Elsevier

Applied Energy

Volume 228, 15 October 2018, Pages 2510-2525
Applied Energy

Coordinating the operations of smart buildings in smart grids

https://doi.org/10.1016/j.apenergy.2018.07.089Get rights and content

Highlights

  • A novel bi-level building demand aggregation and coordination method is proposed.

  • Successive subproblem solving method is introduced to alleviate homogeneous oscillations.

  • Three-phase optimal power flow based aggregation at the distribution primary feeder level.

  • Building electricity cost is reduced while satisfying all distribution operation constraints.

Abstract

With big thermal storage capacity and controllable loads such as the heating ventilation and air conditioning systems, buildings have great potential in providing demand response services to the smart grid. However, uncoordinated energy management of a large number of buildings in a distribution feeder can push power distribution systems into the emergency states where operating constraints are not completely satisfied. In this paper, we propose a bi-level building load aggregation methodology to coordinate the operations of heterogeneous smart buildings of a distribution feeder. The proposed methodology not only reduces the electricity costs of buildings but also guarantees that all the distribution operating constraints such as the distribution line thermal limit, phase imbalance, and transformer capacity limit are satisfied.

Introduction

Increasing integration of intermittent renewable energy resources introduces greater variability and uncertainty into the electricity grid [1]. Thus more ancillary services are required in the electricity market to maintain the reliability of the electricity grid [2], which was provided only by fossil-fueled power plants in the past. Due to the Clean Power Plan that encourages less carbon emissions, more demand response (DR) resources are being procured in the electricity market [3]. With the help of the rapid development of information and control technologies, demand response enables electricity consumers to adjust their electricity usage pattern in response to time-varying electricity price signals, incentive payments and/or direct dispatch instructions. Buildings account for a large amount of the total electricity consumption [4] and Heating, ventilating, and air-conditioning (HVAC) systems consume around a half of buildings’ electricity consumption [5]. Hence, if the thermal energy storage inherent in the building is properly managed, buildings can provide an enormous amount of demand response services to the electricity grid.

There is a large body of work which studies energy efficient smart building operations. Lu et al. modeled the major components of HVAC systems and their interactions in building and presented global optimization technologies for economic operation [6]. Guan et al. improved building energy efficiency by coordinating and optimizing the operation of various energy sources and loads in microgrid [7]. Xu et al. studied coordinating multiple storage devices with HVAC systems and determined the optimal operating strategy of building energy systems under time-of-use electricity prices [8]. Maasoumy et al. presented a hierarchical control architecture for balancing comfort and energy consumption in buildings based on a simplified, yet accurate model of the temperature within each room of the building [9]. Ma et al. presented a stochastic model predictive control (MPC) for building HVAC systems considering the load uncertainty of each thermal zone [10]. Radhakrishnan et al. proposed a token-based distributed architecture for controlling HVAC systems in commercial buildings, which has low deployment cost and is scalable to buildings with more than 300 zones [11]. To reduce the overall operating cost, Afram and Janabi-Sharifi manipulated the temperature set-points of residential building HVAC systems using an MPC based supervisory controller [12]. In addition, occupancy-based control methods for HVAC systems have been well studied. In particular, Dong and Lam designed and implemented a nonlinear MPC which integrated local weather forecasting with occupant behavior detection, and solved it based on the dynamic programming algorithm [13]. Goyal et al. presented experimental evaluation on two occupancy-based control strategies for HVAC systems in commercial buildings and showed that occupancy-based controllers could yield substantial energy savings over the baseline controllers without sacrificing thermal comfort and indoor air quality [14]. Peng et al. used both unsupervised and supervised learning to learn occupants’ behavior, and designed a demand-driven control strategy to make cooling systems automatically adapt to occupants’ actual energy demand [15].

