Peak Load Reduction Research Articles

Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks

Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelligent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.

4th generation district heating (4GDH) in developing countries: Low-temperature networks, prosumers and demand-side measures

The aim is locating low-cost renovation measures to support the efficiency of district heating (DH). The topology analysis and GIS Zulu© are applied for hydraulic simulation and to verify the scenario viability. The scenarios are compared by life cycle cost, levelized cost of heat (LCoH), capital and operational costs, energy production and consumption, network length, heat losses, linear heat density, heat pump usage, peak load reduction, nominal power duration or the equivalent full load time. As demand-side measures (DSM), improved control, thermostats with an indoor temperature sensor and variable frequency drive (VFD) pumps reduce heat demand. Prosumer scenario imply installing a base-load heat pump. It means a consumer no longer receives heat from the DH system, because the households are now adapted to lower space heating (SH) supply temperature. Prosumer concept also allows placing a heat pump on the DH return line. The simplest configuration includes only the booster heat pump and domestic hot water (DHW) tank storages enough to cover morning or evening consumption peak. To limit piping cost, only supply and return pipes are considered for low-temperature DH, no additional (service) pipes or mixing valves are assumed. Converting single-family houses to prosumers decreases energy consumption, primary network flows and temperatures. LCoH ranges 19.7-35.4 EUR/MWh, depends on the decommissioned pipe length and its proportional pipe investment cost. LCoH decreases as the share of consumers converted to prosumers increases. Discounted payback time is 10 years for DSM and 41 years for prosumer case. For a base-load heat pump, the payback time is much higher than the reference scenario. It implies the same share of heat losses (16% of heat supplied from the CHP plant) with generally worse environmental and energy saving effect due to higher greenhouse gas (GHG) emissions and higher consumption of primary energy. Energy production is expected to follow the pattern of the useful energy consumption to compensate heat losses. The effects are decreasing residential heat demand (-4%), additional capacity to connect new consumers (+6%) and decreasing the network length.

Predictive control of power demand peak regulation based on deep reinforcement learning

As urbanization continues to accelerate, effectively managing peak electricity demand becomes increasingly critical to avoid power outages and system overloads that can negatively impact both buildings and power systems. To tackle this challenge, we propose a novel model-free predictive control method called “Dynamic Dual Predictive Control-Deep Deterministic Policy Gradient (D2PC-DDPG)" based on a deep reinforcement learning framework. Our method employs the Deep Forest-Deep Q-Network (DF-DQN) model to predict electricity demand across multiple buildings, and based on the output of the DF-DQN model, applies the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize coordinated control of energy storage systems, including hot and chilled water storage tanks in multiple buildings. Experimental results show that our proposed DF-DQN model outperforms other traditional machine learning, deep learning, and reinforcement learning methods in terms of prediction accuracy, such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, our D2PC-DDPG method achieves superior control performance and peak load reduction compared to other reinforcement learning methods and an RBC-based control method. Specifically, our method successfully reduced peak load by 27.1% and 21.4% over a two-week period in the same regions. To demonstrate the generalizability of our D2PC-DDPG method, we tested it in five different regions and compared its performance with an RBC-based control method. The results showed that our method achieved an average reduction of 16.6%, 7%, 9.2%, and 11% for ramping, 1-load_factor, average_daily_peak, and peak_demand, respectively. These findings demonstrate the effectiveness and practicality of our proposed method in addressing critical energy management issues in various urban environments.

Future of sustainable renewable-based energy systems in smart city industry: Interruptible load scheduling perspective

As the industrial and household loads continue to grow in smart cities, power supply and usage balance is getting more challenging. The increase in supply-side energy production is one method of alleviating peak demand proposed by a few investigations. As peak loads last for a short time, additional installations might be required. In addition, facilities that store energy can be used to reduce peak demand. As a consequence of the previous investigation, interruptible load regulation was not taken into account in optimizing system performance. The regulation of interruptible loads can greatly reduce system peak loads in smart city and within the concept of microgrids (MGs). The study proposes an interruptible load scheduling (ILS) scheme that considers consumer subsidy rates with the goal of reducing the peak load of the MG and its operating prices. To this end, a digital twin is developed in which the consumer interruption load time and minimal per-day load decrease restrictions have been completely met in the scheme. The ILS model is solved using a combination of the Min-Conflict Algorithm and the Gray Wolf Optimization Algorithm. Furthermore, simulations demonstrate the efficiency of the suggested algorithm and the show the substantial benefits of the scheme in reducing peak loads and operating prices in smart cities.

Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility

With the continuous development of the global economy, the demand for natural gas is increasing year by year. At the same time, the widespread adoption of emission reduction measures around the world prompts many countries to switch to cleaner energy sources, which is driving the growth in demand for natural gas. In order to regulate the balance of natural gas supply and demand, maintain the stable operation of natural gas systems and reduce environmental pollution, this paper proposes an incentive-based demand respond (DR) model for natural gas, which takes carbon emissions and load volatility into account, and in the end, it concludes an effective response strategy. In order to get the multi-objective incentive-based DR, first of all, the reward function is constructed by adopting the TOPSIS method, and then it is solved by using the DQN algorithm, in the end, a multi-objective reinforcement learning method based on DQN is proposed. The DR model is evaluated by adopting a real load data from Xi'an city and DR strategies are proposed for different gas users in two periods: the heating and non-heating periods. The DR strategy proposed in this paper can effectively increase gas users motivation, reduce peak loads, maximize the revenue of gas suppliers and gas users, mitigate load volatility and reduce carbon emissions.

Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications

With the rise in electricity, gas and oil prices and the persistently high levels of carbon emissions, there is an increasing demand for effective energy management in energy systems, including electrical grids. Recent literature exhibits large potential for optimizing the behavior of such systems towards energy performance, reducing peak loads and exploiting environmentally friendly ways for energy production. However, the primary challenge relies on the optimization of such systems, which introduces significant complexities since they present quite dynamic behavior. Such cyberphysical frameworks usually integrate multiple interconnected components such as power plants, transmission lines, distribution networks and various types of energy-storage systems, while the behavior of these components is affected by various external factors such as user individual requirements, weather conditions, energy demand and market prices. Consequently, traditional optimal control approaches—such as Rule-Based Control (RBC)—prove inadequate to deal with the diverse dynamics which define the behavior of such complicated frameworks. Moreover, even sophisticated techniques—such as Model Predictive Control (MPC)—showcase model-related limitations that hinder the applicability of an optimal control scheme. To this end, AI model-free techniques such as Reinforcement Learning (RL) offer a fruitful potential for embedding efficient optimal control in cases of energy systems. Recent studies present promising results in various fields of engineering, indicating that RL frameworks may prove the key element for delivering efficient optimal control in smart buildings, electric vehicle charging and smart grid applications. The current paper provides a comprehensive review of RL implementations in energy systems frameworks—such as Renewable Energy Sources (RESs), Building Energy-Management Systems (BEMSs) and Electric Vehicle Charging Stations (EVCSs)—illustrating the benefits and the opportunities of such approaches. The work examines more than 80 highly cited papers focusing on recent RL research applications—between 2015 and 2023—and analyzes the model-free RL potential as regards the energy systems’ control optimization in the future.

Thermal effects on tensile behavior and failure characteristics of granite-concrete bi-materials with different loading directions

The mechanical performance of the rock-concrete interface is greatly affected by high temperature and stress states in many geotechnical projects. It is significant to study the failure mode of the bi-materials subjected to different loading directions and temperature conditions to improve the bearing capacity of the rock or concrete engineering projects. The natural rough surface is obtained through a splitting test, and a surface close to the true roughness of the tunnel is selected from it. The 3D engraving technique is used to ensure that each specimen has the same rough surface. Then, the granite-concrete bi-material discs with a natural rough surface are heated to 100℃, 200℃, 300℃ and 400℃, respectively. A series of Brazilian splitting tests were carried out with an inclination angle between 0° and 90° to study the tensile properties and failure mechanism of the specimens. The results showed that high temperature would significantly reduce the nominal tensile strength (NTS) of the granite-concrete bi-material. The high temperature had the most apparent reduction in peak load at θ = 0°, and the reduction range was between 25% and 85%. The improvement was between 15% and 60% at θ = 90°. The NTS increased rapidly at first and then slowly with the boundary of θ = 45°. The digital image correlation (DIC) technology was also used to analyze the failure propagation process at the surface. Finally, the stress state on the interface and acoustic emission (AE) signals are analyzed to study the crack types in the failure process. The proportion of shear cracks increases first and then decreases with the increase of inclination angles, with the lowest proportion of 20% at 0° and the highest proportion of 38% at 15°. With the increase in temperature, the proportion of shear cracks increases.

