Grid Losses Research Articles

From Reactive to Proactive Infrastructure Maintenance: Remote Sensing Data and Practical Resilience in the Management of Leaky Pipes

The introduction of remote sensing technologies, AI and big data analytics in the utility sector is warranted by the need to provide critical services with the least disruption to customers, but also to enable preventive maintenance, extend the life cycle of infrastructure components and reduce grid loss—or overall, to exhibit ‘durability’ and ‘resilience’ when faced with the certainty of breakage and decay. In this paper, we first explore the concept of ‘resilience’ and the nature of practice from a performativist perspective in order to set the scene for discussing the impact of ‘datafication’ on maintenance practices and infrastructure durability. We then describe an instance of introducing remote sensing technologies in district heating network surveillance and leak detection: drone-operated thermographic cameras and underground wire sensors. Based on insights from this case study, we discuss the specificity of data-driven infrastructure maintenance practices, and what it means to exhibit practical resilience in relation to how such practices unfold, interrelate and evolve over time. We reflect on how the use of remote sensing technologies and data analytics (1) potentially changes district heating workers’ epistemic worlds (i.e., how knowledge emerges, is negotiated and ordered in practice) and (2) provides opportunities for ‘messy’ pipe repair work to tacitly adopt proactive and preventive logics to meet continuously evolving organizational and societal needs.

Evaluation Method for Voltage Regulation Range of Medium-Voltage Substations Based on OLTC Pre-Dispatch

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model.

Analytical distributed PV inverter reactive power support strategy for reduction of grid losses and voltage deviations

Analytical distributed PV inverter reactive power support strategy for reduction of grid losses and voltage deviations

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

Reactive compensation in distribution systems and volt/var control analysis: Mutual performance of inverters and curve slope effects

The high penetration of photovoltaic distributed generation is causing overvoltage in distribution networks due to the reverse active power flow. Proposals to mitigate this problem through Volt/Var control (VVC) by photovoltaic inverters are being discussed. However, the configuration of the appropriate Volt/Var curve depends on the topological factors of each system. In this context, this paper presents a practical methodology for calculating the reactive compensation of the local VVC. In addition, the impact of the slope of the curve on power quality and the problems of mutual actuation of inverters in the VVC are analyzed. The studies were conducted in quasistatic time series (QSTS) mode on the IEEE 33 bus system. The slope of the curve affects grid losses and the substation power factor, and can mitigate the negative effects of the mutual actuation of the inverters in the VVC. Based on this, guidelines were established to adapt the definition of the slope to the characteristics of each system.

The impact of different PV system grid integration approaches on voltage profile, grid losses and behaviour of PV system owners

Distribution grids in individual countries inside the European Union have different properties, while the grid operators use different approaches to integrate photovoltaic (PV) systems into the grid, which influences the voltage profile, grid losses and grid’s hosting capability. This work deals with a 0.4 kV distribution gird characteristic for the Slovenian countryside. The grid users are relatively sparsely distributed in the space. The distances between them are relatively long, while the load distribution among individual phases is unbalanced. Three different approaches to the integration of PV into the grid are discussed. The one used before 2012 with the concentrated larger PV units placed close to the transformer busbars, the one introduced with a net-metering scheme where larger PV units are randomly distributed in the grid, and the one where smaller PV units are evenly distributed throughout the grid. The case study discusses the impacts of the three approaches on the proper voltage profile provision, network losses and behaviour of the PV system owners.

Lagrange Theory-Based Optimization Strategy to Minimizing Power Loss of an Electric Power Grid: Theoretical and Experimental Study

Lagrange Theory-Based Optimization Strategy to Minimizing Power Loss of an Electric Power Grid: Theoretical and Experimental Study

Research on a Self-Coordinated Optimization Method for Distributed Energy Resources Targeting Risk Mitigation

This passage discusses a rapid restoration optimization strategy for short-term global coordination and the synergistic autonomous regulation of distributed energy sources based on a model predictive control framework. A short-term rapid recovery global coordination optimization model is established through the prediction of network states under abnormal conditions. This model includes the energy management of distributed energy sources and recovery plans for critical loads. In terms of the autonomous regulation of distributed energy sources, based on the results of global coordination optimization and aiming to minimize load shedding losses and grid losses, an ultra-short-term rolling control strategy is formulated using power output and load switching as control variables. Finally, simulation analysis on the IEEE 33-node distribution network system indicates that the proposed model and method significantly accelerate the recovery speed of the distribution network and effectively enhance its resilience level.

