• Energy Research

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments. 30 pages, 14 figures, 4 tables

In the evolving energy sector, where buildings are recognized as dynamic components of energy networks and smart grids, the implementation of new regulations and guidelines is crucial to optimize the interaction between buildings and the grid. The imperative to reduce building energy consumption facilitates the promotion of new technologies that rely on renewable energy generation and innovative materials. As technology has progressed, the multitude of energy vectors involved has made controlling energy systems increasingly challenging. This poses a barrier to the widespread adoption and implementation of cutting-edge technologies. In this framework, this paper explores an energy-efficient solution using an integrated photovoltaic/thermal collector and an active phase-change material storage system. The study optimizes the integration of technologies through a resistance capacitance model, assessing the impact on thermal comfort, energy savings and costs. A novel cascade methodology, combining particle swarm optimization search with model predictive control, is designed to select the optimal mode of operation for the proposed technologies. The economic feasibility of the proposed system is analyzed across different tariff structures, whereas the interaction with the grid is evaluated using energy flexibility key performance indicators. The energy and economic performance, as well as the flexibility of the system are assessed through a proof-of-concept conducted in an office building scenario. The results demonstrate an increase in energy efficiency, with savings ranging from 9% to 28% compared to a suitable baseline scenario, and a significant energy shift from on-peak to off-peak periods, potentially accounting for up to 46% of the total building load. This energy flexibility enables the grid to receive reduced demand during morning hours.

This paper investigates the energy and economic performance of several energy schemes that could potentially be applied to agricultural and zootechnical communities contributing to the international objectives of sustainable development. The proposed energy schemes involve integrated energy efficiency technologies and novel system layouts aiming at reaching the zero-energy goal at a community level, by considering collective energy actions with provision of benefits for members and stakeholders. The proposed scenarios include different innovative technologies, such as anaerobic digestion, cogeneration, biogas upgrading, solar, district heating and cooling. These layouts are modelled in TRNSYS simulation environment to perform dynamic simulations and parametric analyses of the pivotal system parameters. Such analyses are conducted to find out the best scenario and the size of its system components which optimize different energy and economic objective functions. To assess the feasibility of all proposed scenarios and energy schemes, as well as to investigate the potential of the developed models, proposed scenarios are studied for an existing community. This existing agricultural community named “La Bellotta”, is served through different technologies, including a gas fuelled co-generator and an anaerobic biodigester. Simulation results show that the investigated scenarios allow for achieving very high self consumption ratios of energy produced on-site (from 57 to 100%), high economic performance (measured by the profitability index up to 1.35 for the best investigated scenario) and environmental benefits. The case study provides examples of energy schemes in which citizens and communities have a major benefit to invest in projects including renewables technologies, energy efficiency, and positive energy services.

Building energy modeling is essential for designing energy-efficient and flexible buildings that seamlessly integrate with the electrical grid. This study introduces a data-driven, control-oriented methodology using Resistance-Capacitance thermal network models to accurately forecast building thermal loads. It differentiates the impacts of fast and slow dynamics associated with different heating types—radiant and/or convective. A Model Predictive Control (MPC) framework optimizes coordination between the different building thermal dynamics, considering weather forecasts and price signals. The Varennes Library, a Net Zero Energy Institutional Building located in Québec (Canada), serves as a case study for performance assessment. Validation of the developed model demonstrates its efficacy in enabling MPC to formulate effective control strategies. Findings reveal that high-mass radiant heating is strategically used before indoor setpoint variation or demand response events. Up to 70% of the building thermal load is delivered to the active envelope for off-peak heat storage and on-peak release. Conversely the ventilation heating is prioritized in proximity of the change in setpoint or grid tariff with percentages over 80%. Results show the adoption of weather clusters for generalizing the optimal control setting, highlighting their influence on thermal loads while maintaining robust ventilation and active envelope heating coordination. The comparison between the predictive control strategy and the existing rule-based control shows improvements in indoor temperature and energy flexibility. During the MPC routine, a constant price signal reduces grid stress, achieving Load Factor (LF) values up to 0.72 compared to 0.60 with rule-based control, while demand response, though critical peak pricing, optimally shifts up to 100% of the thermal load during peak price hours.

The present study investigates the use and implementation of energy efficient measures and strategies for building applications, toward the Nearly Zero Energy Buildings target. Specifically, objective of the study is to implement building integrated photovoltaic thermal devices coupled with a phase change materials heat exchanger acting as an active thermal storage building component, with the aim to add flexibility to the building while still maintaining indoor comfort conditions. To show the potentials of the novel configuration proposed in this paper, a multi-zone grey-box model is developed and validated to capture the thermal dynamics of a building, and a control strategy applied to the whole system is developed for energy management purpose. The whole simulation model, including thermophysical properties of the building-system and the control features, is implemented in a MATLAB environment. To assess the model and application potentials toward the optimal design and operation of the proposed system for energy efficiency and flexibility goals, a suitable case study analysis is conducted. Thus, a sensitivity analysis, using an evolutionary algorithm, is performed by considering economic and energy objective functions which focuses on the reduction of the building energy demand, load variability and economic aspects. In this regard, the optimal design configuration is underlined in a way that the operation of the components can be maximized to provide flexibility to the building: in average working conditions one single layer of PCM can provide around 186.3 Wh/K per unit of temperature and width. A rule-based management strategy is proposed to prove the possibility to shift and shave the energy peaks during high energy request periods, demand response events. Finally, by considering an approximate economic calculation, the simple payback, taking into account only the positive effects on the winter management, is around 13.5 years.