Co-Simulation of Electric Power Distribution Systems and Buildings including Ultra-Fast HVAC Models and Optimal DER Control

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for the co-simulation of electric power distribution systems and distributed energy resources (DERs) is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. The simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical white-box implementations. Hundreds of these models, each with different properties, are randomly populated into a modified IEEE 123-bus test system to represent a typical U.S. community. Advanced VPP controls are developed based on the Consumer Technology Association (CTA) 2045 standard to leverage HVAC systems as generalized energy storage (GES) such that BTM solar PV is better utilized locally and occurrences of distribution system power peaks are reduced, while also maintaining occupant thermal comfort. An optimization is performed to determine the best control settings for targeted peak power and total daily energy increase minimization with example peak load reductions of 25+%.

How flexible electrification can integrate fluctuating renewables

To phase out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost-minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid.

A multi-objective scheduling model for a flexible manufacturing system to reduce peak load using an energy storage system

A multi-objective scheduling model for a flexible manufacturing system to reduce peak load using an energy storage system

Evaluation of deploying data-driven predictive controls in buildings on a large scale for greenhouse gas emission reduction

Buildings consume more than 70% of electricity in the U.S. In order to reduce building energy consumption, advanced building controls have been developed. However, most building controls are using physics-based models and lack of scalability. Recent development of data-driven control models could overcome this challenge and be automatically developed and implemented on large scale. The purpose of this study was to evaluate the effectiveness, robustness, and scalability of automatic and systematic data-driven predictive control (DDPC) for a large-scale real-world deployment. We first used collected data from 78 buildings in RTEM database to train deep neural network models. Then we applied the models to optimize the HVAC control for energy savings. We focused on over 1000 HVAC units in five different commonly used types, including air handling units, rooftop units, variable air volume systems, fan coil units, and unit ventilators. Next, we evaluated the energy-saving potential and the reduction of greenhouse gas emissions of the proposed method. We found that DDPC was robust and scalable in buildings, with an average energy saving of 65% and peak load reduction of 15% compared to current control systems. The average reduction of GHG emissions for CO2, CH4, and N2O was 15.18 kg, 5.76e-4 kg, and 5.48e-5 kg per m2 per year, respectively. New York State can benefit 11% reduction in carbon emission from DDPC in buildings. For scalability, we also identified and categorized the challenging conditions when DDPC may not work properly and summarized the lessons learned from large-scale DDPC deployment.

Testing and evaluation of a smart controller for reducing peak loads and return temperatures in district heating networks

A smart demand response control system aiming towards real-time operational optimisation of district heating (DH) network temperature levels, both in the return and supply pipes, has been developed in the TEMPO project. The return temperature is mainly dependent on the demand side. The controller optimises its value through control of the customers’ heat load. The network supply temperature, however, is directly controllable on the production side. The capabilities of supply temperature control are twofold. On the one hand, lowering the network supply temperature as close as possible to the limits determined by customer thermal demands. On the other hand, activating the intrinsic thermal capacity of the piping to temporarily store heat and thereby shifting the heat load in time. This provides additional energy flexibility potential on top of building demand response.In this study, the two features of the smart control system have been tested in a part of the DH network of Brescia (Italy). A cloud-based platform is used to collect real-time data from various sources and to communicate control signals calculated by the smart controller. The article presents the results of the tests and an evaluation of the controller performance. The analysis indicates that daily flow-weighted average return temperature reductions of almost 1 K on average can be achieved, and up to 15 K instantaneously. Using supply temperature control, the daily peak load energy supply could be reduced by 262 kWh (34%) on average, by shifting the heat load.

