Day-ahead Scheduling Research Articles

Resiliency-informed optimal scheduling of smart distribution network with urban distributed photovoltaic: A stochastic P-robust optimization

An increased worldwide emphasis on resilience has resulted in a greater concentration on the distribution networks' resiliency. Therefore, the conventional approach to advance scheduling for power distribution networks is no longer sufficient, thereby requiring the creation of resilient scheduling approaches. This paper examines a proactive day-ahead scheduling method for distribution networks that are linked with significant renewable energy sources. The suggested approach employs a probabilistic scenario-generating strategy to quantify the uncertainties related to wind speed, solar irradiation, catastrophe duration, and load fluctuations. This study utilizes a hybrid stochastic p-robust optimization technique to enhance the analysis and provide robust strategies against risks. The present methodology employs a regret-based metric to quantify the adverse impacts of uncertainty inside the proactive scheduling procedure. The suggested methodology is verified using an IEEE 33-bus test system that includes significant distributed photovoltaic sources. The findings indicate a significant decrease of 43% in the highest level of regret, accompanied by a relatively limited rise of 1.22% in the operational expenses of the distribution network. This framework efficiently manages natural occurrences in the day-ahead scheduling process, making it a very promising method for improving the robustness of power distribution networks.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

Balancing operational efficiency and regulation performance, for guiding pumped-storage day-ahead scheduling

Pumped-storage plants (PSPs) have significant potential to regulate intermittent energy sources. However, achieving coordinated optimization of regulation stability and operational efficiency has been a long-standing challenge. This study proposes a new soft linking model to address the limitation that current scheduling methods fail to account for both efficiency and second-level stability simultaneously, aiming to improve the day-ahead scheduling strategies for PSP. Firstly, a Pumped-Storage Hydroelectric System (PSHS) model is developed to reflect the regulation stability of PSP, considering the transient characteristics of hydraulic, mechanical, and electrical subsystems. Then, by analyzing transient responses under various operating working conditions, the study integrates the transient variation laws of the power plant into a regulation stability dataset (RSD). Finally, using RSD as the coupling interface, with regulation stability and operational efficiency as the objective functions, a Pumped-Storage Power Day-ahead Scheduling (PSPDS) model is established. An actual PSP is used as a case study to evaluate the effects of four operational scenes with varying optimization objectives on operational efficiency and subsystem performance. The results indicate that controlling load change magnitude and increasing the load decrease working conditions effectively enhance the stability of PSP subsystems. In addition, although the proposed scheduling scheme increases water consumption by 0.0213% compared to traditional scheduling method, it improves regulation stability by at least 15.14%. The findings suggest that a minor compromise in operational efficiency can lead to a significant improvement in regulation stability. This study provides new perspectives and methods for the management and optimization of PSPs.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Integrated real-time dispatch of power and gas systems

The integrated operation of the electricity and gas systems has attracted the attention of many researchers due to the ever-increasing interdependency between the two systems. In this paper, a novel framework for the real-time rolling dispatch of the integrated system is presented, targeting to attain the economically optimal and technically secure gas system real-time operation through the control of the available flexibility procured by various resources. A decoupled day-ahead scheduling is initially executed to determine unit commitment and gas linepack target decisions, which are then utilized as inputs to the proposed integrated real-time dispatch model. Intra-day gas system control is executed in a hierarchical procedure through the deployment of four control actions from various inter-system flexibility providers. The presented analysis illustrates that, based on the selection of control parameters, the activation of flexibility resources from both systems can be steered in such a way as to alleviate linepack deviations, even in cases of severely limited scheduled gas supply. The proposed control framework is tested on the Greek power and gas systems, providing significant insights regarding the activation of the control actions for the real-time gas system balancing in different look-ahead horizons.

Power prediction considering NWP wind speed error tolerability: A strategy to improve the accuracy of short-term wind power prediction under wind speed offset scenarios

Short-term wind power prediction is of great significance for wind power participation in day-ahead scheduling. However, unavoidable numerical weather prediction (NWP) errors bring severe challenges to high-precision prediction of wind power, especially in the power peaks and valleys periods, the extreme error is significant. In this regard, this paper proposes a strategy to improve the accuracy of short-term wind power prediction taking into account the wind speed offset scenario and the weighted improved offset loss function (WIOLF). Introducing a multi-level directed acyclic graph structure for identification of wind speed offset scenarios, and a Wasserstein GAN (WGAN-GP) network with gradient penalty is used to solve the problem of sample imbalance. In the power prediction part, WIOLF is integrated into the combination model of temporal convolution network (TCN) combined with multi-head self-attention mechanism (MHSA) to improve its decision-making mechanism, so as to train a wind power offset prediction model to improve the power prediction accuracy in wind speed offset scenarios. The proposed method is applied to several wind farms in Western Inner Mongolia, China, the results show that compared with the direct prediction method, the RMSE and MAE of the proposed method are reduced by 7.41 % and 6.10 %, and the R2 is increased by 9.06 %, respectively, which verifies the effectiveness of the method.

