Residential Load Research Articles

Load frequency and virtual inertia control for power system using fuzzy self-tuned PID controller with high penetration of renewable energy

AbstractReliance on renewable energy is increasing, and generating units are being added to the network. Since renewable power fluctuates greatly, the frequency deviation of the grid becomes a crucial problem with access to renewable power generation. The fluctuation of the renewable power output of the system puts forward a higher demand for load frequency control of the power grid to increase the penetration of renewable power in the system. PID controller has proven its effectiveness for the LFC due to its simple structure and clear concept. In this article, the virtual synchronous generator is introduced and a fuzzy self-tuned PID controller is proposed for inertia control. The proposed controller is implemented in light of the significant integration of renewable energy and virtual inertia. The efficacy of the suggested controller is evaluated against the traditional PID controller for the Egyptian Power System as a case study under various load disturbance scenarios. The control technique is employed for variable loads with photovoltaic and wind turbine generation systems. Three instances of load changes are studied and the controller design is performed based on grey wolf optimizer in each case. The overshot and integral time absolute error are considered as comparison measures. The new contribution is applied to the proposed controller for the grid and virtual inertia. In the case of many load variations imposed, the disturbances of residential and industrial loads varied from 0.05, 0.01, 0.15, and 0.02 pu. The maximum overshoot is 0.005 for the proposed controller and 0.0078 for the classic PID controller. The integral time absolute error is 0.06429 for the proposed controller and 0.11481 for the classic PID controller. The results demonstrate the efficacy of the proposed controller for inertia control with high penetration of renewable energy. The results show that the proposed fuzzy self-tuned PID controller has an overshot less than the classical PID controller by 25% and integral time absolute error by 45%. These results show that the use of the proposed fuzzy self-tune controller for the grid and inertia gave a better performance in terms of the overshot value and the integral time absolute error.

A deep reinforcement learning based charging and discharging scheduling strategy for electric vehicles

Grid security is threatened by the uncontrolled access of large-scale electric vehicles (EVs) to the grid. The scheduling problem of EVs charging and discharging is described as a Markov decision process (MDP) to develop an efficient charging and discharging scheduling strategy. Furthermore, a deep reinforcement learning (DRL)-based model-free method is suggested to address such issue. The proposed method aims to enhance EVs charging and discharging profits while reducing drivers' electricity anxiety and ensuring grid security. Drivers' electricity anxiety is described by fuzzy mathematical theory, and analyze the effect of EV current power and remaining charging time on it. Variable electricity pricing is calculated by real-time residential load. A dynamic charging environment is constructed considering the stochasticity of electricity prices, driver’s behavior, and residential load. A soft actor-critic (SAC) framework is used to train the agent, which learns the optimal charging and discharging scheduling strategies by interacting with the dynamic charging environment. Finally, simulation with actual data is used to verify that the suggested approach can reduce drivers' charging costs and electricity anxiety while avoiding transformer overload.

Optimizing a hybrid wind-solar-biomass system with battery and hydrogen storage using generic algorithm-particle swarm optimization for performance assessment

This paper investigates the optimal design of a hybrid renewable energy system, integrating wind turbines, solar photovoltaic systems, biomass, and battery and hydrogen storage to ensure a reliable energy supply at the lowest annual cost for a residential load in Kern County, USA. The hybrid generic algorithm particle swarm optimization (GAPSO) algorithm was adopted to determine the optimal configuration of parameters and cost-effectiveness, considering technical, economic, environmental, and social performance indicators. The generic algorithm (GA) and particle swarm optimization (PSO) validate the effectiveness of the proposed technique, showcasing its efficiency in system optimization. The findings indicate that GAPSO outperforms GA and PSO due to its rapid convergence, lowest final fitness value, and stable optimization process. The hybrid GAPSO's performance, combined with the different capacities of wind turbines (4,561 kW), solar PV (8,480 kW), biomass (2,261 kW), battery banks (8,000 kWh), and fuel cells (2,392 kW), resulted in an annual cost of $6,239,193; energy cost and net present value of $0.48/kWh and $101,333,937. The system maintained a supply loss of 0.8 %, achieved an availability index of 99.2 %, a renewable energy fraction of 88.87 %, GHGs emission of 953,615 kg, land use of 3,842,875 m2, and water consumption 528,678 L respectively. GAPSO achieved a 2.17 % and 0.01 % improvement in cost-effectiveness and 11.11 % increase in reliability compared to GA and PSO.

