Load Profile Research Articles

Multi-objective modeling of price and pollution in large-scale energy hubs with load management

This study introduces a novel multi-objective model that addresses emission costs, large-scale user operational expenses, and the efficiency of the electric storage system within an integrated energy hub encompassing heating, water, power, and gas sources. A key objective of this model is to maximize the performance of the electrical storage system. The study employs the epsilon-constraint method to construct the Pareto front, aiming to balance profitability and emission reduction from virtual power plant units. The final decision-making process utilizes a fuzzy decision-making technique. Additionally, demand response program (DRP) is incorporated to optimize peak-hour demand and align the load profile with defined objectives. The proposed approach is evaluated across various operational scenarios within a sample system, demonstrating its effectiveness and potential benefits in terms of cost reduction and environmental impact mitigation. Focusing on environmental impact, the carbon dioxide (CO2) emissions are also lower under the DRP scenario, amounting to 10253.92 kg without DRP versus 10127.74 kg with DRP. This reduction in emissions aligns with sustainable energy management goals, showcasing DRP's capability to mitigate environmental impacts associated with energy generation and consumption. The percentage improvements in total costs and emissions further highlight the advantages of employing DRP. The reductions in total costs range from approximately 1.2%–1.4%, demonstrating cost savings across different cost categories. Similarly, the decrease in CO2 emissions by approximately 1.2% underscores DRP's role in promoting environmentally friendly energy practices.

An enhanced energy management system for coordinated energy storage and exchange in grid-connected photovoltaic-based community microgrids

This paper introduces an enhanced coordinated community energy management system (CEMS) for a community microgrid. It is designed to optimize residential energy consumption and facilitate energy sharing among smart homes. The proposed solution integrates constrained mixed-integer programming (CMIP) approach along with advanced optimization technique and cooperative game theory-based pricing mechanism to address critical challenges in existing energy management strategies, including load scheduling and efficient peer-to-peer energy trading. The CEMS approach is adopted for grid-connected community power systems that incorporate local energy sources such as photovoltaic and battery storage systems. This solution is implemented under a time-of-use dynamic pricing model for energy transfer from the main grid to the community peers, a modified mid-market pricing mechanism for the P2P energy exchange, and feed-in tariff for energy transfer from the community peers to the main grid. Besides, seagull optimization algorithm is applied to relax the CMIP problem to get the optimal rational solution of the scheduling problem which is subjected to a rounding process to obtain the best feasible solution. The obtained results indicate a substantial reduction in electricity costs, achieving daily savings of 72.21 % and reducing grid dependency by 41.95 %. Moreover, 82.2 % of the community's energy demand was met through locally generated resources with 18.39 % enhancement of the overall CMG self-consumption ratio comparing to the reference operating scenario. Ultimately, the study demonstrates the effectiveness of the management system in improving load profiles, maximizing the use of local renewable energy sources, and reducing electricity expenses, thereby providing a sustainable and economically viable model for future smart grids.

Research on the Configuration of a 100% Green Electricity Supplied Zero-Carbon Integrated Energy Station

In the context of rapid growth in renewable energy installations and increasingly severe consumption issues, this paper designs a 100% green electricity supplied zero-carbon integrated energy station. It aims to analyze its configuration focusing on the following three core features: zero carbon emissions, 100% green electricity supply, and a centralized–distributed system structure. It discusses equipment selection and provides models for configuring upstream green electricity resources, power generation, energy storage, transformer, and energy conversion. The study examines the synergy between lithium-ion battery storage and modular molten salt thermal storage, along with the virtual energy storage characteristics formed by thermal load inertia, supported by mathematical models. Based on the data from a green electricity system in an Eastern Chinese city and typical load profiles, the paper validates a specific configuration for a 100% green electricity supplied zero-carbon integrated energy station, confirming model accuracy and calculating the required scale of upstream green electricity resources. It proves that establishing an electro-thermal storage synergy system is crucial for addressing the significant fluctuations in renewable energy output. It also argues that leveraging thermal load inertia to create virtual storage can reduce the investment in energy storage system construction.