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

Two-Stage Optimization Scheduling of Integrated Energy Systems Considering Demand Side Response

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment.

Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy

Hydropower, characterized by its quick ramping speed and energy buffering capacity, has the ability to respond to the flexibility demand caused by the forecast uncertainty of variable renewable energy (VRE). However, how to determine the flexibility supply of each hydropower station to ensure the reliable execution of the day-ahead scheduling in the intraday operation is a challenging issue for a hydro–wind–solar hybrid system (HWSHS). Due to complex characteristics of cascade hydropower stations, there is a mutual influence between the day-ahead scheduling and its execution. Therefore, flexibility response mode (FRM) is proposed to determine the flexibility supply of each hydropower station according to the flexibility demand of VRE, and a co-optimization model is constructed to optimize the day-ahead scheduling and the parameters of FRM considering the forecast uncertainty of VRE. A HWSHS in Guizhou province, China, is employed as a case study. The results suggest that the FRM can ensure the reliable execution of the day-ahead scheduling in the intraday operation. Compared to a contrast model without FRM, the execution deviation and its duration are decreased by more than 22% and 37%, respectively, in both long-run and out-of-sample analyses.

Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators

Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.

Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage

Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.

Flexibility Assessment of Ternary Pumped Storage in Day-Ahead Optimization Scheduling of Power Systems

To explore the advantages in the flexibility of ternary pumped storage units (T-PSUs) compared with fixed-speed pumped storage units (FS-PSUs) and variable-speed pumped storage units (VS-PSUs) in the day-ahead optimal scheduling of power systems, this paper firstly establishes the mathematical model of T-PSUs which is suitable for the target application, and establishes a new hybrid pumped storage model combining both FS-PSUs and T-PSUs for the purpose of improving existing FS-PSUs. Secondly, a day-ahead optimal scheduling model used for a wind-thermal-pumped storage bundled transmission system with the goal of obtaining the lowest operating cost is established. Additionally, various indicators are proposed to comprehensively evaluate the flexibility of different pumped storage unit types on day-ahead optimal scheduling. Finally, a sensitivity analysis is conducted from three aspects: covering the wind power capacity, load rate, and pumped storage capacity. The results indicate that T-PSUs are superior to FS-PSUs and VS-PSUs in maintaining the smooth, economic, and robust operation of thermal power units, as well as in promoting wind power consumption. This provides a significant reference for evaluating the technical and economic benefits of the different types of pumped storage units in applications of future power grids.

Data-driven two-stage scheduling of multi-energy systems for operational flexibility enhancement

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for enhancing operational flexibility. This paper investigates a data-driven two-stage scheduling strategy of multi-energy system to promote uncertain renewable energy integration and improve economic benefits. Dynamic models for DHNs and HCNG networks are established, and the flexibility of multi-energy system is quantified through distribution-level power aggregation. To compromise flexibility enhancement and operational cost reduction, the multiobjective day-ahead scheduling optimization is conducted based on adaptive batch-ParEGO method. Taking day-ahead scheduling as a baseline, a data-driven real-time dispatch method is proposed based on the deep deterministic policy gradient algorithm, which adaptively modifies the energy management scheme in smaller temporal dimension to address uncertainties on both the load and source sides. The performance of the proposed two-stage scheduling method is demonstrated through numerical tests.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Interval Constrained Multi-Objective Optimization Scheduling Method for Island-Integrated Energy Systems Based on Meta-Learning and Enhanced Proximal Policy Optimization

Multiple uncertainties from source–load and energy conversion significantly impact the real-time dispatch of an island integrated energy system (IIES). This paper addresses the day-ahead scheduling problems of IIES under these conditions, aiming to minimize daily economic costs and maximize the output of renewable energies. We introduce an innovative algorithm for Interval Constrained Multi-objective Optimization Problems (ICMOPs), which incorporates meta-learning and an improved Proximal Policy Optimization with Clipped Objective (PPO-CLIP) approach. This algorithm fills a notable gap in the application of DRL to complex ICMOPs within the field. Initially, the multi-objective problem is decomposed into several single-objective problems using a uniform weight decomposition method. A meta-model trained via meta-learning enables fine-tuning to adapt solutions for subsidiary problems once the initial training is complete. Additionally, we enhance the PPO-CLIP framework with a novel strategy that integrates probability shifts and Generalized Advantage Estimation (GAE). In the final stage of scheduling plan selection, a technique for identifying interval turning points is employed to choose the optimal plan from the Pareto solution set. The results demonstrate that the method not only secures excellent scheduling solutions in complex environments through its robust generalization capabilities but also shows significant improvements over interval-constrained multi-objective evolutionary algorithms, such as IP-MOEA, ICMOABC, and IMOMA-II, across multiple multi-objective evaluation metrics including hypervolume (HV), runtime, and uncertainty.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

