End-use Loads Research Articles

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization

Abstract The hybrid microgrid (HMG) possesses power generation, storage, and distribution capabilities, serving as an integrated foundational structure for advanced localized distribution networks. However, due to significant discrepancies in load demands and energy consumption among different users, the photovoltaic (PV) generation output and end-user load requirements exhibit unpredictability, resulting in relatively low self-sufficiency ratio (SSR) and self-consumption ratio (SCR) of the HMG PV system. To address this issue, this study proposes a virtual energy dispatching method for hybrid microgrid demand response based on Lyapunov optimization. Initially, a bidirectional virtual energy dispatching system model based on energy provider/buyer classification is constructed, optimizing load demands and energy management of both the load side and the main utility grid (MUG) while considering demand response. Subsequently, a Lyapunov optimization-based bidirectional virtual energy dispatching algorithm (LOBVED) is introduced, optimizing queueing systems in stochastic networks based on drift-plus-penalty technique to resolve the dynamic interactions among stability, performance, and energy self-sufficiency in the HMG environment, aiming to maximize the PV SSR and SCR of the HMG while ensuring the reliability of battery energy storage (BES) energy queues. Finally, the effectiveness of the proposed method is validated through MATLAB simulations.

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

Distributionally robust planning for power distribution network considering multi-energy station enabled integrated demand response

—To copy with the peak valley difference and peak load problem of power distribution network (PDN), this paper proposes a distributionally robust planning model for PDN considering the flexible management via the multi-energy station (MES) based integrated demand response (IDR). The planning covers reinforcement of the existing substation and feeders as well as the investment of MES and IDR. The IDR involves the incentive interruptible load response (ILR), transferable load response (TLR) and price-based power-gas substitution (PGS) which is implemented on MES. The MES integrates multiple energy storages and energy coupling devices, which is served as a platform for PDN operator to centrally track the final demand and improve the response potential of IDR. The proposed planning method aims at minimizing the present value of PDN investment cost and operation cost during the planning period. To handle the uncertainty of PDN end-use loads, a two-stage Kullback–Leibler divergence (KLD)-based distributionally robust optimization (DRO) planning model is formulated. Finally, the columns and constraints generation algorithm is utilized to solve the KLD-DRO planning model. Case studies are carried out on a practical 152-bus urban distribution system to validate the effectiveness of the proposed methodology and quantitatively analyze the key influence factors of PDN investment.

Smart Meter Tampering Detection Using IoT Based Unsupervised Machine Learning

Abstract: This work presents a novel smart grid tampering detection system re-engineered for end user monitoring and pipeline automation. The research focused on distributed energy resources. In context, the end user load profile, and generation capacity were processed in the cloud environment for tampering management. Computational pipelined methodology was adopted using baseline data from an independent electricity consumption data from 2018-2021 Abuja. First, a smart grid (SG) survey was carried using existing home estate at Abuja to ascertain tampering procedures in distributed energy resource domain. From the energy survey, the system architecture was developed and implemented based on computational model curve for dynamic attack vector mitigation.

Flux potentials of local talc in low voltage electro-porcelain insulator production

Increasing demand for accessories like porcelain electric insulators have continued in developing countries causing overdependence on imported products. Meanwhile, raw materials for their local production are available in commercial quantities with high firing temperature being the major challenge. Hence, the need for fluxing agents that can reduce the working temperature led to the investigation of Talc as a partial replacement of conventional Feldspar. An optimum replacement of 10% Feldspar with Talc was adopted, and this recipe showed microstructurally enriched glassy phase, primary and secondary Mullite with other crystalline phases like Cristobalite, Microcline and Albite at 1200oC with micropores evident in both the Talc and Feldspar formulations. The produced insulator fired at 1200oC offered an optimum thermal conductivity of 0.2768W/mK with reductions in water absorption, apparent porosity and linear shrinkage by 78.29%, 76.02% and 19.75% respectively with a 9.48% rise in bulk density. The electrical performance comprised Inception, Withstand and Breakdown Voltages of 10.2 kV, 14 kV and 20 kV respectively at leakage currents of 0.2 mA, 1.2 mA and 2 mA accordingly. With a withstand voltage above 11 kV and breakdown voltage below 22 kV, this pin-type porcelain insulator will find wide application in both electric substations and end user load points like commercial and residential electric power lines. The novelty lies on the fluxing and filler potentials of Talc as a locally-sourced mineral in porcelain insulator production which increases the market value of Nigerian Talc while reducing importation, costs of energy and raw materials in porcelain production.

