Peak Demand Reduction Research Articles

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. This paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

Quantifying the potential of load flexibility for building HVAC system using model predictive control strategy

Building as a Battery (BaB) is a concept aimed at utilizing building energy systems for load shifting purposes. However, there is a lack of study to accurately quantify building load flexibility and analyze how it is impacted by various factors. This study addresses this gap by first quantifying building load flexibility using battery parameters, specifically charging power, energy storage capacity, and round-trip efficiency. Subsequently, an analysis is conducted to examine the impacts of internal and external factors on building load flexibility, Specifically, we studied how ToU utility structure, load shifting duration, comfort range, ambient weather conditions, and building thermal properties influence charging power, energy storage capacity, and round-trip efficiency. Our simulation results revealed that an increase in the peak-to-valley ratio, load shifting duration, building thermal capacity, and cooling demand leads to higher charging energy and energy storage capacity, while an increase in building thermal resistance and widening of the comfort range result in their decrease. Regarding the round-trip efficiency, we found most parameters, except for building thermal capability, have a positive relation with the round-trip efficiency. Furthermore, sensitivity analysis showed that ToU utility structure and ambient weather conditions have the largest impacts on charging power, energy storage capacity, and round-trip efficiency. Additionally, our study indicates that leveraging buildings as virtual batteries for peak demand reduction is a feasible and cost-effective method compared with other energy storage techniques such as electric batteries. Overall, our findings offer valuable insights for parameterizing building load flexibility using battery parameters, analyzing factors that impact building load flexibility and informing grid operator to design property utility structures to meet their load shifting goals.

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

Smart Grid Technologies: Advancements and Applications in Nigeria

This study explores the impact of smart grid technologies on the modernization of power grids in response to evolving energy demands and the integration of renewable energy sources in Nigeria. It aims to empirically assess advancements in smart grid technologies, focusing on four key objectives: eval_uating the impact of smart meters on energy consumption and peak demand reduction, analyzing the effectiveness of Advanced Distribution Management Systems (ADMS) in enhancing grid reliability and efficiency, assessing the role of demand response programs in balancing supply and demand, and examining the integration and management of Distributed Energy Resources (DERs) within smart grids. The research employs a quantitative methodology, collecting and analyzing data from utility companies that have implemented smart grid technologies. Key findings reveal that smart meters significantly reduce energy consumption and peak demand by providing real-time monitoring, while ADMS improves grid reliability and operational efficiency through enhanced fault detection and automated control. Demand response programs effectively balance energy supply and demand, reducing peak loads and energy costs. The integration of DERs increases renewable energy utilization and grid stability but also presents challenges related to variability in power output and management complexity. The study concludes that smart grid technologies play a crucial role in achieving a more resilient and efficient power grid, though addressing challenges related to DER integration and system management is essential for maximizing their benefits.

Demand response with PCM-based pipe-embedded wall in commercial buildings: Combined passive and active energy storage in envelopes

Demand response (DR) allows Heating Ventilation and Air Conditioning (HVAC) systems to reduce or shift their electricity consumption during peak periods through the global temperature adjustment strategy. Phase change materials (PCM) based walls are the effective energy storage facilities, storing energy in the pre-cooling period and releasing energy during DR event. However, the traditional configuration of PCM-based wall only allows the passive heat transfer between the walls and the indoor air, requiring a long time to complete the phase change process in the pre-cooling period. This paper presents a novel application of PCM-based pipe-embedded wall (PCM-based PE-wall) in building demand response, which combining active and passive heat transfer mechanism to enhance energy storage rates. The effect of PCM-based PE-walls on demand response is evaluated under two different pre-cooling modes: active pre-cooling only (Envelope-2A) and combined passive and active pre-cooling (Envelope-2B). The results show that the implementation of the PCM-based PE-wall can significantly enhance the overall heat transfer rate of the cooling storage in envelopes and thus improving the demand response performance. Compared to traditional PCM walls (Envelope-1), PCM-based PE-walls (Envelope-2B) could cause additional peak demand reductions of 15.78 % and additional energy reductions of 10.18 % during DR events.

