This paper studies an energy storage (ES) sharing model which is cooperatively invested by multiple buildings for harnessing on-site renewable utilization and grid price arbitrage. To maximize the economic benefits, we jointly consider the ES sizing, operation, and cost allocation via a coalition game formulation. Particularly, we study a fair ex-post cost allocation based on nucleolus which addresses fairness by minimizing the minimal dissatisfaction of all the players. To overcome the exponential computation burden caused by the implicit characteristic function, we employ a constraint generation technique to gradually approach the unique nucleolus by leveraging the sparse problem structure. We demonstrate both the fairness and computational efficiency of the method through case studies, which are not provided by the existing Shapley approach or proportional method. Particularly, only a small fraction of characteristic function (less than 1% for 20 buildings) is required to achieve the cost allocation versus the exponential information required by Shapley approach. Though there exists a minor increase of computation over the proportional method, the proposed method can ensure fairness while the latter fails in some cases. Further, we demonstrate both the building-wise and community-wise economic benefits are enhanced with the ES sharing model over the individual ES (IES) model. Accordingly, the overall value ¹ of ES is considerably improved (about 1.83 times). ¹ The proportion of total electricity bill reduction relative to the ES capital cost.

This article studies a scalable control method for multizone heating, ventilation, and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort (TC) and indoor air quality (IAQ) (represented by CO ₂ ) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings, as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with an upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone TC with minimal energy cost, and then, the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy-saving performance of upper level. As both the upper and the lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost, while the latter fails. Moreover, we demonstrate that our method outperforms the demand-controlled ventilation (DCVs) strategies for IAQ management with about 8%–10% energy cost reduction. Note to Practitioners: The high portion of building energy consumption has motivated the energy saving for heating, ventilation, and air-conditioning (HVAC) systems. Concurrently, the living standards for indoor environment are rising among the occupants. Nevertheless, the status quo on improving building energy efficiency has mostly focused on maintaining thermal comfort (such as temperature), and the indoor air quality (IAQ) (usually represented by CO ₂ level) has been seldom incorporated. In our previous work with the similar setting, we observed that the CO ₂ levels will surge beyond tolerance during the high occupancy periods if only thermal comfort (TC) is considered for HVAC control. This deduces the IAQ and TC should be jointly considered while pursuing the energy cost saving target and thus studied in this article. This task is computationally cumbersome due to the complex system dynamics (thermal and CO ₂ ) and tight correlations among the different control components (variable air volume and fresh air damper). To cope with these challenges, this work develops a two-level distributed computation paradigm for HVAC systems based on problem structures. Specifically, the upper level control (ULC) first calculates zone mass flow rates for maintaining comfortable zone temperature with minimal energy cost, and then, the lower level strategically regulates the computed zone mass flow rates as well as ventilation rate to satisfy IAQ while preserving the near energy-saving performance of the ULC. As both the upper and lower level calculations can be implemented in a distributed manner, the proposed method is scalable to large multizone deployment. The method’s performance both in maintaining comfort (i.e., TC and IAQ) and energy cost saving is demonstrated via simulations in comparisons with the centralized method, the distributed token-based scheduling strategy, and the demand-controlled ventilation strategies.

Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate the increasing distributed energy resources (DERs). However, how P2P to be integrated into the existing power systems remains to be investigated. In this paper, we apply network charge as a means for the grid operator to attribute transmission loss and ensure network constraints for empowering P2P transaction. The interaction between the grid operator and the prosumers is modeled as a Stackelberg game which yields a bi-level optimization problem. We prove that the Stackelberg game admits an equilibrium network charge price. Besides, we propose a method to obtain the network charge price by converting the bi-level optimization into a single-level mixed-integer quadratic programming (MIQP) that can handle a reasonable scale of prosumers efficiently. Simulations on the IEEE bus systems show that the network charge mechanism is favorable as it can benefit both the grid operator while securing the prosumers’ profit, and achieves near-optimal social welfare. Moreover, the results show that the presence of energy storage will make the prosumers more sensitive to the network charge price changes and impact the P2P market.

