Peak Load Reduction Research Articles

Energy profiles and electricity flexibility potential in apartment buildings with electric vehicles – A Norwegian case study

Energy flexibility in buildings has the potential to reduce the grid burden of neighbourhoods, yet its practical implementation remains limited. This paper presents a data-based case study from Norway, examining the electricity flexibility potential of electric vehicles, within the context of apartment building loads and PV generation. The results highlight the significant electricity flexibility potential in apartment buildings with EVs, where EV charging can be shifted in time by means of a shared energy management system. Energy profiles are presented, showing how EV charging can increase the average electricity use in apartments by a factor of 1.5 and the power use by a factor of 3.5 to 8.6. Furthermore, the study demonstrates how electricity flexibility KPIs of optimised EV charging in apartment buildings are affected by different energy tariffs, PV generation, V2G technology, and the location of the billing meters. The simulated scenarios showed a maximum reduction of peak loads of 45 %, while a maximum of 38 % of the EV charging was covered by PV generation. The study confirms that residential EV charging emerges as a viable frontrunner in the practical realization of end-user flexibility, paving the way for effective solutions in real-life applications.

Престелген түйіршіктерді өндіру құрылғысын жетілдіру

The article is devoted to the development of a device for the production of pressed granules. Granular feed is considered as a promising product for animal husbandry due to their high nutritional value. The description of the feed granulator as the basis for the production of pellets from fodder crops is given. One of the key elements of the granulator is a matrix, which is a steel part with many holes through which the nutrient mass is passed under pressure during granulation. The matrices are divided into planar and annular. Granulators by design are also divided into roller, drum, disc, blade, vibrating and rotary-centrifugal. Granulators can also be domestic and industrial, which differ from each other in different overall dimensions and performance. To date, the production of granulators is carried out at some plants in Russia. Based on the message of the President of the Republic of Kazakhstan, it follows that Kazakhstan needs to establish its own production of equipment for processing raw materials of the agro-industrial complex, including feed pellets. In the course of our research, we have developed a feed granulator. The design of the developed granulator will reduce peak loads on the working bodies, increase reliability, and improve the feed mixture supply to the area of operation of the working bodies. The granulator consists of a base (housing), shafts, an unloading blade, a die, a washer, a screw with an internal hexagon, a spring lock washer, a nut, a screw with an internal hexagon, bearings, a lock washer, a prismatic key, a ring, a screw, a roller and a distribution blade. The developed feed granulator has a simple design and compact appearance. Its design will allow the production of granular feed of good quality and optimal consistency.

Assessing Peak Load Reduction Potential on Distribution Network: A Case Study of Residential Households in Freetown, Sierra Leone

Assessing Peak Load Reduction Potential on Distribution Network: A Case Study of Residential Households in Freetown, Sierra Leone

Control strategy and capacity optimization of energy-storage-based railway power conditioner

Electrified railroad has the characteristics of high safety factors, high comfort, large transportation capacity, and less time-consuming, which is an effective means to solve traffic congestion. However, with the rapid development of China’s electrified railroad, harmonic, negative sequence and other power quality problems have also been widely concerned, while more and more regenerative braking energy in the traction power supply system can not be effectively utilized. The input of railroad power regulators makes the power quality problem be solved effectively, this paper studies the control strategy of traditional railroad power regulators. Combined with the actual working conditions of railroad vehicles, the simulation model of traction network, vehicles, and railroad power conditioner is established, and the load characteristics of traction substation are simulated by this model. Secondly, for a domestic substation field collection data processing to obtain the actual train operation load changes, to the battery energy storage device life within the maximum economic returns as the optimization goal, for the peak load reduction judgment threshold and the capacity of the battery energy storage device through the genetic algorithm to process the data of previous years to optimize the design.

