Peak Demand Reduction Research Articles

Assessment of peak power demand reduction available via modulation of building ventilation systems.

Peak power demand strains electrical grids and increases cost of electricity generation, transmission and distribution infrastructure. Many studies have examined ways of reducing peak power demand, including modification of room air temperature setpoints or reduction of lighting levels. However, very few or no studies have examined the peak power reduction resource offered by temporary curtailment of building ventilation systems. For this reason, we conducted a simulation campaign in which we examined the resource offered by temporary ventilation curtailment in commercial buildings of different use types across the United States and in residences in the state of California, with the essential constraint that any changes resulted in air quality acceptable to occupants through additional ventilation to compensate for the curtailment. To do this, we employed previously validated building models implemented in the airflow and contaminant transport tool CONTAM and building thermal and systems modeling tool EnergyPlus, in some cases co-simulated. Results show savings are highly dependent on building type and climate but range from 0–2 W/ft2 and up to 40% of total peak building power demand. Depending on building type, this power shed can be conducted for 1.5–8 h before acute exposure or odor concerns are expected, assuming a safety factor of 2. This reduction is of the same magnitude as that offered by thermal control strategies such as setpoint increase, or from lighting reduction strategies.

Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology

The ongoing electrification of the heat and transport sectors is expected to lead to a substantial increase in peak electricity demand over the coming decades, which may drive significant investment in network reinforcement in order to maintain a secure supply of electricity to consumers. The traditional way of security provision has been based on conventional investments such as the upgrade of the capacity of electricity transmission or distribution lines. However, energy storage can also provide security of supply. In this context, the current paper presents a methodology for the quantification of the security contribution of energy storage, based on the use of mathematical optimization for the calculation of the F-factor metric, which reflects the optimal amount of peak demand reduction that can be achieved as compared to the power capability of the corresponding energy storage asset. In this context, case studies underline that the F-factors decrease with greater storage power capability and increase with greater storage efficiency and energy capacity as well as peakiness of the load profile. Furthermore, it is shown that increased investment in energy storage per system bus does not increase the overall contribution to security of supply.

A Novel Domestic Electric Water Heater Control Method

As one of important thermostatically controlled loads (TCLs), domestic electric water heaters (DEWHs) show considerable potential for utilities to shave peak load working with direct load control (DLC). The electricity consumption of DEWHs is high in regard to daily domestic electricity consumption, which shows the potential for peak shifting services through the direct control of DEWHs. In practice, off-control is commonly used to control DEWHs, and finding suitable DEWHs to turn off is the main objective of DLC. However, it is difficult to decide which DEWH can be turned off when end-user comfort is taken into consideration, especially with the absence of temperature measurement information. In this paper, a novel control method is proposed to control DEWHs without temperature information. A time-varied weight matrix is generated based on patterns of DEWHs, and the temperatures of DEWHs can be estimated by the weight matrix and their states. The weight matrix and heating durations are used to generate a customer satisfaction prediction index, which can be used as a reference during DLC. The index can be used to select suitable DEWHs to turn off or to turn back on to maintain end-user comfort. The proposed method only needs the on-off states of DEWHs, which are available in practice. Simulation results prove that the proposed method can work well to reduce peak demand without affecting end-user comfort.

An Incentive-Compatible Energy Trading Framework for Neighborhood Area Networks With Shared Energy Storage

Here, a novel energy trading system is proposed for demand-side management of a neighborhood area network (NAN) consisting of a shared energy storage (SES) provider, users with non-dispatchable energy generation, and an electricity retailer. In a leader-follower Stackelberg game, the SES provider first maximizes their revenue by setting a price signal and trading energy with the grid. Then, by following the SES provider's actions, the retailer minimizes social cost for the users, i.e., the sum of the total users' cost when they interact with the SES and the total cost for supplying grid energy to the users. A pricing strategy, which incorporates mechanism design, is proposed to make the system incentive-compatible by rewarding users who disclose true energy usage information. A unique Stackelberg equilibrium is achieved where the SES provider's revenue is maximized and the user-level social cost is minimized, which also rewards the retailer. A case study with realistic energy demand and generation data demonstrates 28\%~-~45\% peak demand reduction of the NAN, depending on the number of participating users, compared to a system without SES. Simulation results confirm that the retailer can also benefit financially, in addition to the SES provider and the users.