Achieving zero-bill for the grid-connected PV systems in Saudi Arabia governmental schools: a techno-economic analysis

Governmental schools are characterized as ideal places for installing grid-connected PV systems due to the availability of large spaces on their roofs. Schools are also characterized by their good annual load profile, in which most of the loads occur during the day, and there are no loads on nights or weekends or during summer and vacations. Moreover, in the winter, the loads drop dramatically due to the lack of air conditioning. This special annual load profile provides a relative property to government schools with regard to exporting the energy generated during off days to the general electricity grid. The main objective of this research is to attempt to design a grid-connected PV system that can balance imported and exported energy to the grid to achieve an annual zero bill based on the energy exchange tariff in Saudi Arabia. Three different schools in Buraidah City were selected for investigation. The annual energy consumption was estimated from energy bills for 3 years and compared with actual installed loads. The performance analysis was done by applying three widely used indicators: yield factor, capacity factor, and performance ratio. Also, the economic analysis was done using the life cycle analysis methodology based on the local market prices to find the levelized cost of energy (LCOE) and the payback time. The results of economic and performance analysis revealed the professionality of installing grid-connected PV systems in government schools.

Adaptive Charging Simulation Model for Different Electric Vehicles and Mobility Patterns

Electric mobility is a sustainable alternative for mitigating carbon emissions by replacing the conventional fleet. However, the low availability of data from charging stations makes planning energy systems for the integration of electric vehicles (EVs) difficult. Given this, this work focuses on developing an adaptive computational tool for charging simulation, considering many EVs and mobility patterns. Technical specifications data from many EVs are considered for charging simulation, such as battery capacity, driving range, charging time, charging standard for each EV, and mobility patterns. Different simulations of charging many EVs and analyses of weekly charging load profiles are carried out, portraying the characteristics of the different load profiles and the challenges that system planners expect. The research results denote the importance of considering different manufacturers and models of EVs in the composition of the aggregate charging load profile and mobility patterns of the region. The developed model can be adapted to any system, expanded with new EVs, and scaled to many EVs, supporting different research areas.

Resilience enhancement of power distribution system using fixed and mobile emergency generators based on deep reinforcement learning

Growing extreme weather-related events have increased the need for a resilient emergency response in the Power Distribution Systems (PDSs). This paper proposes a novel restoration scheme that involves forming Micro-Grids (MGs) and dispatching Mobile Emergency Generators (MEGs) between them. Due to the PDS variability and extensive decision space, conventional model-based approaches aren't suitable for implementing the proposed scheme. Therefore, a model-free framework is designed to control the restoration process. The proposed problem is formulated as a Markov Decision Process (MDP) considering uncertainties associated with load profile, and renewable resources. Deep Q-Network (DQN), a fundamental algorithm in Deep Reinforcement Learning (DRL), is employed to solve the proposed MDP model. DQN algorithm can learn optimal control strategy by interacting with the simulated environment at the training phase and then deploy the learned policy in a real-world application. The main objective of the designed DQN model is to maximize the restored loads and ensure a high level of post-restoration reliability for Critical Loads (CLs). MATLAB software is considered as the simulated environment to perform power flow calculation and MEGs routing. The efficiency and applicability of the proposed method are evaluated by conducting experiments on the modified IEEE 13-bus, 37-bus, and 123-bus networks.

Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.

Novel load allocation analysis for energy management and carbon emissions reduction of chiller system in hotel buildings

The operation of chiller systems for cooling energy generation in hotel buildings consumes the highest proportion of electricity. This study aims to introduce load allocation analysis to assess the potential for energy savings by a chiller system. The cooling load profile of a hotel in a subtropical climate is simulated using EnergyPlus. Eleven design options for the system include 4 to 8 chillers of the same or different capacities. Restoring chiller sequencing with high load operation can reduce the system's energy use intensity (EUI) by 12.7 %. An optimal design of four large and two small chillers with proper sequencing can reduce the EUI by up to 26.82 % or carbon emissions by 551,516 kgCO2-e. Increasing capacity steps above ten brings no significant energy savings and imposes complicated chiller staging. The area of the load allocation region can be evaluated simply from the chiller system design and used to analyze the system's energy performance. The EUI can be reduced by 0.3261 % for a 1 % drop in the area of load allocation region. The novelty of this study lies in using the load allocation region to assess energy savings of a chiller system without referring to sophisticated operating data at short timesteps. The significance of this study is to highlight the limitations of energy savings from the optimum chiller system design and address the need for incorporating more low energy technologies to enhance decarbonization.