Adaptive Load Shedding as Part of Primary Frequency Response To Support Networked Microgrid Operations

Global changes in the deployment of distributed energy resources, control and communications technologies, business models, and regulatory policy are increasing the operational options for future distribution systems. One such option is the coordinated operation of distributed resources to form microgrids and networks of microgrids to support traditional bulk power systems during normal operations and critical end-use loads during outages. While individual standalone microgrids have been extensively studied and deployed, the coordinated operation of networked microgrids is operationally more challenging due to the dynamic boundaries and changing mix of generation resources. This paper presents a method of using a distributed control architecture to support primary frequency response in networked microgrids operations. The support of primary frequency response is accomplished using the Open Field Message Bus reference architecture and Grid Friendly Appliance controllers.

Topology optimization method of district heating system considering load uncertainty

With the implementation of the carbon peaking and carbon neutrality strategy, China's heating system has witnessed various transformative trends, including source-network-load integration and the synergistic utilization of fossil and renewable energy sources. The district heating system, serving as a vital intermediary between heat sources and end-users, plays a crucial role in ensuring a stable energy supply. Particularly, as renewable energy integration increases and end-user requirements become more refined, the demand-supply matching aspect of district heating systems becomes increasingly critical. Therefore, it is essential to quantify and analyze user demand while establishing an adaptive pipe network capable of accommodating fluctuations in end-user load. This paper proposes an optimal topology structure method for district heating systems, taking into account the uncertainty of end-user load. Specifically, a quantitative model of end-user load uncertainty is developed, utilizing the Gram-Charlier A expansion method to capture the factors influencing end-user load uncertainty comprehensively. Furthermore, an optimization model for the pipe network topology is formulated, which considers the uncertainty of end-user load as a constraint and aims to minimize pipeline construction costs. This model optimizes the layout, size, and location of heating sources within the district heating system. The proposed method's practical applicability is validated through a case study of a district heating system in a northern Chinese city that involves 21 end-users. The results indicate that the end-user load uncertainty model outperforms the conventional normal distribution fitting model when dealing with bimodal load distributions. Additionally, the proposed method reduces construction costs by 6.24%, decreases heat loss by 5.30%, and eliminates redundant structures within the district heating system. This research significantly contributes to the planning and design of heating pipeline networks and the optimization and refurbishment of existing pipeline networks within district heating systems.

Techno-economic assessment of the solar-assisted heat pump latent heat thermal energy storage system for water heating

A novel solar-assisted heat pump heating system including the latent heat thermal energy storage component that enables flexible switching between different connection modes was developed in this study to provide a cost-effective and energy-efficient solution for hot water supply. The feasibility, energy efficiency, and economic performance of the system under different design parameters and operation modes were evaluated via the experimental validated numerical model of the heating system. The end-user loads and meteorological parameters highly affected the system performance under the same design scheme. The optimal system configuration and component size for promoting renewable energy penetration and decreasing electricity cost were further determined. A certain increment in the collection area substantially benefits system performance, resulting in an approximately 56.6 % reduction in energy consumption. The larger capacity of the heat pump reflects a slight improvement in the system performance; however, it would require a longer payback period. Compared with the single solar and air heat source, the energy consumption of the hybrid heat source system declined by 26.6 % and 68.0 %, respectively. The system implementing the demand-side management proved to be a more profitable solution, exhibiting a low carbon emission of 188.1 kg, significant potential to flatten peak load, and a large lifecycle cost saving of $1834.64.

A Comprehensive Framework for Data-Driven Building End-Use Assessment Utilizing Monitored Operational Parameters

The availability of building operational data such as energy use and indoor temperature provides opportunities to enhance the traditional building energy performance calculation. Disaggregated building energy use facilitates informed decision-making to identify cost-saving measures efficiently at the individual building and building stock levels. The existing energy performance analysis techniques with measured input data in the literature are fragmented. Moreover, they frequently approach this issue with varying degrees of complexity depending on the available input data, expertise, and time. The procedure of choosing an appropriate method is often cumbersome with limited indication of the usefulness of the outcomes. This study proposes a data-driven framework for end-use load disaggregation through techniques that exploit various kinds of building consumption data. The results demonstrate the use of different techniques for varied applications. Calibrated theoretical calculation, data-driven heat loss coefficient (HLC), and energy signature curve (ESC) are among the proposed methods in the framework that facilitate individual, and urban scale energy decomposition. It is observed that different methods yield unalike outcomes, while their performance is predictable. While the HLC methods are flexible but also highly sensitive to the input parameters, the ESC needs high-frequency time series but provides stable energy decomposition. The ESC is efficient for large-scale analysis and the HLC method for detailed case-specific applications. Calibrated theoretical energy decomposition has a simple workflow and can supplement the current energy performance assessment method, although it entails sufficient input data.

