Grid Load Research Articles

Framework and Outlooks of Multi-Source–Grid–Load Coordinated Low-Carbon Operational Systems Considering Demand-Side Hierarchical Response

In the context of advancing new power systems, a multi-source–grid–load interactive operation framework considering low-carbon demand hierarchical response is developed to further explore the support value of the multi-source–grid–load interaction mechanism for the low-carbon economic operation of the power system. The framework analyzes the support mechanisms of carbon tracking and load-side demand response for the low-carbon economic dispatch of the system and derives the carbon flow calculation method based on the network node correlation matrix, laying the foundation for developing low-carbon demand response strategies. Meanwhile, considering the marginal contribution of each load-side node to the system carbon emissions, a combined Shapley–Topsis low-carbon demand hierarchical response mechanism is designed to guide load nodes in implementing accurate low-carbon hierarchical responses, thereby ensuring the optimal allocation and efficient utilization of system resources. Finally, based on the proposed framework, promising future research perspectives are proposed to provide critical insights for constructing a low-carbon and reliable new energy system.

Network-aware electric vehicle charging/discharging scheduling for grid load management in a hierarchical framework

The increasing adoption of electric vehicles (EVs) poses significant challenges for power system operations, requiring scalable coordination to mitigate their negative impacts and leverage their potential to enhance grid conditions. This paper introduces a scalable, three-layer hierarchical framework for optimal EV charge and discharge scheduling (EVCDS) that coordinates key agents: EVs, EV aggregators (EVAs), and the distribution network operator (DNO). The optimization problem is developed as an exchange problem and solved using the alternating direction method of multipliers (ADMM) in a decentralized approach. The proposed EVCDS addresses economic factors by minimizing battery degradation costs at the EV level and charging costs at the EVA level, while managing technical aspects at the DNO level by minimizing load curve variance and limiting power capacity. Moreover,voltages at network nodes are calculated using the DistFlow model to simplify the optimization and ensure compliance with standard operational limits. Compared to uncoordinated EV charging, EVCDS reduces load profile deviations by 85% and total costs by 91%, while also improving bus voltage profiles.

Active domain adaptation in support of reliable operating mode detection for wind turbines

Wind turbines operate under different regimes depending on the contextual conditions, such as meteorology, operational constraints, or grid loads. Knowing when a turbine changes operational regime (its current operating mode) is crucial for a proper assessment of its performance, as only then can one compare it to the expected performance. However, often operators overseeing the turbines do not have access to the operating mode (OM) a turbine is in, leading to incomplete or inaccurate evaluation of its current capacity. We have devised an approach to leverage the knowledge across different wind farms aiming at reliably transferring, using domain adaptation, an OM detection model from a source turbine, for which OM labels are available, to a target turbine, for which they are not. This is achieved by an active learning process, enabling the adaptation to take place with expert supervision, while minimizing the required effort. Furthermore, an extension based on density-based clustering allows to post-process the outcome of the active learning to further characterize and discriminate between multiple non-primary modes of the turbine. Our approach is validated on a real-life dataset of onshore wind turbines, and we demonstrate that our active domain adaptation method achieves optimal OM detection performance.

Cooperative scheduling strategy for electric vehicles with Vehicle-to-Grid technology considering renewable energy generation

The randomness and intermittency of electric vehicles (EVs) charging can lead to a peak-on-peak phenomenon in the distribution network load. As the scale of EVs continues to expand, uncoordinated charging demands exacerbate the differences between peak and off-peak loads. To address this issue, this study introduces an innovative optimization method for EVs charging. First, by analyzing the charging patterns of power batteries and driver behavior, EVs charging demand is simulated and deduced using the Monte Carlo method. Subsequently, considering the constraints of EVs and the power grid, an objective function to optimize grid load is constructed based on Vehicle-to-Grid (V2G) and time-of-use pricing strategies. Finally, an Improved Backbone Particle Swarm Optimization (IBBPSO) algorithm is designed, which is further combined with a Bayesian Optimization (BO) algorithm to dynamically adjust the allocation coefficients of the objective function and the initial parameters of the IBBPSO. This approach facilitates the attainment of the optimal EVs charging and discharging scheduling strategy. Simulation results indicate that the implemented strategy effectively reduces the peak-to-valley load disparity within the power grid, flatting the adverse effects of uncoordinated EVs charging.

Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented.