Exploring Degradation in PEM Water Electrolysis Cells Due to Metal Migration: Insights from X-Ray Fluorescence Analysis

Green hydrogen production through proton exchange membrane (PEM) water electrolysis offers a viable renewable energy storage and transport solution. The Hydrogen Evolution Reaction (HER) and the Oxygen Evolution Reaction (OER) are central to this technology, with the latter’s slow kinetics posing a significant bottleneck. Despite research efforts, iridium (Ir) is the only element which currently meets the stability and activity needs of OER, challenging the search for alternatives and shifting focus towards enhancing catalyst stability [1]. Recent studies have identified metal migration, particularly of Ir, as a critical factor in the degradation of PEM water electrolyzers [2]. This finding has highlighted the importance of understanding metal migration mechanisms and the need to investigate the contribution or interaction of less studied elements such as platinum (Pt), titanium (Ti), and iron (Fe) to the degradation process.This study investigates the migration of both catalytically active (Ir, Pt) and inactive (e.g., Ti, Fe, Ca) metals within PEM water electrolysis membranes, using ex-situ 2D X-ray fluorescence (XRF) mapping (s. Fig. 1). Tests were conducted on PEM water electrolysis single cells (active area: 4 cm2) with a NafionTM 115 catalyst-coated membrane, with anode and cathode loadings of 2.0 mgIr/cm2 and 1.0 mgPt/cm2, respectively. The study entailed varying operating times (10 h, 100 h, 500 h, 1500 h) under constant load, different load profiles (e.g., open-circuit voltage (OCV) and accelerates stress test (AST) phases) for 500 h, and the introduction of specific impurities like iron (Fe) and calcium (Ca) through the anodic feed water. Complementary ICP-MS analysis of the anode and cathode waters was also performed. Selected segments of the catalyst-coated membranes (CCMs) were investigated using synchrotron radiation-based XRF at microXAS beamline (X05LA) of the Swiss Light Source (Paul Scherrer Institute, Villigen, Switzerland) at 11.0, 11.4, and 11.7 keV (below and above the Ir L3- and Pt L3-edges, for IrO2, see e.g. ref. [3]). The beam was focused using a Kirkpatrick-Baez (KB) mirror system. The spatial resolution of the measurements was approx. 1 μm, allowing selective identification and relative concentration of elements to be mapped within the entire cell cross-section (electrodes and polymer layer).Through our XRF analysis, it is possible to detect migration of the catalytically active materials (Ir, Pt) through the Nafion membrane depending on the degradation time. Interestingly, while impurity tests with Ca led to its accumulation in the membrane, Fe impurities introduced similarly did not show significant accumulation. These results, demonstrating the intricate details of metal migration and their varying behaviors under different operational conditions, underscore the value of high-resolution XRF mapping with synchrotron radiation in providing critical insights. This understanding is vital to unraveling the complex degradation mechanisms of PEM water electrolyzers. Further experiments may in principle allow for precise quantification of metal migration, with measurement of appropriate elemental standards. Figure 1. Schematic overview of the study: Catalyst-coated membranes (CCMs) at various stages of degradation are investigated using synchrotron radiation-based X-ray fluorescence (XRF) mapping. This method provides a 2D elemental distribution map, as demonstrated with Iridium (Ir) in this example. Analysis of the cross-sectional distribution of the element within the membrane allows for inferences regarding its migration. We thank the SLS for providing beam-time through an accepted proposal (20221749) and appreciate the micro-XAS beamline team’s help during measurements and data processing.

