• Energy Research

In forced air heating systems, there are basically two approaches to set the temperature of each zone within a multi-zone building: regulating the supply airflow rate and regulating the supply air temperature (or a combination of these two parameters). Conventional variable air volume (VAV) systems usually maintain the supply air temperature constant within all rooms and regulate only the supply airflow rate to the room. Recently, a novel multi-zone air heating and ventilation (MZHV) system has been developed, which is capable of regulating both the supply airflow rate and the supply air temperature to the rooms independently of each other. This paper investigates the flexibility of the novel MZHV system and compares it with the conventional VAV systems. The MZHV system as well as a multi-zone case study building are modeled in a high-fidelity simulation environment (IDA ICE). Instead of traditional rule-based control schemes, a model predictive control (MPC) scheme is developed to control real-time power consumption of the multi-zone building. For this purpose, a second-order state space model is identified for each zone and calibrated against the results from the high fidelity simulations. MPC is implemented in MATLAB environment by using the second-order state space models. Two co-simulations are performed to compare the flexibility of the novel MZHV system with a conventional VAV system. The results show that the MZHV system provides a significant flexibility in terms of the energy usage, without affecting the comfort levels. This flexibility can for example be exploited to reduce the overall energy usage or provide ancillary services for a smart electricity grid.

Due to the new green energy policies, district heating companies are being increasingly encouraged to exploit power-to-heat assets, e.g., heat pumps and electric boilers, in their distribution networks besides the traditional central combined heat and power units. The increased utilization of these assets will generate a more complex interaction between power distribution grids and district heating networks including markets for provision of ancillary services. Enabling the participation of power-to-heat units in the ancillary service markets, e.g., frequency reserves, may increase the revenue streams for assets’ owners. However, some technical challenges must first be addressed, including optimization of portfolios of assets that accounts for ancillary service markets, new coordination and operational schemes for portfolio of assets, increase data exchange and interactions with transmission system operators, and new local control schemes for units. This paper proposes a systematic model based design approach for assessment of provision of frequency regulation by power-to-heat assets using the smart grid architecture model. The proposed approach is demonstrated in a Real-Time Control Hardware-in-the-Loop laboratory environment.

New green energy policies are encouraging district heating (DH) companies to exploit heat production by power-to-heat (P2H) assets, e.g., heat pumps (HPs) and electric boilers (EBs). Therefore, they are shifting to exploit these assets in their distribution network besides the traditional central combined heat and power (CHP) units in their production chain. It will lead to an increasingly complex interaction between DH networks and electricity markets. This article proposes an economic model predictive control (EMPC) framework to investigate how DH companies can manage this extra complexity and if they can contribute in an efficient way towards balancing the production and consumption of electric power in smart grids through playing in the reserve capacity markets. The framework is applied to a real world case study and the results are discussed. The results show the new paradigm increases operation costs. However, reserving a part of the capacity for playing in reserve capacity market will not only make up for the extra costs to some extent, but also improve the capability of providing regulating power.

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.