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Machine learning surrogate of physics-based building-stock simulator for end-use load forecasting

Dynamic optimization and thermo-economic assessment of a solar parabolic trough combined heat and power system with thermal storage: A case study

The solar-driven combined heat and power system provides an efficient and green approach for the energy demand in remote areas. In this study, a two-tank molten salt thermal storage system is coupled with solar parabolic trough collectors and feeds an organic Rankine unit that produces electricity and heating. Specially, the organic Rankine unit can operate in off-design conditions following the end-user electricity load demand. The configuration of the complete system is parametrically investigated and dynamically optimized aiming to identify the operating conditions for which the net present value is maximized. The optimal arrangement for Lhasa is found to be a 1175-m2 solar parabolic trough field and two 50-m3 molten salt storage tanks. The monthly results show that this optimal case is able to run uninterruptedly the whole month in November and December with a 100% monthly electricity supply reliability rate. Furthermore, the application of this system for typical cities in western China is optimized and compared. The best economical location for the apparatus is in Ejinaqi, with a maximum net present value of 1471 thousand dollars, of which carbon reduction benefit account for 8.8%, and the payback period is only 5.4 years. Moreover, the yearly energy efficiency, exergy efficiency, electricity supply reliability rate and solar abandonment rate for optimal case of Ejinaqi are 55.0%, 20.2%, 93.9% and 25.4%, respectively. The study demonstrates that the proposed apparatus can be a promising option to provide energy for remote areas faced with poorly power grid.

PEMBANGUNAN GAME LOST CHAIN MENGGUNAKAN BLOCKCHAIN DAN GASLESS TRANSACTION

Blockchain technology has emerged as a disruptive innovation, opening the way for the creation of decentralized apps (dApps). However, the current implementation of blockchain technology requires gas fees, which are transaction fees paid to miners for transaction validation. Gas fees restrict the adoption of decentralized applications because users must pay these fees to interact with the network. The method to reduce consumer burden by utilizing transactions without gas is using meta-transactions. Meta-transactions allow third parties to pay fees on behalf of users, so users don't have to pay expensive gas fees. These solutions have the ability to simplify the user experience, reduce end-user cognitive load, and increase dApp adoption. The results of this study indicate that gasless transactions are a promising method for relieving user tension and increasing dApp adoption. Overall, this research offers useful insights into the problem of gas costs in blockchain technology and suggests practical solutions to improve user experience.

The effect of components capacity loss on the performance of a hybrid PV/wind/battery/hydrogen stand-alone energy system

A stand-alone hybrid renewable energy system (HRES) comprising a photovoltaic (PV) array and a small wind turbine for energy production, a lead-acid battery for short-term energy storage, and a hydrogen subsystem for long-term energy storage is analyzed in this study. The hydrogen subsystem comprises a PEM electrolyzer for hydrogen production, a hydrogen compressor and a hydrogen tank for hydrogen storage, and a PEM fuel cell for power generation. Twenty-year hourly input weather data were acquired from the national meteorological service for the Mediterranean location of Split, Croatia. Two different end-user load profiles are considered, namely a year-around constant load profile and the stochastically variable load profile. Furthermore, a comparative analysis is performed for two different energy management strategies – one based on energy balance within the system, and the other based on double hysteresis loop control. The HRES performance is simulated using MATLAB. The dynamics of capacity loss of the batteries, PEM electrolyzer, and PEM fuel cell is a function of how the system operates, but at the same time, this dynamics of capacity loss affects the overall system performance. The double hysteresis control strategy results in a significantly longer battery lifetime for both load profiles considered, while the PV and battery loss of capacity causes more frequent electrolyzer and fuel cell operation for all considered cases.

Enhanced Automated Deep Learning Application for Short-Term Load Forecasting

In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability.

County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers

Customer concerns over electric system resilience could drive early adoption of behind-the-meter solar-plus-storage (BTM PVESS), especially as wildfire, hurricane, and other climate-driven risks to electric grids become more pronounced. However, the resilience benefits of BTM PVESS are poorly understood, especially for residential customers, owing to lack of data and methodological challenges, making it difficult to forecast adoption trends. In this paper, we develop a methodology to model the performance of BTM PVESS in providing backup power across a wide range of customer types, geography / climate conditions, and long duration power interruption scenarios, considering both whole-building backup and backup of specific critical loads. We combine novel, disaggregated end-use load profiles across the continental United States with temporally and geospatially aligned solar generation estimates. We then implement a PVESS dispatch algorithm to calculate the amount of load served during interruptions. We find that PVESS with 10 kWh of storage can meet a limited set of critical loads in most United States counties during any month of the year, though this capability drops to meeting only 86% of critical load, averaged across all counties and months, when heating and cooling are considered critical. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points in Harris County correspond to a 40% range in winter backup performance and a 20% range in summer performance. Economic calculations show that a customer’s resilience value of PVESS must be high to motivate adoption of these systems.