Optimizing shared charging services at sustainable bus charging hubs: A queue theory integration approach

Integrating solar photovoltaic (PV) systems into bus charging infrastructure offers a promising solution to mitigate carbon emissions and reduce grid loads. However, a mismatch between the output of solar PV energy and the electric bus (EB) charging demand can arise due to the instability and intermittency of solar PV energy. To address this issue, this study introduces a novel shared charging business mode that allocates charging facilities to private electric vehicles (PEVs), leveraging idle infrastructure to maximize solar PV utilization. We develop an optimization framework to jointly determine the EB charging schedules and the allocation of charging facilities between EBs and PEVs within an EB network integrated with solar PV systems. Additionally, a loss queue model with time-dependent arrival rates is proposed to model the stochastic nature of PEV charging demand. A case study is conducted on a bus network in Beijing to validate our proposed operational strategy. The findings demonstrate that integrating shared charging in bus depots equipped with solar PV systems improves solar PV utilization from 79.30 % to 92.91 %. This integration reduces CO2 emissions and overall daily system costs by 7.93 % and 14.82 %, respectively, without negatively impacting the grid load during peak periods.

A novel analytical method for optimal management of network congestion caused by electric vehicle charging stations

Congestion management with the emergence of electric vehicle charging stations (EVCSs) can be considered as one of the challenges facing the network operator, whose incorrect management causes the price difference to increase and creates market power. In general, the effect of EVCSs on the congestion rent (CR) of grid lines has been studied in a few studies, which are considered a research gap. In this paper, the share of each EVCS to the variations in line CR and total congestion rent (TCR) is precisely determined. Also, the uncertainty regarding the capacity of the EVCSs is considered to make the problem more real. In addition, the share of other factors as well as marginal, expensive, and cheap units, grid loads, and congested lines are calculated on the CR of grid lines. The proposed method is divided into two parts, in which the share of the five factors to variations in the buses’ locational marginal price (LMP) and the flow of lines is determined in part 1. In part 2, the share of EVCSs to variations in the CR of lines and TCR is computed. The proposed method is implemented using the Kahn-Tucker condition method. This approach is implemented on an IEEE 24-bus system. The analysis of results indicate that the congestion of network lines in the best and worst cases of placing 3 EVCSs in the network increases by 0.0013 % (0.65 $/h) and 0.1 % (48.94 $/h), respectively. Therefore, determining the appropriate placement of the EVCS is of particular importance.

An integrated model of electric bus energy consumption and optimised depot charging

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Optimal Design of Wind-Solar complementary power generation systems considering the maximum capacity of renewable energy

This paper proposes constructing a multi-energy complementary power generation system integrating hydropower, wind, and solar energy. Considering capacity configuration and optimization of the complementary power generation system, a dual-layer planning model is constructed. The outer layer aims to maximize the accessible scale of wind and solar energy, while the inner layer considers the matching degree between power output and grid load. The optimization uses a particle swarm algorithm to obtain wind and solar energy integration's optimal ratio and capacity configuration. The results indicate that a wind-solar ratio of around 1.25:1, with wind power installed capacity of 2350 MW and photovoltaic installed capacity of 1898 MW, results in maximum wind and solar installed capacity. Furthermore, installed capacity increases with increasing wind and solar curtailment rates and loss-of-load probabilities. When the loss-of-load probability is set to 3 %, the impact of wind and solar curtailment rates on the installed capacity ratio is relatively small. The complementary characteristics of wind and solar energy can be fully utilized, which better aligns with fluctuations in user loads, promoting the integration of wind and solar resources and ensuring the safe and stable operation of the system.

Power Quality Enrichment and Power Management using an Adaptive Control Scheme in Microgrid System

This article proposes an ANFIS-based control scheme for improved power quality and power management that will provide optimal and sustainable energy in a grid-connected renewable energy system. The system consists of a wind energy conversion system (WECS), a photo voltaic system (PVS), and a battery energy storage system (BESS). To increase the PVS's and WECS output powers, a unique control approach is suggested in this paper. The adaptive Neuro-Fuzzy Inference System (ANFIS) controller is incorporated into the proposed controller to generate three-phase reference current signals for harmonic elimination in the system during system dynamic conditions. The proposed control technique can work under voltage quality problems such as voltage sag, voltage swell, neutral currents, and reactive power. Renewable energy sources (PV and Wind) are interfaced to improve the DC-link overall performance by minimizing short-term and long-term voltage problems. The proposed controller regulates the energy flows between the renewable energy sources to the end users with a unity power factor. A supervisory control scheme is implemented for optimal power management in the system. Based on the load requirement, and how the renewable, battery, and grid sources are sharing the power, a detailed analysis is given in the paper. Simulation results from the MATLAB/Simulink platform under several test conditions at the grid side and load side illustrate the efficacy of the proposed control mechanisms in the environment of power optimization and energy management. The comparative analysis is performed to show the efficacy of the proposed system. Finally, the proposed system is validated and the THD content of the grid currents is found good.