The control of heating, ventilation, and air-conditioning (HVAC) systems has raised extensive attention due to their high energy consumption cost and operation patterns far from being energy-efficient. However, most of the existing methods suffer limitations in scalability and computational efficiency for large buildings due to the centralized structures. To compensate for such defects, this article studies the scalable control of multizone HVAC systems with the target to reduce energy cost while maintaining thermal comfort. In particular, the thermal couplings due to heat transfer among the adjacent zones are incorporated, which has been ignored or not well studied due to complexity in the literature. To overcome the computational challenges of the nonlinear and nonconvex problem caused by the complex system dynamics, this article proposes a decentralized approach composed of three main steps: 1) relaxing the bilinear system dynamics; 2) solving the relaxed problem in a decentralized manner using the accelerated distributed augmented Lagrangian (ADAL) method; and 3) recovering the recursive feasibility of the solution. Through a comparison with the centralized method, the suboptimality of this approach is demonstrated. In addition, the superior performance of this approach is illustrated through a comparison with the distributed token-based scheduling strategy (DTBSS). The numerical results imply that for buildings with a relatively small number of zones (less than 20), the two methods are competitive. However, for larger cases, the proposed approach performs better with a considerable reduction both in energy cost and computation time.

The heating, ventilation, and air-conditioning (HVAC) system account for substantial energy use in buildings, whereas a large group of occupants is still not actually feeling comfortable staying inside. This poses the issue of developing energy-efficient HVAC control, i.e., reduce energy use (cost) while simultaneously enhancing human comfort. This brief pursues the objective and studies the stochastic optimal HVAC control subject to uncertain thermal demand (i.e., the weather and occupancy). Particularly, we involve the elaborate predicted mean vote (PMV) thermal comfort model in the optimization. The problem is computationally challenging due to the nonlinear and nonanalytical constraints imposed by the system dynamics and PMV model. We make the following contributions to address it. First, we formulate the problem as a Markov decision process (MDP) which is a desirable modeling technique capable of handling the complexities. Second, we propose a gradient-based learning (GB-L) method for progressively learning a stochastic control policy off-line and store it for on-line execution. Third, we prove the learning method's converge to the optimal policies theoretically, and its performance (i.e., energy cost, thermal comfort, and on-line computation) for HVAC control via simulations. The comparisons with the existing model predictive control based relaxation (MPC-R) method which is assumed with accurate future information and supposed to provide the near-optimal bounds show that though there exists some discount in energy cost reduction (i.e., 6.5%), the proposed method can enable efficient on-line implementation (less than 1 s) and provide a high probability of thermal comfort under uncertainties.

A building’s environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelligent control, and building commissioning. Though many solutions for environmental measuring based on wireless sensor networks exist, they are not easily accessible to households and building owners who may lack time or technical expertise needed to set up a system and get quick and detailed overview of environmental conditions. Building-in-Briefcase (BiB) is a portable sensor network platform that is trivially easy to deploy in any building environment. Once the sensors are distributed, the environmental data is collected and communicated to the BiB router via the Transmission Control Protocol/Internet Protocol (TCP/IP) and WiFi technology, which then forwards the data to the central database securely over the internet through a 3G radio. The user, with minimal effort, can access the aggregated data and visualize the trends in real time on the BiB web portal. Paramount to the adoption and continued operation of an indoor sensing platform is battery lifetime. This design has achieved a multi-year lifespan by careful selection of components, an efficient binary communications protocol and data compression. Our BiB sensor is capable of collecting a rich set of environmental parameters, and is expandable to measure others, such as CO ${}_2$ . This paper describes the power characteristics of BiB sensors and their occupancy estimation and activity recognition functionality. We have demonstrated large-scale deployment of BiB throughout Singapore. Our vision is that, by monitoring thousands of buildings through BiB, it would provide ample research opportunities and opportunities to identify ways to improve the building environment and energy efficiency.