Electric Load Management Research Articles

Resilience-centered optimal sizing and scheduling of a building-integrated PV-based energy system with hybrid adiabatic-compressed air energy storage and battery systems

This study investigates the economic and resilience co-optimization of a decentralized hybrid energy system (HES) within scenarios involving limited energy sources and a hybrid energy storage solution. The HES is comprised of a building-integrated Photovoltaic (PV) system incorporating an adiabatic compressed air energy storage (A-CAES) and batteries, with the main grid, serving as a backup. A two-stage sizing-scheduling model is proposed to optimize the configuration, minimize lifetime costs, and enhance both long and short-term resiliency while achieving an optimal schedule. The charging/discharging transition time of A-CAES is also adopted in the operational model. The results indicate a substantial annual resiliency improvement of approximately 41.1 %, with optimal sizing and energy storage integration. Additionally, they highlight the cost-effectiveness of the PV/A-CAES system while emphasizing the higher self-sufficiency achieved by the PV/A-CAES/battery system, attaining an electrical load management ratio of 47.3 % and a PV self-consumption rate of 96 %, a 6 % improvement over HESs with individual A-CAES. Furthermore, it is presented that under an optimal operational condition, even with the highest PV power availability during grid interruptions, HES relying solely on individual A-CAES could meet 94 % of the load demand. Integrating a fast-response battery enhances this resiliency to 100 %, effectively reducing PV-power curtailment, particularly during grid interruptions and A-CAES transitions.

Evolution of the (Energy and Atmosphere) credit category in the LEED green buildings rating system for (Building Design and Construction: New Construction), from version 4.0 to version 4.1

LEED (Leadership in Energy and Environmental Design) is a certification program for quantitatively assessing the qualifications of homes, non-residential buildings, or neighborhoods in terms of sustainability. LEED is supported by the U.S. Green Building Council (USGBC), a nonprofit membership-based organization. Worldwide, thousands of projects received one of the four levels of LEED certification. One of the five rating systems (or specialties) covered by LEED is the Building Design and Construction (BD + C), representing non-residential buildings. This rating system is further divided into eight adaptations. The adaptation (New Construction and Major Renovation) or NC applies to newly constructed projects as well as those going through a major renovation. The NC adaptation has six major credit categories, in addition to three minor ones. The nine credit categories together have a total of 110 attainable points. The Energy and Atmosphere (EA) credit category is the dominant one in the NC adaptation, with 33 attainable points under it. This important credit category addresses the topics of commissioning, energy consumption records, energy efficiency, use of refrigerants, utilization of onsite or offsite renewable energy, and real-time electric load management. This study aims to highlight some differences in the EA credit category for LEED BD + C:NC rating system as it evolved from version 4 (LEED v4, 2013) to version 4.1 (LEED v4.1, 2019). For example, the updated version 4.1 includes a metric for greenhouse gas reduction. Also, the updated version 4.1 no longer permits hydrochlorofluorocarbon (HFC) refrigerants in new heating, ventilating, air-conditioning, and refrigeration systems (HVAC & R). In addition, the updated version 4.1 classifies renewable energy into three tiers, differentiating between onsite, new-asset offsite, and old-asset offsite types.

Comprehensive techno-economic analysis of a standalone renewable energy system for simultaneous electrical load management and hydrogen generation for Fuel Cell Electric Vehicles

This research conducts a comprehensive analysis of renewable energy systems across five distinct geographic regions, focusing on economic power and hydrogen generation for community-based electrical load and Fuel Cell Electric Vehicles (FCEVs). The considered geographical regions include diverse regions across Pakistan and include Karachi, Awaran, Hyderabad, Nok Kundi, and Pasni. The objective is to identify systems with the least Levelized Cost of Energy (LCOE) for electricity and Levelized Cost of Hydrogen (LCOH) for hydrogen production, tailoring solutions to each location's unique characteristics. The study undertakes an evaluation of nine prospective configurations of renewable energy systems for a comprehensive techno-economic analysis, aiming to ascertain the most cost-effective and optimal system. Findings highlight the effectiveness of the combination of PV, WT, electrolyzer, battery bank, and hydrogen tank in Pasni, Balochistan, with a notable LCOE of 0.30 $/kWh and LCOH of 9.52 $/kg. This optimal configuration not only meets energy and hydrogen load demands but also presents considerable environmental benefits by reducing carbon emissions. The study emphasizes the effectiveness of Hybrid Optimization Model for Electric Renewables (HOMER) for modeling hybrid energy system for the considered case studies and underscores the potential of hydrogen technologies as alternatives to traditional battery banks for long-term energy storage, offering valuable insights into sustainable power generation and emission-free transportation.