The existing building energy simulation and control models can be categorized as physics based (white box) models, data-driven (black box) models, and those in between (gray box models) [16]. The white box models can capture the building dynamics well by using detailed physics-based equations. The white box models such as EnergyPlus [17] and TRNSYS [18] can capture the building thermal dynamics with high accuracy. However, they require detailed information of buildings via extensive energy audit and energy survey. Moreover, the simulations with white box models are extremely time-consuming and not appropriate for real-time applications. The gray box models use simplified physical models to simulate the behavior of building energy systems. For example, Resistance and Capacitance (RC) network model is widely used in online building optimal control and demand response applications, in which different buildings are represented by different RC model parameters [16]. The model parameters are identified based on the operation data using statistics or parameter identification methods, such as nonlinear regression [19], global and local search [20], and genetic algorithm-based parameter identification [21]. However, detailed RC model is still very complicated, which makes the parameter identification and state calculation procedure time-consuming. Hence, model reduction techniques are used to simplify the model which sacrificed some accuracy [22]. The black box models, or the data-driven models, capture the relationship between building energy consumption and operation data based on on-site measurements over a certain period. For example, Vaghefi et al. combined a multiple linear regression model and a seasonal autoregressive moving average model to predict the cooling and electricity demand [23]. Recently, with the rapid development of machine learning (ML) technologies, the ML-based data-driven approaches (black box models) have been well studied. Huang et al. proposed an artificial neural network model to predict the temperature change of multi-zone buildings, and proposed an MPC-based method to maintain the comfortable temperature while reducing energy consumption [24]. Yang et al. presented a reinforcement learning model to control building consists of a PV/T array and geothermal heat pumps [25]. Wei et al. formulated the HVAC control as a Markov decision process and developed a deep reinforcement learning based algorithm to minimize the building energy cost and occupants’ discomfort [26]. Behl et al. provided a model-based control with regression trees algorithm, which allows users to perform closed-loop control for DR strategy synthesis for large commercial buildings [27]. Smarra et al. proposed a data-driven MPC using random forests, in which the classical regression tree and random forest algorithms were adapted to determine a closed-form expression for the states prediction function [28]. The data-driven models are model free and require no expert knowledge. After model training, black box models need less computation overhead and are much faster than the gray box models during online optimization. However, black box models often require a large amount of training data and long training period. Moreover, when the operating conditions, weather pattern or building structure change, the trained model is often not usable and needs retraining. Therefore, each of these models has its own advantages and disadvantages. In this paper, we choose the simplified RC model, which is analytically tractable.

It is inefficient and impractical to manage millions of smart buildings directly in the electricity market. Thus load aggregation is one of the key requirements for implementing buildings’ DR mechanism. There are already lots of studies on load aggregation. One popular aggregation method is the coordinated aggregation method, which aggregates all the loads into one cluster through linear addition and determines the operation schedule by solving the optimization problem on the cluster level. For example, all the loads under the building cluster are considered together and optimized in a decentralized approach [29]. In the demand response aggregation mechanism [30], the electricity sent to each household is determined by solving a conic quadratic mixed-integer problem at the aggregation node. In [31], the particle swarm optimization is performed to determine the operation strategies for all loads under the building cluster. Regarding the building to grid integration frameworks in both [32], [33], all buildings under a transmission network node are regarded as a cluster during optimization. However, this method is unsuitable and inaccurate for the situation where loads are distributed in a large distribution network. Another aggregation method is the bottom-up aggregation, which aggregates loads starting with those connected to low-voltage feeders (residential and small commercial loads fed from distribution transformers), and moving upward toward distribution substations [34]. The bottom-up aggregation method has been widely used in industrial and commercial loads for implementing smart grid functions due to its advantages including easy implementation, fast computation, and wide applicability to load types and variations in power demands [35]. However, this method has limited accuracy and is highly dependent on accurate measurements. Moreover, both methods have not considered the network operating constraints during aggregation, which may lead to issues such as voltage violation, equipment overloads and phase unbalance [36].

To ensure reliable operation of the distribution network, the distribution network constraints should not be ignored. Some distribution network operating constraints are considered in the DR management schemes. In [37], the day-ahead prices for all building loads are calculated based on social welfare maximization while considering the network operational constraints. The original integer programming optimization problem is relaxed into linear programming problem and solved iteratively in a decentralized approach by the alternating direction method of multipliers (ADMM) based algorithm. In [38], the joint building and grid optimization is implemented in a two-level approach. First, each building is optimized to reduce the electricity cost based on forecasted prices and environment information. Then, based on the optimized load profiles, a distribution grid power flow analysis is carried out. In case of security constraint violation, the maximum allowed load is calculated and sent back to buildings. These two steps are performed iteratively until all the network operating constraints are met. However, in aforementioned work, the active power losses on the distribution lines are not modeled. Furthermore, the optimization procedures are performed on all buildings in the distribution network in each iteration, which increases the model complexity. Furthermore, the proposed approach is time-consuming and not suitable for real-time operations. In [39], most of the distribution network operating constraints are taken into account based on linear programming. The linear approximation of all the constraints and load models improves computational efficiency. However, the approximation could result in performance degradation. In summary, there is a lack of robust algorithm which is capable of coordinating the operations of a large number of smart buildings while considering the distribution network operating constraints. There are two challenges in developing such an algorithm. Firstly, the optimization model for a single building can be nonlinear. Thus the building coordination problem can be very complicated when all the buildings in a distribution feeder are considered. Secondly, the optimal power flow problem in the distribution network is non-convex [40], which makes the load aggregation problem non-convex and hard to solve.