Analysis of the Potential Carbon Emission Reduction Through Interaction Between New Energy Vehicles and Smart Grids

Abstract: This paper analyzes the potential for carbon emission reduction through the interaction between new energy vehicles and smart grids, discussing how the optimization of energy efficiency and balancing of grid loads can be achieved through Vehicle-to-Grid (V2G) technology. The interaction between new energy vehicles and smart grids can enhance energy utilization efficiency, optimize grid management, and reduce reliance on fossil fuels by leveraging renewable energy sources, thereby achieving the goal of carbon emission reduction. However, this process faces multiple challenges including technological integration, cost-effectiveness, policy support, and societal acceptance. The paper proposes a series of strategic recommendations, including promoting technological standardization, enhancing policy and economic incentives, and increasing public awareness and participation, to support the effective interaction between new energy vehicles and smart grids, and to achieve long-term carbon emission reduction goals.

Electrical vehicles impact on unbalanced distribution networks considering different loads and energy prices

The expansion of distributed generation and erratic loads create many challenges for electricity distribution networks (DN), like grid congestion and load unbalance. Technological advances in recent years have made the use of electric vehicles (EVs) more economical and justified for network connection. The use of smart EVs in the network scale, in addition to the peak shedding, if it is used in a single phase, it can also lead to the improvement of load unbalance. This paper proposes an energy and cost management model for the appropriate and distributed deployment of single phase EVs in distribution networks to provide network support for a DN including renewable resources. By connecting or disconnecting single-phase smart EVs to the DN, unbalanced loads in this network will be balanced as much as possible. To evaluate the proposed model, a network of 13 buses has been used, and the calculations of these energy costs have been measured during 24 h by two types of household and industrial loads. These loads are unbalanced in the DN and single-phase EVs have the ability to improve the unbalanced load of the DN. Also, the energy price is variable during the day and depends on the peak load. From the sensitivity analysis and with the relative increase or decrease of load, load change effects on the cost of each DN per-unit power have been measured and compared with the presence or absence of single-phase smart EVs to obtain more practical results. According to this paper, the use of EVs, in addition to improving the load of the DN, greatly reduces the costs of DN.

Numerical simulation of a hybrid energy system proposed for low carbon data center

Low-carbon energy application for both large grid and distributed energy consumers is an urgent problem to be solved. Data centers (DC) are the typical distributed large-size energy consumers, and the application of renewable energy and energy storage is a promising solution for data center decarbonization. This paper developed a complete hybrid energy system (HES) simulation process for data center applications. The photovoltaic (PV) cell, hydrogen electrolysis, hydrogen storage, proton exchange membrane fuel cell (PEMFC), lithium battery (BAT), grid, and data center load are considered in the HES. A one-dimensional two-phase flow PEMFC model is developed. The detailed mass transfer process inside the PEMFC cell is modeled. The PEMFC model-solving algorithm is developed and integrated into the HES simulation program. The energy management system (EMS) based on a logical flow chart energy management strategy is integrated by editable subfunction. The HES simulation program is coded and implemented based on the MATLAB-SIMULINK environment. The yearly HES performance simulations are conducted and analyzed for a 200 kW data center in Xi’an with different HES parameters and an appropriate HES parameter arrangement is provided. The main contribution of this work is the development of an editable HES simulation program that is extensible for data center applications.

Research on Power Grid Load Forecasting Method Based on Support Vector Machine

Abstract In order to improve the accuracy of power load forecasting, a power load forecasting model with the support vector machine (SVM) was proposed in the existing load forecasting methods. Firstly, establish the basic analysis on the SVM theoretical foundation. Through the establishment of SVM prediction model, the real power data is sent into the support vector machine prediction model for learning and forecasting once it has been processed. Think about the electricity grid load change rule states, our enterprises accurately understand the local power grid operation and scheduling measures by estimating the electricity uses. Meanwhile, it optimizes the power grid management through careful scheduling means, prevent excessive consumption of resources and large energy consumption. To achieve the prediction effect, this paper builds the prediction model and analyses the data with the MATLAB. In the experiment, the real value is compared with the anticipated value. Through the analysis of more than 8000 load data, the accuracy of the training set is 96.184%, and the accuracy of the test set is 96.6134%. The outcomes demonstrate the great accuracy of the SVM prediction model in predicting electricity load.