ОПТИМАЛЬНЫЕ РЕЖИМЫ ИМПУЛЬСНОГО УПРАВЛЕНИЯ ПРИВОДОМ ЭЛЕКТРИФИЦИРОВАННОЙ ПЛАТФОРМЫ

In autonomous drives of highly maneuverable vehicles with adaptive control systems, DC motors have become the most widespread. To control them, pulse width modulation is used, in which the simplicity of the algorithm is combined with high-frequency current fluctuations in the motor windings. When optimizing these modes, it is necessary to choose both the control mode and its parameters correctly, taking into account the specifics of the power source, for example, a battery with limited capacity. (Research Purpose) The research purpose is analyzing the optimal parameters for stream and batch modes of pulse-width control of DC motors from rechargeable batteries. (Materials and methods) For the analysis of control modes, generally accepted dependencies describing the relationship of controlled parameters in transient processes, as well as methods for solving differential equations, are used. A general approximate solution of the equation relating current and voltage is obtained, and its approximation for the flow control mode is determined. (Results and discussion) The obtained solution is studied for the practical feasibility of the stream mode. The corresponding analytical condition is derived, in case of violation of which it is necessary to apply the batch control mode. (Conclusions) The optimal parameters of pulse width modulation are analyzed when controlling drives with DC motors from autonomous power sources. Based on the general solution obtained, the application area of the streaming control mode and the optimal values of the corresponding parameters, as well as the application area of the batch control mode and its optimal parameters are highlighted. The use of the proposed equations makes it possible to improve the quality of drive control, to increase its accuracy, to reduce peak loads and unproductive power losses when driving autonomous vehicles.

Two-Stage Approach for Demand Side Management in Multi-Smart Grids

In the face of escalating electricity demand driven by technological advancements and a growing population, efficient management becomes imperative. This study explores demand-side management (DSM) within smart grids (SGs) to minimize customer bills, alleviate peak loads, and mitigate losses. The research employs diverse strategies and compares their effectiveness, focusing on optimal load consumption across smart microgrids. Key contributions include a DSM strategy for cost reduction, efficient peak load reduction, and active power loss minimization in smart microgrids. The proposed approach is validated through simulations, with results contributing to real-world SG implementation. The study highlights a 23.4% and 31.6% cost saving for residential and commercial customers, respectively, and emphasizes the significance of load management in enhancing operational efficiency and cost-effectiveness within the energy hub framework. The research provides valuable insights for developing advanced consumption management strategies, ensuring energy efficiency and sustainability in future power systems.

An integrated approach for cost-and emission optimal planning of coastal microgrid with demand-side management

In the realm of sustainable power systems, the pivotal role of demand-side management (DSM) in Microgrids (MGs) design and planning is increasingly recognized. This study accentuates the significance of DSM in improving operational efficiency, economic viability, and environmental sustainability of MGs, focusing on a remote community in the Gulf Al-Aqaba, Sinai Peninsula, Egypt. An innovative four-level approach is proposed for the techno-enviro-economic modelling, simulation, and optimization-based DSM of MGs, addressing the challenges posed by renewable energy integration and fluctuating electricity demands. The research explores three demand scenarios: a base load without DSM (SC-1), load shifting-based DSM (SC-2), and a combined load shifting/peak shaving DSM (SC-3), to demonstrate the adaptability and robustness of MGs under different conditions. The findings reveal significant improvements in MG performance, with peak load reduction from 73.66 kW in SC-1 to 50.82 kW in SC-2 and 44.82 kW in SC-3, highlighting the effectiveness of the applied DSM strategies. These strategies also yield economic benefits, evident in an 8% and 23% decrease in the total life cycle cost for hybrid and 100% renewable MG configurations under SC-2 and SC-3, respectively, compared to SC-1. Particularly noteworthy is the reduction in required capacities of key MG components in the hybrid MG scenario under SC-3, with reductions of 10% for solar photovoltaic, 33% for microturbine generator, and 40% for battery energy storage, implying cost savings and a more efficient MG setup. Additionally, the environmental impact is profound, with an 80.3% decrease in emissions for the hybrid MG setup under SC-3 compared to a standalone 50 kW MTG system under SC-1. This outcome highlights the potential of DSM in contributing to environmental sustainability, particularly in MG configurations involving renewable energy sources. In conclusion, the study provides comprehensive insights into the synergy between DSM and MG optimization, underscoring its essential role in addressing climate change and transitioning to sustainable energy systems.

Simulation-based analysis of thermochemical heat storage feasibility in third-generation district heating systems: Case study of Enschede, Netherlands

In this article a dual heat storage system comprising thermochemical heat storage (TCS) and hot water storage for managing the mismatch between heat generation and demand in district heating systems (DHs) is evaluated. TCS is known as technology suitable for long-term heat storage due to its high energy density and negligible heat losses over a longer period. However, the integration of TCS in DHs is significantly influenced by the operating conditions of DHs. Here we evaluate the feasibility of integrating TCS into DHs in the Enschede region of the Netherlands. DHs models are established to simulate heat generation, demand, and storage, and a control strategy is designed to manage storage coordination. The obtained results show that the dual storage system outperforms the single hot water storage system in reducing peak load generation. Depending on the TCS's operational condition, annual energy generation from peak load in dual storage systems could drop by 30–60 % compared to the single hot water storage system. It is achieved mainly by managing the energy capacity remaining in the storage system. The technical feasibility and benefits of implementing a TCS system in DHs with a dual storage system are shown to be more energy efficient.

Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm

The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm.

Highly energy efficient housing can reduce peak load and increase safety under beneficial electrification

Climate change is driving urgent investments in decarbonization. One core decarbonization strategy is to electrify energy services that currently directly use fossil fuels, because electricity can be generated from zero greenhouse gas energy resources. Shifting fossil-based services to electricity, however, requires a major expansion of electricity supply and increases dependence on electricity for critical services. Home heating is a particular challenge, especially in very cold climates. Unserved heating loads can be fatal. Electrified heating is expected to drive peak loads (and thus overall grid size) due to high coincident and nondeferrable loads. This study shows that highly efficient housing presents an opportunity to simultaneously protect people and structurally reduce peak load, reducing the need for electricity supply infrastructure while increasing people’s resilience to weather extremes. This study uses seven building efficiency scenarios from the National Renewable Energy Laboratory’s End Use Saving Shapes to investigate the impact of residential building efficiency on grid size in 2050, using the example of Pierre, South Dakota as a very cold weather location that might also experience substantial new housing demand due to climate-induced human mobility. We find that the deepest efficiency electrification scenario we investigate reduces peak demand by about half relative to low-efficiency electrification. Costs of about $3900/kilowatt (kW) peak load reduction are competitive with the cost of new decarbonized supplies capable of meeting peak load, though building efficiency costs are usually privatized while supply expansion costs are distributed across ratepayers. Decarbonization scenarios suggest the US grid might need to expand by a factor of 5–8 in the next 25 years: extremely rapid growth will be needed regardless, but targets might not be reachable with inefficient end users. Residential building efficiency presents an urgent opportunity to reduce peak demand and provide safer and more resilient housing.

A Comparative Study on Wind Power Forecasting Models Based on the Use of LSTM

In the context of wind power generation's growing significance, this research tackles the critical problem of improving power system stability by reducing peak load and frequency control pressures using sophisticated wind power forecasting methods. Using the rapidly developing area of artificial intelligence and neural networks in particular, the study explores the efficacy of Long Short-Term Memory (LSTM), a recurrent neural network designed for event forecasting in time series data with long intervals and temporal delays. The research presents a novel forecasting model, LSTM that enhances prediction accuracy. This is corroborated by the calculated Root Mean Squared Error (RMSE) of 0.6782 and Mean Absolute Error (MAE) of 0.4614. The sustainability and dependability of wind energy integration into power systems may be enhanced by these findings, which highlight the possibility for more efficient and rapid convergence processes in wind power forecasting.

Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior

The rise in popularity of plug-in electric vehicles (PEVs) and the increasing use of renewable energy sources (RESs) have paved the way for advanced energy management systems (EMSs) that optimize energy usage and distribution in home microgrids (H-MGs). This study focuses on the integration of PEVs in H-MGs using an EMS and analyzes its impact on electrical power grids (EPGs). We examine the effect of PEV charging and discharging patterns on H-MG energy flow, considering various scenarios such as high renewable energy generation, different levels of PEV penetration, and EPG connection status. Our findings indicate that an efficient EMS can significantly enhance H-MGs’ overall efficiency by intelligently scheduling PEV charging and discharging, maximizing the use of locally generated renewable energy, and reducing peak load on the EPG. We demonstrate that a cooperative multi-agent system EMS, driven by a robust continuous and real-time fuzzy Q-learning (FQL) method, can reduce electricity market prices by 15% by increasing the use of renewable energy generation by 25%. To fully realize the benefits of EMS, we address challenges such as reducing dependence on EPG by 30%, improving battery state by 12%, and ensuring EPG stability in the face of uncertainties.