A Flow Battery Path to Long Duration Energy Storage

Solar and wind sources are expected to replace fossil fuels in powering grids, but they are intermittent and non-dispatchable, thus requiring electric energy storage to provide reliable power delivery according to load profile. A dramatic growth in storage capacity is forecasted in to face these issues, up to 1.25 TW and 5 TWh by 2050. Pumped hydro is today the largest-capacity form of grid energy storage worldwide, but it is not expected to grow much, due to geographical and environmental issues. Batteries are often indicated as the solution of choice, notably the dominating Li-ion batteries (LIBs), which are expected to continue to expand. However, their use is limited by economically convenient discharge durations, which do not exceed 4 hours, and raw material costs dynamics. Like other batteries, they present issues e.g. life duration, safety and self-discharge. On the other hand, several studies indicate that to achieve full decarbonization Long Duration Energy Storage (LDES) will be essential, i.e. systems with discharge times above eight hours. Flow Batteries (FBs) have the potentials to provide this performance. In this framework, flow batteries (FBs) are emerging as a competitive option for LDES and several other services. They provide independent sizing of energy and power, thus allowing for long discharge times at full power. Their most developed version, the vanadium FB, exhibits very long life, virtually no self-discharge, operation at room temperature and pressure and absence of fires and explosion hazards. The presentation will discuss their potential and challenges and the undergoing research to improve their performance and competitiveness. References S. Department of Energy. Long Duration Storage Shot. https://www.energy.gov/eere/long-duration-storage-shot, 2021.Long Duration Energy Storage Council. Net-zero power: Long duration energy storage for a renewable grid, technical report, 2021.Jeremy Twitchell, Kyle DeSomber, and Dhruv Bhatnagar. Defining long duration energy storage. Journal of Energy Storage, 60 (2023):105787.Sanchez-Diez, E. Ventosa, M. Guarnieri, A. Trovò, C. Flox, R. Marcilla, F. Soavi, P. Mazur, E. Aranzabe, R. Ferret, “Redox flow batteries: status and perspective towards sustainable stationary energy storage”, J. Power Sources, 481, (2021) 228804.Trovò, M. Rugna, N. Poli, M. Guarnieri. Prospects for industrial vanadium flow batteries, Ceram. Int., 49 (14), (2023) 24487-24498.Trovò, V. Di Noto, J. E. Mengou, C. Gambaro, M. Guarnieri. Fast Response of kW-Class Vanadium Redox Flow Batteries, IEEE Trans. Sustain. Energy 12, (2021) 2413–2422.Trovò, N. Poli, M. Guarnieri. New strategies for the evaluation of Vanadium Flow Batteries: testing prototypes, Cur. Opin. Chem. Eng. 37, (2022) 100853.

(Invited) Stack Test Facility for PEM Water Electrolysis up to 400 Kilowatt and Lessons Learned at Stack Scale-up

A new type of energy system based on renewable energy sources and hydrogen technologies is put into operation at Forschungszentrum Jülich, Germany. The aim of the "Living Lab Energy Campus (LLEC)" project is testing and optimizing the systems interactions. Central components are wind power and PV systems, PEM electrolysis system, conditioning and subsequent gas transport in H2 and O2 pipelines, H2 storage in LOHC reactors and hydrogen use in combined heat and power plants.The PEM electrolysis system in Fig. 1, left, is an R&D test platform which was jointly configured by Forschungszentrum Jülich (IEK-14) and Greenlight Innovation Corp. and built by Greenlight. It offers a range of different operating modes and system configurations that will be studied embedded into the LLEC: Equal pressure or differential pressure anode/cathode up to 50 bar(g).Water circulation on anode and/or cathode side.Stack cabin with flexible connections for fluid inlets and outlets as well as electrical current. This makes it easy to connect and operate different stacks (also from other manufacturers).Automated system operation, simulation of load profiles (e.g. wind/PV). The system is CE-certified and can run continuously under automatic control. Critical system conditions, such as exceeding the H2 concentration limit (currently set at 30 % LEL) in the O2 gas flow, immediately initiate an emergency shutdown program. In the emergency shutdown program, the power supply unit is first switched off and the pressure on the anode and cathode side is reduced to ambient pressure. The reason for the fault leading to the shutdown is stored in the system control unit and can be called up. In addition, all measured values and states of the installed sensors and actuators are recorded by the system control unit and stored with an adjustable time resolution. The measured values can be used to monitor the system behavior and detect problems at an early stage, but also to characterize and balance the overall system, system components and the stack.For generating large amounts of hydrogen within the LLEC project, IEK-14 initiated the development of a PEM electrolysis stack. The electric design data was taken from the performance data of a laboratory cell (1.75 V at 3 A cm-2, 20 cm² active area) and scaled up. The stack consists of 60 cells with an active cell area of 1056 cm².The bipolar plate was completely redeveloped. The main features are: Use of porous layer structures with different degrees of fineness as a flowfield structure.Welding of all components of the bipolar plate to form a material-locking unit. Preliminary tests in short stacks with bipolar plates on a smaller scale (100 cm² active cell area) showed promising results. The design and manufacturing process for the bipolar plates have now been patented (EP4128399) and published (Janßen H et al., A facile and economical approach to fabricate a single-piece bipolar plate for PEM electrolyzers, Int. J. of Hydrogen Energy, 2023).An interesting aspect that will be discussed further in the presentation is the scale-up of both the active cell area and the number of cells. Ultimately, the aim is to understand which parameters (and their fluctuations) are mainly influencing the stack performance during scale-up. A data analysis of component properties and a comparison with operating data should provide initial indications. Fig. 1, right, shows a 60-cell PEM stack with an active cell area of 1056 cm², of which short stacks with 4 cells were also constructed. Figure 1