Field Trial of Coordinated Control of PV and Energy Storage Units and Analysis of Power Quality Measurements

Trends support low voltage distribution networks will soon experience significant uptake of customer-owned low-carbon technology (LCT) devices especially rooftop photovoltaics (PVs) and small-scale energy storage (SSES) systems. This paradigm shift will introduce some significant challenges in modern distribution network planning and operations owing to the temporal nature of modern demand. Therefore, it became relevant to investigate the UK low voltage (LV) network operations considering high uptake of PVs and SSESs through both field measurements and desktop studies. The aim was to validate through field trials, the flexibility benefits of peak demand reduction and reverse power flow mitigation through smart control of customer-owned SSESs. It was shown that peak demand of up to 60% could be achieved in UK distribution network through the smart control of these devices. In tandem with the demand reduction, the study revealed that type-tested SSES power interface units do not pose significant power quality risks even for 100% customer penetration.

A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system

Customers are subject to varying charges for their electricity consumption (kWh) as well as monthly maximum demands (kW) depending on the charging schemes for commercial and industrial customers. Generally, maximum demand charges may account for as high as 30% of the total electricity bills. Although on-site photovoltaic (PV) systems can help customers reduce their maximum demand charges, PV may not be as effective in reducing some of the peak demands due to the intermittent power output of PV. The inclusion of a battery-based energy storage system (BESS), on the other hand, can reduce those unexpected peaks by supplying power at the appropriate time and magnitude. Research efforts have therefore been carried out to develop control strategies for BESS to reduce the peak demands of PV customers. However, some of the existing controllers that rely on forecasted next-interval net demands to supply power for the next interval may fail to reduce peak demands effectively when the actual next-interval net demands are different from the forecasted ones. Hence, a spontaneous self-adjusting controller has been developed and presented in this paper to overcome this issue. It employs model predictive control and dynamic programming with anticipatory, preparatory and recovery actions to achieve a maximum demand reduction of at least 11.00% over the monthly maximum demand. Throughout an experimental peak reduction period of 4 months, the controller has also proven to achieve a reduction of at least 74.11% of the ideal reductions as compared to 68.00% and 65.00% reductions demonstrated by the preceding active and fuzzy controllers.

Cluster analysis and prediction of residential peak demand profiles using occupant activity data

Researching the dynamics of residential electricity consumption at finely-resolved timescales is increasingly practical with the growing availability of high-resolution data and analytical methods to characterize them. One methodological approach that is popular for exploring consumption dynamics is load profile clustering. Despite an abundance of available algorithmic techniques, clustering load profiles is challenging because clustering methods do not always capture the temporal aspects of electricity consumption and because clusters are difficult to explain without additional descriptive household data. These challenges limit the use of cluster analysis to better understand behavioral and other drivers of electricity usage patterns.We address these challenges by applying a novel clustering approach to a unique data set of high-resolution electricity and occupant time-use data from UK households. We cluster cumulative rather than raw load profiles to capture their full shape. Our clustering approach identifies two distinct patterns of electricity consumption during evening weekdays (5–9 p.m.), which are primarily differentiated by the timing of their peak demand. Next, we apply several classification algorithms to assess the potential for using time-use activity data to predict membership in these distinct usage clusters. The methods we use are suited to this predictive modeling context and are able to identify key activities driving patterns of electricity demand. We discuss how such an approach can inform more targeted strategies for residential peak demand reduction and response interventions as well as improve our understanding of constraints and opportunities for demand-side flexibility in the residential sector.

