Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Industrial demand response is the main component of demand response, but it is limited by various aspects such as production operation and management, making it difficult to be optimized and utilized. The uncertainty of industrial demand response is analyzed in this paper, and a multi scenario generation method for the uncertainty of industrial resource group demand response is proposed. Then, this paper constructs corresponding optimization models for the main problems of industrial load aggregators participating in the power system demand response in three stages, including the load aggregator day-ahead bidding optimization model, the system side dayahead demand response scheduling plan optimization model, and the load aggregator intraday operation optimization model. Lastly, this paper proposes a dispatch model of optimizing the response and operation of resource groups for multi-scenario two-stage stochastic demand response. The effectiveness of the proposed model is verified through simulation.

To solve the noise problem of unmanned aerial vehicle (UAV) flight parameter data, the paper proposed an effective singular value decomposition method optimized by chaos ratio bat algorithm (CRBA). The basic bat algorithm is improved by using sigmoidal map, energy of singular values is taken as the objective function in proposed algorithm to optimize the structure of Hankel matrix. The number of effective singular value determined by using the singular value's singularity detection ability, and the de-noised data obtained from singular value and its corresponding vector. According to the experiment results of linear signal and flight parameter data, the proposed method is applicable to nonlinear signals without obvious characteristic frequency and linear signal, which has good denoising results.

In this paper, we study path selection and power allocation problems for relay-aided systems with multiple source, relay, and destination nodes. To take fairness among different links into account, we aim at maximizing the minimum source-relay-destination link rate performance. We consider the scenario that the source node can be paired with any destination node. For this scenario, we decouple the original path selection and power allocation problem as two graph-based matching problems and develop algorithms to solve the formulated matching problems with and without sum power constraint, respectively. Numerical results show that our proposed algorithms outperform random matching algorithms in all scenarios and the corresponding performance gains increase with the number of nodes.

The emergence of smart grids has posed great challenges to traditional power system control given the multitude of new risk factors. This paper proposes an online supplementary learning controller (OSLC) design method to compensate the traditional power system controllers for coping with the dynamic power grid. The proposed OSLC is a supplementary controller based on approximate dynamic programming, which works alongside an existing power system controller. By introducing an action-dependent cost function as the optimization objective, the proposed OSLC is a nonidentifier-based method to provide an online optimal control adaptively as measurement data become available. The online learning of the OSLC enjoys the policy-search efficiency during policy iteration and the data efficiency of the least squares method. For the proposed OSLC, the stability of the controlled system during learning, the monotonic nature of the performance measure of the iterative supplementary controller, and the convergence of the iterative supplementary controller are proved. Furthermore, the efficacy of the proposed OSLC is demonstrated in a challenging power system frequency control problem in the presence of high penetration of wind generation.

Heating, Ventilation and Air Conditioning (HVAC) system is largest energy consumer in buildings. Worldwide, buildings consume 20% of the total energy production. Therefore, increasing efficiency of the HVAC system will result in significant financial savings. As one solution, Thermal Energy Storage (TES) tanks are being utilized with buildings to store excess energy to be reused later. An optimal control strategy is crucial for optimal usage. Therefore, this paper presents a novel control framework based on Artificial Neural Networks (ANN) for optimally controlling a TES for achieving increased savings. The presented ANN controller utilizes 3 main inputs: 1) current TES energy availability, 2) predicted building power requirement, and 3) predicted utility load/price. In addition to the design details of the control framework, this paper presents implementation details of the ANN controller. Further, experiments on several test cases were carried out and the paper presents the experimental setup and obtained results for each test case. Performance of the presented ANN control framework was compared against a classical proportional derivative (PD) controller. It was observed that the presented framework resulted in better cost savings than the classical controller consistently for all the experimental test cases.

In this paper, we investigate robust resource allocation for cognitive radio networks. First, a resource allocation scheme based on stable matching is developed, which takes the preferences of both secondary users and primary users into account. To improve its robustness, we then discuss an ϵ-stable resource allocation scheme. With the help of the properties of ϵ-stable resource allocation, three edge-cutting algorithms are proposed. Numerical results show that the modified algorithms are robust to the channel state information variation.

Dynamic reactive power control of doubly fed induction generators (DFIGs) plays a crucially important role in maintaining transient stability of power systems with high penetration of DFIG based wind generation. Based on approximate dynamic programming (ADP), this paper proposes an optimal adaptive supplementary reactive power controller for DFIGs. By augmenting a corrective regulation signal to the reactive power command of rotor-side converter (RSC) of a DFIG, the supplementary controller is designed to reduce voltage sag at the point of common connection (PCC) during a fault, and to mitigate output active power oscillation of the wind farm after a fault. As a result, the transient stability of both DFIG and the power grid is enhanced. An action dependent cost function is introduced to provide real-time online ADP learning control. Furthermore, a policy iteration algorithm using high-efficiency least square method is employed to train the supplementary controller in an online model-free manner. By using such techniques, the supplementary reactive power controller is endowed with capability of online optimization and adaptation. Simulations carried out on a benchmark power system integrating a large DFIG wind farm show that the ADP based supplementary reactive power controller can significantly improve the transient system stability in changing operation conditions.