Real Time Prediction and Modelling of Power Grid System Stability and Load Balancing

Abstract The extensive geographical distribution of the power system determines its highly complex structure. The system includes key components such as generators, substations, transmission and distribution grids, and users. This system has characteristics such as nonlinearity, time-varying, and parameter uncertainty. Power system stability is inherently linked to its regular operational performance. This article uses Simulink models to simulate the working state of generators and circuit breakers during faults. Under the conditions of a synchronous generator with a rated power of 350MW, a voltage of 10.5KV, a three-phase parallel RLC load rated voltage of 115KV, and a power consumption of 300MW, when a short occurs on line, the speed and relative angle of the generator tend to a fixed value, and the system remains stable. When a fault occurs in the circuit within 0.1 seconds, the circuit breaker will cut off the faulty line within 0.6 seconds. Avoiding the occurrence of oscillations and unstable states in typical dynamic systems.

Adaptive Single-layer Aggregation Framework for Energy-efficient and Privacy-preserving Load Forecasting in Heterogeneous Federated Smart Grids

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating data from distributed load networks while ensuring data privacy. However, the heterogeneous nature of smart grid load forecasting introduces significant challenges that current methods struggle to address, particularly for resource-constrained devices due to high computational and communication demands. To overcome these challenges, we propose a novel Adaptive Single Layer Aggregation (ASLA) framework tailored for resource-constrained smart grid networks. The ASLA framework mitigates data heterogeneity issues by focusing on local learning and incorporating partial updates from local devices for model aggregation in adaptive manner. It is optimized for resource-constrained environments through the implementation of a stopping criterion during model training and weight quantization. Our evaluation on two distinct datasets demonstrates that quantization results in a minimal loss function degradation of 0.01% for Data 1 and 1.25% for Data 2. Furthermore, local model layer optimization for aggregation achieves substantial communication cost reductions of 829.2-fold for Data 1 and 5522-fold for Data 2. The use of an 8-bit fixed-point representation for neural network weights leads to a 75% reduction in storage/memory requirements and decreases computational costs by replacing complex floating-point units with simpler fixed-point units. By addressing data heterogeneity and reducing storage, computation, and communication overheads, the ASLA framework is well-suited for deployment in resource-constrained smart grid networks.

Modeling of heat pumps load profiles for power systems integration

Heat pumps (HPs) are one of the most efficient heating technologies; their mass adoption will be required to decarbonize energy systems. However, to do so will require a better understanding of how they will impact electric grid load. Methods are needed to estimate not just their peak demand but also their impact on hourly load profiles. In this paper, we propose two methods, using easily accessible data, for estimating future hourly load profiles following the adoption of large populations of residential HPs. The first method uses feeder load data disaggregation while the second method uses annual space heating end-use energy consumption, both taking into account the temperature dependencies on coefficient of performance and output heat capacity. A case study based on data from Summerside, PE, Canada, is used to demonstrate and evaluate the two methods.

Techno-economic and environmental analysis of biogas-based hybrid renewable energy systems: A case study for a small-scale livestock farm

Biogas technology offers a significant benefit in enabling energy generation from distributed sources of biomass through its easy scalability. This study aimed to conduct the technical, economic, and environmental analysis of biogas-based hybrid renewable energy systems in a small-scale livestock farm located in the rural area of the Central Anatolia region in Türkiye. The HOMER software was used to analyze a total of five scenarios. These scenarios are hybrid combinations of a biogas generator, PV panels and wind turbines. The hybrid scenarios were evaluated by comparing them to each other and to the base scenario, which only has a grid connection. The optimal system has been determined to be the grid-connected Biogas/PV panel hybrid configuration, with an energy cost of 0.0688 $/kWh and a net present cost of $70,777. The optimal system has a renewable fraction of 89.9 % and a CO2 emissions mitigation potential of 13,066 kg/year compared to the base scenario. Furthermore, sensitivity analysis for various input parameters such as grid sellback rate, interest rate and load demand was performed to assess the impact of changes in these parameters on the system. Sensitivity analysis results show that increased grid sellback rates can significantly enhance financial metrics and reduce the payback period from 13.13 years to 6.63 years.

Impact of variable electricity price on heat pump operated buildings

Background Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.