Work profile and activity pattern of load carrying workers in grain market

Work profile and activity pattern of load carrying workers in grain market

Polyethylene Wear Testing of a Non-Mechanically Linked Total Elbow Replacement

The objective of this study was to perform a polyethylene wear test on a non-mechanically linked total elbow arthroplasty implant using a clinically relevant in-vitro elbow wear test methodology that simulated ten years of use in the light to moderate activity of daily living range. The test protocol applied an 80° arc of ulnohumeral motion beginning at 30° shy of full extension and progressing to 110° of flexion. Force was applied at 7° to recreate a valgus load on the elbow. A variable joint load profile at a frequency of 0.5 Hz was employed. The implants were tested for 5 million cycles (Mc) in a bovine serum lubricant. Implant component failure was characterized and polyethylene wear was determined gravimetrically. After 5 Mc the small polyethylene bushing wear rate was 0.56 mg/Mc. The medium size wear rate was 0.28 mg/Mc. Three large sizes were tested and the average wear rate was 0.39 ± 0.07 mg/Mc. No implant failure was identified. The test recreated an in vivo loading environment and measured polyethylene wear rates at specified cycle counts. The test demonstrated less wear than other joint replacements. Further clinical evaluation is necessary to determine if this translates into reduced complications of total elbow replacement associated with wear.

A systematic literature review of the bifacial photovoltaic module and its applications

AbstractBifacial modules can absorb radiation on both sides, increasing energy yield per unit area. Climatic conditions, mounting configuration, and system parameters influence the energy yield. The flexibility of bifacial modules allows for various installation orientations, including vertical and east‐west, which can help balance load profiles and reduce bottlenecks. Bifacial solar cells are found to provide higher current density and power compared to monofacial cells. Under optimum conditions, bifacial modules offer up to 30% more energy than conventional modules. Comparative assessments also demonstrate higher energy output from bifacial modules, especially on cloudy days, with low light intensity. Increasing ground reflectance (albedo) to 0.5 can further enhance the bifacial gain worldwide. The progress in performance modelling and accurate energy yield prediction has led to widespread adoption in residential and commercial applications and integration into emerging systems like floating PV and agro‐photovoltaic systems. Bifacial photovoltaic (PV) technology has received much interest, with the International Technology Roadmap for Photovoltaic (ITRPV) projecting a market share of 85% for bifacial PV cells by 2032. This study highlights the research on bifacial PV technology during the last 13 years and also discusses future trends and challenges. Furthermore, recommendations are made to ensure the bankability and scalability of bifacial PV modules.

Methane slip and other emissions from newbuild LNG engine under real-world operation of a state-of-the art cruise ship

Liquefied natural gas (LNG) use as shipping fuel has increased in recent years. While LNG results in lower carbon dioxide (CO2) emissions as well as benefits in terms of air pollutants, the slip of unburned methane, the main component of LNG, has remained a concern. In this study, methane together with other climate warming agents, CO2 and black carbon (BC), as well as other emission compounds were characterized from 4-stroke low-pressure dual fuel engine on-board a newly build cruise ship utilizing LNG as well as marine gas oil (MGO). The brake specific methane slip was found to vary according to engine load, being 2.3–3.0 g/kWh at 54–80% loads, but increasing to 10 g/kWh at 25% load and 21 g/kWh at 12% load. The LNG combustion also resulted in higher formaldehyde emissions compared to MGO, but reduction in formaldehyde levels was observed over the SCR catalyst present in the exhaust line of the dual-fuel engine, without urea injection, suggesting it may provide a pathway for formaldehyde mitigation. In terms of particle emissions, LNG use reduced particle mass (PM) by 87–93% and BC by 94–99% compared to MGO combustion. Non-volatile particle number above 23 nm (PNnv,>23nm) and 10 nm (PNnv,>10nm) were reduced by 88–97% and 97–99%, except at lowest engine load where PNnv,>10nm increased by 26% compared to MGO utilization. When total greenhouse gas (GHG) emissions including CO2 and BC were considered, LNG use resulted in 13–15% lower GHG at high loads, but the benefit was undermined by the escaping methane at low load conditions. Following the engine activity profile during 8-months of vessel operation on the Mediterranean suggested, however, that in a diesel-electric cruise ship, low load conditions are used mainly during arrivals and departures from harbors, as the engine was operated at loads above 40% for 90% of the operation time. Weighted emission factor, representing the actual engine operation, resulted in methane slip of 2.8 g/kWh or 1.7% of the fuel use, which is below the value considered in the FuelEU Maritime. The results suggest that load specific methane slip, together with engine load profile should be considered when evaluating methane slip on vessel or fleet level.