Physics-informed Graph Neural Network for Dynamic Reconfiguration of power systems

To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task.

Multi-microgrid Energy Management Based on Finite Horizon Twin Delayed Deep Deterministic Policy Gradient

Background: With the increasing integration of renewable energies into smart microgrids, the parameter space of the microgrid system model has increased dramatically, leading to critical computational challenges in achieving the dispatch strategy. Objective: Additionally, in remote areas, connecting renewable energy resources to the national grid is unavailable due to the high costs. Consequently, we have introduced microgrids as a viable alternative solution. Microgrids are small-scale distributed power grids that rely on renewable energy sources and generators to balance the load demand. The aim of this paper is to improve the reliability of microgrids by integrating machine learning techniques into their control. This technology ensures that all loads in the microgrid are met through energy trading. Methods: This algorithm utilizes reinforcement learning as the control mechanism for the trading process. We have established a set of trading rules that facilitate energy trades among three microgrids. We designate one of these three microgrids as the primary microgrid, while the other two function as trading microgrids. Firstly, a deep reinforcement learning algorithm (FH-TD3) is developed as a solution. Subsequently, the performances obtained in the three regions are compared with those derived from traditional algorithms. Finally, the effectiveness of the proposed energy management strategy was validated through simulations. Results: FH-TD3 has shown superior performance to DDPG in three different microgrid experiments, with the FH-TD3 algorithm consistently exhibiting shorter iteration times compared to the DDPG algorithm. Conclusion: The research shows that the algorithm realizes the balance of the power without the loss of the power grid, and realizes the profit in the transaction process.

Investigations on solar PV integration and associated power quality challenges in distribution systems through the application of MCS and GA

In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

CO2 emission from the electricity sector in Iran; calculation, prediction and reduction policies

Fossil fuel power plants produce a significant amount of CO2 emissions, and this pollutant causes global warming, respiratory and heart diseases, and other significant issues. Electricity interprets as a primary and rising demand in each energy system; thus, in this paper, carbon dioxide (CO2) emission reduction was selected as the objective value for 2025. Power plant fuel consumption was surveyed to calculate the CO2 emission caused by each fuel. Also, Esfahan province (an industrial province in Iran) was investigated as the study case. Forecasting the fuel consumption for 2025 was run by two parameters: population and Gross Domestic Production (GDP), which were forecasted by the report of the Iran Statics Center and the Gaussian Process Regression (GPR) method, respectively. The CO2 emission of power plants was obtained using the coefficients of each fuel. Based on Iran's commitment to the Paris Agreement, a 4% reduction of CO2 emissions is the main objective. Thus, this study aims to reach this goal by implementing four scenarios: a) adding renewable energies, b) adding renewable energies and improving the generation efficiency, c) adding renewable energies and decreasing the grid losses, and d) combining the three scenarios mentioned above. According to these scenarios, reformation strategies compensated 10.5% of the required power, which was satisfied by renewable energies, and finally, this province can gradually satisfy a 4% reduction until 2025.

