A stochastic flexibility calculus for uncertainty-aware energy flexibility management

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

The integration of multi-energy systems to meet the energy demand of buildings represents one of the most promising solutions for improving the energy performance of the sector. The energy flexibility provided by the building is paramount to allowing optimal management of the different available resources. The objective of this work is to highlight the effectiveness of exploiting building energy flexibility provided by thermostatically controlled loads (TCLs) in order to manage multi-energy systems (MES) through model predictive control (MPC), such that energy flexibility can be regarded as an additional energy source in MESs. Considering the growing demand for space cooling, a case study in which the MPC is used to satisfy the cooling demand of a reference building is tested. The multi-energy sources include electricity from the power grid and photovoltaic modules (both of which are used to feed a variable-load heat pump), and a district cooling network. To evaluate the varying contributions of energy flexibility in resource management, different objective functions—namely, the minimization of the withdrawal of energy from the grid, of the total energy cost and of the total primary energy consumption—are tested in the MPC. The results highlight that using energy flexibility as an additional energy source makes it possible to achieve improvements in the energy performance of an MES building based on the objective function implemented, i.e., a reduction of 53% for the use of electricity taken from the grid, a 43% cost reduction, and a 17% primary energy reduction. This paper also reflects on the impact that the individual optimization of a building with a multi-energy system could have on other users sharing the same energy sources.

Towards greener production, manufacturing companies face several challenges, for example peak load shaving or flexible production planning as parts of demand-side management (DSM). DSM uses processes that can be shut down, shifted, or controlled. Advances in digitalization in the energy sector and manufacturing systems create transparency which in turn offers new opportunities to commercialize energy flexibility potentials as optimally and automatically as possible. The variety of flexibilities in manufacturing systems and various dependencies of different kinds of complex manufacturing processes complicate the modelling and aggregation of flexibility. To overcome this challenge, we developed a method for the aggregation of energy flexibilities that is based on a generic energy flexibility data model. The method proposes a two-step approach to aggregate flexibilities cost efficiently and considers manufacturing specific limitations. For cost-efficient aggregation, we use in the first step the merit-order model known from the energy industry and in the second step the bin-packing problem originating from combinatorial optimization, adapted according to the generic data model. The two-step approach allows energy flexibilities to be aggregated across industries, facilities, and systems, thus ensuring broad applicability.KeywordsDemand-side managementEnergy flexible manufacturingEnergy flexibility aggregation

Chapter 5 - Thermal energy storage for enhanced building energy flexibility

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

On-site batteries, low-pressure biogas storage, and wastewater storage could position wastewater resource recovery facilities as a widespread source of industrial energy demand flexibility. This work introduces a digital twin method that simulates the coordinated operation of current and future energy flexibility resources. We combine process models and statistical learning on 15 min resolution sensor data to construct a facility's energy and water flows. We then value energy flexibility interventions and use an iterative search algorithm to optimize energy flexibility upgrades. Results from a California facility with anaerobic sludge digestion and biogas cogeneration predict a 17% reduction in electricity bills and an annualized 3% return on investment. A national analysis suggests substantial benefit from using existing flexibility resources, such as wet-weather storage, to reduce electricity bills but finds that new energy flexibility investments are much less profitable in electricity markets without time-of-use incentives and plants without existing cogeneration facilities. Profitability of a range of energy flexibility interventions may increase as a larger number of utilities place a premium on energy flexibility, and cogeneration is more widely adopted. Our findings suggest that policies are needed to incentivize the sector's energy flexibility and provide subsidized lending to finance it.

In this paper, we address the problem of Data Centers (DCs) energy efficiency considering their integration into the electrical and thermal grids by emphasizing the role of the DC Digital Twin model in DC flexibility management. Due to their high digitization and controllable energy systems, the DCs can act as flexible assets, being able to dynamically adapt their energy profiles and valuable energy services. We present a flexibility management solution that is using a Digital Twin model of DC systems to determine action plans for shifting energy load. DC monitored data is acquired by integration with existing DC infrastructure management (DCIM) while energy predictions are computed for DC energy demand, energy flexibility, and heat generation. The flexibility optimization plans for DC operation are determined and enforced after DC manager validation via DCIM integration. Five energy services are identified as suitable to be provided by the DC with the help of described flexibility management solution: energy trading for increasing profit, grid congestion management by decreasing DC energy demand, scheduling by increasing DC energy demand to consume as much as possible the renewable available in the local grid, power factor compensation and sell heat on demand.