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Methods of optimization energy consumption in residential and public buildings with the availability of alternative sources of energy supply

The article is devoted to the analysis of the potential for increasing the efficiency of electricity consumption in residential and administrative buildings in the presence of various energy sources and storage systems. This task is relevant in view of the spread of renewable energy sources both in centralized power supply systems and at local facilities and the significant impact of unstable generation on the cost of electricity in the market. In particular, the main consumers of electricity, which can be used as "consumers-regulators", as well as storage systems available for use in buildings, are considered. Taking into account that a significant amount of electricity is used or can be used in building air-conditioning systems, as well as for the needs of hot water supply, the construction of mathematical models of such systems for existing buildings will allow the creation of effective electrical load management systems to optimize energy costs for traditional buildings and ensure autonomy for buildings with close to zero energy consumption. The article describes the priority tasks to be solved to build an energy management system to minimize energy costs and increase the efficiency of the country's energy system and local energy systems of communities. The formulation of the tasks follows from the analysis of information on the actual consumption schedules of residential and public buildings and the analysis of their mutual correlation, which allows using these findings to develop algorithms for monitoring energy consumption and load management on the consumer side. Further stages of the study include additional measurements of actual energy consumption at the facilities with a discreteness of several seconds to an hour, while simultaneously recording potential influencing factors, which will allow creating a digital model of building energy consumption. A preliminary analysis of the research shows that the most promising mathematical apparatus for building such a model is the use of neural networks.

Optimal planning and configuration of adiabatic-compressed air energy storage for urban buildings application: Techno-economic and environmental assessment

As urbanization and demand for energy increase, the importance of localized renewable energy resources and energy storage system solutions becomes more prominent. Adiabatic-compressed air energy storage (A-CAES) has been identified as a promising option, but its effectiveness in decentralized applications is not widely concerned. This study aims to plan and design a decentralized A-CAES system to enhance its significance within urban building infrastructure. The focus is on the optimal design of a decentralized A-CAES system for urban building utilization through a comprehensive analysis of its thermodynamic, techno-economic, and environmental aspects. A sizing-designing approach inducing simulation and optimization is proposed to customize the A-CAES system according to the specific application requirement. To do so, this study investigates several energy management operation strategies (EMOS) toward different potential applications of decentralized A-CAES to maximize the system's value and adaptability during the project's lifetime. Multiple scenarios concerning managing solar PV-surplus power are proposed to investigate the overall performance of the proposed model. Additionally, a sensitivity analysis (post-optimization) is conducted to ensure the model's applicability across different case studies. The results demonstrate that an energy cost saving in the range of 0.015–0.021 $/kWh is achieved for the optimal hybrid system in which the A-CAES system is planned for solar photovoltaic (PV) integration and seasonal load shifting, leading to shaving the grid peak demand. Adopting such an A-CAES-PV hybrid system allows for achieving a 52 % electrical load management ratio and 65 % carbon emission reduction compared to the primary power system (grid). Furthermore, under the worst-case scenario (zero selling back), such an optimal hybrid energy system (HES) achieves a PV self-consumption rate of around 92 % and a payback time of 15.5 years. This analysis provides useful insights for policymakers, building owners, and energy planners interested in implementing sustainable and energy-efficient solutions, especially in the feasibility study phase.