Optimal Scheduling of Energy Storage and Shiftable Loads in Grid-Connected Residential Buildings with Photovoltaic Micro-Installations

Photovoltaic (PV) systems are becoming increasingly popular, especially in residential buildings. However, the high penetration of prosumer PV micro-installations can have a negative impact on the operation of distribution networks due to the low self-consumption of the energy produced. One way to mitigate this problem is to use a residential energy storage system (RESS) and load shifting under a demand-side management (DSM) scheme. Energy management systems (EMSs) are used to control the operation of RESSs and to implement DSM. There are two main categories of EMSs: rule-based and optimization-based. Optimization-based EMSs provide better results than rule-based EMSs but can be computationally expensive. This article proposes an optimization-based EMS that is designed specifically for residential buildings. The proposed home energy management system (HEMS) uses a particle swarm optimization method to maximize the prosumer’s financial neutrality, which is calculated based on dynamic energy prices. Simulation-based evaluation using the measurements taken in a building equipped with a PV source, RESS, and shiftable loads shows the improved performance of the proposed HEMS compared to rule-based RESS control. The results show that the designed HEMS increases self-consumption, thus reducing the impact of the prosumer’s PV micro-installations on the distribution grid.

Tackle power outage effects for Egypt's energy crisis via localized optimum load shedding

This paper presents an approach for selecting optimal load-shedding values for individual subscribers or consumers. Implementing this method can bring positive outcomes for consumers by minimizing risks and economic losses caused by power outages. It enables network operators to address power supply deficits and overcome network issues, especially those affecting weaker areas. The optimal load-shedding values are determined by employing IGWA (Improved grey wolf algorithm) through a Graded Objective Function Strategy. The proposed method essentially centered on categorizing loads into two levels based on their importance. The first level includes essential loads, the disruption of which results in risks or significant economic losses. The second level comprises less critical loads, and they do not have more risks or economic losses when power outages. This approach applies specifically to residential loads. The control scheme diagram of disconnecting and reconnecting the current is established by estimating the critical load limit during power outage periods. The necessary arrangements can be made, leading to a proactive measure against risks and losses. The proposal has been implemented in Egypt's electricity network. this proposed method provides a simple and effective solution to the ongoing crisis and provides permanent resolution of problems arising from power outages.

A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy.

Energy demand pattern analysis in South Korea using hidden Markov model‐based classification

AbstractUnderstanding energy demand patterns in the residential sector is crucial for improving energy efficiency through demand‐side management. Load curve classification is a useful method for analyzing energy demand patterns. In this paper, we employ a hidden Markov model (HMM)‐based classification to residential load curves in South Korea. We also investigate how the number of hidden states affects classification performance by allowing HMM to train with a different number of hidden states for each class. We compare our HMM‐based method with several state‐of‐the‐art models and find that it outperforms other competing models in multiple datasets. Additionally, we use the fitted HMM model to make inferences about the load curves, gaining deeper insights into energy demand patterns.

