Residential Load Research Articles

Photovoltaic-wind-battery and diesel generator-based hybrid energy system for residential buildings in smart city Coimbatore.

The building consumes almost 40% of the energy generated in the building. Investigating the photovoltaic system, wind, battery, and diesel generators for residential buildings can reduce energy utilization. In this work, various energy sources are combined to form hybrid energy sources, which are designed based on the load of the residential building. The Hybrid Optimization of Multiple Energy Resources tool optimizes various energy sources such as photovoltaic (PV), wind, diesel generator (DG), and battery. An investigation on the residential load in the smart city of Coimbatore, Tamil Nadu, India, is being carried out. This article examined the technological and economic feasibility of solar photovoltaic, wind, diesel generators, and batteries combined to form a hybrid energy source (HES). With 2kW of photovoltaic, 1kW wind, 1kW of DG, 1kW of the power converter, and five batteries, system case 1 (photovoltaic/wind/diesel generator/battery model) had the best results in the simulation and was recommended for use in the proposed residential building. As a result, it has a minimum net present value of $14,568 and an energy cost of $0.312/kWh, which is about 39% cheaper than system base cases. The sensitivity and environmental analysis are carried out to analyze the system's feasibility.

Enhancing peak prediction in residential load forecasting with soft dynamic time wrapping loss functions

Short-term residential load forecasting plays a crucial role in smart grids, ensuring an optimal match between energy demands and generation. With the inherent volatility of residential load patterns, deep learning has gained attention due to its ability to capture complex nonlinear relationships within hidden layers. However, most existing studies have relied on default loss functions such as mean squared error (MSE) or mean absolute error (MAE) for neural networks. These loss functions, while effective in overall prediction accuracy, lack specialized focus on accurately predicting load peaks. This article presents a comparative analysis of soft-DTW loss function, a smoothed formulation of Dynamic Time Wrapping (DTW), compared to other commonly used loss functions, in order to assess its effectiveness in improving peak prediction accuracy. To evaluate peak performance, we introduce a novel evaluation methodology using confusion matrix and propose new errors for peak position and peak load, tailored specifically for assessing peak performance in short-term load forecasting. Our results demonstrate the superiority of soft-DTW in capturing and predicting load peaks, surpassing other commonly used loss functions. Furthermore, the combination of soft-DTW with other loss functions, such as soft-DTW + MSE, soft-DTW + MAE, and soft-DTW + TDI (Time Distortion Index), also enhances peak prediction. However, the differences between these combined soft-DTW loss functions are not substantial. These findings highlight the significance of utilizing specialized loss functions, like soft-DTW, to improve peak prediction accuracy in short-term load forecasting.

Monte Carlo Simulation for Assessing the Voltage Level and Losses with Photovoltaic and Electric Vehicle in Low Voltage Network

Livelab from Alliander is a program which started to measure electrical and power quality data in the Dutch distribution network since 2013. This paper presents a methodology to generate the residential load profiles by cumulative distribution function (CDF) based on Livelab measured data, then applying Monte Carlo simulation method, a statistic three phase Load Flow program is developed in order to investigate the voltage level and network losses (harmonic loss is considered) of a typical low voltage (LV) network with the consideration of variable loads and random locations. Then, the prospective impact to voltage level and losses due to the grid connected photovoltaic power generation systems (PV) and electrical vehicles (EV) are discussed, Monte Carlo simulations are done with the different penetration rates of PV and EV, maximum capacity of grid-connected PVs and EVs can be estimated and the possible network losses are calculated.

Enhancing Renewable Energy Use in Residential Communities: Analyzing Storage, Trading, and Combinations

Renewable energy resources, especially rooftop solar PV, have gained momentum during the past few years. However, the local consumption of PV power is limited due to the negative correlation between peak PV power and residential loads. Therefore, this study analyzes various cases to maximize the consumption of renewables in communities encompassing dwellings both with and without PV installations. The three cases considered in this study are local energy storage, community energy storage, and internal trading. A total of six cases are analyzed by evaluating these cases individually and in combinations. To achieve this, first, a generalized optimization model with specific constraints for each case is developed. Subsequently, different indices are devised to quantitatively measure trading with the grid and the consumption of renewables under varying cases. The performance of these different cases is analyzed for a community comprising five dwellings over a summer week. Furthermore, the performance of each case is evaluated for various seasons throughout the year. Additionally, a sensitivity analysis of different storage capacities (both local and community) is conducted. Simulation results indicate that community storage results in the highest renewable consumption if only one case is considered. However, the overall combination of internal trading and community storage results in the highest cost reduction, lowest dependence on the grid, and the highest consumption of renewables. Finally, a techno-economic analysis is performed on four widely used battery technologies, taking into account diverse cost and technical considerations.