Non-intrusive load monitoring (NEM) is a load monitoring technique proposed to be used in today's residential energy auditor. It is expected to automatically provide the information of the type, energy consumption, and operation status of the electric loads without getting access to the loads. However, there still not exists any commercialized product so far, mainly because of the extraordinary large load sets comparing with the limited learning data. The fast emerging of new types of loads further aggravates the problem. This paper proposes an adaptive non-intrusive load identification model to address this problem. The proposed model is not dedicated to identify all the loads around the world, but it will grasp knowledge from samples that are not identified in the real application, and gradually form a new learning procedure so as to identify more and more new samples correctly. Random forest algorithm is introduced here to realize the objective and a case study is carried out to verify the effectiveness of the model.

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum- EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 50% performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5% performance loss on EE performance compared to the latter one.

This paper presents a methods of utilizing building-level demand response in microgrid frequency regulation. Based on an in-depth study of building loads, a bi-level framework of building demand response control is presented to determine the controllability and demand response participation timescale of different loads. Based on this framework, the internal control scheme of buildings is presented in analogy to negative generators. The contribution of building-level demand response to microgrid frequency regulation is discussed in details. The large power loss on transmission lines in microgrids is considered, and the heterogeneity among buildings in their contribution to system frequency deviation is discussed. A test case is studied to prove the efficacy of building-level demand response in microgrid frequency regulation, and the advantage of building-level demand response over single-load demand response.

Most wind power forecasting methods today take numerical weather prediction (NWP) as their inputs. Therefore, the accuracy of these forecasting methods highly depends on the accuracy of NWP. This paper involves in studying the statistical features of NWP. A total of four error patterns are pre-defined according to the statistical features of NWP. Moreover, an advanced autoregressive integrated moving average (ARIMA) simulator with error information integrated is established to adjust the NWP. Finally, a pair of comparison tests based on support vector machine (SVM) is run with raw NWP and adjusted NWP as inputs respectively. It proves that the adjusted NWP increases forecast accuracy greatly.

Miscellaneous electric loads (MELs) currently consume more electricity than any other single major category of electric appliances. MELs provide valuable energy consumption and performance information which can be utilized to meet the raising needs and opportunities of energy saving, demand response, peak shaving, and building management. A reliable intelligent method to identify different MELs is a prerequisite of all purposes. A support-vector-machine (SVM) based hybrid identification method of MELs is proposed in this paper. Studies on applying only SVM as well as a combination of SVM and supervised Self-Organizing Map (SSOM) are presented. SSOM first cluster a large number of MELs into several classes. MELs with similar feature values fall into the same class. SVM is then utilized to identify similar MELs. The proposed method shows satisfactory accuracy in tests using real-world data.

Plug-in Hybrid Electric Vehicles (PHEVs) are becoming increasingly popular all over the world. Therefore, some associated public infrastructures will have to be established, such as smart parking zones, charging stations, and charging piles, etc. This paper only focuses on the smart charging station. A comprehensive analysis upon the impacts of electricity price on the return of investment (ROI) of the charging station with a coordinating charging is presented. First, a coordinated charging and discharging model is presented which considers the profits of all three partners, namely the utility, the charging station and the customers. Second, the bidirectional money flow model of the station with both utility and customer are presented as well. Third, the hierarchical optimization model is introduced to solve the Mixed Integer Linear Programming problems. Fourth, a simulation based on a real database of a typical city is run and the Bass' diffusion model is applied to forecast the amount of PHEVs arriving at the charging station per day. Lastly, it is proved that an optimal coordinating charging strategy will reduce the years of ROI significantly and a figure presenting the relationship between price and ROI years is given.

This paper presents a new isolated bidirectional DC/DC converter for auxiliary power supplies in electric vehicles where high power density, bidirectional power flow and electric isolation are required. Compared to conventional bidirectional converters, this modified half-bridge circuit further minimizes components to 2 capacitors without any other change based on the existing highest power density circuit. With a novel PWM plus phase-shift (PPS) control strategy, it minimizes device current stresses over a wide input voltage range. Moreover, a natural zero-voltage switching method for the whole switches is achieved without additional device to reduce switching loss and device inrush voltage stresses. These advantages make the new converter promising for high power density applications, especially to overcome the high device current stresses problem caused by wide range ultra-capacitor voltage variation. In this paper, firstly, a complete design consideration and principle of the new topology is presented; secondly, an analysis of static state mode is provided; finally, a 15V/50W prototype is built up after a 60V/4kW modeling simulation on computer and it successfully proved this converter's device current stresses minimization and high power density characteristics.

In building energy management area, many studies have been done on energy saving technologies of the high-power loads like HVACs, pumps and compressors, which will exert a great influence on demand side management. However, another group of loads defined as plug-in loads are also widely used and consume over 37% of the electricity in buildings. Usage of plug-in loads is typically directly affected by the consumer and real time pricing, and an effective management of these loads will also provide significant energy saving. To better understand the loads in residential home, this paper presents a comprehensive study of the plug-in load topologies at residential home.