Management of three-phase electrical loads using phase balancing optimization technique to reduce voltage imbalance in distribution networks

Management of three-phase electrical loads using phase balancing optimization technique to reduce voltage imbalance in distribution networks

A novel AC-DC hybrid microgrid architecture and control strategy for electric vehicle swap station

The characteristics of small volume, modularity, and easy expansion in solid-state transformers (SST), which makes SST widely applied in electric vehicle swapping stations. However, due to various loads and pulsed loads, voltage instability and fluctuation problems inevitably occur in electric vehicle swapping stations. Aiming at these problems, a topology structure based on a Modular Multilevel Solid State Transformer (MMC-SST) is proposed. It is the first choice for realizing the flexible power allocation of AC/DC hybrid system and the interconnection of medium and low voltage. The performance of the DC bus voltage of the charging station has been improved. Based on this topology, an energy management strategy with virtual synchronous generator (VSG) control is proposed to smoothly transition the on-grid and off-grid processes of the distributed power source. In addition, due to the frequency regulation is applied in the control system, the energy storage system has damping and inertia when the output of the new energy power generation equipment changes or load fluctuation, reducing the impact on the load energy storage equipment and improving the stability and safety of the electric vehicle charging station system. Finally, the effectiveness of the proposed topology and its control strategy in different working scenarios are verified by simulation. Simulation analysis shows that the proposed topology has good advantages in DC bus voltage stability, optimization of electric vehicle charging characteristics and load management.

Optimal management of electric hotel loads in mild hybrid heavy duty truck

The problem of engine idling for heavy-duty trucks has been under study for decades with Auxiliary Power Units (APUs) and Truck Stop Electrification (TSE) as the most compelling solutions. With the electrification of trucks approaching feasibility in terms of cost-effective technology, hybridization offers another “degree of freedom” to tackle the problem. This work aims at exploiting a battery pack of a 48 V mild-hybrid heavy-duty truck to store sufficient onboard energy for powering the auxiliary loads during the hoteling. This problem is not trivial, as the battery packs typically cannot recover the entire energy required through regeneration alone; hence an optimal energy management strategy needs to be employed to charge the battery through the engine during drive operation. This strategy optimizes powertrain performances among the four modes: (i) Engine of Coasting (EOC), (ii) Regeneration by braking, (iii) Regeneration by engine, and (iv) engine idling. This paper presents the development of a Dynamic Programming (DP) framework that employs a multi-objective cost function to minimize the fuel consumption and maximize the regeneration using the above-mentioned four modes. A typical heavy-duty truck drive cycle is used to represent the drive phase, with mandatory hoteling stops as per regulations. A comprehensive powertrain model is developed using validated components’ model. The DP employs two state variables: battery State-of-Charge (SOC) and engine mode, and three control inputs: (i) the engine ON–OFF state, (ii) clutch engagement state, and (iii) power request at the Electric Machine (EM) for calculating optimal SOC trajectory. The framework also tackles rapid engine ON–OFF scenarios to avoid the challenges associated with DP and the compromises in fuel cost with those approaches. Finally, the effectiveness of the proposed framework is tested for potential fuel savings on two different battery packs by performing the full cycle simulations. The results show 6.47% of fuel consumption reduction as compared to traditional APU-based heavy-duty truck.

Automatic management of electrical loads in more electric aircraft

PurposeThis paper aims to automatically generate load shedding sequences due to insufficient power supply, to ensure flight safety and complete flight task.Design/methodology/approachIn this paper, a power allocation and load management model, including logical and physical submodels of the distribution system, is first established according to different requirements of the loads in different flight phase and the current total power supply. Then, an optimal load management scheme based on an improved ant colony algorithm is proposed to automatically generate load shedding sequences for both safety-critical and nonsafety critical loads, to achieve a reliable and safe power supply.FindingsTo verify the efficiency and feasibility of the algorithm, the proposed method is verified in a virtual simulation platform. Simulation result illustrates that the proposed algorithm is efficient and feasible.Practical implicationsThe proposed method can provide guidance on load power supply when the civil aircraft is under abnormal power supply situation.Originality/valueAn optimal load management scheme is proposed by considering different requirements of the loads in different flight phase and the current total power supply.

An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand

The expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can comprise up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public locations, and it makes up the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Peak power demand for use of level-2 chargers is 49% to 91% higher among high-risk-sensitive users than among risk-neutral users. Users’ preferences for fast charging rates can change the DCFC demand from 36.4% of the total demand to 53.7% of the total. This study helps to qualitatively analyze the factors that figure in charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.