Revolutionizing power grid loss prediction with advanced hybrid time series deep learning model

Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Effects of the yaw error and the fault conditions on the dynamic characteristics of the 15 MW offshore semi-submersible wind turbine

The floating offshore wind turbines (FOWTs) have a higher failure rate compared to onshore wind turbines, which could lead to aerodynamic imbalance on the floating systems. In addition, yaw error presents an inevitable challenge for FOWTs. This study aims to investigate the effects of the yaw error and the fault conditions on the dynamic properties of the IEA 15 MW semi-submersible FOWT using OpenFAST. The fault conditions involve blade seize without shutdown, blade seize with shutdown and grid loss. The platform motions and structure loads under different yaw errors (−20°, −8°, 0°, 8°, 20°) for different fault conditions were investigated. The dynamic response resulting from the fault was compared with the normal condition. Moreover, the optimal yaw angle under fault conditions was investigated in this paper. The results revealed that the fault shutdown conditions significantly impact the motions and loads of the wind turbine, with substantial reverse tower top and base bending moments. The yaw error, conversely, has less effect on the motions of the wind turbine, but can ameliorate the transient effect in the fault shutdown conditions. Additionally, it is recommended to operate the wind turbine at a yaw angle of 25–30° for wind speeds greater than 12 m/s.

Assessing the performance of hydro-solar hybrid (HSH) grid integration: A case study of Bui Generating Station, Ghana

Renewable energy sources (RES) are rapidly expanding as a result of energy security and environmental concerns. Despite their numerous benefits, they pose significant challenges to power grid operation. Ghana is dedicated to reaching a 10 % renewable energy mix target by 2030 to promote low-emission development. Ghana has the first hybrid power plant made up of 400MW hydropower plant and 50 MW solar PV plant supplying power to the national grid. The study designs a hydro-solar hybrid system configuration for Ghana's Bui generation unit, using data from the 50 MW ground-mounted solar PV and 133.33 MW hydropower units to assess the performance and challenges of the hydro-solar hybrid system at the Bui Generating Station. Methodology involves modeling and simulation in the DIgSILENT power factory software environment. Utilizing quasi-dynamic simulations, the study investigates variations in active power generation, voltage fluctuations, grid losses, and reactive power generation. Results highlight technical challenges such as voltage fluctuations and power loss, and propose mitigation measures. Comparisons between simulated and field data reveal discrepancies attributed to factors such as temperature effects, dust accumulation, and conductor resistance. Mitigation strategies are proposed, including energy storage expansion, smart grid implementation, advanced control techniques, FACTS device deployment and grid monitoring improvements. Despite limitations in data availability and simulation accuracy, the study underscores the system's reliability and provides insights for enhancing renewable energy integration in the region. Generally, the study contributes to advancing renewable energy integration efforts, with implications for sustainable development and climate action in Ghana and West Africa at large.

Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.

Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results show that the application of this algorithm can effectively reduce the active loss in the process of distribution network reconfiguration, thus reducing energy consumption; At the same time, it can reduce the electric heating in the process of electric energy transmission, and then prevent the occurrence of fire. There are three main contributions of this study. Firstly, the resistance in the transmission path is reduced by using this algorithm, so that the power transmission efficiency can be analyzed more accurately. Secondly, the new algorithm enriches the power safety maintenance method; Finally, the fire caused by local overheating of the line is reduced by fusion algorithm.

Influence of Excitation by Idling Rotor on Wind Turbine Ultimate Loads in Storm Conditions

Typical large scale pitch-controlled wind turbines idle their rotors during storm conditions. The design loads of wind turbines are calculated by aeroelastic simulations under various conditions. These include grid loss and failures, which can increase rotor speed and excite the first-mode of the tower bending. In this study, the influences of self-excitation by the idling rotor on the ultimate loads in storm conditions were investigated. Aeroelastic simulations were conducted for a three-bladed 5 MW upwind turbine as an example, under steady and extreme turbulent wind conditions according to the international design standard IEC61400-1 ed.4. As a result, we confirmed that yaw misalignment increases the idling rotor speed and 6P, second order harmonics of blade passing frequency, excites the first-mode tower bending, which can generate a large load on the tower. Pitch stick can increase the rotor speed but not as noticeably as yaw error. Although no clear provisions exist in wind turbine design standards or guidelines for the self-excited vibration during wind turbine idling, these results indicate that conditions must be set that consider self-excited vibration.