Energy retrofits for smart and connected communities: Scopes and technologies

Wen L., Zhou K., Feng W., Yang S.

Demand side management (DSM) is an important way to achieve smart energy management. Herein, a dynamic price (DP)-based demand response (DR) model is developed for DSM in smart grid. The proposed DR model can shift the peak electricity demand, thereby improving the power system stability and reliability. In a district-scale smart grid with a high photovoltaic power penetration, the energy service provider (ESP) optimizes the DP to maximize its utilities and reduce the load fluctuation while minimizing the bills and dissatisfaction of electricity consumers (ECs). The game theory model is used to explore the interaction between ESP and ECs, and the existence of Nash equilibrium is proved. The proposed DR model is validated with real-world data from a commercial and residential cluster in Suzhou City, Jiangsu Province, China. The results show that the peak electricity demands of commercial and residential ECs decreased by 4.99% and 9.99%, respectively, through the proposed DR model. Meanwhile, the ESP's net profits increased by 7.13% and 2.37%, respectively, while ensuring the ECs’ benefits. The results also demonstrate that the proposed DR model is robust in different scenarios. This article contributes to the effectiveness and efficiency of energy engineering management.

Alsolami M., Alferadi A., Lami B., Ben Slama S.

With the price of green energy now more reasonable, users can now produce enough electricity to meet their needs and make a profit by selling the surplus on the underground P2P energy market. For households, energy trading and demand management can reduce electricity costs. However, consumers generally obtain market offers based on their expectations and the forecasts of other households. However, the P2P exchange system is not able to quantify the gap between these offers and the best market. The objective of this paper is to apply deep reinforcement learning techniques to optimal energy trading and demand response (DR) methods within a peer-to-peer (P2P) market. The main objective is to maximize cost reductions. The best approach to achieve this objective was investigated as part of this project. The complexity of domestic energy trading and energy recovery is formally characterized as a partially observable Markov decision process (POMDP). Through decentralized training and performance-based learning, the strategy maximizes policy and value functions. In order to identify the most effective proactive solutions, a comparative analysis is carried out between the two parties. Based on the simulation results, it was observed that implementing the recommended reinforcement learning strategy to optimize peer-to-peer (P2P) energy exchange can lead to a significant improvement in the average household reward. Specifically, the average household reward can be increased by 7.6% and 12.08% by employing the aforementioned approach.

Liu B., Penaka S.R., Lu W., Feng K., Rebbling A., Olofsson T.

This paper presents an open digital ecosystem based on a web-framework with a functional back-end server for user-centric energy retrofits. This data-driven web framework is proposed for building energy renovation benchmarking as part of an energy advisory service development for the V¨asterbotten region, Sweden. A 4-tier architecture is developed and programmed to achieve users’ interactive design and visualization via a web browser. Six data-driven methods are integrated into this framework as backend server functions. Based on these functions, users can be supported by this decision-making system when they want to know if a renovation is needed or not. Meanwhile, influential factors (input values) from the database that affect energy usage in buildings are to be analyzed via quantitative analysis, i.e., sensitivity analysis. The contributions to this open ecosystem platform in energy renovation are: 1) A systematic framework that can be applied to energy efficiency with data-driven approaches, 2) A user-friendly web-based platform that is easy and flexible to use, and 3) integrated quantitative analysis into the framework to obtain the importance among all the relevant factors. This computational framework is designed for stakeholders who would like to get preliminary information in energy advisory. The improved energy advisor service enabled by the developed platform can significantly reduce the cost of decision-making, enabling decision-makers to participate in such professional knowledge-required decisions in a deliberate and efficient manner. This work is funded by the AURORAL project, which integrates an open and interoperable digital platform, demonstrated through regional large-scale pilots in different countries of Europe by interdisciplinary applications.

Yang H., Zhang S., Zeng J., Tang S., Xiong S.

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.

Chen Y., Han B., Li Z., Zhao B., Zheng R., Li G.

