Energy flexibility investigation of advanced grid-responsive energy control strategies with the static battery and electric vehicles: A case study of a high-rise office building in Hong Kong

Quantification of energy flexibility of residential net-zero-energy buildings involved with dynamic operations of hybrid energy storages and diversified energy conversion strategies

Heuristic battery-protective strategy for energy management of an interactive renewables–buildings–vehicles energy sharing network with high energy flexibility

A novel hybrid agent-based model predictive control for advanced building energy systems

Review of building energy modeling for control and operation

A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems

Real-life implementation of a linear model predictive control in a building energy system

Conventional key performance indicators (KPI) assessed in building simulation lack specific measures of how the building interacts with the grid and its energy flexibility. This paper aims to provide an overview of specific energy flexibility performance indicators, together with supporting control strategies. If applied correctly, the indicators help improving the building performance in terms of energy flexibility and can enable minimization of operational energy costs. Price-based load shifting, self-generation and self-consumption are among the most commonly used performance indicators that quantify energy flexibility and grid interaction. It has been found that the majority of performance indicators, specific to energy flexibility, are combined with rule-based control. Only a limited amount of specific energy flexibility KPIs are used in combination with optimal control or model predictive control. Both of these advanced control approaches often have a couple of economic or comfort objectives that do not take into account an energy flexibility KPI. There is evidence that recent model predictive control approaches incorporate some aspects of building energy flexibility to minimize operational cost in conjunction with time varying pricing.

Thermal mass of buildings and domestic hot water tanks represent interesting sources of thermal energy storage readily available in the existing building stock. To exploit them to their full potential, advanced control strategies and a coupling to the power grid with heat pump systems represent the most promising combination. In this paper, model predictive control (MPC) strategies are developed and tested in a semi-virtual environment laboratory setup: a real heat pump is operated from within a controlled climate chamber and coupled with loads of a virtual building, i.e., a detailed dynamic building simulation tool. Different MPC strategies are tested in this laboratory setup, with the goals to minimize either the delivered thermal energy to the building, the operational costs of the heat pump, or the CO2 emissions related to the heat pump use. The results highlight the ability of the MPC controller to perform load-shifting by charging the thermal energy storages at favorable times, and the satisfactory performance of the control strategies is analyzed in terms of different indicators, such as costs, comfort, carbon footprint, and energy flexibility. The practical challenges encountered during the implementation with a real heat pump are also discussed and provide additional valuable insights.

• Studies on energy flexibility of residential buildings have been reviewed. • Energy flexibility technologies by types, purposes, and scopes are summarized. • Modeling techniques, tools, and characterization of flexible resources are presented. • Quantification methods and metrics of energy flexibility are analyzed. • Research trends, open questions, and future research opportunities are identified. With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Multi-scale Simulation for Energy Flexible Factories and Factory Networks: A System of Systems Perspective

Over the last few years, the cost of energy from renewable resources, such as sunlight and wind, has declined resulting in an increasing use of Renewable Energy Sources (RES). As a result, the energy produced by RES is fed into the power grid while their share is expected to significantly increase in the future. However, RES are characterized by power fluctuations and their integration into the power grid might lead to power quality issues, e.g., imbalances. At the same time, new energy hungry devices such as heat-pumps and Electric Vehicles (EVs) become more and more popular. As a result, their demand in power, especially during peak-times, might lead to electrical grid overloads and congestions. In order to confront the new challenges, the power grid is transformed into the so-called Smart Grid. Major role in Smart Grid plays the Demand Response (DR) concept. According to DR, Smart Grid better matches energy demand and sup- ply by using energy flexibility. Energy flexibility exists in many individual prosumers (producers and/or consumers). For instance, an owner of an EV plugs-in his EV for more time than it is actually needed. Thus, the EV charging can be timely shifted. The load demanded for charging could be moved to time periods when production from wind turbines is high or away from peak-hours. Thus, RES share is increased and/or the electrical grid operation is improved. The Ph.D. project is sponsored by the Danish TotalFlex project (http://totalflex.dk). Main goal of the TotalFlex project is to design and establish a flexibility market framework where flexibility from individual prosumers, e.g., household devices, can be traded among different market actors such as Balance Responsible Parties (BRPs) and distribution system operators. In order for that to be achieved, the TotalFlex project utilizes the flex-offer concept. Based on the flex-offer concept, flexibility from individual prosumers is captured and represented by a generic model. However, the flexible loads from individual prosumers capture very small energy amounts and thus cannot be directly traded in the market. Therefore, aggregation becomes essential. The Ph.D. project focuses on developing aggregation techniques for energy flexibilities that will provide the opportunity to individual prosumers to participate in such a flexibility market. First, the thesis introduces several flexibility measurements in order to quantify the flexibility captured by the flex-offer model and compare flex- offers among each other, both on an individual and on an aggregated level. Flexibility is both the input and the output of the aggregation techniques. Aggregation techniques aggregate energy flexibility to achieve their goals and, at the same time, they try to retain as much flexibility as possible to be traded in the market. Thus, second, the thesis describes base-line flex-offer aggregation techniques and presents balance aggregation techniques that focus on balancing out energy supply and demand. Third, since there are cases where electrical grid congestions occur, the thesis presents two constraint-based aggregation techniques. The techniques efficiently aggregate large amounts of flex-offers taking into account physical constraints of the electrical grid. The produced aggregated flex-offers are still flexible and when scheduled, a normal grid operation is achieved. Finally, the thesis examines the financial benefits of the aggregation techniques. It introduces flex-offer aggregation techniques that take into account real market technical requirements. As a result, individual small flexible loads can be indirectly traded in the energy market through aggregation. The proposed aggregation techniques for energy flexibilities can con- tribute to the use of flexibility in the Smart Grid in both current and future market frameworks. The designed techniques can improve the services offered to the prosumers and avoid the very costly upgrades of the distribution network.

Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics

Flexibility quantification and enhancement of flexible electric energy systems in buildings

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 48 data-driven energy flexibility KPIs from 87 recent and relevant 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, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

The integration of renewable energy sources into electricity grids has emphasized the need for demand-side management to enhance grid stability. Among various strategies, utilizing building thermal mass as a form of energy storage by adjusting indoor temperature setpoints presents a promising approach to improving energy flexibility. However, despite its significance, energy flexibility is often overlooked in the architectural design process, where regulations focus primarily on energy efficiency rather than the flexibility potential of building components. This study investigates the impact of different design parameters, including building design based on regulatory and Net Zero energy targets, varying levels of thermal mass, HVAC configurations, and setpoint control strategies on building energy flexibility. Simulations were conducted to assess performance across winter and summer periods. The findings reveal that Net Zero buildings exhibit lower flexibility in winter but greater adaptability in summer. VRF (DOAS) systems demonstrate superior flexibility in winter, whereas All-Air system (VAV) and Chilled Ceiling systems perform best in summer. Thermal mass influences energy flexibility more significantly in summer than in winter, with optimal levels varying by HVAC system and control strategy. The results underscore the importance of integrating energy flexibility considerations into early-stage design decisions to enhance building performance in energy flexibility.