Modelling long-term operational dynamics of grid-connected hydro- photovoltaic hybrid systems

The development of variable renewable energy sources (VRESs), such as wind and solar photovoltaic (PV), has become a strategic choice for ensuring the security of electricity supplies and the decarbonization of power systems. The hybridization of hydropower with VRESs is an important measure for guaranteeing the smooth and cost-effective integration of VRESs into the power system. However, the highly variable weather- and climate-driven spatial and temporal fluctuations in VRES performance have significant potential to affect the optimal trajectory of hydropower operations. Therefore, it is essential to understand how hydropower responds to VRESs over long-term horizons, thereby allowing informed operation decisions to be made for hybrid generation systems (HGSs). In this study, a long- and short-term nested operation model is developed for hydropower operations considering PV integration. Specifically, the long-term model determines the day-by-day hydropower production over many years, and the short-term model simulates the day-ahead generation scheduling and real-time operation processes of the HGS. Performance metrics are established for evaluating the complementary operation in comparison with separate operation of hydropower and PV plants. China's two representative hydro-PV HGSs (Zhongyu and Longyangxia) are selected as case studies. The results indicate that the hybrid generation of hydropower and PV could, on average, increase total electricity generation by 11.33 %, reduce the load loss rate by 9.31 %, and decrease the PV curtailment rate by 36.60 %. However, hydro-PV hybridization may lead to an increase of 9.22 % in the water spillage rate and a decrease of 0.094 kWh/m3 in the water–electricity conversion coefficient. Therefore, hybrid generation of hydropower and PV could further enhance energy yield and supply reliability. Also, the importance of improving hydropower flexibility is highlighted.

Customized decentralized autonomous organization based optimal energy management for smart buildings

In recent years, the unprecedented rising urbanization level and proliferation of distributed energy sources (DES) are motivating the transition of smart building from consumer to prosumer. In the context of mitigating upstream emission via reducing reliance on utility grids, the key challenge becomes realizing autonomous and economically optimal energy management within smart buildings. This paper innovatively proposes a Decentralized Autonomous Organization (DAO)-based energy management framework for smart buildings, where the mechanism of day-ahead scheduling DAO considering peer-to-peer (P2P) energy sharing is specifically investigated. Besides, a sequential preference satisfaction mechanism is designed to customize participants’ special demands, thereby achieving effective energy matching. Considering potential communication failures between buildings in practical operations, this study develops a communication failure robust distributed algorithm to minimize operational costs and safeguard building’s privacy. Extensive numerical studies for highly DES penetrated smart buildings successfully offers an operation cost reduction of 33.7% when considering preference satisfaction and P2P energy sharing in the day-ahead scheduling problem. Simulation results also demonstrates that the algorithm in this work excels in resisting potential communication failure during operation.

Short-term load distribution model for giant cascade serial diversion-type hydropower stations

With the development of complex cascade serial diversion-type hydropower plants (CSDHP) in Southwest China, it has become imperative to study short-term scheduling challenges that are distinctive to this type of hydropower facility. Unlike conventional cascade hydropower stations, which have large reservoirs to regulate the outflow between stations, the CSDHP system consists of several diversion-type hydropower plants connected in series, requiring strict matching of turbine discharge between upstream and downstream facilities. In addition, these plants impose complex operation constraints, such as multiple units sharing one tunnel, which greatly increases the difficulty of short-term scheduling. This paper proposes a short-term optimal scheduling model for CSDHP systems aimed at achieving efficient load distribution among units while minimizing water consumption. The model takes into account the strict matching of turbine discharge and the complex constraints of multiple units sharing one tunnel. Subsequently, the model is transformed into a mixed integer linear programming problem using specified linearization techniques, with complexity further addressed via an iterative resolution method. A case study of a cascade illustrates the model’s efficacy in facilitating effective unit load distribution under complex hydraulic and electrical constraints, which substantially reduces the system’s water consumption by up to 6.1% during the dry season and 1.8% during the flood season, and reducing the number of crossing forbidden zones by 50.0% and 52.0%, respectively. The result shows the models’ potential to enhance day-ahead scheduling for the CSDHP system.

Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.