A Cautionary Note on Using Smart Plugs for Research Data Acquisition

The availability of low-cost smart plugs that measure and control loads has encouraged their use as a research tool to disaggregate end-user loads. While there has been extensive research on the cybersecurity implications, and demand response capabilities, of smart plugs, there has been little investigation of their primary function: Measuring loads. In this study, we analyze the accuracy of power and energy measurements reported by 5 smart plugs from different manufacturers, with a specific focus on the accuracy of the meters when load levels change rapidly, as is often the case with power electronic loads. The study included Belkin, TP-Link, Etekcity, Emporia, and Sonoff smart plugs, with loads switching at frequencies of 0.01Hz to 2Hz, in addition to the steady-state loads often used for testing these devices. Data indicate significant errors in both energy and power measurement when used to monitor highly variable loads. While maximum energy measurement error for the Belkin product never exceeded 1.7%, maximum energy measurement errors were 54–100% for the other 4 brands. Power measurement accuracy was highly dependent on load variability, with all units exhibiting substantial errors when loads varied, even at rates as low as 0.01-0.05 Hz. All units also exhibited reporting delays of 3–6 seconds, with some delays as high as 20 seconds. The large, load-dependent, errors, call into question the use of these devices for research data acquisition, particularly for monitoring highly variable power electronic loads.

Multi-objective optimization of integrated energy systems based on demand response

ABSTRACT The implementation of demand response (DR) in the operation optimization of the integrated energy system has become one of the research hotspots worldwide with the rapid development of the energy network. Integrated gas-electricity system (IGES) operators can allocate different energy carriers more rationally and decrease energy cost and exergy input considering the DR scheme. However, DR was mainly applied to electric power systems in the past, while natural gas systems were less considered. In this paper, based on the theory of price-elasticity, and simultaneously with the consideration of the DR for end-user (EDU) of electricity and natural gas, the optimization model of the IGES is established, which includes two objective functions, the minimum operating cost of the integrated system and the maximum EDU satisfaction. The constraints include compressors and gas storages in the natural gas system, coal and gas generator units in the electric power system, Pow to gas units, and EDU gas/electric load changes. Subsequently, the proposed model is employed for an integrated 7-node natural gas system and 6-node power system with the sensitivity analysis for operating cost. The results demonstrate that the implementation of DR brings specific economic benefits to the operation of the entire system, the total operating cost is reduced by 16.6%, and the natural gas load is reduced by 89.9%. The implementation of DR balances the 24-hour load, reduces the fluctuation of the EDU load, increases the efficiency of the load shifting, and alleviates the instability of upstream production and transportation.

Application of the artificial bee colony algorithm to power contract capacity optimization

In recent years, the artificial intelligence of imitation human neuron operation has become an aid to human thinking. Therefore, we applied this technique to forecast medium-term load systems. Power loads represent an indispensable energy in our life, so effectively forecasting power load usage may not only decrease energy consumption but also reduce the cost of contract capacity. Currently, people have used end-use models, load density, trend analysis, and statistical methods to predict Medium-Term Load Forecasts (MTLF) and have employed artificial neural networks to predict Short-Term Load Forecasts (STLF). In this paper we propose an optimized structure to predict the power load by combining an artificial bee colony (ABC) and an artificial neural network (ANN) to forecast MTLF. After considerable trial-and-error testing, the influence of different structures, max iteration, and datasets leading to different errors all became points in our study. These structures were also put into both a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to test the experiment. The experimental results verified that ABC demonstrated better performance in max iteration 30 and structure 7-n-1. An appropriate contract capacity prediction can be obtained by applying this structure. To show the effectiveness of the proposed structure, we verified it with six years’ statistical data of the health care service industry from Taiwan Power Company and then discuss the results.

Power Quality Improvement using Parallel Connected FACTS Device with Simplified d-q Control

INTRODUCTION: Distribution network is mainly affected by the end user load nature. Ruinous influence on power system operation based on the type of load used by the end user and due to the presence of non-linear loads causes Harmonics in power system. The concept of reducing the harmful effect of harmonics on power system attracted the research attentiveness. OBJECTIVES: To reduce these harmonics and reactive power problems FACTS devices are found reliable. Out of various FACTS devices, Active Power Filter (APF) is one FACTS device which identifies and controls the harmonic contamination in power system with non-linear loads. This work mainly presents a parallel APF concept for the compensation of harmonics to improve the reliability of the system. METHODS: The main contribution of this work is proposal of single control strategy for compensation of Power quality. Corrective back currents (for parallel APF) to compensate the identified harmonics are generated using modified d-q reference-based control methodology. RESULTS: The models are developed, and analysis is presented using MATLAB/SIMULINK software CONCLUSION: d-q reference theory control strategy is explained in detail. Load current, Source Voltage and DC link voltage which are measured using various sensors and send to processor. modified d-q reference the signals to be measured are same