Integrating Internet-of-Things-Based Houses into Demand Response Programs in Smart Grid

This paper presents a novel framework that mathematically and optimally quantifies demand response (DR) provisions, considering the power availability of Internet of Things (IoT)-based house load management for the provision of flexibility in the smart grid. The proposed framework first models house loads using IoT windows and occupant behavior, and then integrates IoT-based house loads into DR programs based on a novel mathematical optimization model to provide the optimal power flexibility considering the penetration of IoT-based houses in distribution systems. Numerical results that consider a 33-bus distribution system are reported and discussed to demonstrate the effectiveness of flexibility provisions, from integrating IoT-based houses into DR programs, on peak load reduction and system capacity enhancement.

Study of effect of provision of cut-outs on axial collapse behaviour of circular aluminium tubes

Geometrical modification of thin-walled tubular structure is one of the innovative ways in the development for vehicle safety design. This research is developed in accordance with the need of investigation on behaviour of tubular structures with geometrical modifications like cut-outs under dynamic loading technique. Diamond and elliptical cut-out shapes are provided in multiple number arranged circumferentially in a single row on a tube surface. Tubes with cut-outs are loaded axially at three different velocities, corresponding to three different strain rates. Energy absorption characteristics like energy efficiency, specific energy absorption, peak collapse force, mean collapse force and efficiency of peak force reduction at the initiation of tube deformation are calculated and compared using finite element (FE) based ABAQUS/Explicit® software. Split-Hopkinson Pressure Bar (SHPB) is modelled in the FE software to understand the material behaviour under high strain dynamic loading. Johnson-Cook material model parameters are incorporated in the study to understand the strain rate sensitive behaviour of aluminium alloy material. Results revealed that providing diamond and elliptical cut-outs on tube is much efficient than providing circular cut-outs on tube, in terms of percentage peak load reduction as well as energy efficiency of structure. It was observed that number of cut-outs influences the polygonal deformation pattern of tube with cut-outs. Overall study significantly helps to achieve the aim of improving vehicle crashworthiness characteristics by implementing cut-outs in a crash frame.

Co-optimization of integrated energy systems in the presence of renewable energy, electric vehicles, power-to-gas systems and energy storage systems with demand-side management

AbstractWith the widespread penetration of renewable energy sources and energy storage systems, the problem of energy management has received increasing attention. One of the systems that network owners consider today is the power-to-gas (P2G) system. This system causes surplus electricity generated from renewable energy resources or batteries in the network to be converted into gas and sold to the gas network. Two reasons for the existence of gas distributed generation resources and P2G systems cause the two power and gas networks to interact. Energy management and profit making considering these two networks, as a co-optimization of integrated energy systems, is a topic that has been discussed in this study to achieve the best optimal answer. Since the production of renewable energy resources and the purchase price of energy are uncertain, a scenario-based method has been chosen for modelling. Demand-side management is also one of the important problems in optimal operation of the electricity network, which can have a significant impact on reducing peak load and increasing profits. In this paper, a mixed-integer quadratic programming model for co-optimization of electric distribution and gas networks in the presence of distributed generation resources, P2G systems, storage facilities, electric vehicles and demand-side management is presented. The 33-bus distribution network is intended to analyse the proposed model. The results of different scenarios show the efficiency of the proposed model. Several key points are deduced from the obtained results: (i) demand-side management is able to reduce the peak load of the network, (ii) the presence of renewable resources and batteries can cause the network to convert excess electricity into gas and sell it to the gas network in the market and (iii) distributed generation can reduce the purchase of energy from the upstream network and cause a 36% reduction in the cost function.

An energy demand-side management and net metering decision framework

Demand side management (DSM) and net metering (NM) are two important strategies that can be used in smart energy management systems to help utilities reduce peak loads and increase the penetration of renewable energy, while also enabling customers to save energy and reduce energy bills. In order to effectively implement DSM and NM programs, it is important to identify the customers who are most likely to benefit from these programs and to quantify the economic impact and benefit for both utilities and end-users. This paper proposes a decision framework that utilizes smart meter data and clustering analysis to identify representative load patterns, and a five-stage method for segmenting load patterns to create a consumption intensity matrix. A fuzzy ensemble based multi-criteria decision making framework is then applied to identify the most potential customer groups and quantify the economic benefit and impact. This paper presents a novel approach for identifying potential customers for DSM and NM programs and developing personalized services for energy demand management. The proposed framework is evaluated through a case study, which demonstrates its robustness in decision making and identifies 13 typical load patterns, seven for weekdays and six for weekends, representing diverse consumption behaviors. The weekday load patterns perform better than the weekend load patterns in terms of the five-stage method.