Performance Investigation of Wind/Photovoltaic/Fuel cell Hybrid Energy System for Stand-alone Power Supply

Large numbers of households in isolated areas in Nigeria were reported to have lacking access to national grid. Their basic electricity demand is served through fossil fuel generators which are costly and environmentally unfriendly. The fact that extension of national grid lines to those areas becomes difficult, renewable energy resources exploitation is the best alternatives. Wind and solar energy as clean abundantly available resources could be harnessed in a hybrids form with long-term storage devices for enhancing energy optimization and power stability. This paper investigated the performance of Wind-PV-Fuel cell Hybrid Energy Systems for stand-alone application. The performances of Wind-Battery-Fuel cell and PV-Battery-Fuel cell Hybrid Energy Systems were investigated by using Hybrid Optimization Models for Energy Renewable (HOMER), through cycle charging controls. The techno-economic Figure of merits for the optimal configurations from these systems was compared. The results revealed that Wind-Battery-Fuel cell Hybrid had been the best system for serving4.37kWh/day (1595kWh/yr) residential load demand. The system was reported with 321 kWh/yr excess electricity, 8.59 kWh/y unmet load and 10.5 kWh/yr capacity shortage. While, the Net Present Cost (NPC), Operation and Maintenance Cost (O&MC) and Cost of Energy (COE) were $25,951, $185 /yr and $1.229/kWh respectively. The result of sensitivity analysis, predicted the unmet load and capacity shortage to be declined by 0.0% each, with system autonomy of 7.9 days. Meanwhile, the NPC, O&MC and COE have decreased by 24.6 %, 8.1 % and 25.06 % respectively. This analysis confirmed that Wind-Battery-Fuel cell-Hybrid Energy System could be an economically viable and technically feasible for stand-alone power supply for isolated areas.

Residential load forecasting by a PSO-tuned ANFIS2 method considering the COVID-19 influence

The most important feature of load forecasting is enabling the building management system to control and manage its loads with available resources ahead of time. The electricity usage in residential buildings has increased during the COVID-19 period, as compared to normal times. Therefore, the performance of forecasting methods is impacted, and further tuning of parameters is required to cope with energy consumption changes due to COVID-19. This paper proposes a new adaptive neuro-fuzzy 2 inference system (ANFIS2) for energy usage forecasting in residential buildings for both normal and COVID-19 periods. The particle swarm optimization (PSO) method has been implemented for parameter optimization, and subtractive clustering is used for data training for the proposed ANFIS2 system. Two modifications in terms of input and parameters of ANFIS2 are made to cope with the change in the consumption pattern and reduce the prediction errors during the COVID-19 period. Simulation results obtained by MATLAB software validate the efficacy of the proposed ANFIS2 in residential load forecasting during both normal and COVID-19 periods. Moreover, the performance of the proposed method is better than that of the existing adaptive neuro-fuzzy inference system (ANFIS), long short-term memory (LSTM), and random forest (RF) approaches.

Implementation of battery storage system in a solar PV-based EV charging station

Commercial production of solar-powered battery charging stations for electric vehicles has recently started largely as a result of the growing popularity of electric vehicle (EV) technology as well as the falling cost of photovoltaic (PV) systems. The rise in popularity of EVs may be attributed to both the progression of technology and rising awareness of environmental issues. In this work, a 400V DC bus voltage-based EV charging station is designed which is powered by both a PV system and a utility grid. Also, battery energy storage units are used to overcome the unpredictable behavior of the environment. An Energy management algorithm (EMA) has been presented to balance the power flow among PV, EVs, BESS, grid, and residential load. A sharing coefficient-based power sharing is done between the grid and battery to reduce the stress from the battery units. Then, a multi-step constant current charging method is used to charge the EVs in the charging station to maintain the proper state of charge (SOC) of EV batteries. During this study, different operating conditions are considered according to available PV power and load to validate the power management scheme in the EV charging station. The model is run using MATLAB simulation and opal-RT simulation. The obtained results have been demonstrated with an explanation and system performance verification.