Techno – economic and environmental design of a three – phase hybrid renewable energy system for UNVDA Ndop Cameroon using meta-heuristic and analytical approaches

A three – phase hybrid renewable energy system have been proposed in this study as an alternate current grid power supply for an agro – company in the North – West Region of Cameroon. In this study, an industrial load profile with an energy demand of 383.45 kWh/day, 72.79 kWp peak and a residential load with energy demand of 33.26 kWh/day, 2.7 kWp peak have been considered in solving the economic, technical and environmental aspects of the optimization problem. To achieve the best results, a two step methodology has been implored to design the system. The first method requires an analytical approach used to determine the required battery, inverter, diesel generator capacities and the number of three phase PV arrays (NF). The second methodology is a techno – economic and environmental analysis conducted for different number of NF using three meta-heuristic algorithms; ABC, GA and PSO. The obtained results from these methodologies showed that for different values of NF, the REF was in the range 62.9–99.8 % in the Photovoltaic hybrid system (PVHS). The lowest cost of energy was 0.1441 €/kWh at a final REF of 99.8 % compared to a standalone system with an energy cost of 0.1655 €/kWh. The PVHS consist of a 117 kW PV array, 30 kW inverter, 160 kW diesel generator operating at a load factor of 75 % and a battery bank capacity of 43.2 kWh. The Standalone option consist of a 126 kW PV array, 135 kW inverter and a battery bank capacity of 475.2 kWh. The large inverter capacity and battery bank in the standalone system accounts for the high energy cost compared to the PVHS. This study can be used to evaluate and validate the performance of hybrid renewable energy systems for agro – companies whose load profile lack time series data and require a three – phase supply for industrial applications.

A Short term Electricity Load Forecasting for Community Residents Based on Federated Learning and Considering Privacy Protection

INTRODUCTION: As the penetration rate of renewable energy increases and patterns of energy demand evolve, fluctuations on both the supply and demand sides of electricity are becoming more pronounced. Consequently, accurate forecasting of community residential electrical loads has become crucial. OBJECTIVES: Although the widespread adoption of smart meters among residents provides abundant data for model training, strict challenges arise during the training process due to the need for privacy protection and data security. METHODS: This paper proposes a privacy-preserving community residential short-term electric load forecasting method based on federated learning. Initially, the method applies shared random masking encryption to the sensitive data of community residents, ensuring data privacy while maintaining consistency with the original data after preprocessing. Subsequently, a private data aggregation scheme is established to perform dynamic clustering of the community’s electrical load. RESULTS: The clustered model then serves as the basis for establishing individual load forecasting models for each category of community residents to predict short-term electrical loads. Finally, an empirical analysis is performed using the electrical load data from 120 households across 6 communities in a city in Southern China. CONCLUSION: The analysis demonstrates that the proposed method can achieve the prediction of community residential electrical loads without sharing residents’ data, thus verifying the effectiveness of this approach.

MLFGCN: short-term residential load forecasting via graph attention temporal convolution network.

Residential load forecasting is a challenging task due to the random fluctuations caused by complex correlations and individual differences. The existing short-term load forecasting models usually introduce external influencing factors such as climate and date. However, these additional information not only bring computational burden to the model, but also have uncertainty. To address these issues, we propose a novel multi-level feature fusion model based on graph attention temporal convolutional network (MLFGCN) for short-term residential load forecasting. The proposed MLFGCN model fully considers the potential long-term dependencies in a single load series and the correlations between multiple load series, and does not require any additional information to be added. Temporal convolutional network (TCN) with gating mechanism is introduced to learn potential long-term dependencies in the original load series. In addition, we design two graph attentive convolutional modules to capture potential multi-level dependencies in load data. Finally, the outputs of each module are fused through an information fusion layer to obtain the highly accurate forecasting results. We conduct validation experiments on two real-world datasets. The results show that the proposed MLFGCN model achieves 0.25, 7.58% and 0.50 for MAE, MAPE and RMSE, respectively. These values are significantly better than those of baseline models. The MLFGCN algorithm proposed in this paper can significantly improve the accuracy of short-term residential load forecasting. This is achieved through high-quality feature reconstruction, comprehensive information graph construction and spatiotemporal features capture.