The development of smart grids allows residential customers to participate in demand response (DR) programs to aid power grid management through HEMS (Home Energy Management Systems), but similar electricity consumption behavior among customers based on time-of-use electricity prices may lead to the problem of peak load shift, also known as peak rebounds. This article proposes a multi-level interactive optimization model considering individual sensitivity for DR. The model consists of community energy aggregators (CEAs), which perform as an intermediate processing layer between customers and power grid. Customer terminals perform energy management for home appliances, electric vehicles, energy storage systems, and renewable energy generation. The scheduling problem is decomposed into smaller parallel decision problems that are easier to solve. Renewable generation especially photovoltaic power generation is predicted and used to mitigate the influence of energy generation uncertainty. By introducing numerical responsiveness of customers, the model deals with uncertainty on the subjective level of customers. As indicated in numerical analyses, the model is a good compromise between stochastic optimization depending on idealized probability models and robust optimization sacrificing cost to meet worst case scenarios. The proposed method was compared with existing optimization-based methods for peak shaving. Compared with coordinated load management, our method reduced the peak load and average cost by 13.36% and 18.96%, respectively. Compared with robust optimization, our method achieved similar effect while handling the uncertainty in customers and PV.

Kumar J., Saxena D., Singh A.K., Vasilakos A.V.

Andreadou N., Thomas D., De Paola A., Kotsakis E., Fulli G.

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.

Couraud B., Andoni M., Robu V., Norbu S., Chen S., Flynn D.

The transition towards a more decarbonised, resilient and distributed energy system requires local initiatives, such as Smart Local Energy Systems (SLES), which lead communities to gain self-sufficiency and become electricity islands. Although many SLES projects have been recently deployed, only a few of them have managed to be successful, mostly due to an initial knowledge gap in the SLES planning and deployment phases. This paper leverages the knowledge from the UK’s largest SLES demonstrator in the Orkney Islands, named the Responsive FLEXibility (ReFLEX) project, to propose a framework that will help communities to successfully implement a SLES. First, this paper describes how the multi-services electrical SLES implemented in Orkney reduces the impact of the energy transition on the electrical infrastructure. We identify and discuss the main enablers and barriers to a successful SLES, based on a review of SLES projects in the UK. Second, to help future communities to implement SLES, we extend the Smart Grid Architecture Model (SGAM) into a comprehensive multi-vector Smart Local Energy Architecture Model (SLEAM) that includes all main energy services, namely power, heat and transport. This extended architecture model describes the main components and interaction layers that need to be addressed in a comprehensive SLES. Next, to inform successful deployment of SLES, an extensive list of key performance indicators for SLES is proposed and implemented for the ReFLEX project. Finally, we discuss lessons learnt from the ReFLEX project and we list required future technologies that enable communities, energy policy makers and regulatory bodies to best prepare for the energy transition.

Madler J., Harding S., Weibelzahl M.

The shift towards renewable energy sources (RES) in energy systems is becoming increasingly important. Residential energy generation and storage assets, smart home energy management systems, and peer-to-peer (P2P) electricity trading in microgrids can help integrate and balance decentralized renewable electricity supply with an increasingly electrified power, heat, and transport demand, reducing costs and CO2 emissions. However, these microgrids are difficult to model because they consist of autonomous and interacting entities, leading to emergent phenomena and a high degree of complexity. Agent-based modeling is an established technique to simulate the complexity of microgrids. However, the existing literature still lacks real-world implementation studies and, as a first step, models capable of validating the existing results with real-world data. To this end, we present an agent-based model and analyze the corresponding microgrid performance with real-world data. The model quantifies economic, technical, and environmental metrics to simulate microgrid performance holistically and, in line with state-of-the-art research, consists of self-interested, autonomous agents with specific load profiles, RES generation, and demand-response potential. The model can simulate a P2P marketplace where electricity is traded between agents. In the second part of the paper, we validate the model with data from a medium-sized German city. In this case study, we also compare microgrid performance in 2022, during the energy market crisis in Europe, with historical data from 2019 to assess the effects of energy market shocks. Our results show how microgrids with P2P trading can reduce electricity costs and CO2 emissions. However, our trading mechanism illustrates that the benefits of energy-community trading are almost exclusively shared among prosumers, highlighting the need to consider distributional issues when implementing P2P trading.

Liu C., Xue Z.

