the grid’s carrying capacity risk is validated through simulation, while the economic cost of mitigating the grid’s carrying capacity risk and the line overload probability is assessed across varying parameter values.

Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios

This study aimed to optimize the sizing and allocation of renewable distributed generation (RDG) systems, with a focus on renewable sources, under N-1 faulty line conditions. The IEEE 30-bus power system benchmark served as a case study for us to analyze and enhance the reliability and quality of the power system in the presence of faults. The firefly algorithm (FFA) combined with the crow search (CS) optimizer was used to achieve optimal RDG sizing and allocation through solving the optimal power flow (OPF) under the most severe N-1 faulty line. The reason for hybridization lies in leveraging the global search capabilities of the CS optimizer for the sizing and allocation of RDGs and the local search proficiency of the FFA for OPF. Two severe N-1 faulty conditions—F27-29 and F27-30—were separately applied to the IEEE 30-bus distribution system. The most severe N-1 faulty line of these two faulty lines was F27-30, based on a severity ranking index including both the voltage deviation index and the overloading index. Three candidate buses, namely 27, 29, and 30, were considered in the optimization process. Our methodology incorporated techno-economic multi-objectives, encompassing overall costs, power losses, and voltage deviation. The optimizer can eliminate the impractical buses/solutions automatically while remaining the practical one. The results revealed that optimal RDG allocation at bus 30 effectively alleviated line overloading, ensuring compliance with the line flow limit, reducing costs, and enhancing voltage profiles, thereby improving system performance under N-1 faulty conditions compared to the equivalent case without RDGs.

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).

A Review and Comparison of FACTS Optimal Placement for Solving Transmission System Issues

The current social impact of new transmission and distribution lines and the growth in environmental requirements has not lead to the expansion of the electric power grid but to the optimization of the existing assets. Flexible AC Transmissions Systems (FACTS), developed during the last decades of the past century, have become one of the most remarkable solutions for the optimization of the electrical power grid. Due to their flexibility, different types of FACTS have been proposed to solve similar transmission system operation problems. This paper reviews the FACTS devices used for solving these problems and the techniques used to optimize their location. The objective of the paper is to serve as a guide for selecting the right power system analysis and optimization technique for a given transmission system problem, and the most used FACTS for this purpose. The main operation issues that have been considered are: voltage control, assets optimization, line overloads and grid congestion, voltage stability problems, angle stability problems, contingencies and economic issues. In this study, the power system analysis and optimization methods have been divided into four main groups: classic optimization methods, technical criteria based methods, heuristic or meta-heuristic based methods and simulation based methods.

Impact of OLTC equipped transformer operation on PV installation in urban distribution network

Presented paper addresses the problem of proper large-scale integration of photovoltaic (PV) systems in urban area distribution network (DN). A methodology for minimization of annual energy losses in DN by optimal placement of PV systems, which simultaneously considers rooftop PV potential and time dependent network operation, is presented. Optimal reactive power generation of PV systems and optimal setting of on-load tap changer (OLTC) equipped transformer in secondary substation are determined as well. Furthermore, proposed methodology enables evaluation of the impact of OLTC equipped transformer on the share of PV installation that urban DN can accommodate. Proposed methodology is based on the optimization tool called differential evolution, with the objective function set to minimize network’s annual energy losses, while preventing the voltage violations and thermal overloading of lines. Results of the case study performed on a real urban low voltage distribution network, with consideration of different scenarios of OLTC operation are presented. The results indicate that simultaneous consideration of chosen variables yields greater benefits to DN operation in terms of reduction of annual energy losses and greater PV penetration.

Line Congestion Management in Modern Power Systems: A Case Study of Pakistan

The surging electricity demand in Pakistan has led to frequent blackouts, prompting government initiatives to expand power plant capacities and improve the national grid. The government prioritizes integrating large‐scale renewable energy sources, such as wind and solar power, to reduce dependence on conventional power plants. However, the intermittency of renewables leads to forecasting errors, requiring extra power reserves from conventional units, thereby escalating operational costs and CO2 emissions. The country currently utilizes a manual mechanism for power balancing operations, overlooking critical grid constraints of the transmission line loadings. In such conditions, injecting large‐scale power from renewables can lead to significant fluctuations in line power flows, risking transmission line loadings and compromising the system’s secure operation. Hence, this paper has developed an automatic generation control (AGC) model for the highly wind‐integrated power system to alleviate line congestions in the network and enhance the economic operation of the system. The study utilizes the Pakistan power system as a case study to simulate the proposed model. The developed real‐time power dispatch strategy for the AGC system considers the constraints of the transmission line to avoid congestion. It integrates wind energy as operating reserves to enhance the economic operation of the system. When managing line congestion, it identifies overloaded bus lines and adjusts power regulation accordingly while compensating for shortfalls by augmenting transmitted power from regional grid stations. However, it maintains a constant dispatch ratio without line overloads, aligned with generation capacities. Additionally, the strategy integrates reserve power from the wind power plant and traditional generating units to further improve economic operations. Simulations have been conducted using PowerFactory software, employing the eight‐bus and five‐machine models to replicate the characteristics of the Pakistan power system. The results demonstrate the effectiveness of the proposed AGC design in mitigating transmission line congestion of power systems that are heavily integrated with wind energy sources while simultaneously ensuring the economic operation of generating units.