Effects of pre-existing damage on vertical load-bearing capacity of masonry arch bridges

In-service masonry arch road bridges, mainly realised before the first half of the last century, represent a wide portion of the entire worldwide infrastructural asset. Given their age, during their service life these structures could have experienced damage due to anthropic (i.e. traffic) and natural (i.e. earthquakes, soil settlements, degradation, etc.) actions which may have inevitably affected their load-bearing capacity. The present study addresses the problem of the residual capacity estimation of damaged bridges by investigating the impact of previous loading on the actual strength of the structure. In particular, reference to a past experimental activity retrieved from the literature on reduced-scale bridges subjected to concentrated vertical loads has been made to calibrate a reliable detailed finite element model in Abaqus software. Then, damage of different extent has been introduced by simulating the transit of vehicles of various weights on the structure and the residual capacities of the bridge have been assessed and compared against the undamaged configuration. The results confirm that pre-existing damage due to traffic loading may significantly influence the capacity of such structures, with peak load reductions up to 60% estimated through the proposed methodology.

Development of a dynamic demand response quantification and control framework for fan-coil air-conditioning systems based on prediction models

The air-conditioning demand response has gradually become an effective approach to reduce peak load of the national grid. However, with the increasing penetration of renewable energy generation sources, static performance evaluation of demand response that only fits one certain scenario cannot meet the demandsofthe times. To address this issue, a novel dynamic demand response quantification and control framework was built based on the secondary development of building energy simulation software and multilayer perceptron algorithm. In this framework, the demand response evaluation method can be used for different global temperature adjustment strategies under different external disturbances and internal loads, and the dynamic demand response control method could be further adopted for load reduction prediction and setpoint adjustment recommendation. The performance prediction model based on artificial neural network can predict the maximum load shedding during demand response period with an average absolute percentage error of 15 %, and the prediction error of load shedding intensity is ± 1 W/m2 for over 80 % of samples. Besides, the setpoint adjustment prediction model achieved an average absolute percentage error of 3 % while its prediction error is ± 0.25 °C for over 99 % of samples. The results prove that this research can provide reasonable suggestions for customers and load aggregators to achieve accurate control of setpoint adjustment in dynamic demand response conditions.

Demand side management using a novel archimedes optimization algorithm in a smart grid environment

Demand side management (DSM) is a smart grid technology that enables consumers to make decisions about their energy use, lowers energy suppliers’ peak hour demand, and changes the load profile. Demand Side Management (DSM) is regarded as the most significant method used in a Smart Grid (SG), as it helps consumers produce accurate information about their electrical energy usage and assists the utility in reducing peak load demand and reshaping the demand curve. By effectively utilising storage with Renewable Energy Systems (RES), DSM seeks to reduce peak demand, electricity costs, and emission rates. In this paper, we have proposed a load-shifting method for the DSM with a large number of controllable devices. The load-shifting issue has been handled hourly, throughout the course of a 24-hour day, in order to reduce the peak demand, lower the power cost, and minimise the Peak to Average load Ratio (PAR). The Archimedes Optimization (AO) method has been utilised in residential loads in SG to achieve the goal of load shifting by minimising of the problem to the DSM. The simulation findings demonstrate that the suggested demand side management technique generates significant cost savings while lowering the smart grid’s peak load demand.

Development of design calculations for radiant ceiling panels incorporating phase change materials (PCMs)

Thermally active building systems (TABS) show significant benefits such as providing peak load shifting, peak load reductions, and energy savings. Phase change material (PCM) applications with water circulation have benefits similar to TABS, but PCMs can store more heat per unit volume than concrete due to the latent heat exchange. Radiant panels with PCM can be applied in building retrofit, which gives this application a significant advantage compared to TABS. A design methodology for determining heat flux does not exist though for dimensioning a radiant ceiling system that incorporates PCM. Design methods according to standards for TABS – ISO 11855-2 and for radiant panels – ISO 18566-3 were investigated. Five radiant ceiling panels incorporating PCM were selected from Denmark, Canada, Germany, the Czech Republic and Latvia. The resulting heat flux between the panel and conditioned space, i.e. the room, following methods represented in the standards, were compared with the measured or simulated values reported in the literature. The comparison showed that the calculation for TABS (ISO 11855-2) led to similar results to the measured or simulated values.