Fuzzy-Logic-Based Cascaded Decentralized Control and Power Quantification of Residential Buildings for Effective Energy Load Management

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables.

Optimal Hybrid Renewable Energy System to Accelerate a Sustainable Energy Transition in Johor, Malaysia

As the world’s second-largest palm oil producer, Malaysia heavily depends on its extensive oil palm cultivation, which accounts for nearly 90% of the country’s lignocellulosic biomass waste. Approximately 20–22 tonnes of empty fruit bunches (EFBs) can be derived from an initial yield of 100 tonnes of fresh fruit bunches (FFBs) from oil palm trees. The average annual amount of EFBs produced in Johor is 3233 tonnes per day. Recognising that urban areas contribute significantly to anthropogenic greenhouse gas emissions, and to support Malaysia’s transition from fossil fuel-based energy to a low-carbon energy system, this research employed HOMER Pro software 3.18.3 to develop an optimal hybrid renewable energy system integrating solar and biomass (EFB) energy sources in Johor, Malaysia. The most cost-effective system (solar–biomass) consists of 4075 kW solar photovoltaics, a 2100 kW biomass gasifier, 9363 battery units and 1939 kW converters. This configuration results in a total net present cost (NPC) of USD 44,596,990 and a levelised cost of energy (LCOE) of USD 0.2364/kWh. This system satisfies the residential load demand via 6,020,427 kWh (64.7%) of solar-based and 3,286,257 kWh (35.3%) of biomass-based electricity production, with an annual surplus of 2,613,329 kWh (28.1%). The minimal percentages of unmet electric load and capacity shortage, both <0.1%, indicate that all systems can meet the power demand. In conclusion, this research provides valuable insights into the economic viability and technical feasibility of powering the Kulai district with a solar–biomass system.

Hi‐Z fault location identification on low voltage distribution systems using IoT‐based digital twin model computation

AbstractDistribution feeders carry and supply power to industrial, commercial and residential loads from the point where sub‐transmission (33 kV level) ends after stepping down to suitable voltages, such as 11 kV and further down to 400/230 V. In recent times, high impedance (Hi‐Z) faults on distribution systems are creating unique challenges to utilities both from operational and safety perspectives. Most of these Hi‐Z faults occur at distribution voltages of 15 kV or below, with the problem being worse at lower voltages. Hi‐Z fault detection technologies emerged and were developed and incorporated on embedded platforms, such as relays, reclosers and sensors, which protect and monitor distribution systems. Although these technologies can detect Hi‐Z fault on feeders, most of them cannot identify the exact location of the fault. Specifically, there is no solution available in literature for detecting Hi‐Z fault location on low voltage (LV) circuits like 3‐phase 4‐wire 400 V distribution network. In this paper, we introduce a novel and a unique algorithm to identify the location of Hi‐Z faults using proposed smart metres IoT data‐based distribution system load flow and digital twin model representation of the network. Furthermore, a case study on the standard 33 bus LV system clearly depicts the functionality of the proposed algorithm.

Optimal scheduling of multiple entities in virtual power plant based on the master-slave game

As the energy market evolves into a dynamic and interactive landscape, the distributed nature of virtual power plant (VPP) becomes increasingly significant. This shift highlights the limitations of traditional centralized optimization approaches in capturing the complex interplay among various stakeholders. In response, this article introduces a novel multi-entity distributed collaborative optimization strategy for an integrated energy system (IES) that incorporates a VPP, electric vehicles, and carbon capture technologies, under the Stackelberg master-slave game framework. Within this framework, the VPP operator (VPPO) assumes the role of the leader, while the energy supply aggregator (ESA), customer side residential load aggregator (CSRLA), electric vehicles aggregator (EVA), and carbon treatment system aggregator (CTSA) are positioned as the followers. The paper delves into the development of interaction strategies for each entity, aiming at achieving optimal objectives. The study undergoes by presenting the mathematical model of the VPP-integrated energy system, which is seamlessly integrated into the master-slave game structure. This integration facilitates the creation of a distributed multi-entity cooperative optimization model featuring a single leader and multiple followers, with the uniqueness of the Stackelberg equilibrium being rigorously established. Therefore, the research employs a hybrid nutcracker optimization algorithm and quadratic programming (NOA-QP) method to address the engineering challenge of optimizing operator energy pricing. The case study conducted demonstrates that the proposed scheduling methodology improves the VPP system profit by 10.80%, increases the aggregation profit by up to 1317.22%, optimizes the carbon emissions by 15.59%, and significantly improves the solution efficiency of the VPP by 99.29%.