Hybrid energy management with respect to a hydrogen energy system and demand response

A Hybrid Energy System (HES) is a mechanism that combines multiple sources of energy connected together to achieve synchronised energy output. However, increased energy consumption, operator energy expenses, and the potential environmental impact of increased emissions from the exhaustion of non-renewable energy resources (fossil fuel) pose major challenges to HES. This research is to conduct energy management strategy based on a demand response (DR) program and a hydrogen storage system by designing a Program Logic Controller (PLC) unit. The hybrid system is evaluated by comparing different scenarios such as a hydrogen energy system and demand response. The purpose of this research is to reducing peak demand, minimise the cost of the system and also to extract surplus power generation out of the rate of the battery. This can be achieved by improving the system performances and by eliminating any degradation at the early stages. Organisations or companies must be sure their systems are working properly and that their investments will pay off.

Smart Scheduling of a Batch Manufacturer’s Operations by Utilization of a Genetic Algorithm to Minimize Electrical Demand

Abstract Power utilities currently manage unpredictable electrical demand through the use of fast-ramping plants and punitive demand fees. Unpredictable demand makes it more difficult to onboard variable renewable energy sources, such as solar and wind, because of their intermittency, which can contribute to grid instability. Optimal utilization of variable renewable energy sources requires a flexible, resilient electrical grid and thus a transformation of the current system to a proposed “smart grid” that would adapt in real time to grid signals. Batch manufacturers are well suited to reduce the burden of intermittency by considering demand side management techniques when scheduling process operations. The goal of such participation is a reduction in electrical demand and associated costs for both the facility and the utility. This study investigates how a novel application of the genetic algorithm could be used to schedule a batch manufacturer’s operations in a manner that reduces overall peak demand and is compatible with process constraints. The genetic algorithm is chosen because it is highly efficient at finding minima and handling complex constraints and large data sets. A major highlight of this article is the use of measured demand profiles to log real-time energy consumption of process equipment. The scenarios investigated use a genetic algorithm to determine the optimum scheduling when given a fixed set of interdependent operations that must be executed within the day. All investigated scenarios have a reduction in peak demand after rescheduling via the genetic algorithm. Baseline operations demonstrated reductions between 13 % and 28 %, with an average reduction of 21 %. The results of this study demonstrate that automated load shifting can easily be applied to an industrial facility and paves the way for future work to investigate the integration of scheduling algorithms with process control systems.

최대 수요 전력 저감을 위한 LSTM 기반 ESS 운영 스케줄링 기법

최대 수요 전력 저감을 위한 LSTM 기반 ESS 운영 스케줄링 기법

Energy, cost, and environmental analysis of individuals and district cooling systems for a new residential city

In hot climates, the active cooling system of buildings is the main cause of high demand for electrical energy. A district cooling (DC), water-cooled chiller system based, proved the success of reducing such demand. However, this advantage of DC system was not investigated for low rise residential buildings. In this study, a comparison of the energy consumption, life-cycle-cost, and environmental impact of the DC, rooftop unit (RTU), and variable refrigerant flow (VRF) is performed. DC shows a reduced peak demand by around 50 % and 49 %, in addition to a less annual energy consumption by 55 % and 18.4 % compared to RTU and VRF systems, respectively.The life-cycle-cost analysis (LCC), which conducted in a new design city “Almutlaa” in Kuwait as a case study, indicated that the DC system is the most cost-effective system over a life cycle of 24 years. The LCC of the three systems (RTU, VRF, and DC) for the new city is 22.7, 20.8, and 17.4 billion dollars, respectively. This is means that DC saves 5.28 and 3.44 billion dollars compared to RTU and VRF, respectively. Moreover, the amount of CO₂ emission avoided using DC compared to RTU and VRF is 35,000 and 11,000 metric tons, respectively.

