The increasing importance of coordinated energy management in residential districts has led to a shift from individual end-user optimization to a broader energy community perspective. This transition, however, necessitates efficient data communication and processing tools. In this context, the Internet of Things (IoT) plays a pivotal role by seamlessly connecting energy meters, sensors, data processing units, and controllable energy assets within these districts. This empowers homeowners and utility providers with real-time data and intelligent automation, leading to more efficient energy consumption through predictive analytics. IoT sensors monitor energy usage patterns, weather conditions, and energy market fluctuations, allowing residents to remotely control and optimize their appliances and heating/cooling systems, ultimately reducing energy waste and costs. This paper presents an IoT-powered demand response management simulation study in a building district, validated using data from the Großschönau Municipality in Austria. This community encompasses various building types connected to both electric and local district heating (DH) networks. Data is collected by IoT-enabled sensors and transmitted via the internet for pre-processing and backend services. These services primarily involve an optimization-based coordinated management of energy assets in the community. This study aims to assess, in the simulation phase, the optimal operation scheduling of heat pumps (HP) with energy storage units that connect each building in the energy community to the DH network. The simulation outcomes demonstrate a notable improvement in the community's energy self-efficiency, resulting in lowered energy expenses facilitated by real-time monitoring of energy market data. This approach also leads to a reduction in estimated total CO2 emissions related to HP's operation.

In recent years, there has been a growing acknowledgment of the vital significance of energy flexibilitywithin local energy communities (LECs) as a fundamental strategy to optimize the utilization of adiverse array of available resources. At the district level, where flexibility is indispensable for theefficient operation of controllable assets within centralized substations, energy storage systems (ESSs)emerge as central players in achieving this objective. The primary aims encompass reducing electricitycosts and maximizing the self-consumption of interconnected renewable energy systems (RES) withinLECs, all while ensuring the secure and efficient operation of substation components. This challengeinvolves translating these objectives into a nonlinear optimization problem. Numerous optimizationtechniques have been explored and validated in this pursuit, applied on a real data for the heatingdemand of the ENVIPARK energy district in Turin, Italy. For this regard, a virtual scenario wasconstructed, suggesting the installation of two key energy storage technologies: battery electric storagesystem (BESS) and sensible thermal energy storage (TES). As a long-term assessment, the impact ofenergy flexibility margin, specifically BESS state of charge (SOC) and TES maximum temperature, hasbeen accurately evaluated and quantified. Essentially, adjusting BESS SOC lower limit from 50 % to10 % and the variation interval of the TES maximum temperature from 15 °C to 20 °C led to asubstantial improvement of up to 13.9 % in energy costs. Which underscores the central role of theoptimization-driven energy flexibility in reducing the heating expenses of local energy communities.

This study investigates the thermal and energetic dynamics of primary school classrooms in a Mediterranean climate in Khoualed Abdel Hakeem, Ain Temouchent County, Algeria. The research highlights significant optimizations by focusing on passive strategies such as external shading devices, Window-to-Wall Ratio (WWR), glazing types, and building envelope adjustments. Our simulations, validated rigorously, showcase a remarkable congruence with actual electricity consumption, affirming the reliability and efficacy of our simulation model as a valuable predictive tool. A Vertical Shading Angle (VSA) of 60° proves optimal, resulting in an impressive 11% reduction in Annual Energy Consumption (AEC). A recommended WWR of 30% demonstrates an 11% decrease in AEC and improves thermal and energy efficiency. Double Low Emissivity (Double-Low E) glazing is found to be superior, resulting in a significant 14% decrease in AEC. Achieving a WWR of 50% in shaded areas helps maintain a well-balanced thermal environment, resulting in a 12% reduction in heating and cooling requirements. The integration of passive strategies in the optimized model showcases a remarkable 44% overall reduction in energy consumption. The results highlight the efficacy of passive strategies, promoting energy-conscious and ecologically responsible practices, advocating for their incorporation in educational facilities, and offering valuable insights for sustainable school building design.

Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55 ◦C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short- term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models.

Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes.

Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.

District heating and cooling (DHC) networks are becoming, recently, promising solutions for the decarbonization of heating and cooling systems. The low supply temperature of 4th and 5th generation DHC make it possible lowering the heat losses in the pipes and integrating renewable energy sources (RES)-based heat generators. DHC network owners aim, continually, to keep large enough the temperature difference between supply and return in their plant generators in order to make heat transfer to the end-user more efficient. However, in summer scenario, this task is not trivial due to the low heat demand, and in summer, it can be even more challenging. This article is addressing and validating a potential solution based on the use of sorption energy thermal system (SETS) to enhance the seasonable DHC network performance, such as return temperature and heat pump setpoints. With an optimal real-time operation, SETS can fulfil the cooling demand in summer and help in lowering the DHC return temperature to a tolerable range.

Photovoltaic (PV) systems are recently the most used sustainable energy source to fit with the energy demand growth. Generally, batteries, as storage systems, are installed along with PV modules. When it comes to an optimal power management of PV/battery hybrid systems, the uncertain and intermittent behavior of PV power production can provoke some challenges, with which, the real-time operation of the hybrid system can be degraded, therefore, PV power forecast is highly needed. Data-driven models are became nowadays very efficient methods to build regression models for the purpose of PV power forecast. In this paper, Adaptive Neuro-Fuzzy Inference (ANFIS) is chosen as a data-driven technique, to build up forecasting models. Standard ANFIS, which uses only weather data, cannot avoid the confusing scenarios like PV modules covered by the snow in clear-sky days. This work proposes an improved ANFIS model taking historical generated power into account. The developed model is validated on a real case, using the PV system of the institute of energy system technologies in Offenburg. When adding the average of produced power of the last 72 hours as additional input, the model was able to follow the rapid changes in weather conditions and overcome the unexceptional situations like the problem of snow on the PV modules.          

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic(PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltageprofiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes intoaccount the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions arerequired. In this context, the grid status and weather data need to be transmitted continuously, via wirelesscommunication technology, to an edge computer. Based on the transmitted data, and using the system mathe-matical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpointfor each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWERIEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the gridwith a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordinationof PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In thiscontext, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. Animprovement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved.Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.