Synchronization and Rebound Effects in Residential Loads

Synchronization and Rebound Effects in Residential Loads

Multilayer Iterative Stochastic Dynamic Programming for Optimal Energy Management of Residential Loads with Electric Vehicles

This work introduces a multilayer iterative stochastic dynamic programming (MISDP) framework for optimizing energy management in smart residential settings, incorporating electric vehicles to reduce energy costs while enhancing operational efficiency. The study investigates the complexities of managing residential loads with integrated EV batteries, set against the backdrop of unpredictable charging demands and fluctuating energy prices. The proposed method is designed to optimize charging and discharging schedules, ensuring cost‐effective energy consumption without compromising the longevity of EV’s battery operations. The proposed MISDP strategy encompasses multi‐iteration processes, both at internal and external levels, that not only highlight the method’s capacity for precise, real‐time decision‐making but also underscore its adaptability to the dynamic nature of energy systems. The external iteration primarily focuses on adapting to broader operational variables, such as fluctuating prices and demand patterns, setting a framework for optimization. Concurrently, the internal iteration updates the details of EV battery operation, fine‐tuning charging and discharging strategies to refine the control law sequence for each operational period, ensuring optimal energy management. Throughout the iteration process, the framework ensures the performance index function remains bounded, adhering strictly to the evolving control law sequence. Through comparative analysis, the MISDP framework is evaluated against different optimization techniques, demonstrating its superior capability in achieving significant energy cost savings and operational effectiveness while ensuring convergence under stochastic conditions.

Power-to-Frequency Dependency of Residential Loads in a Wider Frequency Range: An Experimental Investigation

Power-to-Frequency Dependency of Residential Loads in a Wider Frequency Range: An Experimental Investigation

Short-Term Residential Load Forecasting With Baseline-Refinement Profiles and Bi-Attention Mechanism

Short-Term Residential Load Forecasting With Baseline-Refinement Profiles and Bi-Attention Mechanism

Machine Learning Data-Driven Residential Load Multi-Level Forecasting With Univariate and Multivariate Time Series Models Toward Sustainable Smart Homes

Machine Learning Data-Driven Residential Load Multi-Level Forecasting With Univariate and Multivariate Time Series Models Toward Sustainable Smart Homes

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

Hybrid Renewable Energy Microgrids: A Genetic Algorithm Approach to System Design

The paper examines the use of genetic algorithm (GA) methods to optimize hybrid renewable energy microgrids by merging various renewable sources and energy storage technologies. An examination of meteorological data over many days reveals fluctuations in solar irradiance ranging from 4.8 kW/m² to 5.5 kW/m² and wind speed oscillating between 3.9 m/s and 4.5 m/s, indicating the presence of dynamic weather conditions. An analysis of energy generating capabilities reveals a wide range of potentials, with solar capacities varying from 80 kW to 150 kW and wind capacities ranging from 60 kW to 120 kW across different sources. An analysis of Energy Storage System (ESS) specifications shows a range of values for maximum capacities, charge/discharge efficiencies (ranging from 85% to 96%), and maximum charge/discharge rates (from 60 kW to 100 kW), highlighting the need for flexible energy storage systems. The examination of microgrid load profiles reveals the presence of diverse energy needs, with residential loads oscillating between 48 kW and 55 kW, commercial loads ranging from 40 kW to 47 kW, and industrial loads spanning from 30 kW to 36 kW. A percentage change study reveals the ability to adapt, with solar irradiance and wind speed showing mild fluctuations of roughly 14% and nearly 15% respectively. In contrast, renewable source capacity demonstrate significant percentage changes ranging from around 40% to 50%. These results highlight the ever-changing characteristics of renewable energy sources, underlining the need for strong optimization tactics in microgrid systems. The study emphasizes the potential of GA-based approaches in developing efficient microgrids, promoting sustainable and dependable energy solutions in the face of changing environmental circumstances and varied energy requirements.

The One-Node Approach to Implement Smart Grid Functions for Residential Loads

The One-Node Approach to Implement Smart Grid Functions for Residential Loads

Energy Management of a Battery Storage System Considering Variable Load and Controllable Renewable Generation (Solar Study Case) to Keep the Grid’s Frequency Stability

This paper presents the research of analytical functions related to the energy generation of photovoltaic systems and the residential and commercial load demanded by end users, concerning a statistical function. To test this model, a linear cost function was considered to compute its overestimation and underestimation due to its maximum and minimum production limits, where energy consumption is obtained at each instant of time, within the established production ranges, through the analytical equations that determine solar energy generation and demand load. The result obtained by applying the Uncertainty Quantification (UCF) theory in these equations, in the same way through the Monte Carlo (MC) simulation for comparison, is the expected value of energy for a hypothetical storage system E (Cu, Co). Better accuracy of results via this model can be improved upon when the energy generation parameters are structured as analytical functions each instant of time associated with probability distributions based on the uncertainty costs of controllable sources, instead of statistical functions.