Mitigating peak load and heat stress under heatwaves by optimizing adjustments of fan speed and thermostat setpoint

ABSTRACT Heatwaves are becoming more frequent and severe, intensifying cooling demand and reducing air conditioner efficiencies. This causes peaks in electricity demand that pose operational challenges to power grids. This paper provides methods to mitigate demand peaks and heat stress under heatwaves by jointly adjusting fan speeds and thermostat setpoints in buildings. The methods involve (1) learning baseline models to predict load and thermal comfort, (2) fitting perturbation models that relate fan speed and thermostat setpoint adjustments to perturbations in load and thermal comfort, and (3) optimizing peak load and thermal comfort. The methods are implementable in real buildings, providing fast, accurately predicted optimized solutions that flatten demand peaks and mitigate personal heat stress. This paper demonstrates the methodology through simulation-based case studies of a single building and a six-building neighbourhood. In case studies, the methods reduce peak load by 8–10% while maintaining occupants' thermal comfort within safe and comfortable ranges. Highlights This paper develops data-driven methods to reduce peak demand and mitigate heat stress during heatwaves. The methods are designed for straightforward implementation in the field. In case studies, the methods reduce peak demand by 8–10% while maintaining thermal comfort within safe and comfortable ranges. To achieve the same level of peak load reduction, jointly adjusting fan speed, rather than solely thermostat setpoint, improves thermal comfort by 5% in the test case.

Performance and economic efficiency analysis of an integrated, outdoor fan-ventilated cooling device

Recently, the importance of mechanical facilities in charge of the safety and comfort of occupants in buildings has once again been highlighted in accordance with global social issues such as the spread of COVID-19. In response, various ventilation systems are being developed to improve indoor air quality, and efforts are being made to satisfy the indoor comfort of the occupants. Such advanced facilities allow occupants to secure indoor air quality, while frequent ventilation systems can affect the cooling and heating load in the building, and there is also a problem that it can occupy a relatively large amount of space in the building. This study proposes an integrated, outdoor fan-ventilated cooling device and analyzes its performance and economic efficiency. The EnergyPlus simulation program was used to model two types of systems for comparison: an existing (base) model with a condenser located in the outdoor unit, and a developed model with the condenser integrated within the cooling system. The state of the air passing through the condenser was analyzed prior to comparing the efficiency of the integrated, outdoor fan-ventilated cooling device, followed by an in-depth analysis of the performance and economic efficiency based on total energy consumption. In Case 1, the air passing through the cooling system was approximately 5 °C lower than the base model and showed 11% peak load reduction in comparison to the maximum energy consumption. Additionally, a comparison between regions with different outdoor air temperatures showed an average cost reduction of 16% in Daejeon and Busan City.

Peak load reduction and resilience benefits through optimized dispatch, heating and cooling strategies in buildings with critical microgrids

This research analyzes the combined peak reduction and resilience increase in critical buildings through the use of microgrids configured using a specific control system to provide correct power quality for local loads that can be either connected or disconnected from the grid, operating grid-connected or islanded mode. In case of grid failure, energy storage combined with one or several local generators can provide backup power and consider both conventional and renewable energy systems. This research focuses on the design of building energy systems that are able to maintain the energy supply and establishes a methodology to evaluate the resilience benefits of a microgrid integrated into critical buildings when power outages occur and the particular case of a hospital building is presented. The optimization of the dispatch and heating and cooling strategies are analyzed and a case study characterizing an electric polygeneration microgrid feeding critical loads is presented. Results show the benefits and the increased energy resilience achieved when using solar PV, electrochemical batteries, combined heat and power, TES water tanks, and absorption chillers and propose a design and optimization scheme that can be applied for similar buildings and extend to any facility with critical loads. Results show that these microgrids can be optimally designed to improve the resilience of critical energy systems and, simultaneously, achieve economic benefits. Results show that in the event of an outage the positive monetary effects last longer than the duration of the outage.

Intelligent energy management scheme‐based coordinated control for reducing peak load in grid‐connected photovoltaic‐powered electric vehicle charging stations

AbstractSolar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup.