Demand-side Energy Management Research Articles

Towards simulating the constraint-based nature-inspired smart scheduling in energy intelligent buildings

Appliance scheduling on the user’s defined preferences plays a pivotal role in smart home energy management systems. In this context, energy management controllers are largely used to satisfy the users’ demand preferences within their financial budget constraints. This work proposes a robust technique based on demand-side energy management for efficiently monitoring and controlling the domestic loads. A nature-inspired crescive consumer satisfaction algorithm (NCSA) is proposed for the optimal scheduling pattern based on time and device preferences. The proposed algorithm maximizes user satisfaction at the preset user budget by producing optimum appliance scheduling patterns. It considers household appliances input data such as time of use, power ratings, and the absolute maximum satisfaction for optimal scheduling. The proposed algorithm is evaluated on three budget schemes, and the simulation results reveal that the proposed algorithm achieves a better satisfaction index at a lower cost per unit satisfaction. Results also show that the proposed algorithm has a good convergence rate and is generalizable to any random budget scenario.

Demand side energy management for smart homes using a novel learning technique–economic analysis aspects

In this study, a new distributed pattern of demand side energy management in residential smart grids regarding Improved Sparse Bayesian Learning (ISBL) method along different types of domestic electrical appliances have been presented. Considering the overall expense of electricity usage, the restrictions of supply quantity is sustained through distribution set-ups and the obtained power demands for individual appliances, the optimal energy management (OEM), for the demand side users, such as households, became an elaborated optimization issue in relation to the coupled objective performance across spatially and temporally of doubled constraints. Usually, in distributed model, figuring out of this kind of issue is problematic. Hence, in this study, we had changed it into ISBL method, and developed a distributed algorithm in order to obtain an optimal quantity of the unified OEM across the procedure of implementation. The presented scheme does not need any personal information about individual operators, meanwhile each of optimality and convergence are achieved. Additionally, this article demonstrated that the presented scheme has high robustness in unreliable communications. In addition, the suggested scheme is demonstrated and confirmed through simulations.

A Short Term Forecasting Method for Regional Power Consumption Considering Related Factors

Analysis and prediction of power consumption law is the basis of power grid planning and construction, and is also an effective guide for energy demand side management. With the rapid development of economy and the complex change of industrial structure in recent years, the internal structure of power demand is changing to some extent. Therefore, a short-term forecasting method of regional electricity consumption considering the related factors is proposed. Based on the analysis results, a short-term prediction model of regional electricity consumption considering the related factors is established, and the short-term prediction is realized by the calculation of the model. Through the example analysis, it is verified that the forecasting deviation of the short-term forecasting method is low and meets the basic requirements of electric quantity forecasting.

AI-Based Home Energy Management System Considering Energy Efficiency and Resident Satisfaction

Demand-side energy management is becoming increasingly important owing to concerns related to global warming and energy shortages. In particular, as the development of Internet of Things (IoT) enables the precise control of home appliances, the demand for home energy management systems (HEMSs) is expected to increase. This article proposes an artificial intelligence-based HEMS (AI-HEMS) that provides both energy efficiency and resident satisfaction. To this end, we implemented three prediction mechanisms: 1) derivation of comfort temperature; 2) device-free sleep prediction; and 3) occupancy-probability-based outing prediction. Based on these mechanisms, we present four intelligent heater control strategies: 1) outing; 2) occupancy; 3) comfort; and 4) sleep-based control. For evaluation, an experimental testbed was constructed and measurements were taken. To increase the reliability of the evaluation by excluding natural energy fluctuation factors, a treatment group (100 households) and a control group (2281 households) were recruited to measure the energy savings of the treatment group compared to the control group. In a 48-day evaluation, AI-HEMS was found to provide an energy-saving rate of approximately 14% and resident satisfaction of approximately 91%. These results imply that the proposed system can save energy while maintaining a high level of satisfaction.

Blockchain-based solution for energy demand-side management of residential buildings

Smart homes, connected through a network, can optimize the energy consumption and general load shape of their area. In this work, a blockchain-based smart solution is presented for demand-side management of residential buildings in a neighborhood to improve Peaks to Average Ratios (PAR) of power load, reduce energy consumption, and increase the thermal comfort of occupants by modeling heating, illumination, and appliance systems. For real-time power and temperature monitoring of the neighborhood, a transient numerical physical model has been developed. The simulator has been validated with data measured from a building in Northern Italy. Then, a neighborhood with 2,000 households has been modeled for different occupancy patterns, initial values, and boundary conditions. Two different control scenarios, namely basic and smart, have been considered. In the basic scenario, everything is managed by occupants except the boiler, which is controlled by the indoor temperature of the home. Instead, in the smart scenario, a blockchain-based network has been introduced for buildings to exchange a parameter called the Probability of the Next Hour (PNH). Ethereum Solidity has been deployed for smart contract development in the blockchain. The results show that using blockchain-connected smart controllers aimed at demand-side management can improve PAR, comfort level, and energy efficiency of buildings, which can bring about CO2 reduction on an urban and even global scale.

A critical review of control schemes for demand-side energy management of building clusters

The proliferation of smart meters, energy storage systems (ESS), and renewable energy systems (RES) creates a unique opportunity for the building sector to enhance the efficacy of demand-side energy management. The objective of this review is to capture the trends, strategies, and lessons learned from studies focused on building-to-grid interactions at the cluster level, including residential and mixed-use neighborhoods, microgrids, and multi-unit residential buildings. In approximately 75% of the 82 reviewed studies, ESS such as electric vehicles or batteries were used to reduce the peak load of the building cluster and capture excess renewable energy generation. All the reviewed studies ensured that computers and televisions were non-controllable loads to ensure occupant needs while HVAC, washing machines, and dishwashers were targeted as controllable loads to help reduce and shift energy peaks. A 20% and 30% reduction in total energy cost and peak energy consumption respectively were achieved under the various control schemes, with approximately 50% of the studies using price-based strategies to shift energy consumption away from peak hours. Most importantly, the reviewed studies achieved these reductions while ensuring or maximizing occupant comfort, thus presenting buildings as a great opportunity to help manage energy consumption for the grid.

Load shifting and energy conservation using smart thermostats in contemporary high-rise residential buildings: Estimation of runtime changes using field data

As the proportion of the population who resides in urban centers grows, the number of dwellings in high-rise residential buildings is also increasing. In Canada, heating, ventilation, and air conditioning (HVAC) typically accounts for over half of all energy use in this building type and, as such, consideration of these loads is key to effective energy demand-side management. Smart thermostats present a new opportunity conserve energy and shift electricity load to off-peak periods in these buildings through modifications to HVAC control strategies. In this study, three thermostat control strategies were tested in 49 suites across two contemporary high-rise residential buildings: 1) baseline operation (thermostat operating as a standard programmable thermostat); 2) occupancy-based control (thermostat operating using an occupancy-reactive control strategy); 3) load shifting control (thermostat operating using a pre-conditioning strategy). HVAC runtime data was collected from smart thermostats installed in the suites and was used to develop data-driven regression models to estimate baseline suite HVAC runtime based on weather conditions and suite characteristics. Thermostat-measured HVAC runtime data for periods in which occupancy-based or load shifting control strategies were in use in the suite was then compared with estimated baseline runtime for similar outdoor conditions. Occupancy-based control was found to reduce HVAC system runtime during the cooling seasonby 5.9% ± 46% on average. On aggregate, the load shifting control strategy was not effective at reducing peak period HVAC load during the cooling season. The strategy was able to reduce peak period loads in a subset of suites substantially, which may be leveraged to reduce electricity demand during capacity-constrained periods of grid operation, however, based on marginal emissions factors for Ontario’s electricity grid, even in these suites, the strategy was not effective in reducing GHG emissions overall.

Personality-based normative feedback intervention for energy conservation

Residential sector is considered as a key to energy conservation due to its significant share and fast growth. Normative feedback has been widely implemented in residential sector for its low cost and high effectivity. However, the potential of energy saving generated from the method has not been fully achieved, as the influence of personality traits and benchmark setting strategy on individual behavioral response has been overlooked. Therefore, this paper incorporates personality traits into the analysis of individual behavior patterns, and proposes a personality-based normative feedback mechanism which optimizes the benchmarks setting strategy in real time by optimal control algorithm. The agent-based modelling method is adopted to develop improved Deffuant-optimal control (DOC) model and to simulate the personality-driven energy conservation of residents under the proposed intervention mechanism. The study finds that intervention effect of the dynamic benchmark setting strategy is superior to normative feedback with fixed benchmarks in terms of energy savings and response agility, and individuals with different personality traits are significantly different in behavioral characteristics. Increase in openness promotes the residents to save energy quickly, and agreeableness has an inverted U-shaped relationship with residents' response agility. Open-minded and agreeable individuals perform well in energy saving while showing stronger boomerang effect. In addition, residents are classified according to their behavioral response patterns and personality traits, in order to analyze the time-dependent response characteristics and boomerang effect of different groups of individuals. The DOC model is evaluated by energy conservation experiments conducted in residential communities of Hangzhou, China. The proposed intervention mechanism improves the effectivity of normative feedback in both short-term and long-term, and provides an innovation approach for optimizing energy demand side management.

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

DEMAND-SIDE MANAGEMENT FOR ENERGY PRODUCTION USING MICRO COMBINED HEAT AND POWER SYSTEM – A REVIEW

World development increased dramatically ever since the Industrial Revolution, in particular after second world war (WWII), which drove the rise of energy consumption. Thus, energy consumption in the World has been growing continuously in the past 50 years. Using micro Combined Heat and Power (mCHP) allows energy scheduling and demand-side management depending on different variables which will benefit users and suppliers. Different researches have been conducted due to increasing interest from researchers to increase and optimise the advantages of energy scheduling. In addition to the mCHP system, optimisation process also includes distributed energy sources (solar panels) with electricity storage. On the demand side, various devices with different load profiles which can be controlled over time can be considered. This study therefore, being a desktop based one, sought to review the energy demand-side management as it applies to the use of mCHP in residential settings.

FORECASTING IMPACT OF DEMAND SIDE MANAGEMENT ON MALAYSIA’S POWER GENERATION USING SYSTEM DYNAMIC APPROACH

Rapid economic growth, increasing population, industrialization and high living standards have increased the electricity demand more than ever before. Efficient energy planning and management is always considered as the greatest challenge in all over the world. Among the other factors availability of electricity is the main bottleneck to the economic growth and industrial revolution. Considering this fact, it becomes necessary for academicians, government agencies and electricity companies to construct more efficient methodologies and procedures to predict long-term electricity demand. The objective of this article represents the initiative towards understanding and analyzing the importance of demand-side management (DSM) in forecasting electricity demand by using a system dynamics approach. This study examines the long term impact of demand-side management variables including HER (Home energy report), MEPS (Minimum Energy Performance Standards) and NEEAP (National Energy Efficiency Action Plan). The future installation capacity of Malaysia’s power generation is evaluated considering the factors of population, per capita electricity consumption, efficiency, capacity margin and DSM. The forecasting horizon of the simulation model is 15 years from 2016 to 2030. Keywords : Energy forecasting, System Dynamics, Energy efficiency, Energy Demand Side Management JEL Classifications: O18, Q21 DOI: https://doi.org/10.32479/ijeep.9716

Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes

All-electric buildings are playing an important role in the electrification plan towards energy-neutral smart cities. Batteries are key components in all-electric buildings that can help the demand-side energy management as a flexibility asset and improve the building survivability in the case of power outages as an active survivability asset. This paper introduces a novel methodology and indexes for determining cost-effective battery sizes. It also explores the possible trade-off between energy flexibility and the survivability of all-electric buildings. The introduced methodology uses IDA-ICE 4.8 as a building performance simulation tool and MATLAB® 2017 as a post-processing calculation tool for quantifying building energy flexibility and survivability indexes. The proposed methodology is applied to a case study of a Norwegian single-family house, where 10 competitive designs, 16 uncertainty scenarios, and 3 dynamic pricing tariffs suggested by the Norwegian regulators are investigated. The methodology provides informative support for different stakeholders to compare various building designs and dynamic pricing tariffs from the flexibility and survivability points of view. Overall, the results indicate that larger cost-effective batteries usually have higher active survivability and lower energy flexibility from cost- effectiveness perspective. For instance, when the time of use tariff is applied, the cost-effective battery size varies between 40 and 65 kWh (daily storage). This is associated with a cost-effective flexibility index of 0.4–0.55%/kWh and an active survivability index of 63–80%.

Demand-Side Energy Management Method for Building Clusters Applying Reinforcement Learning

Demand-Side Energy Management Method for Building Clusters Applying Reinforcement Learning

Understanding energy demand behaviors through spatio-temporal smart meter data analysis

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences.