Evaluation of Dynamic Insulation Systems for Residential Buildings in Barcelona, Spain

Abstract This paper evaluates the potential energy cost savings when high R-value static insulation layers as well as dynamic insulation materials (DIMs) are applied to residential housing located in Barcelona Spain. The analysis considers three dwelling prototypes to characterize the existing housing stock in Barcelona including detached attached and apartments. In addition three vintages for each housing prototype are defined: before 1979 when building envelope insulation took effect in Spain between 1980 and 2006 and after 2006 when the building envelope insulation code became more restrictive. Using a modified 3R2C network model to determine thermal loads the performance of both static and dynamic insulation systems is evaluated when applied to exterior wall for various housing prototypes in Barcelona. The dynamic insulation R-value is selected based on a 2-step control strategy. The analysis results indicate that DIMs with the largest R-value step (i.e. difference between the high and the low R-values) achieve the highest savings in source energy reaching up to 19% reduction in source heating and cooling energy for the entire housing stock of Barcelona. The annual energy savings achieved by DIMs are valued to be 181 M€/year for the entire existing housing stock in Barcelona. In addition electrical peak demand reduction associated with retrofitting exterior walls for the existing Barcelona housing stock can result in future avoidance of building new power plants and can provide additional 144 M€ and 162 M€ for respectively static and dynamic insulation systems. Considering the current energy mix applying dynamic wall insulation systems for Barcelona existing housing stock could reduce annual CO2 emissions by more than 300 000 tons or 6.80% of the total carbon dioxide currently emitted to heat and cool homes.

Flywheel vs. Supercapacitor as Wayside Energy Storage for Electric Rail Transit Systems

Energy storage technologies are developing rapidly, and their application in different industrial sectors is increasing considerably. Electric rail transit systems use energy storage for different applications, including peak demand reduction, voltage regulation, and energy saving through recuperating regenerative braking energy. In this paper, a comprehensive review of supercapacitors and flywheels is presented. Both are compared based on their general characteristics and performances, with a focus on their roles in electric transit systems when used for energy saving, peak demand reduction, and voltage regulation. A cost analysis is also included to provide initial guidelines on the selection of the appropriate technology for a given transit system.

Demand Smoothing in Military Microgrids Through Coordinated Direct Load Control

In small microgrids and individual branches of a bulk electrical grid, the aggregate electrical load can contain significant and frequent peaks caused by large individual loads. These peaks can reduce overall system efficiencies if generation resources, e.g., diesel generators, are dispatched based on peak demand. This problem is particularly severe in military forward operating base (FOB) microgrids, in which the load profile is dominated by environmental control units (ECUs) that operate under thermostatic control. Leveraging the intrinsic energy storage capabilities associated with large loads such as these ECUs and coordinating their operations across neighboring facilities provides an opportunity to reduce peak demand while maintaining system performance. Using a military FOB microgrid as a use case, this paper presents two direct load control (DLC) algorithms for coordinating ECU operations and reducing peak demand. This coordinated control is demonstrated through simulations and field tests at the U.S. Army’s Base Camp Integration Laboratory using novel controller hardware. Both simulation and field tests indicate that the DLC algorithms can reduce peak loads by 25% or more without sacrificing thermal comfort.

Hierarchical price coordination of heat pumps in a building network controlled using model predictive control

Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time.

Combined Impact of Network Reconfiguration and Volt-VAR Control Devices on Energy Savings in the Presence of Distributed Generation

Reduction of energy consumption and energy losses is a major concern of the distribution network operator in the present scenario. Traditionally, on-load tap changers, a shunt capacitor bank, and a voltage regulator have been employed as volt-var control (VVC) devices for savings in energy consumption and losses. In this paper, an efficient and optimally coordinated operation of traditional VVC devices, a distribution network reconfiguration (DNR), and a photovoltaic smart inverter (PVSI) for energy savings has been proposed. In order to achieve the optimal solution, a modified binary gray wolf optimization (MBGWO) algorithm has been proposed. Besides, the proposed method in association with PVSI droop control has been employed to control the voltage violations during cloudy day condition. Furthermore, the proposed method has been employed for service restoration considering voltage regulation and peak demand reduction under faulty condition. For validation, the performance of the proposed algorithm has been tested on balanced as well as unbalanced distribution systems. The test results demonstrate the significance of the DNR associated with VVC devices and PVSI for energy savings under different load models. Outcome of the proposed algorithm has been compared with other existing metaheuristic algorithms and test results demonstrate the benefit of the proposed method.