To overcome these difficulties, a novel bi-level aggregation methodology is proposed in this paper, which coordinates the operations of smart buildings in smart grids while considering the operating constraints of the distribution network. The main contributions of this paper are listed below.

  • A novel bi-level building load aggregation and coordination methodology is proposed, which not only reduces the building electricity costs, but also satisfies the distribution system operating constraints. The development of the bi-level aggregation is inspired by the physical structure of the distribution network.

  • In level-1 aggregation, the joint optimization problem is formulated to coordinate the operations of individual buildings subject to transformer maximum capacity constraint. The SSS method is introduced to decompose the mixed integer linear programming problem (MILP) problem into a series of small coordinated MILP subproblems. This method addresses the homogeneous oscillations problem. Furthermore, the level-1 aggregation can be performed in parallel under each secondary feeder system which makes the approach computationally efficient.

  • In level-2 aggregation, the three-phase optimal power flow based aggregation algorithm is developed which not only aggregate the demand bids but also satisfy all the distribution operating constraints. To the best of the authors knowledge, this is the first attempt to develop building aggregation algorithm with a three-phase optimal power flow based approach.

The rest of the paper is organized as follows. Section 2 presents an overview of the proposed smart building operation coordination framework. Section 3 presents the individual building energy scheduling algorithm without coordination. Section 4 presents the proposed bi-level aggregation/disaggregation methodology to coordinate the operations of smart buildings. Section 5 demonstrates the effectiveness of the proposed bi-level aggregation approach with comprehensive simulations, and Section 6 concludes the paper.

Section snippets

Overview of smart building operation coordination framework

The overall framework of the proposed smart building operation coordination methodology is illustrated in Fig. 1. The proposed framework is an extension of the proactive demand participation scheme [41]. The overall framework can be divided into three parts: transmission system, distribution system, and individual buildings. There are three types of intelligent decision making entities: the independent system operator (ISO) in the transmission system, the distribution system operators (DSOs) in

Smart building operation without coordination

At the individual building level, it has been shown that appropriately managing flexible energy loads can effectively reduce the total energy cost of buildings [45]. In this section, each smart building in the distribution network will be operated without coordination. In the following subsections, we will introduce the building thermal dynamics model, the MPC-based building energy scheduling algorithm, and the demand bid curve generation methodology for an individual building. Finally, all the

Smart building operation coordination based on bi-level aggregation/disaggregation

To coordinate the operations of smart buildings, the proposed bi-level aggregation and disaggregation approach will be illustrated in details in this section. The distribution network operating constraints are carefully considered in the aggregation and disaggregation process.

Simulation and analysis

In this section, we investigate the impact of smart building operations on the distribution grid, and demonstrate the effectiveness of the proposed bi-level aggregation methods.

Conclusion

This paper proposes a novel bi-level building demand aggregation methodology to coordinate the operations of smart buildings in smart grids. The proposed method improves upon the existing work by taking the key distribution system operating constraints including the line thermal limit, phase imbalance, and transformer capacity limit into consideration during the aggregation process. At the distribution secondary feeder level, a joint optimization problem is formulated to perform the level-1

Acknowledgement

This work was supported by National Key Research and Development Program of China under grant (2016YFB0901905), National Natural Science Foundation of China under grants (61472318, 61632015, 61772408, 6180022135, U1766215, U1736205), National Science Foundation (NSF) under awards (#1637258, #1637249), Department of Energy under award (#DE-OE0000840), Fok Ying Tong Education Foundation (151067), and the Fundamental Research Funds for the Central Universities.

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