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users.

Study on thermo-electric-hydrogen conversion mechanisms and synergistic operation on hydrogen fuel cell and electrochemical battery in energy flexible buildings

This research explores socio-technical perspectives of the demand-side management strategy of using the built environment for short term thermal energy storage. Here conceptualised as a niche innovation within the Danish socio-technical district heating landscapes, the research explores potentials and limitations of this building energy flexibility strategy from the perspective of district heating sector professionals, actors at the centre of the low-carbon energy transitions. Results of the mixed-methods abductive research enquiry suggest that this energy flexibility strategy facilitates (I) solving local network congestion challenges in smaller parts of existing networks and (II) reduces needed network capacity in new heat supply areas. Sector professionals assess this (III) energy flexibility strategy as most practicable in large-scale/commercial buildings and industries. Challenges include hardware balancing, service and maintenance, and the sometimes counterproductive incentive structures among stakeholders involved. Research evidence suggests that business models appealing to environmental values and priorities may incentivise sustainable heat-use behaviours more than economic benefit alone among some groups of end users. Building energy flexibility and demand-side management strategies may become integral to future ‘smart’ energy systems throughout the world. However, their successful implementation necessitates understanding the local socio-technical dynamics involved. Multidisciplinary research approaches as the one taken here facilitate these necessary insights.

Variable water demands in growing season, spatial altitude difference in hydrant points, incompatibility in irrigation time, crop pattern alternation and the other environmental factors are among the most important dynamic factors affecting the operation of pumping stations in an irrigation system. Pumping stations could be effectively operated using a dynamic or time-dependent approach. In this study, the performance of an agricultural pumping station, which will be equipped with variable speed pumps, was analyzed. The station is located in an agricultural area in Qazvin Province, northern Iran. The dynamic model of the pumping station was developed for simulating five defined operation scenarios. The results showed that using variable speed pumps is capable of reducing energy consumption up to 67 %, in comparison with current constant speed modes. The ratio of energy consumption for pumped water was determined equal to 0.37 kwh/m3 in variable speed mode, implying up to 45% reduction in comparison with use constant speed pumps.

This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by residential districts. However, managing and implementing energy demand response, in particular energy flexibility activations, in real case studies still presents issues to be solved. This study, within the framework of the European project “SABINA H2020”, addresses the development of a multi-level optimization algorithm that has been tested in a semi-virtual real-time configuration. Results from a two-day test show the potential of building’s flexibility and highlight its complexity. Results show how the first level algorithm goal to reduce the energy injected to the grid is accomplished as well as the energy consumption shift from nighttime to daytime hours. As conclusion, the study demonstrates the feasibility of such kind of configurations and puts the basis for real test site implementation.

Managing complex and large-scale building facilities requires reliable, easily interpretable, and computationally efficient models. Considering the electrical-circuit analogy, lumped-parameter resistance–capacitance (RC) thermal models have emerged as both simulation surrogates and advanced tools for energy management. This review synthesizes recent uses of RC models for building energy management in large facilities and aggregates. A systematic review of the most recent international literature, based on the analysis of 70 peer-reviewed articles, led to the classification of three main areas: (i) the physics and modeling potential of RC models; (ii) the methods for automation, calibration, and scalability; and (iii) applications in model predictive control (MPC), energy flexibility, and digital twins (DTs). The results show that these models achieve an efficient balance between accuracy and simplicity, allowing for real-time deployment in embedded control systems and building-automation platforms. In complex and large-scale situations, a growing integration with machine learning (ML) techniques, semantic frameworks, and stochastic methods within virtual environments is evident. Nonetheless, challenges persist regarding the standardization of performance metrics, input data quality, and real-scale validation. This review provides essential and up-to-date guidance for developing interoperable solutions for complex building energy systems, supporting integrated management across district, urban, and community levels for the future.

In this paper, we present the domOS solution, which brings three mains contributions to the digitalization of buildings: it specifies an IoT ecosystem enabling the decoupling of the infrastructure layer (in-building sensors and actuators, smart appliances, gateways) and the service layer; it develops a set of compliant services for energy efficiency, energy flexibility and demand-side management; and it tests services and infrastructure on significant scale demonstrators. The paper will focus on the implemented methodology and preliminary results of the solution's implementation in a Swiss site. A Living Lab approach which uses methods to co-design services with users is also applied. The expected outcome of the methodological approach is new user centred business models for smart energy services.