In smart elderly care communities, optimizing the design of building energy systems is crucial for improving the quality of life and health of the elderly. This study pioneers an innovative adaptive optimization design methodology for building energy systems by harnessing the cutting-edge capabilities of deep reinforcement learning. This avant-garde method initially involves modeling a myriad of energy equipment embedded within the energy ecosystem of smart elderly care community buildings, thereby extracting their energy computation formulae. In a groundbreaking progression, this study ingeniously employs the actor–critic (AC) algorithm to refine the deep deterministic policy gradient (DDPG) algorithm. The enhanced DDPG algorithm is then adeptly wielded to perform adaptive optimization of the operational states within the energy system of a smart retirement community building, signifying a trailblazing approach in this realm. Simulation experiments indicate that the proposed method has better stability and convergence compared to traditional deep Q-learning algorithms. When the environmental interaction coefficient and learning ratio is 4, the improved DDPG algorithm under the AC framework can converge after 60 iterations. The stable reward value in the convergence state is −996. When the scheduling cycle of the energy system is between 0:00 and 8:00, the photovoltaic output of the system optimized by the DDPG algorithm is 0. The wind power output fluctuates within 50 kW. This study realizes efficient operation, energy saving, and emission reduction in building energy systems in smart elderly care communities and provides new ideas and methods for research in this field. It also provides an important reference for the design and operation of building energy systems in smart elderly care communities.

Mousavi S., Gheibi M., Wacławek S., Behzadian K.

The rise in greenhouse gas emissions in cities and the excessive consumption of fossil energy resources has made the development of green spaces, such as green roofs, an increasingly important focus in urban areas. This study proposes a novel smart energy-comfort system for green roofs in housing estates that utilises integrated machine learning (ML), DesignBuilder (DB) software and Taguchi design computations for optimising green roof design and operation in buildings. The optimisation process maximises energy conservation and thermal comfort of the green roof buildings for effective parameters of green roofs including Leaf Area Index (P1), leaf reflectivity (P2), leaf emissivity (P3), and stomatal resistance (P4). The optimal solutions can result in a 12.8% increase in comfort hours and a 14% reduction in energy consumption compared to the base case. The ML analysis revealed that the adaptive network-based fuzzy inference system is the most appropriate method for predicting Energy-Comfort functions based on effective parameters, with a correlation coefficient greater than 97%. This novel smart framework for the optimal design of green roofs in buildings offers an innovative approach to achieving energy conservation and thermal comfort in urban areas.

Mäkivierikko A., Siepelmeyer H., Shahrokni H., Enarsson D., Kordas O.

Households can provide demand-side flexibility by changing their consumption behaviour and shifting energy-intensive activities – such as vacuuming, cooking or charging electric cars – to off-peak hours. Such behaviour-based demand response management could balance out consumption peaks without the need for costly smart home devices or automation technologies. However, existing research has struggled to motivate consumers to adapt their behaviour or maintain behaviour change over extended periods of time. This field study explored a scalable and cost-effective behavioural demand response tool and investigated its short- and long-term impacts on average and peak hour electricity consumption under realistic conditions: A smartphone app provided users with social comparison feedback on their electricity consumption and invited them to participate in “pause hours” by avoiding energy-intensive activities during peak hours. To appeal even to hard-to-reach energy users and elicit longer-term engagement, the app also contained a neighbourhood newsfeed and was framed as a local social network. In a 15-month trial with 550 student apartments in Sweden, more than half of the residents voluntarily installed the app, many app users stayed engaged over an extended period of time and pause hour participants achieved substantial peak-load consumption reductions of 46% on average. These results indicate that smartphone apps can achieve peak load consumption reduction and long-term engagement, although they may be particularly attractive to already energy efficient households. Avenues for future research are discussed.

Alden R.E., Gong H., Rooney T., Branecky B., Ionel D.M.

As the smart grid involves more new technologies such as electric vehicles (EVs) and distributed energy resources (DERs), more attention is needed in research to general energy storage (GES) based energy management systems (EMS) that account for all possible load shifting and control strategies, specifically with major appliances that are projected to continue electrification such as the electric water heater (EWH). In this work, a methodology for a modified single-node model of a resistive EWH is proposed with improved internal tank temperature for user comfort modeling and capabilities for conservation voltage reduction (CVR) simulations as well as Energy Star and Consumer Technology Association communications protocol (CTA-2045) compliant controls, including energy storage calculations for “energy take”. Daily and weekly simulations are performed on a representative IEEE test feeder distribution system with experimental load and hot water draw (HWD) profiles to consider user comfort. Sequential controls are developed to reduce power spikes from controls and lead to peak shavings. It is found that EWHs are suitable for virtual power plant (VPP) operation with sustainable tank temperatures, i.e., average water temperature is maintained at set-point or above at the end of the control period while shifting up to 78% of EWH energy out of shed windows per day and 75% over a week, which amounts to up to 23% of the total load shifted on the example power system. While CVR simulations reduced the peak power of individual EWHs, the aggregation effect at the distribution level negates this reduction in power for the community. The EWH is shown as an energy constant load without consistent benefit from CVR across the example community with low energy reductions of less than 0.1% and, in some cases, increased daily energy by 0.18%.

