• Energy Research

The uncertainties of renewable energy may bring impacts to power system reliability. This paper proposes a novel generation rescheduling algorithm, which adjusts generation outputs to mitigate the variations of branch power flows and relieve the overload probability. This algorithm helps prevent power congestion and balance the aggregate renewable generations and loads in power systems. Besides, the paper also proposes a probabilistic method evaluating the system uncertainty issue in the power system considering generation rescheduling operation. The proposed generation rescheduling algorithm has been tested on the Arizona transmission system. The generation rescheduling method enhances power system reliability analysis with a high penetration of renewable energy resources.

To unlock the potential of flexible resources, a multi-time-scale economic scheduling strategy for the virtual power plant (VPP) to participate in the wholesale energy and reserve market considering large quantity of deferrable loads (DLs) aggregation and disaggregation is proposed in this paper. For the VPP multi-time-scale scheduling including day-ahead bidding and real-time operation, the following models are proposed, namely, DLs aggregation model based on clustering approach, economic scheduling model considering DLs aggregation, and DLs disaggregation model satisfying consumers’ requirements, respectively. The proposed strategy can realize the efficient management of massive DLs to reduce the energy management complexity and increase the overall economics with high computation efficiency, which indicate its promising application in the VPP economic scheduling.

A deeper coupling of information and power would make active distribution networks (ADNs) evolve into typical cyber-physical systems (CPSs). The unreliability of cyber systems can affect the security of ADNs. In this article, a methodology based on cyber-power joint analysis is presented to quantitatively evaluate the operational risk of ADNs caused by cyber contingencies. First, a typical CPS structure of ADNs is demonstrated and the influence ways of cyber contingencies are discussed. Then, a CPS modeling framework is proposed to analyze the information flow and cyber contingencies of cyber systems. To determine the best data transmission paths in cyber networks, a routing algorithm based on the link-state protocol is presented. Furthermore, in view of the response of ADNs to physical failures, an optimization model is developed to minimize the load shedding that results from the over-limited voltage. Finally, the expected load curtailment and the expected fault coverage used to quantify potential system losses are calculated using Monte Carlo simulation. The simulation results explain the effectiveness and practicability of the proposed models and methods.

The heat and electricity integrated energy system (HE-IES) has high energy efficiency and bright application prospects. In the HE-IES, the heat transmission in the heating network and the indoor temperature variation in the buildings are governed by (partial) differential equations, which bring the heat dynamics problem in the system operation. In this paper, we propose a comprehensive approach for the operation of the HE-IES to coordinate the electricity and heat dynamics, including modeling, evaluation, and dispatch procedure. First, we formulate a high-resolution model for the heating network and buildings to describe the heat dynamics; two indices are proposed to measure the impact of heat dynamics, which are then used to select the time resolution. Second, an optimal dispatch model with high resolution for the heat dynamics is formulated for the HE-IES; a decentralized and parallel solution method is proposed based on the alternating direction method of multipliers. Third, a two-stage procedure for the time resolution selection is proposed, which consists of a dispatch decision stage and a time resolution evaluation stage. A small illustrative case is studied to verify the effectiveness of the proposed method. A big case based on a real heating network in Jilin Province, China, is also simulated.

This paper proposes an extended-state-observer-based distributed robust secondary voltage and frequency control for an autonomous microgrid (MG) with inverter-based distributed generators (DGs) considering the uncertainties from models and measurement noise. The MG is considered as a multi-agent system where each DG is defined as an agent and its controller only requires its own information and the information of its neighbors, but each DG obtains noisy measurements of the states of itself and its neighbors easily due to stochastic noise. Therefore, in this paper, an extended state observer is employed to estimate the accurate state information of each DG, which is significantly influenced by measurement noise. Furthermore, the distributed controllers based on a fast terminal sliding mode surface and an adaptive super-twisting algorithm are designed to track the voltage reference and to fasten the convergence rate against disturbances and uncertainties caused by parameter perturbation. Moreover, the distributed frequency controllers are also designed to restore the frequency and to guarantee the accurate active power sharing without power information of DGs. Finally, the effectiveness of the propose control strategy is illustrated by the simulation of an autonomous MG in MATLAB/Simulink.

Integrated energy systems (IESs) are composed of multiple heterogeneous subsystems, i.e., electrical power system, natural gas system, and district heating system (DHS), which endow the whole system with excellent performance in overall efficiency and renewable energy utilization. The paper aims to offer a concise and analytical model for the thermal dynamic characteristics (i.e., thermal inertia) of the district heating network (DHN) and buildings to facilitate the analysis, planning, and operation of IESs. Firstly, an equivalent start network is introduced for modeling the radial DHN, and a synchronous response model is proposed for buildings to approximate the optimal response of heat load. Secondly, the thermal inertia aggregation model (TIAM) is proposed, which offers an accurate DHN and buildings model for the planning and operation of IESs. Finally, some properties of the TIAM are derived to reveal its potential in general applications such as analysis and evaluation. Simulation results of different scale systems demonstrate the performance of the proposed model and reveal its advantages in the computational efficiency and sensitive information protection of DHN.

This study proposes a fully decentralized secondary voltage control scheme which employs the state estimation method in autonomous microgrids. Based on a large-signal dynamic model of a microgrid, a linear parameter varying based state estimator that is localized in each distributed generation (DG) unit serves as an alternative communication role and obtains the dynamics of the other DG units independently. A linear matrix inequality formulation for pole placement condition is derived for the state estimator design. A decentralized secondary voltage controller is, thus, able to achieve accurate reactive power sharing and average voltage restoration without any additional communication links. Our approach offers superior reliability, flexibility, and economic efficiency because of the irrelevance of communications to its performance, which is essential when conventional centralized or distributed methods are likely to yield towards poor performance or even instability under communication latency or data drop-out conditions. Simulation results that verify the effectiveness of the proposed methodology are provided.

This paper proposes an optimal design algorithm for distributed secondary voltage control in islanded microgrids (MGs), including communication topology and controller gains. First, upon the consensus-based secondary voltage control, the sufficient condition for network connectivity of communication topology is revealed by the reachability matrix. A multi-objective optimization criterion is first proposed for the network design, taking the convergence performance, network-relevant time delays, and communication costs into consideration. After obtaining the Pareto frontier of this multi-objective model, an optimal network is selected to meet the practical requirements. Based on static output feedback, a small-signal dynamic model of an MG installed with a secondary voltage controller is established, where the distributed secondary voltage controller can be converted into an equivalent decentralized controller. Thereby, a linear quadratic regulator is formulated for the near-optimal design of controller parameters. Our approach customizes the optimal design framework of the topology and controller, which have been largely ignored in the existing literatures. Therefore, it promises to improve the performance of distributed secondary control. The effectiveness of the proposed methodology is verified by a simulation study.

In the future integrated energy market, the traditional centralized planning method cannot solve the planning problem for multi-stakeholder integrated energy systems (MSIESs). To cope with this problem, this paper proposes a synchronously decentralized adaptive robust programming (SDARP) model for MSIESs based on the analytical target cascading (ATC) algorithm. In this model, different stakeholders are planned in parallel under the source-load uncertainties, aiming at minimizing the annual investment and operation costs and fully taking into account the interactive heating/cooling power transmitted by the district heating/cooling network. In each stakeholder, the column-and-constraint generation algorithm is used to address the robustness problems in the three-layer adaptive robust programming model. In addition, a tractable alternating optimization procedure is used to solve the non-convex SDARP model with finite convergence. Case studies verify the superiority and effectiveness of the SDARP method in dealing with the planning problem of MSIESs.