Demand response strategy study of a radiant roof cooling system based on the thermal inertia of the building envelope

There is an imbalance between supply and demand in the power system. Implementing demand response control strategies for air-conditioning systems is beneficial to optimize the allocation of power resources. Here, we use two single strategies and a combination strategy for the radiant roof cooling system: passive energy storage, global temperature reset, and the passive energy storage-global temperature reset combination strategy to implement demand response control, all of which achieve peak load reduction or shifting by changing the indoor controlled parameters. Based on the thermal inertia of the building envelope, we utilize a TRNSYS model to analyze the performance of three demand response strategies of radiant roof cooling systems in terms of thermal comfort, energy consumption, operating costs, and peak load shifting rates. The findings reveal that implementing demand response strategies can reduce the operating energy consumption of radiant roof cooling systems and facilitate peak load shifting. Among them, the combined response strategy shows the best peak load transfer effect, with a transfer rate of 19.84% and a better operating economy. Meanwhile, we find that the outdoor temperature affects the implementation of demand response strategies for the radiant roof cooling system based on the thermal inertia of the building envelope. Practical application The study has significant application value in the following aspects: Implementing a demand response strategy for the radiant roof cooling system, based on the thermal inertia of the building envelope, can reduce operational energy consumption and achieve peak load shifting. This approach effectively addresses the issue of supply-demand imbalance in the power system. The application of the work could facilitate improved operational energy efficiency, contributing to emissions reduction goals and optimizing the use of intermittent renewable energy systems in power grids.

Incentive based emergency demand response effectively reduces peak load during heatwave without harm to vulnerable groups

The incentive-based emergency demand response measure serves as an important regulatory tool during energy system operations. However, whether people will sacrifice comfort to respond to it during heatwave and what the effect on heat vulnerable populations will be are still unclear. A large-scale emergency demand response pilot involving 205,129 households was conducted in southwestern China during continuous extreme high temperatures in summer. We found that the incentive-based emergency demand response causes a statistically significant decline in electricity use with no additional financial burden on vulnerable groups. The electricity conservation potential of urban households was higher than that of rural households. Households with children did not respond to the emergency demand response, while the response of households with elderly individuals proved to be more positive. The repeated and frequent implementation of this policy did not result in an attenuation of the regulatory effect. This research can serve as a reference for countries with similar regulated power markets.

Detailed evaluation of electric demand load shifting potential of heat pump water heaters in a hot humid climate

Heat pump water heaters (HPWH) are a proven method of reducing water heating energy use over prevailing electric resistance systems (ERWH). Both technologies lend themselves to enhanced control for peak load reduction. Laboratory tests were conducted in Central Florida using the CTA-2045 standard to evaluate load shifting strategies with connected water heaters. Four HPWHs from three manufacturers, including two different tank volumes, were tested alongside an ERWH in a garage-like environment. Tests aimed to shift energy use away from utility peak load periods to off-peak times when excess renewable energy resources are often available. Two load-shifting strategies were shown effective, Shed and Critical Peak, with variation by manufacturer. Beyond draw volume, other factors influenced HPWH load shifting: Florida winter conditions, which increase the energy used per draw, provided the greatest challenges to complete load shift. Inlet water temperature had a large impact on the success of load reduction. Ground temperatures in which water pipes were buried largely determined inlet water temperatures. HPWH efficiency setting: Heat pump water heaters often default to a “hybrid” mode that may use some electric resistance heat to minimize risk of running out of hot water. Operational mode can impact load shifting potential.

Potential evaluation of energy flexibility and energy-saving of PCM-integrated office building walls

Building energy flexibility is important for reducing peak-to-valley differences in air-conditioner loads. Integrating phase change material into office building envelopes and precooling can effectively reduce peak load, but precooling may increase total energy consumption. Therefore, it is imperative to investigate its energy flexibility and energy consumption features. In present work, a heat transfer model of phase change material (PCM)-integrated office building walls was established firstly and verified by experiments. Then, the energy flexibility and energy-saving potential of the PCM-integrated wall under different precooling strategies were studied, and the effects of various PCM parameters on energy flexibility and energy-saving potential were evaluated. Finally, the influence of different peak-to-valley electricity tariff differences on electricity costs was analyzed. The results show that the optimized precooling strategy and peak-to-valley electricity tariff difference make the energy flexibility index of the PCM-integrated wall up to 69.7% at a total load reduction of 1.3% and save the electricity cost by 51%. The PCM location and melting point have the most significant effect on the energy flexibility and energy-saving potential of the PCM-integrated wall under precooling operating conditions. This study guides the selection of energy flexibility and energy-saving precooling strategy and PCM parameters for PCM-integrated office building walls and the formulation of time-of-use tariff policies.