Capacity and Resistance Diagnosis of Batteries with Voltage-Controlled Models

Capacity and internal resistance are key properties of batteries determining energy content and power capability. We present a novel algorithm for estimating the absolute values of capacity and internal resistance from voltage and current data. The algorithm is based on voltage-controlled models. Experimentally-measured voltage is used as an input variable to an equivalent circuit model. The simulation gives current as output, which is compared to the experimentally-measured current. We show that capacity loss and resistance increase lead to characteristic fingerprints in the current output of the simulation. In order to exploit these fingerprints, a theory is developed for calculating capacity and resistance from the difference between simulated and measured current. The findings are cast into an algorithm for operando diagnosis of batteries operated with arbitrary load profiles. The algorithm is demonstrated using cycling data from lithium-ion pouch cells operated on full cycles, shallow cycles, and dynamic cycles typical for electric vehicles. Capacity and internal resistance of a “fresh” cell was estimated with high accuracy (mean absolute errors of 0.9% and 1.8%, respectively). For an “aged” cell, the algorithm required adaptation of the model’s open-circuit voltage curve to obtain high accuracies.

Gaussian-based plug load profile prediction in non-residential buildings archetype

This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

Energy management algorithm development for smart car parks including charging stations, storage, and renewable energy sources

In this study, a photovoltaic system and stationary energy storage unit integrated vehicle charging station energy management algorithm were developed using a long-short term based prediction model (LSTM). The aim of the proposed system is to develop intelligent car parks with controlled charging stations aggregated by centralized charging stations instead of individual or dispersed uncontrolled charging stations to eliminate the imbalance in the demands of charging. The system has four energy sources: grid, vehicle batteries, PV system, and the stationary battery group. By calculating the power demand for vehicles in the car park, a dynamic energy management algorithm has been developed that provides efficient use of energy resources by considering the power demand density. Moreover, the forecasting model has been created by LSTM) not only to adjust charging timing and effectively use energy sources. The model was performed by forecasting not to take energy from the grid at critical times (overloaded times). The grid load is analyzed by the energy management algorithm and run for different times of the year, creating a load profile for 16 vehicles. The results of this study show that energy demand during the critical time interval can be reduced by 8–20 % solely through load shifting before and after the critical time interval without any additional resource support. Moreover, while the integration of only the PV system supports the grid by 15–20 %, the optimal utilization of other energy sources during the relevant time frame supports the grid by 65–75 %. In addition, by collecting charging stations at a central point, energy storage capacity is increased and effective energy management is achieved. In conclusion, it is proposed that the infrastructure of such a parking system should be set up before the use of electric vehicles increases significantly.

Model development and validation of a vapor-compression system integrating water-based thermal energy storage using a three-fluid heat exchanger (TriCoil™)

This paper presents a comprehensive simulation suite for a residential heating and cooling system that combines a vapor-compression system (VCS) with a water-based thermal energy storage (TES) using a three-fluid heat exchanger (TriCoil™). The VCS configuration includes the TriCoil™ as the indoor coil, a fin-and-tube heat exchanger as the outdoor coil, a variable-speed compressor, and an isenthalpic expansion device. To minimize computational cost for annual simulations, an artificial neural network (ANN) was employed to develop a data-driven model for the TriCoil™. The suite integrates models of the VCS, TES, and control systems, factoring in utility rates, building load profiles, and weather data. Validation against experimental data demonstrated accurate predictions, with TES temperature profiles having a root mean square error of 0.3 K and VCS heating capacity predictions showing mean absolute errors of 4.3% for the R410A side, 5.0% for the water side, 4.9% for the air side, and 4.3% for the air-sensible side.Simulations for a cooling season in Stillwater, OK, demonstrated significant electricity cost reductions by shifting cooling loads from peak to off-peak hours. The proposed system minimizes expenses using time-of-use rates and optimized load scheduling, showcasing substantial benefits even with a small TES unit.