Corrigendum: Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

Corrigendum: Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

The Effect of Smart Grid Technology on Energy Consumption Management in Canada

Purpose: The aim of the study was to evaluate the effect of smart grid technology on energy consumption management in Canada. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Smart grid technology has significantly enhanced energy consumption management in Canada. By integrating advanced metering infrastructure and real-time data analytics, smart grids optimize energy distribution, reduce peak demand, and enhance grid reliability. This technology enables better demand-response programs and promotes renewable energy integration, leading to more efficient energy use and reduced environmental impact. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & the theory of planned behavior (TPB) may be used to anchor future studies on the effect of smart grid technology on energy consumption management in Canada. Smart grid technology enables more efficient energy usage by providing consumers with real-time data and insights into their consumption patterns. The vast amounts of data generated by smart grids necessitate robust policies to protect consumer privacy and ensure data security. Policymakers should establish clear regulations governing the collection, storage, and use of smart grid data.

A constrained price-based demand response framework employing utility functions in three-state Overlapping Generation and Gift and Bequest based model in distribution system

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

Use of a low-cost phase change material emulsion in de-centralized thermal energy storage for district heating network enlargement

A detailed heat transfer model of a storage tank was developed to assess the performance of a district heating system with de-centralized storage solutions. The performance of two cases, with water and a low-cost phase change material emulsion as storage medium was analysed for 40 residential buildings in Zaragoza (Spain). The chosen configuration is a typical cylindrical tank with internal coils through which the district heating water flows for the charge and discharge. This decentralized thermal energy storage unit reduced peak demand and mass flow in the network, allowing additional buildings to connect to a saturated grid. Four configurations were examined: (1) A 180 m3 water tank reduced district heating power demand by 36.6 %. (2) The same 180 m3 tank using the emulsion required fewer coils, lowering costs, stabilizing TES temperature, and increasing water supply temperature, thus extending system lifespan. (3) A 130 m³ tank with the emulsion offered operational characteristics similar to the water tank, with economic and structural benefits from reduced size. (4) An 180 m³ optimised tank with the emulsion achieved a 38.8 % reduction in district heating power demand. These configurations demonstrate the efficiency and cost-effectiveness of using phase change material emulsion in thermal energy storage systems.

Non-intrusive demand response management strategy to mitigate the impacts of residential electric vehicle charging on distribution systems

The proliferation of electric vehicles (EVs) is expected to significantly increase electricity demand. However, if left uncontrolled, the aggregated demands from EVs have the potential to affect the power balance and quality of distribution systems adversely. Given this complex framework, this work presents a non-intrusive demand response management (DRM) approach to mitigate the impacts of residential EV charging on distribution systems. After analyzing a database comprising 157,250 operations, 74,316 charging patterns described by 17 attributes are obtained. These patterns are used for sampling EV fleets distributed in the Institute of Electrical and Electronics Engineers (IEEE) 33-bus test system. From scenarios with different levels of EV penetration, critical regions in the demand curve are identified, while actions for demand response (DR) are taken. Despite the increase in total energy consumption, larger EV fleets have greater potential for participation in DR. At a penetration level of 5%, it is not possible to shift excess energy consumption during the peak period, even with the participation of about 47% of the fleet, which corresponds to all users involved in EV charging during the peak period. At a penetration level of 50%, the energy goal can be achieved with the participation of about 15% of the fleet. In this latter scenario, the proposed management strategy can achieve a peak demand reduction of 9–11%.

Three-tier integrated demand-supply energy management for optimized energy usage in institutional building

Sustainable Energy Plan is an indispensable field of study in the context of Building Energy Management (BEM) which yearns for a cutting-edge technique to raise consumers’ level of contentment with energy usage. This paper introduces a three-tier strategic approach for demand cum supply side management at the prosumer end named TID-SEM (Three-tier Integrated Demand cum Supply Energy Management) framework. The framework commences with data collection, machine learning, and Random Forest classifier-based Demand side management to reduce peak demand. Followingly, a feasibility study on available sources (Solar PV + Battery + Diesel Generator + Grid) was done using the HOMER simulator. The quality techno-enviro-economic shows distinguished optimal one under each criterion. Ultimately to overcome, an ideal computative multicriteria decision-making approaches Fuzzy Analytic Hierarchy Process, the Fuzzy Preference Ranking Organization Method for Enrichment Evaluations, and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution employed to highlight the most optimal configuration. The study focuses on a department building within a higher education institution in the Madurai district, Tamil Nadu, India. On implementing the TID-SEM, ‘PV + Grid’ after DSM configuration was ranked as the most optimum. Lastly, a sensitivity analysis was conducted to evaluate the techno-enviro-economic impact of uncertain parameters on the optimal configuration, ensuring the robustness of the proposed framework.

A deep learning approach for fairness-based time of use tariff design

Time of use (TOU) tariffs aim to shape demand by reducing peak demand. This is significant given the increasing pressure on peak energy supply, particularly in Europe, where peak gas prices have dramatically increased bills. This study defines fairness as a reform measure that carries both distributional and transitional effects on different income users and utilities. A flexible fairness-driven TOU tariff design is proposed, which (1) assigns households to different tariffs by income and estimated price responsiveness calculated from a long short-term memory (LSTM)-based counterfactual electricity consumption model and (2) filters potential tariffs to simultaneously lower total costs, distribute cost savings progressively, and maintain constant utility profitability. Using the 2009-10 Irish Smart Metering Trial based on more than 2000 users, under the proposed frameworks (b) and (c) with fairness constraints, no lower-income users have been observed to pay a higher bill than the higher-income users on average. Specifically, lower-income groups can achieve 8% and 10% bill reduction, respectively, under framework (b) partial bill protection and framework (c) full bill protection for the lower-income users. Our fairness-based TOU framework has successfully achieved fairness of distribution and transition, paving the way for a more equitable and sustainable electricity system.

Development and performance evaluation of active insulation systems using solid-state thermal switches

Traditional building envelopes have passive insulation systems that cannot respond to dynamic changes in the environment. An Active Insulation System (AIS) consists of Active Insulation Materials (AIMs) that dynamically vary the thermal conductivity of the insulation system. Several researchers have evaluated the impact of AIS on building thermal and energy performance by using simulation tools. Up to 70% savings in annual heating and cooling energy and significant reductions in peak demand have been predicted for some climates with wall systems employing AIS. However, materials and assembly development still need a cost-effective product that achieves the required performance. In this study, we present the process of developing an AIS that we will install in a test hut for its performance evaluation. Minimum performance criteria of the AIS system are developed based on R-low/R-high ratio, required time and efficiency to switch states, and cost estimates. The following steps during this study are creating the concept to meet the requirements, predicting the performance via simulations, developing the experimental setup for bench-scale testing, and finally, constructing a full-scale wall assembly and monitoring the performance when exposed to environmental chamber tests. The selected approach uses off-the-shelf products to create an AIS that can switch R-value between 0.98 ft2·°F·h/BTU (0.173 m2·K/W) and 5.81 ft2·°F·h/BTU (1.02 m2·K/W) and have a switching time of less than one minute between R-high and R-low.

Design of a model predictive control methodology for integration of retrofitted air‐based PV/T system in school buildings

This paper presents a model predictive control (MPC) methodology for integrating air-based photovoltaic/thermal (PV/T) systems in school buildings. The methodology is developed based on a case study for an archetype fully electric school building in Québec, Canada. A data‐driven grey‐box model for the classrooms is calibrated with measured data, and a PV/T model is developed. These models are integrated to apply MPC to the school building using the established dynamic tariffs for morning and evening peaks. Three scenarios are investigated and compared: (1) A reference case without a PV or PV/T system, (2) Integration of a PV system and MPC, and (3) Integration of a PV/T system and MPC under a demand response scenario. Results show that using the MPC with PV/T integration can reduce peak demand by up to 100% during high-demand periods for the grid. This methodology is scalable and can be transferable to other institutional buildings. Abbreviations: PV: Photovoltaics system; BIPV/T: Building integrated photovoltaic and thermal system; COP: Coefficient of performance; CV-RMSE: Root-mean-square error; DR: Demand response; DSM: Demand-side management; HRV: Heat recovery ventilator; HVAC: Heating ventilation and air conditioning; IEQ: Indoor environmental quality; MPC: Model predictive control; RC model: Resistance-capacitance model; RES: Renewable energy sources

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

A metaheuristic approach to compare different combined economic emission dispatch methods involving load shifting policy

AbstractDistributed generators (DGs), which can be traditional fossil fuel generators or renewable energy sources (RES), must be appropriately planned in order to reduce a power network’s overall generating cost. Renewable energy sources (RES) should be prioritized because they provide a clean and sustainable energy supply and are abundant in nature. Demand side management (DSM) optimizes the scheduling of flexible loads to reduce peak demand and improve the load factor, while keeping daily demand unchanged. The test system in this research employs a dependable and effective hybrid optimization tool to plan the DGs of a dynamic system in a way that matches low active power production costs with low pollutant emissions. The fitness functions used in the test system were non-linear due to the presence of the valve point effect (VPE). The costs and emissions were evaluated for various fitness functions which included involvement of wind, DSM, and different types of combined economic emission dispatch (CEED) methods. The test system’s peak demand was cut by 12% and the load factor was raised from 0.7528 to 0.85 when DSM technique was used. The generation cost has been reduced from $1,014,996 to $1,012,182 using CSAJAYA algorithm which was further reduced to $1,007,441 after incorporating DSM. Likewise, the CEEDppf was also observed to be reduced to $1,231,435 and $1,216,885 with and without DSM compared to $1,232,001 from reported literature. Numerical results show that both the cost and emission were reduced significantly using the proposed CSAJAYA compared to a long-sighted list of algorithms published in literature. Graphical Abstract

Optimizing Energy Efficiency in Smart Grids Using Machine Learning Algorithms: A Case Study in Electrical Engineering

The increasing demand for power driven by the integration of renewable energy sources has created an urgent need to improve energy efficiency in smart grids Conventional power grids with unidirectional power supply and midstream industries struggling to accommodate variable renewable energy and increased demand consumption. In response, this research explores the use of machine learning (ML) algorithms as a solution to increase energy efficiency, and presents a data-driven approach to address the challenges of modern smart grids meeting the solution for. The effectiveness of the algorithms is examined. Specifically, the research aims to (1) investigate how ML models can improve power delivery and reduce power consumption, (2) differ in key metrics such as accuracy and responsiveness, among others and regression, clustering, and neural networks -Time for performance testing ML algorithms, and (3) ML applications in smart networks Address practical challenges, such as data quality and computational requirements. To achieve these objectives, the study seeks to provide actionable insights for practitioners and researchers aiming to adopt ML solutions for sustainable energy use. The results of this study show that the ML algorithm significantly increases the energy consumption of smart grids. Through predictive modeling and optimization, the ML model achieved a 15% improvement in energy efficiency, a 25% reduction in peak demand, and an annual cost savings of approximately $200,000 Furthermore, predicting a ML-driven maintenance enabled early detection of potential grid issues, reducing downtime , technical losses and it has been reduced. These findings highlight the potential of ML to address today’s complex energy systems, delivering robust, scalable, and efficient solutions that support the integration and powering of renewable energy sources sustainable planning goals are advanced.

Impact of Insulation Strategies of Cross-Laminated Timber Assemblies on Energy Use, Peak Demand, and Carbon Emissions

Cross-Laminated Timber (CLT) panels have many structural benefits but do not have much thermal resistance. We have developed a solution to insulate CLT structures that uses high-performance insulation panels that provide R-values up to R40/inch. The CLT panels are made of layers of wood laminates (three, five, seven or more). The solution replaces some of the wood laminates in the CLT production with the insulation panels in a staggered fashion so that the wood laminates maintain contact throughout the panel, ensuring the CLT panel’s structural integrity. The insulated CLT panels have factory-installed water-resistive barriers reducing the installation time by eliminating installing insulation and water-resistive barriers on site. Per simulations, the CLT/insulation panel achieved code-required insulation levels with commonly available insulation materials. The significance of the thermal mass of CLT/insulation hybrid building envelopes was quantified by comparing the whole building energy performance and peak demand of traditional low mass and CLT wall assemblies resulting in up to 7% reduction in peak demand for cooling in Knoxville, TN, in a multifamily building. Buildings contribute over 40 percent of carbon emissions. The proposed CLT/insulation hybrid building envelope addresses both operational and embodied carbon by having high thermal resistances due to the embedded insulation sections and eliminating the use of high embodied carbon materials such as steel and concrete. The carbon benefit is estimated.