Cost-efficiency based residential power scheduling considering distributed generation and energy storage

The permeation of renewable energy into smart house is a key characteristic of the future power system that brings a significant challenge to the peak load management in the power sector. In this paper, we propose cost-efficiency based residential power scheduling scheme considering distributed generation and energy storage. In which, a cost-efficiency model designed by the ratio of consumption benefit to its cost is introduced to capture the residential users' electricity consumption behaviors and reflect their economic efficiency. A discomfort cost is constructed to represent the losses associated with power scheduling when facing the incentives given by the smart grid. Specially, not only residential load consumption utility, but also that of the renewable energy generation and energy storage devices are applied to compose the consumption benefit，and an energy storage model to be established to plan the charging and discharging of energy storage batteries. Day-Ahead Bidding Price and Real-time consumption management models are introduced to test the performance of the proposed scheduling scheme. Simulation results demonstrate that the proposed method increases the residential consumer's daily cost efficiency by 1.5834, and reduces its daily electricity bill by 38.43 % and that about 36.86 % of energy is saved in the smart grid while the proposed scheduling strategy for energy consumption can effectively smooth the load curve in smart grid.

A novel energy management method for multiple residential energy systems with energy exchange

Energy saving and emission reduction are the worldwide outstanding problems in the development of economy and society, which lead to wide attention of residential energy management. This paper presents a novel energy management method for multiple residential energy systems with energy exchange based on action-dependent heuristic dynamic programming (ADHDP). The major contributions are displayed as follows: (1) a novel energy management model is established for multiple residential energy systems with real-time electricity price and time-varying residential loads; (2) a novel ADHDP based energy management method is presented to obtain the energy scheduling scheme of multiple residential energy systems; (3) the continuous control action of the overall system is transformed into discrete control space which avoids training the action network. First, the structure and system models of multiple residential energy systems are introduced, and the energy management problem is formulated. Then, a brief introduction of ADHDP method is provided. Based on this, an energy management method is presented and an effective discretization method of control action is developed for solving the presented energy management problem with less computational burden. Simulation results show that the presented method can reduce the energy cost effectively. Finally, the comparison results verify the superiorities over the traditional energy management methods.

Battery capacity design and optimal operation control of photovoltaic-battery system considering electrochemical aging

In recent years, the distributed photovoltaic battery (PVB) system is developing rapidly. To fully utilize photovoltaic production and increase the penetration of renewable energy, battery storage in distributed photovoltaic systems becomes essential. Despite plenty of studies dedicated to the capacity design and system control strategies under different work conditions, few research pay attention to the sophisticated battery storage aging problem and its effect on the system performance. This paper develops a five-parameter photovoltaic model and the electrochemical lithium battery model for the PVB system considering the residential load uncertainty in the distributed photovoltaic system. The battery and system performance under different capacity design and operation strategies are discussed. The results show that the oversize of the battery capacity design contributes to the capacity loss, leading to the increasement of levelized cost of storage, and the capacity design of 6, 8, 10 kWh under 100 %, 80 %, 70 % state of charge (SOC) charging limit is recommended in this case. Under the 100 % SOC limit, the battery capacity declines by 22 %–28 % annually, which is much higher than the limit of 80 % and 70 %. Meanwhile, the battery lifetime based on recommended capacity design can be improved further by 8.6 %, 4.0 % and 2.0 % with controlling the battery charge rate. Besides, a comparison between the electrochemical and traditional battery models is conducted, demonstrating that both models present a high consistency in system energy flow, while the traditional battery model lacks the internal electrochemical aging mechanism. The electrochemical battery model gives out a more precise battery aging result and reveals the different performances under varied control strategies.

FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid

In the contemporary energy landscape, power generation comprises a blend of renewable and non-renewable resources, with the major supply of electrical energy fulfilled by non-renewable sources, including coal and gas, among others. Renewable energy resources are challenged by their dependency on unpredictable weather conditions. For instance, solar energy hinges on clear skies, and wind energy relies on consistent and sufficient wind flow. However, as a consequence of the finite supply and detrimental environmental impact associated with non-renewable energy sources, it is required to reduce dependence on such non-renewable sources. This can be achieved by precisely predicting the generation of renewable energy using a data-driven approach. The prediction accuracy for electric load plays a very significant role in this system. If we have an appropriate estimate of residential and commercial load, then a strategy could be defined for the efficient supply to them by renewable and non-renewable energy sources through a smart grid, which analyzes the demand-supply and devises the supply mechanism accordingly. Predicting all such components, i.e., power generation and load forecasting, involves a data-driven approach where sensitive data (such as user electricity consumption patterns and weather data near power generation setups) is used for model training, raising the issue of data privacy and security concerns. Hence, the work proposes Federated Smart Grid (FedGrid), a secure framework that would be able to predict the generation of renewable energy and forecast electric load in a privacy-oriented approach through federated learning. The framework collectively analyzes all such predictive models for efficient electric supply.