Rapidly increasing renewable energies bring more fluctuations to the power system and put forward higher requirement on operating reserve for maintaining the system balance. Traditional generating units are phasing out and may be insufficient to satisfy this reserve requirement in the near future. Therefore, much attention is paid to demand-side regulation resources. Inverter air conditioners (IACs) account for about 30% of the total power consumption in cities and have huge regulation potential. However, the control of large-scale dispersed IACs is intractable. Most existing studies adopt the centralized control scheme to regulate IACs, while it requires a high-cost communication infrastructure and induces privacy concerns. To address this issue, this article investigates the distributed control scheme of large-scale dispersed IACs for providing operating reserve. First, a normalization approach is proposed to uniformly quantify the regulation capacities of heterogeneous IACs. Then, a distributed consensus algorithm with a nonlinear protocol is developed for IACs to achieve the regulation objective under guaranteeing customers’ comfort requirements. Based on the Lyapunov stability theorem, the convergence of the proposed algorithm on large-scale dispersed IACs is proved to be always guaranteed. Finally, numerical studies verify the feasibility and performance of the proposed method, which features better data privacy protection and less communication and computation burden than the centralized control methods.

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.

This paper investigates the multi-objective optimal operation problem of an integrated energy system (IES) which integrates grid-connected photovoltaic (PV) generator, gas boiler, battery energy storage system, and thermal storage to satisfy energy demand in forms of electricity and heat. To handle the changes from the system uncertainty (e.g., PV generation, electrical loads, thermal loads, etc.) and unknown thermal dynamic model for temperature control, deep reinforcement learning-based model-free optimization method is proposed to solve the multi-objective optimization problem in which the multi-objective optimization problem is firstly formulated as a multi-objective Markov decision process (MDP) problem. The multi-objective MDP problem is converted to many single-objective MDP problems by the sum technique which are solved by multi-agent deep deterministic policy gradient (DDPG) algorithm. To improve the performance of multi-agent DDPG algorithm, evolutionary computing-based parameter-tuning method is further proposed to fine-tune the policy parameters in DDPG algorithm. The proposed methods are verified on real data. Experiments results illustrate that the multi-agent DDPG algorithm can efficiently solve the multi-objective optimal operation problem of the IES while the evolutionary computing-based policy parameter-tuning method can further improve the approximation of Pareto frontier.

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency.

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

In this paper, the modeling and estimation of a class of dynamic multiscale system, subject to colored state equation noise and measurement equation noise, is proposed. The state equation is whitened firstly and then the measurement equation. The state space projection equation is used to link the scales, then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. A Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.

MOTIVATION: Since the gap between sharply increasing known sequences and slow accumulation of known structures is becoming large, an automatic classification process based on the primary sequences and known three-dimensional structure becomes indispensable. The classification of protein quaternary structure based on the primary sequences can provide some useful information for the biologists. So a fully automatic and reliable classification system is needed. This work tries to look for the effective methods of extracting attribute and the algorithm for classifying the quaternary structure from the primary sequences. RESULTS: Both of the support vector machine (SVM) and the covariant discriminant algorithms have been first introduced to predict quaternary structure properties from the protein primary sequences. The amino acid composition and the auto-correlation functions based on the amino acid index profile of the primary sequence have been taken into account in the algorithms. We have analyzed 472 amino acid indices and selected the four amino acid indices as the examples, which have the best performance. Thus the five attribute parameter data sets (COMP, FASG, NISK, WOLS and KYTJ) were established from the protein primary sequences. The COMP attribute data set is composed of amino acid composition, and the FASG, NISK, WOLS and KYTJ attribute data sets are composed of the amino acid composition and the auto-correlation functions of the corresponding amino acid residue index. The overall accuracies of SVM are 78.5, 87.5, 83.2, 81.7 and 81.9%, respectively, for COMP, FASG, NISK, WOLS and KYTJ data sets in jackknife test, which are 19.6, 7.8, 15.5, 13.1 and 15.8%, respectively, higher than that of the covariant discriminant algorithm in the same test. The results show that SVM may be applied to discriminate between the primary sequences of homodimers and non-homodimers and the two protein sequence descriptors can reflect the quaternary structure information. Compared with previous Robert Garian's investigation, the performance of SVM is almost equal to that of the Decision tree models, and the methods of extracting feature vector from the primary sequences are superior to Robert's binning function method. AVAILABILITY: Programs are available on request from the authors.