A novel techno-economic risk-averse strategy for optimal scheduling of renewable-based industrial microgrid

Nowadays, increasing concerns regarding the harmful effects of fossil fuel-based energy production systems have accelerated the decarbonization efforts particularly in equipping the electric power grid with a high level of renewable energy sources. In deregulated energy networks with a high share of renewables, adopting an effective strategy for appropriately dealing with intermittences of stochastic producers is necessary to provide a continuous and reliable energy supply. This paper proposes a novel techno-economic risk-averse strategy for optimal scheduling of the industrial microgrid considering both the technical and economical objectives. The robustness function of the information gap decision theory method is exerted for developing the risk-averse strategy aiming to properly manage the fluctuations of uncertain parameters in the system. The demand-side energy management is carried out by developing the price and load response schemes as the demand response programs. The industrial microgrid equipped with renewable energy sources and energy storage systems is intended as a real case study for examining the effectiveness of the proposed strategy. The problem is studied under two models, which Model I assesses the scheduling of the industrial microgrid without intending the stochastic behaviors of uncertain parameters while Model II models uncertainties based on the proposed strategy. The results indicated the effectiveness of the proposed strategy in realizing robustness condition for the renewable-based industrial microgrid to supply reliable and continuous energy in the deregulated environment.

Design and Performance Evaluation of a Home Energy Management System for Power Saving

In the context of the ongoing global warming, with environmental concerns regarding the greenhouse gas emissions due to our increasing energy consumption, smart energy management solutions have gained popularity as they have the potential to reduce our impact on the environment and also on our budgets. This paper proposes one of the most affordable designs for an autonomous, microcontroller-based demand-side energy management system to be installed in a home environment where it reduces the standby power consumed by the controlled devices. As a secondary function, it monitors and controls the lights to further save energy. The proposed system is designed to operate independently and also to limit the new wireless sources of electro-magnetic radiation introduced in the home environment. Six homes have been analyzed in terms of the measured energy consumption and to evaluate the energy management capabilities of the system, a prototype was built and tested. Promising results have been obtained and are detailed in the Results and Conclusion sections. A very low purchase price and good performance make this design a viable solution for intelligent home energy management, in today’s economic context.

Decoupling economic and energy growth: aspiration or reality?

Energy has long been a driving force of economic growth; however, it comes with environmental costs and security challenges. This study analyzes the energy–economy nexus and explores their decoupling possibilities by using cross-country data over the years 1971–2014. The results indicate that, while energy use and economic growth exhibit a typical inverted U-shaped decoupling relationship for the industrialized countries, they have been rising in tandem for the developing economies. Among factors, it is the economic scale, population size, and energy intensity that are the decisive factors. Among countries, it is the U.S., China, and India, which mainly dominate the global economy–energy trend. Overall, we conclude that any global economy–energy decoupling may confront challenges and uncertainty. To better decouple economic growth from energy use, we propose policies for more structural reforms, a clean energy system, improved energy efficiency, and efficient energy demand-side management.

Energy demand side management in stand-alone power supply system with renewable energy sources

The article presents the demand side management for electricity consumption in the conditions of autonomous power supply systems with several sources of generation–traditional and renewable energy sources. The paper gives a classification of household electrical appliances in accordance with this, even devices with a high priority, which are often referred to as non-shiftable load, can participate in the program. An algorithm for the interaction of the demand side management control system and the user is proposed. It is shown that with the participation in the demand management program even some types of equipment can achieve a significant economic effect, increase the service life of the equipment and reduce the negative impact on the environment.

Computer‐assisted demand‐side energy management in residential smart grid employing novel pooling deep learning algorithm

Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: Novel pooling-based deep neural network is applied for residential energy management in a smart grid. The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.

A qualitative-computational cataloguing of the EU-level public research and innovation portfolio of clean energy technologies (2014–2020)

To better allocate funds in the new EU research framework programme Horizon Europe, an assessment of current and past efforts is crucial. In this paper we develop and apply a multi-method qualitative and computational approach to provide a catalogue of climate crisis mitigation technologies on the EU level between 2014 and 2020. Using the approach, we observed no public EU-level funding for multiple technologies prioritised by the EU, such as low-carbon production and use of cement and chemicals, electric battery, and a number of industrial decarbonisation processes. We observed a rising trend in the funding of solar power and onshore wind, the adjacent to them power-to-X technology, as well as recycling. At the same time, the shares of funding into fuel cell, biofuel, demand-side energy management, microgrids, and waste management show a decline trend. With note of the exploratory character of the present paper, we propose that the EU Horizon 2020 funding of clean technologies only partially reflected the expectations of key institutionalised EU actors due to the existence of many non-funded prioritised technologies.