Gough M., Rakhsia K., Bandeira T., Amaro H., Castro R., Catalão J.P.

Water heating accounts for approximately 25% of household energy use in developed countries. Therefore, the optimal control of water heating through the deployment of intelligent residential Electric Water Heaters (EWH) brings significant benefits. This paper presents an innovative design and implementation of an easy-to-use device for intelligent residential water heating. The device relied upon machine learning techniques to forecast a consumer’s hot water demand and optimize the operation of an EWH using a novel data collection process that relied on non-intrusive vibration data alone. The device was deployed in a six-month pilot project on the island of São Miguel, Portugal. The major difficulties were the novel use of vibration data to forecast the volume of hot water used and the uncertain behavior of the consumers. The challenges of only using vibration data were solved by careful data collection and artificial intelligence methods. To tackle the issue of uncertain consumer behavior, an innovative ‘heat now’ function was incorporated into the device to override the novel control framework. Results show that the device could accurately forecast hot water demand and optimally operate the EWH to meet this demand. The results showed an average reduction of 1.33 kWh/day per consumer, which equates to an average decrease of 35.5% in water heating costs. Calculations show that these devices can reduce the total energy used by 2832 kWh daily or 0.21% of total electricity generated. Furthermore, a fleet of these devices could reduce thermal generation by 0.37%, reducing emissions by 693.31 tons of CO2 per year. The results from the consumer survey show that the device did not affect the consumer’s comfort, validating the benefits and efficacy of the proposed device. Hence, the paper shows that a simple-to-use, novel device using an innovative forecasting algorithm based on non-intrusive vibration data brings numerous quantifiable benefits to actual consumers and electrical utilities.

Eckhoff S., Hart M.C., Brauner T., Kraschewski T., Heumann M., Breitner M.H.

The urgency of climate change mitigation, rising energy prices and geopolitical crises make a quick and efficient energy transition in the building sector imperative. Building owners, housing associations, and local governments need support in the complex task to build sustainable energy systems. Motivated by the calls for more solution-oriented, practice-focused research regarding climate change and guided by design science research principles, we address this need and design, develop, and evaluate the web-based decision support system NESSI. NESSI is an open-access energy system simulator with an intuitive user flow to facilitate multi-energy planning for buildings and neighborhoods. It calculates the technical, environmental, and economic effects of 14 energy-producing, consuming, and storing components of the electric and thermal infrastructure, considers time-dependent effects, and accounts for geographic as well as sectoral circumstances. Its applicability is demonstrated with the case of a single-family home in Hannover, Germany, and evaluated through twelve expert interviews.

Valsan V., Vuppala N.S., Koganti S.S., Kalla L.S., Pappala K.A., P. K., Ramesh M.V.

Khaled K., Berardi U.

Croitoru C., Calotă R., Lemian D., Civiero P., Aelenei L.

This paper highlights the role of building retrofitting in developing energy-resilient communities as a part of sustainable urban regeneration. Different approaches and technologies are covered, with the role of improving the energy performance of existing buildings by utilizing, among others, innovative insulation materials or renewable sources for heat supply combined with advanced smart control systems. The case studies from different parts of the world illustrate that this techno-economically viable retrofitting approach can reduce around 40 % energy consumption and emissions, making buildings more sustainable. The analysis of the new economic and regulatory is connected with the government’s incentives as well as public engagement in developing positive energy communities. This paper also documents an extensive evaluation of retrofit technologies and their application, demonstrating the critical contribution energy retrofits can make towards achieving enduring urban sustainability.

Shu L., Hong T., Sun K., Zhao D.

YesuJyothi Y., Venkateswarlu Y.

Poyyamozhi M., Murugesan B., Rajamanickam N., Shorfuzzaman M., Aboelmagd Y.

The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.