Impact on railway infrastructure of wayside energy storage systems for regenerative braking management: a case study on a real Italian railway infrastructure

Today, in the railway sector there is considerable interest in studying the best ways of exploiting train braking energy, in order to achieve a reduction in energy costs and better stabilisation of grid voltage. Among the various on-board or wayside measures proposed, one of the most promising solutions is based on using wayside energy storage systems (WESSs). A WESS is a storage installation which can be integrated into mass transit systems in urban areas as well as into long-distance railway lines. It can operate as a smart storage system able to provide relevant benefits in terms of recovering surplus regeneration braking energy, voltage stabilisation, reduction of peak power demand. For these purposes, an effective and flexible simulator is essential to provide all the elements needed to focus and compare feasible configurations and operations. This study examines the problem of introducing the WESS technology in real railway infrastructure. It proposes a method based on a simulator of the WESS system integrated into the infrastructure and carries out the results of dynamic simulations referring to real operating data of the system and vehicles. The results highlight the performance of the WESS in terms of energy and power exchange, also discussing economic aspects.

Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing

The power demand (kW) and energy consumption (kWh) of data centers were augmented drastically due to the increased communication and computation needs of IT services. Leveraging demand and energy management within data centers is a necessity. Thanks to the automated ICT infrastructure empowered by the IoT technology, such types of management are becoming more feasible than ever. In this paper, we look at management from two different perspectives: (1) minimization of the overall energy consumption and (2) reduction of peak power demand during demand-response periods. Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustively reviewed the potential mechanisms in data centers that provided flexibilities together with flexible contracts such as green service level and supply-demand agreements. We extended state-of-the-art by introducing the methodological building blocks and foundations of management systems for the above mentioned two perspectives. We validated our results by conducting experiments on a lab-grade scale cloud computing data center at the premises of HPE in Milano. The obtained results support the theoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT: 33% of overall energy savings and 50% of power demand reduction during demand-response periods in the case of data center federation.

The value of flexible load in power systems with high renewable energy penetration

Demand Side Management (DSM1) and in particular load shifting has considerable potential for facilitating renewables integration. At present, dispatchable loads in power systems are often operated to run late at night when electricity demand is lowest. However, they might also be managed to facilitate increasing levels of variable renewable generation. A key challenge in estimating this potential, however, is a lack of data showing how dispatchable loads behave at an aggregated level in large systems. This paper helps to fill this gap using a case study of residential hot water systems in the Australian National Electricity Market (NEM). Drawing on detailed interval meter data from the Smart Grid Smart City trial, the potential benefit of optimized control of these systems for possible future high renewable penetrations in the NEM is quantified. Optimized dispatch within the PLEXOS® for Power Systems software environment demonstrates the potential to decrease power system operating costs as well as reducing conventional generator cycling requirements, reducing peak demand and improving renewables utilization in high renewables scenarios.

Performance improvement of distribution networks using the demand response resources

Despite a great focus of attention dedicated to demand response (DR) resources, their potential benefits for the distribution networks have not yet been quantitatively studied. Considering the DR programmes in the present study, the load profile is determined for a real distribution network and the impacts of the DR resources on different aspects of the network operation such as the power losses, voltage profile, and reliability are evaluated. For doing so, two different types of customers are considered at first. Some data loggers are then installed on two real distribution feeders in Golpayegan city and the maximum load profile is selected with a comparison of the recorded load profiles. Based on the given mathematical equations, a new load profile is obtained using the proposed DR programmes. Both load profiles, before and after doing the DR programmes, are applied to the above-mentioned real network and the system is simulated using the CYME software. The simulation results show well that the DR resources are able to reduce peak demand, thereby alleviating the need to operate high-cost high-emission generation units. In this study, time of use and emergency DR programmes are considered together.