Design of a Smart Distribution Panelboard Using IoT Connectivity and Machine Learning Techniques

Electric load management through continuous monitoring and intelligent controlling has become a pressing requirement, particularly in light of rising electrical energy costs. The main purpose of this work is to realize a low-voltage electrical distribution panelboard that allows for real-time load monitoring and that provides a load forecasting feature at the household level. In this regard, we demonstrate the design and the implementation details of an IoT-enabled panelboard with smart features. An IoT dashboard was used to display the most significant information in terms of voltage, current, real power, reactive power, apparent power, power factor, and energy consumption. Additionally, the panel system offers visualization capabilities that were integrated into a cloud-based machine learning modeling. Among several algorithms used, the Gaussian SVM regression exhibited the best training and validation results for the load forecasting feature. It is possible for the proposed design to be simply developed to add more smart features such as fault detection and identification. This assists in an efficient management of energy demand at the consumer level.

Comparative case study analysis of conventional and solar energy systems

Energy management is the process for the conservation of energy as energy consumption tends to rise day by day, as energy demand rises. The process involved is to determine when, where, why, and how energy is used in a plant or building. This paper aims to carry out electrical load management in organizations /domestic/industries. The main objective is to evaluate the use of Electrical energy in an organization for different appliances and determine the opportunities for energy saving with energy-efficient equipment or techniques to make it more energy-efficient. This paper presents mathematical modeling, examination, and analysis for the institution and estimates the savings obtained by switching from a conventional energy source to a solar energy source.

Using blinds, day-lighting, and geyser temperature settings to reduce electricity consumption and pricing patterns in energy-efficient buildings

Depending on the building architecture, usage, and energy consumption patterns, over US$ 60 billion was expended annually on electric lighting in commercial buildings. Therefore, the paper focuses on the development of energy-efficient buildings that minimize energy consumption through integrated energy-efficient design processes. This can serve as a practical guide to design buildings that can lower the energy requirements and a strategy to reduce energy consumption. In this study, predictive analytics were used to examine how blinds, daylighting, and geyser temperature settings can reduce electricity consumption and pricing patterns. A panel of expert judges was used to validate the 5-point Likert scale residential electricity load management questionnaire used to gather survey data for the statistical analysis in a Windhoek suburb, Namibia. The main goal of this study was to investigate how blinds, day-lighting, and geyser temperature settings can be used to save energy, reduce electricity consumption, and costs for sustainable growth and development. The results from this investigation indicate a perfect Gaussian histogram of 15 electricity price jumps confirming 15 four-way stepwise interaction effects. Optimal 0.5 Quetelet curve index offers average citizen energy efficiency awareness, education, and behavior modification for affordable electricity. Females generally set hotter geyser temperatures and are higher energy consumers. Blinds reduce electricity consumption by 50% in summer, 25% in winter, and day-lighting by 25%. These were the least cost and optimal solutions to the rising electricity consumption and pricing patterns problem. Adopting the findings or the outcomes of this paper could provide more optimal and sustainable energy consumption thereby reducing pressure on the power grid.

Optimisation of the eccentricity of the pyriform diagram for balancing electrical power systems loading

Electricity is a vital resource need in any modern society for an enhanced lifestyle. Furthermore, electricity load management covers optimal power generation, transmission, distribution, and utilisation. Also, demand-side management is electricity consumption beyond the meter, and the ever-increasing electricity demand because of rising population and higher standards of living place a limitation and a constraint on its accessibility to all the citizens in any community. The eccentricity of the pyriform scatter diagram data shape was used to characterise the statistical distribution of the electricity consumption data points around a common axis. The Markov process, the Jordan Canonical transformation, and the Martingales were used to generalise the independent electricity consumption to depend only on the outcome preceding it and not after it. The results show a balanced light loading of 50.0%. The pyriform was symmetrical, convex, and even about the midpoint, which served as the globally optimised solution of the electricity consumption balanced loading problem. A balanced electrical power systems loading enables utilities to supply more customers, increases operational efficiencies, and places less stress on electricity generators, transmission, and distribution networks. It is recommended that optimisation of electrical power systems loading will lead to energy efficiency, energy savings, lower-cost operations for reliable and sustainable supply, growth, and development. Balanced light loading of electrical power systems components facilitates optimal unit commitment at lower economic and social costs. Whenever service taxes are reduced because of cheaper electricity prices, utilities witness flatter load curves and avoided production costs across power systems operations. This further, lessens blackouts and extends life of utility facilities.

Energy Conservation and Customer/Utility Electric Load Management. (Dept. E)

Energy Conservation and Customer/Utility Electric Load Management. (Dept. E)

Data-Driven Optimal Design of a CHP Plant for a Hospital Building: Highlights on the Role of Biogas and Energy Storages on the Performance

Combined heat and power (CHP) generation plants are an assessed valuable solution to significantly reduce primary energy consumption and carbon dioxide emissions. Nevertheless, the primary energy saving (PES) and CO2 reduction potentials of this solution are strictly related to the accurate definition and management of thermal and electric loads. Data-driven analysis could represent a significant contribution for optimizing the CHP plant design and operation and then to fully deploy this potential. In this paper, the use of a bi-level optimization approach for the design of a CHP is applied to a real application (a large Italian hospital in Rome). Based on historical data of the hospital thermal and electric demand, clustering analysis is applied to identify a limited number of load patterns representative of the annual load. These selected patterns are then used as input data in the design procedure. A Mixed Integer Linear Programming coupled with a Genetic Algorithm is implemented to optimize the energy dispatch and size of the CHP plant, respectively, with the aim of maximizing the PES while minimizing total costs and carbon emissions. Finally, the effects of integrating biogas from the Anaerobic Digestion (AD) of the Spent Coffee Ground (SCG) and Energy Storage (ES) technologies are investigated. The results achieved provide a benchmark for the application of these technologies in this specific field, highlighting performances and benefits with respect to traditional approaches. The effective design of the CHP unit allows for achieving CO2 reduction in the order of 10%, ensuring economic savings (up to 40%), when compared with a baseline configuration where no CHP is installed. Further environmental benefits can be achieved by means of the integration of AD and ES pushing the CO2 savings up to 20%, still keeping the economical convenience of the capital investment.

Optimal operation of energy hubs integrated with electric vehicles, load management, combined heat and power unit and renewable energy sources

As an integral part of multi-energy systems, the energy hub acts a major task in developing the flexibility, efficiency, and reliability. Due to the increasing progress of science and human communities and the rise in air pollutants and Earth temperature, the need for renewable energies and electric vehicles has increased. The only challenge to the use of new energies is the uncertainty in their production due to the lack of continuous solar irradiation and wind in different hours of the day. Accordingly, this paper addresses an optimal load dispatch form for an energy hub to decrease the total costs of the energy hub, such as exploitation costs and CO2 emission costs. This energy hub includes a heat storage unit, combined heat and power (CHP) unit, photovoltaic (PV) arrays, gas boiler, wind turbine (WT), and electric vehicles (EV). EV uncertainty is modeled via Monte Carlo simulation and a developed algorithm based on grasshopper search is adopted for dealing with future uncertainties in electricity price. Moreover, the proposed model considers the electric and thermal demand response (DR) methods comprehensively. Herein, three scheduling scenarios are discussed with different charge/discharge and DR settings. The numerical and graphical results demonstrate that, by choosing a coordinate charge/discharge mode for the EVs, the final costs are successfully reduced. Compared to Scenario 1, the total costs of Scenario 2 are reduced by 12%. Consequently, it can be obvious that the EVs’ matched charge/discharge is successfully decreased the energy costs for the consumers. Compared to Scenario 2, the total costs of Scenario 3 are decreased by 5.76%. The results also indicate that by implementing the DR programs, total consumer costs can be further decreased.

Electric Vehicle Load Management: An Architecture for Heterogeneous Nodes

Integrating electric vehicle (EV) charging infrastructures into the utility grids requires solutions for coordinated charging of EVs. Load management systems aim at computing coordinated charging schedules for electric vehicles based on predetermined charging objectives. Contributions on coordinated charging for EVs predominantly assume that the EVs or the charging stations are controllable entities in the load management systems. However, in practice, charging infrastructures may consist of controllable and uncontrollable entities. This paper proposes architecture and control strategies for EV charging infrastructures consisting of controllable and uncontrollable entities. Simulations based on real-world charging sessions show how the share of uncontrollable entities in a charging infrastructure affects the performance of different control strategies in the system architecture. We show that a certain number of uncontrollable entities in a charging infrastructure does not affect the scheduling objectives significantly. EV fleet and charging infrastructure operators can develop pragmatic investment and operation strategies based on the proposed control strategy and architecture.