Short‐term load interval prediction with unilateral adaptive update strategy and simplified biased convex cost function

AbstractThis article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time.

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

AbstractThe transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

Non-intrusive residential load identification based on load feature matrix and CBAM-BiLSTM algorithm

Non-intrusive residential load identification based on load feature matrix and CBAM-BiLSTM algorithm

The role of pit lake thermal dynamics on the thermal performance of ground heat exchangers

The addition of ground heat exchangers (GHEs) to a pit lake’s basin has the potential for abundant, clean and renewable geothermal energy extraction using shallow geothermal systems. Basin-embedded GHEs avoid direct interaction with mine water, which has been shown to impact efficiency and longevity in mine open-loop geothermal systems negatively. The now accelerated closure of open-pit coal mines presents itself as an opportunity to use this technology. However, no guidelines currently exist for designing or operating GHEs embedded in the sediment of water bodies. Furthermore, the two-way coupling between the complex annual thermal fluid dynamics that lakes are naturally subjected to and heat fluxes on the sediments and the GHE system has not been explored. In this study, we develop and validate finite element models to assess the relevance of lake thermal stratification in the performance of a geothermal system embedded in water bodies basins, e.g., on open-pit mine closures, under temperate residential thermal loads. The results show that the pit lake’s role as a thermal sink improves significantly when the lake’s thermal dynamics are accounted for, with an increase of up to 292% in the lake’s available energy budget. A minor variation in energy budget (~8%) was found whether the lake is modelled explicitly or simplified as a transient Dirichlet temperature boundary condition. This small difference vanishes if horizontal circulation along the lake is considered, highlighting the lake’s thermal energy potential. Finally, the impact on the GHE Coefficient of Performance (COP) is evaluated, with a maximum of ~15% difference among all cases.

A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer

A Medium- and Long-Term Residential Load Forecasting Method Based on Discrete Cosine Transform-FEDformer

An open data-based model for generating a synthetic low-voltage grid to estimate hosting capacity

This study develops and applies an open data-based reference electricity grid analysis (REGAL) model designed to create a synthetic representation of a low-voltage (LV) grid for a country-size geographic area. The model enables large-scale grid simulation in which new loads, such as electric vehicle charging, can be added to estimate their impacts on the current LV grid. The modeling is carried out in three steps: (1) generation of a synthetic LV grid; (2) addition of residential loads, including electric vehicle charging; and (3) evaluating if the grid capacity is exceeded. The grid is generated by selecting transformers and cables so that the system can fulfill the current demand while meeting national regulations and standards for distribution grids, all at the lowest total cost. This paper presents the results of calibration and validation against real-world data for the predicted electricity demands and synthetic grid generated by the model. Different calibration values were explored, and the accuracy of the estimations of grid capacities was calibrated using proprietary real-world data from grid operators. For a region with multiple grid cells, an average deviation from real-world data of ±10 % was achieved. For an average area of 1 km2, the error was 44.5 %, which means that the model is not suitable for analysis on this geographic level. However, the level of accuracy is deemed sufficient for initial estimations of hosting capacity for larger geographic areas, such as a region or a country, thereby enabling estimations of hosting capacity in new areas that lack publicly accessible grid capacities.